{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aed0bf9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tqdm\n",
    "import uvtools\n",
    "from astropy import units, constants\n",
    "from pathlib import Path\n",
    "from pyuvdata import UVData\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b6135ae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4 [00:00<?, ?it/s]key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.768939234480634 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.60814042154186 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.811123869742005 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.04750987321144 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.96909137235316 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.86872586701134 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.811950363446655 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.7332293751178 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.34728480893466 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.57549039903128 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.45692902512806 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.417360119535616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.338150209363405 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.2588448037402 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.179444667028164 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.38409605460845 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.02036326058423 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.94068352239387 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.86091211617534 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.781049809082944 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.70109736880127 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.28994157303816 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.54092516209816 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.46070693369823 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.38040164814444 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.300010075730455 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.21953298725863 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.42177290574358 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.05832534779027 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.977596340842894 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.89678490591852 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.81589181622398 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.73491784543967 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.65386376769658 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.57273052655242 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.73397850526433 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.41022881006418 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.32886205489517 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.247419070062094 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.16590063230977 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.0843075187871 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.002640507060434 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.920900375082425 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.94295750635656 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.757203864180738 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.49604050820642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.552170593735019 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.511130167201017 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.42896767243697 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.346737513784433 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.90082325522452 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.182077328838417 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.3050805406216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.22002380950187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.934599106274774 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.851979375383024 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.238300442235065 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.68655412531841 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.0353318199644 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.52088655275209 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.437963704249086 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.354982584041537 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.23040336064804 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.188848667138558 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.064101103545198 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.02249108191863 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.939230377251576 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.855916112264865 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.772549073033233 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.689130045859926 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.605659817271416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.95787222060949 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.438568902973643 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.76056637475757 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.27128262643123 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.187568195758217 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.105774819804484 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.133428633768037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 147.01588892991447 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.187940576268023 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.515768614541344 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.241391098175963 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.26771814494747 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.293779569402506 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.41161677141515 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.395673408532764 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.37036994457594 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.395368509869503 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 135.69368659093698 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.444567954252943 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.468768725809934 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.49270346239105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.516372115934303 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.539774640659832 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.56291099306993 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.585781131949126 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.34009434114014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.630722615663945 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.652793889481423 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.296870317106743 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.696137340600796 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.717409460577773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.111254381116005 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.75915436316904 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.779627102150993 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.79983334096967 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.819773063514656 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.87066397601163 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.52192664315206 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.87799296704129 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.896866509552368 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.915473489448992 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.41748316388 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.60421822436842 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.80660358777784 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.987235768572933 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.830195172239705 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.021517577671073 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.03825867901277 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.05473326216108 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.07094134292409 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.086882939384694 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.10255807190527 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.117966763122173 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.133109037950266 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.1479849235778 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.162594449468962 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.176937647363335 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.191014551272403 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.204825197483046 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.218369624553826 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.231647873316017 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.8976115388679 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.1708613452772 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.26988596642673 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.282099956239147 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.2940480059588 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.305730170036064 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.31714650518906 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.32829707040255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.339181926927388 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.34980113827933 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.360154770239383 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.0405145142525 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.380065570420435 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.389622881517216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.7011891948264 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.40794169986877 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.77681349775124 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.29301968560496 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.433431608019117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.441398358767383 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.03306721419888 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.45653755918286 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.09710578362023 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.47061830695845 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.477262000352912 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.652353472052935 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.489756524340923 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.495607549050273 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.501194577053976 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 81.08806818578432 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.511577042331187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.62143776539685 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.520904803659878 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.525173460135647 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.62513164806839 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.5856900231105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.0664671054061 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.539616080420966 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.542569361239106 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.26290398812134 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.6703520249439 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.549853592802226 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.55175691795356 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.55339809845581 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.554777276470652 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.44759554461278 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.556750204807457 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.557344250571106 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.55767688472773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.557748260548355 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.9602416307652 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.557365805636213 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.79377680033085 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 147.01332802872525 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.5548406033998 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.553477897645063 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.551854983794296 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.15899216183703 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.54782921346156 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.545426703319677 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.62760606698372 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.067431585475658 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.536662797272985 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.53322330629066 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.5575514377242 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.525568149166794 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.5213528601319 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.516879352503015 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.512147820306723 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.507158459755214 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.501911469244572 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.496407049353202 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.4906454028397 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 85.63514622951129 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.478351251870247 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.47181916381484 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.465030681934962 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.454367703801744 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.450685408856 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.44312903648926 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.435317137821173 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.351044903231184 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.98459256490776 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.41035051772731 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.622826998332213 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.392432605737966 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.383092292921777 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.37349805513582 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.36365012974846 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.353548756272446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.343194176364015 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.34311878758928 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.321726374575018 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.310613646700578 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.299248700401243 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.287631788014068 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.04089989923254 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.26364308496349 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.65538283871879 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.238649598781073 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 117.8401413559168 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.70094610918707 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.7829585589103 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.185656720254773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.81731642122823 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.157661592250705 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.64515061366767 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.12867026479613 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.113801721905926 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.098684968108117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.083320287422527 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.06770796596161 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.051848291926355 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.07721129497457 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.019388049372616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.00278806768255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.892181665192446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.9688498661406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.951512245583018 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.923124473545755 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.91610147866564 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.067911000477324 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.879712053156638 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.401780531708354 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.84234644889828 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.05696648300109 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.60300543921946 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.78447321298874 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.764696829404272 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.50570139406945 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.72441789764752 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.50324267874885 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.37452994160105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.75368642828099 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.640964884172185 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.619500410987797 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.597796121899115 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.1597444854481 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.553669456222135 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.531247764775035 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.03626229387419 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.48568939259996 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.462553409064306 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.97561052169253 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.415569605093342 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.391722493845247 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.367639052536983 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.343319640737686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.08461795474074 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.293974353876827 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.99021900108171 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.243689549472737 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.21819574815249 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.192468229141188 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.16650725872285 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.140313265571212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.11388662700608 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 94.16588066952579 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 94.26588090177164 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.03321464133151 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.005861233363802 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.97827709584483 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.85447912890453 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.4101858102774 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.894144205674063 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.865641059306178 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.83690914771536 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.807948869545857 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.778760625357997 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.74934481762768 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.71970185074266 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.6898321309969 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.618961877509975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.62941406761046 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.598866546062176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.75229718218318 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.53709659268718 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.50587499429862 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.034324517938245 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.93370829778294 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.410868819098685 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.378754334389033 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.346417690897546 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 96.7516061326506 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 111.7237280181981 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.386821862137296 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.214858134941327 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.18141716814615 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.147756643778095 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.11387700183668 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.079778684156253 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.09245993531769 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.0109277980908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.976176122528184 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.941207556864303 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.906022552062492 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.87062156089901 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.83500503796033 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.799173439635744 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.90910564755343 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.72686685140528 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 98.52320500500852 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 110.41396019729724 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.61680541684101 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.4351332233681 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.542368932741645 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.50483337723169 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.99159740043446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.62541230752649 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.39096328575645 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.352587033572597 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.73783431008692 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.53927328978037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.59893820599486 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.19699704791759 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.157580726574192 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.11795784905717 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 100.08417454618987 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.03809438693884 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.07332749548137 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.957410601642035 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.576008151608804 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.87591046068255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.03253801465554 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.50323969013225 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 77.03675654613879 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 130.38889660262492 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.668614835082188 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.62655207548134 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.584289174620196 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.39357060104482 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.499165260832296 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.456305276897307 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.07738752366946 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 118.68572468033742 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.32653942570321 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.19156870951464 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.68598608707048 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.33827153491168 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.128580251794777 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.106341324789707 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.061718186451515 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.016901778932475 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.971892635550677 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.541041729508706 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.09880046561912 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.81285057631221 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.56216005772299 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.743974659288558 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.69782039022952 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.68055000981363 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.6049455246545 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.5582260218287 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.487795822142793 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.63974068992246 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.416945926854662 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.55951245089176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.321829921326625 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.273994821631593 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 105.96682136079443 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.177773220859606 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 105.85338636683942 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.66043373223037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 78.75642424145582 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.98314014803588 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.9340289523446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.884737721658226 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.83526702660713 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.64107232244376 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.735789533385272 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.685783883923474 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.635601067507995 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 83.36518303002867 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.53470624747982 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.483995404378952 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.43310971533018 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.382049764232207 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 104.58080963787336 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.279409418749495 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.227830306947922 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.176079175963498 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.124156724088905 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.42823968097201 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.01980022920137 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.967367375493836 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.914765579501804 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.86199543940055 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.809057554765744 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.755952526563192 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.59072843987542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.649243450280526 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 81.66526989909934 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 81.76893362207497 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.08236676767815 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.67274517015593 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.18417615113127 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.02555086779805 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.270585655716634 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.215842548210038 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.16093899833974 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.105875623409446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.050653042038174 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.96752201439743 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 80.15211082175682 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.884036381415225 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.828183187196327 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.772173899368592 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 83.32166162174552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.31433732464091 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.327673919341535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.54658836074464 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.48980802767877 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.43287537907845 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.37579104907088 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.10202255586723 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.05504810474904 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.20363433073413 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.14594964146143 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.088116460184043 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.030135428490556 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.97200718920007 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.91373238634502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.855311665179958 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.85745876732344 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.738035054944156 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.67918046239576 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.620182544559402 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.561041952675765 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.501759339156873 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.442335481446325 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.32815174008881 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.32340538511255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.263221882826493 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.203238988617056 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.127960767389155 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.08285961277466 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.328908704654976 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.9619331962072 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.20079211325911 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 117.5989088460642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.77952948357965 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 99.43184726370832 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.804988994126234 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.595924859764406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.68713080828662 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.47286352671021 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.626807010548724 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 99.44030160664074 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.30418774209191 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.22518526711278 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.162945558407767 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.1005791466499 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.038086710902412 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.975468931277398 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.91272661965103 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.8498601969716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.786870476912217 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.72375814367975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 88.56210277473214 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.597168379562337 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.533692322120835 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.470096398377795 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.406381297532338 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.342547709770077 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.2785963262443 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 87.10048911648957 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.15034294137646 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.640731290038445 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.989375958007468 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 114.11599319400585 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.49310806948728 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.15778368163707 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.76282786669284 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.69784758141299 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.350919798888736 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 114.54415253578478 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 116.56603219693912 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.43682514143916 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.71743624233376 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.30566312190351 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.239921776911114 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.174074529357686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.108122086948885 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.89196477144642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.975904452694937 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.90964068055101 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.843274552941818 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.776806781828345 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.676916103041545 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.643569161078428 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 115.92018026943254 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.509933530525334 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.442968250214616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.719963638200372 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.30874634384224 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.2414911538927 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.174140764828703 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.10669589665864 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.03915727017694 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.05290020800167 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.887054837798786 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.87323885709779 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.768079623516368 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.700083043865956 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.63199704649936 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.563822499449305 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.495559845620203 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.33833476451931 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.35877355367293 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.290251372606097 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.221644140677686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.152952588157024 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.08417744600234 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.015319445848117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.623884502994 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.865333962955779 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.80825562381976 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.739073521411695 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.669812229170232 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.600472482677764 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.531055018162098 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.461560572475909 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.391989883097606 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.322343688125072 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.252622726262535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.182827736829621 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.112959459734261 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.043018635488535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.97300600518192 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.902922310488405 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.832768439770105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.762544843764834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.09818170455792 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.03847585081564 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.55146401637627 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.480969542393202 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.410409211473619 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.339783769711216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.233725157605077 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.198340540647584 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.127524248124706 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.9511870835574 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.52505540459205 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.91470563792186 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.843645354109539 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.772525946544496 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.701348165804594 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 128.6253328469561 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.896291922883526 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.487472097364067 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.41606833902547 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.77972644282516 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.273097735522496 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.201532397371741 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 98.18636826533066 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.54642896780281 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.986525437646305 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.914755219742501 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.842935670303097 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.77106754549114 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.699151601851502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.809201786886796 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.555179286169261 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.483124429087914 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.411024783090047 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.338881106552256 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.26669415819171 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.194464697072823 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.1160448228685 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.049881274471515 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.99797633486813 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.669502962194425 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.832706290252942 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.760237711105466 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 100.1390554929218 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 100.23613405704953 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.5426118780584 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.469999107943243 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.397352195349084 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.324671902916618 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.25195914494135 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.84165027329775 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.106438528333987 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.033632348052477 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.960796604855778 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.887932062824431 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.815039486259918 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 123.74141469172197 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.669173287797127 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.596201195529053 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 123.96890458878431 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.217066984353513 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.276965792933785 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.304070727544298 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.230981413607955 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.157870952595514 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.084740110633254 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.011589653999044 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.22680469464444 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.865232962492291 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.54813978166655 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.718807010956715 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.645569979737905 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.572317934214082 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.02709295261127 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.42577202137054 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.352479536046728 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 84.10308936077831 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.6039243335908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.132533478322719 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.58096656596229 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.985853280522567 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.912500636780022 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.839140653452935 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.765774098332768 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 137.31486120157015 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.619024343973221 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.545642680436606 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.47225751647859 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 126.12034610039291 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.865125818860378 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.252088700797513 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.178697213835212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.105306065737436 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.031916024297955 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.95852785728663 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.885142332427191 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.811760217397688 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.01162811664136 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.6650092872690765 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.52480429547967 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.518281359843212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.44492780702559 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.371582268755849 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.298245512202475 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.7375003705465 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.151601412426655 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.078295603022132 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 134.24020239260417 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.931720298843257 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.687877122586434 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.785198524253201 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.420133276840616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.638736409397507 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.5655296393918166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 106.88610894922641 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.4191685028575565 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.346015667269879 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.2728823404637994 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.308169475987357 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.126677273037129 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.053607061846731 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.861418132766474 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.944322880185457 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.8345348905638135 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.761559534125436 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.688609952548068 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.6156866053875705 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.542790407544271 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.995762976634476 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.397082510620362 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 108.36202763265895 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 132.79868719495835 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.178743345710569 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.106026052563867 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.033341242416026 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.96068967609212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.888072114090403 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.0021230248703 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.41374490350216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.99909034155187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.664223918723879 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.73642798994628 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.812008237362317 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.780649533021171 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.819382020740948 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.858075968895848 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.61414759146598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.55473633298806 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.973923495859191 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.51802733063422 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.050956761148514 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.0894130114008735 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.127828571494918 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.166203135477248 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.204536397720391 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.2428280529252 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.779883444911388 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.883567341844017 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.50399191331542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.2854399862552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.433651561193232 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.471687181578931 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.13506347001116 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.849146168173384 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.72749803213372 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.623389291672298 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.661203216041843 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.698971896277444 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.73669503156532 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.64822302187406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.812003465869241 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.42916977227373 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.8871261197545 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.924617030902711 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.77440367839097 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 86.43348533825421 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.036804441380484 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.84585580359607 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.111355418459185 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.148557811182174 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.1857110781531635 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.222814923453159 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.259869051558306 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.786772483421542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.3338269760598624 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.370730183389712 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.407582495399327 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.4443836185623 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.369020132010437 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.517831126282745 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.44106710133839 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.591070366526809 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.808196172596308 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.241443827147954 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.700533622888843 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.736914717676202 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.88896261563718 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.809515181944578 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.809276889981337 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.45052448039672 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.918007156154873 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.954061047504547 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.0923588821763 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.18400657065143 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.77281455558138 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.097718331782817 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.97352604831154 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.46860718085985 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.72024912439176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.891285609425598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.276012383131553 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.91192829461322 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.23568097421442 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.382293812652913 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.472078437306166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.452853806100167 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 134.7470497187535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.523175741231983 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.877799417574234 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 125.71050334020117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.628207385293395 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.663096481663304 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.697924388607946 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.35449277456053 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.767395524983565 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.802038128970006 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.836618508924163 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.87113631720476 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.905591278844783 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.939983119378242 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.234332343640716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.008576341765663 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.042777177205382 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.03628188989552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.4903942608502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.144993312688642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.29644539122371 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.212812714375852 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 135.7568912858427 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.280369842723447 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.314049381345685 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.347662545019817 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.381209065975563 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.414688676966295 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.448101111278065 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 125.04673383115353 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.64524181105382 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.547932695047203 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.651900310272637 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.614146335237267 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 101.54763886718439 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.516176203883596 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.712950328742453 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.745746259850053 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 119.13427867924204 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.811128419096457 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.385770702787653 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.876229239022921 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.908673498078942 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.941046645032424 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.97334842196726 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 119.70331698323093 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.037736836958773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 88.49153933050289 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.101836691108533 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.13377776914257 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.08103302687734 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.197440954725318 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.229162555051973 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.9916751433033 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.86002580873071 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.323884355105372 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 108.37978306002965 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.68115729380216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.417936377157575 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.26284886330006 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.48026222911957 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.511311812426579 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.542285482215364 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.573182991697054 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.60400409468705 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.634748545606927 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.73418060947199 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 122.15358382156687 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.726519539329717 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.695671314977716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.787312466728425 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.817591882372733 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.847792944079105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.877915411199163 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 105.3521482428271 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.937923602218513 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 121.44737721922672 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.99761454277934 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 118.2725813290242 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.056986330319562 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.086551949930426 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.116037072425756 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.145441462853833 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.17476488689701 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.204007050097333 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.4243396032988 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.262246966801529 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.291244195242365 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.320159295421059 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.14894211147737 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.377742189955066 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.33826585128548 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.434993814878334 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.463494830514522 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.491912345202552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.520246132474812 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 123.70835810272497 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.323133396228116 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.58618582608987 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 108.47594573791034 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.39100367508422 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.688477982256105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.36155960805975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.743875287995012 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.77144544787909 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.798929652572015 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.826327683023445 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.853639320872029 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.880864348440442 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.908002492340865 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.935053649258991 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.005974022876348 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.988893970676715 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.015682706048112 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.165239451281195 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.068996258522832 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.095520650686476 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.121956504677089 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.148303609780685 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.174561755990469 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.200730734005832 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.226810335233973 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.78059098111275 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.02329672318034 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.304510802949396 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.23282428184151 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.355860438696668 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.381399438687632 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.406847621736711 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.432204784984785 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.457470726293375 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.02463511221134 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.053286666312943 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.532719208100326 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.557618203071387 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.582425028254585 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 116.54551303671474 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.631761223160256 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.65629019956537 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.680726167620856 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.705068932513088 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.729318300170847 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.753474077262014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.147501988670523 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.801504090150035 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.82537794301267 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.84915743944528 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.872842389853462 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.577913093155544 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.919927897978413 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.943328080271167 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.82015677513482 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.98984236842251 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.362536388113423 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.035973986320835 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.89303629991908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.554918898144386 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.0683795995735 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.127083338131088 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.149619222978979 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.172058119409998 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.194399941906513 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.90319059384288 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.30958880609642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.260840907150355 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.76260945812062 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.304646034564003 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.326401418262117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 123.44496098218616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.369616958135099 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.391076769653468 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.412437713761117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.433699620093899 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.33157282940218 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.475925641905834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.496889420596036 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.51775348793529 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.538517677506471 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.559181823689864 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.57974576165824 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.724246389499 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 112.6626186949851 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 137.673703548122 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.04406567088708 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.49755089046552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.70101584581248 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.720873778449482 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.11379965107776 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.760284992019324 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.779837958541245 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.799288956630152 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.81863783110486 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.681714371213786 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.857028592549186 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.876070173222617 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.89500901769003 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 130.78240158986677 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.932577894390244 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.951207626860965 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.75948691632625 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.73253156268847 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.006476219408713 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.024691725280777 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.44853518977634 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.060810826177212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.078714132966448 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.4288473550323 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.17127138657793 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.13179736667639 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.149282411645219 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.166662504132178 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.183937577780037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.89385779147707 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.21817200900715 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.235131093334603 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 132.00071911820456 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.67861138878473 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 98.64096300842104 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.3496562508703 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.50284367319686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.33466328604189 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.350880363326626 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.366990914971234 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.382994812355998 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.398891927704577 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.414682134093185 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.430365305446147 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.44594131654033 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.217242477496653 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.476771361314755 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.32559588258716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.507171283681602 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.522209644972792 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.97784819045414 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.55196256728352 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.56667686016536 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.581282934972203 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.19587276571481 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 119.74908992942969 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.379150245943162 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.63862242574257 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.65268552380009 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.22375926965495 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.34774562234234 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 126.75931401674777 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.707847447220688 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.721364759994787 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.10375176332032 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.51565470808906 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.761259363390502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.12145075921031 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.273973240731591 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.800165742066541 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.812914443761295 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.825552929307664 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.83808109768883 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.850498848765158 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.862806083276135 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.875002702839007 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.15641462373094 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.13590788251724 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.910927876628614 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.922681070406215 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.59878196984652 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.94585408390863 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.957273718329526 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.968581982468388 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.979778785884792 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.990864039026052 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.0018376532247 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.007282568255166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.02344961456801 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.034087788820658 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.044613978347558 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.055028098928036 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.065330067231033 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.075519800817831 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.085597218142725 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.095562238551574 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 88.8390728329673 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.115154770478542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.48590967921731 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 89.14482726933869 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.139011800994782 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.152987621874745 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.19501477226161 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.00960497584416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.180176677585054 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.007143268935874 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 123.48226517197213 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.206347370165654 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.250112773720645 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.71925944000114 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.18344732641013 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.482240319638038 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.559763735131867 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.637359711977737 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.1518084095972 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.792766411317857 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.87057566267687 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.948454533127908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.026402285211098 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.397039091885613 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.182501480661763 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.260651445382067 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.508632388131655 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.417148406707966 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.01673038445027 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.329100562717112 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.65237530049351 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.730909679828542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 100.60616021120217 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.888161929821727 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.966878472758566 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.04565424440773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.124488497391226 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.203380483660844 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.282329454520507 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.361334660612968 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 137.89758335245398 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.51951077789449 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.598680187188855 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.67790282793905 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.757177947643385 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.447086471302395 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.596712398224593 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.995310646254964 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.442551483589442 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.15431435044843 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.39294522723822 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.611090933504283 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 133.096936926858 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.4688184408505 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.55264950703976 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.632452059281064 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.71229801439729 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.792186446068087 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.7798794816366 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.95208852804386 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.032100489493644 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.11215204866582 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.15219243911286 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.272370909371737 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.352536684260635 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.43273900359813 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.300278771962848 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 111.67306827310439 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.673557580563376 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.93184514798346 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.83427199175574 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.914677506136975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.99511420347274 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.075581316037812 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.156078075681954 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 139.57426477949784 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.31715746147475 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.397738549223604 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.47834620726634 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 139.75563607615086 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.63963815299502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 95.79860452444292 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.80102696430714 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.8817559128952 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.962506804778634 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.043278867493044 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.124071328225376 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.204883413809107 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.28571435074521 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.366563365187424 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.44742968297801 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.17336243793581 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.780893681029255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.69012470997793 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.90903622640702 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.851993704158176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.5027098790386 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.01391330535416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.094890142709616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.17587730222577 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.56190364798985 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.33787947911472 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.41889294154079 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.499913616245372 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.08430865380846 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.661973489932933 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.743011132068503 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.80264807879908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.882660160930016 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.98614536525109 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.067194727138315 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.922963702758196 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.229295615721014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.31034558273224 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.36317957727924 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.29176299169694 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.523504059736275 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.594004650014057 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.715559436033615 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.764111406232495 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.299317888113986 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.95863085917631 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.03964049941966 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.120641753739 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.20163384065325 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.282615978582506 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.36358738585301 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.444547280692312 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.08459672755846 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.60642940561879 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.76897072534637 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.817809123137955 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.419190207529724 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.57805772480745 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.010878344258863 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.517881328688997 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.172539079686246 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.291329620181095 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.38702823246857 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.414883225349037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.49562228928535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 89.76578391439469 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.657032246254218 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.737701574655908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.33674048976496 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.43764468843608 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.875166329754954 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.06012296810821 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.093364561697527 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.22116916209451 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.904372015357204 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.382097345435582 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.462515228274924 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.54290126279983 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.62325466729542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 88.33922093177162 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.78386045981329 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.46303181254667 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.94432618713781 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.024504719317903 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.10464593335975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.18474904833147 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.264813283444354 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.344837858054433 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.42482199166578 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.753726545936416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.5846658147084 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.83382910401152 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.577227162094598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.82410873884192 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.903833845367757 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.983513052214803 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.063145580330627 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.0139933801602 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.222267485228322 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.30175530497953 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.381193331960795 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.460580788232534 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.539916896097402 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.61920087811118 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.69843195707972 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.777609356072034 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.856732298421775 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.93579984321252 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.014811543516586 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 106.95245404522834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.172663813734253 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.251502832892243 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.330282741418834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.409002764710024 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.48766212847653 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.56626005875502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.644795781917857 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.686562418360552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.801677514083238 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.07049772990598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.958301142828482 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.12851591404637 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.11466049174325 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.32840237626086 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.270749390350915 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.348690494191672 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.42656167432481 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.50436216120091 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.58209118567985 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.65974797904407 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.3825928877065 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.814841799641123 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.89227713045529 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.487886658912615 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.04692144292693 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.12412873067059 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.201258418429447 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.984818605040786 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.355281933799958 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.43217423250878 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.4393648513754 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.585716093363914 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.662364129544187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.72690708255888 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.744378907025673 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.891807515919083 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 79.87601926760141 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.71628496592166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.120483839477586 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.196535276198176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.272498446040927 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.34837259105481 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.424156953840736 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.66070673540125 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.575453306017522 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.650963783490255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.78192382528584 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.80170540911324 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.09937697572516 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.952069934773988 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.02710884339769 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.10205117961526 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.176896192004165 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.251643129775445 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.32629124278411 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.400839781516 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.98015424284891 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.572340845774725 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.62388046680466 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.69802322648212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.77206267424053 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.19425680503511 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.91982865263759 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.99355369433191 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.06717244622352 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.532645974214596 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.21408811042963 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.58881512706465 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.98220287993576 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.43364588853511 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.5066113296758 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.57946529716642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 89.9049603194901 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.72483571050327 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.79735083473541 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.92593182382093 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.94203709024671 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.72330353527227 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.08625979549162 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.164494181071284 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.230013089921826 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.3017118801493 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.373291125772774 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 122.60309972793488 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.51608806827698 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.68957171819076 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.658398095839416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.729368700924255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.80021540024858 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.87093746991218 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.941534186865454 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.012004828936654 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.08234867481377 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.15256500407631 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.22265309718253 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.29261223548019 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.3624417012151 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.20215704070551 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.60977838391726 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 92.63586885174601 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.64044837103509 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.70961873972823 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.778655148501755 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.8280173892439 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 80.15803559915716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.110816615500156 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.18192741089467 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.69675518057275 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.19002067603688 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.01456941454213 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.32603870447946 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.39383757971108 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.46149542516362 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.529011539244635 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.59638522136287 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.66361577193855 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.697177154556144 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.797644685226416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.86444165388295 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.93109270289336 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.99759713781965 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.06395426525839 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.13016325519891 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.19622369200283 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.26213474746353 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.50965194301139 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.393505958797775 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 84.89377817142307 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.71504989709735 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.58942522181085 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.65442555469767 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.5605205628309 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.78396299089074 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.84849873228346 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.91287824978656 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.977100865248296 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.04116590164368 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.10507268308654 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.1688205348327 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.825803415526025 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.295836756018524 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.359103781738526 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.757782317270994 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.485152312876465 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.547932481587395 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.610549029890294 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.78590851832598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 97.4179086406065 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.79741017525319 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.859365600312245 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.977674704608866 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.982774984942736 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.04422762700793 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.105511359271546 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.16662552605422 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.7960108315349 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.28834254663944 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.34894409529335 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.40937346817627 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.469630015854406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.74349696948602 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.58962204419592 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.64935623234787 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.0169443586105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.408397030055227 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.8275037645769 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.88653245873873 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.94538317827242 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.52866215124532 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.06254814341729 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.120861117297764 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.178993573105416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.23694487829542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.29471428158952 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.352301393638044 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.30777251265425 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.98749289225595 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.523961981193295 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.46168410835736 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.637478827753014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.69395831826935 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.23684682592614 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.806356332171745 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.862273619071686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.91800227060277 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.97354167201314 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.028891209956626 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.084050272496924 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.13901824911598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.76883962594914 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.93489122925885 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.30276957960652 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.35696713585345 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.410970575006104 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.46477929515055 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.51839269582406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.334063259550604 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.62503114420828 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.22579287588482 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.730881035940385 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.783508883937785 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 133.77965304097225 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.914647913158486 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.940196720065146 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.99202546728773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.043652971416535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 147.01568419536738 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.146301915293094 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.197322191278445 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.24813889665626 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.2987514533573 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.34915928483785 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.39936181610291 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.44935847368883 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.015734781680194 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.548731881739 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.598107493041596 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.64727495235462 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.69623369400627 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.74498315389644 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.589591346019187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.84185197987169 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 125.62960025823237 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 104.75195998242529 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.985571494937375 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.033053507196215 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.08032233339968 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.127377422190534 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 88.85027777164241 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.22084419020469 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.2672547748098 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.31344943278111 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 126.35817323726792 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.382335369422606 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.45073242275073 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.49605795825495 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.541164867386684 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.58605261514792 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.6307206682056 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.67516849488989 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.307756282390347 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.83502805865974 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.80718532509776 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.93001207462615 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.89408574148828 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 89.00434984895665 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.8050742814076 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.33810588121611 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.06520176526148 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.10741845665176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.14940918456166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.170319652356355 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.78334693153172 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.274020482796516 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.92348635611711 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.355955536004984 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.39657980572767 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.436974569064844 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.113642306511125 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.83865103622094 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.55677683791686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.59624860165884 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 108.37907415381991 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.67449568498745 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.71327002566049 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.75181091602893 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.79011787108834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.8281904076278 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.64781406496117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.22652060359544 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.733406578297604 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.42932742652992 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.01502002576872 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.05167592833645 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.08809415687995 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.12427416609451 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.248824069845718 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.195917660663625 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.5707707426742 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 92.37201953379196 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.30158464396398 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.33632559595567 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.370825098712814 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 77.09426348902156 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.43909794226008 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.47287038056979 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 81.87362771445075 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 77.51113143312662 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.9816283245819 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.60552313557575 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.638074855976896 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 82.39158440133805 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.52007724391777 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.50793496444578 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.49564681934692 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 82.75387642570563 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.11850630610618 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.859039438736175 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.83628417593266 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.91994214337187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.90808777205923 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.97984757677615 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.009425244037885 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.038752322857576 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.53242565094024 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.09665306032714 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.12522589640545 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.15354649890578 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 84.14897343434798 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.20942937944152 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.236990850497435 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.888357084473085 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.29135185142887 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.318150586095086 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.344694283367446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.3709825505704 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.397014997003666 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.42279123394715 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.44831087466548 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.10369394850692 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.49857883042711 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.52332638195071 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 137.24176252292807 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.62770006951589 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.725314340742585 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.82298089129238 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.2448354754051 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.137711417401988 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.11629105952793 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.2141639737852 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.312087993206255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.410062923277586 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.3547194532298 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 92.53405204853682 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 136.6105953289035 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.80246785636743 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.900694418531735 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.92769686165325 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.16100624980103 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.19567177299338 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 83.01582494852077 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 133.58202861978623 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.491091526924265 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.58966217664787 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.83098306572637 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.78694839910808 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.88566357996666 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.98442654583394 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.083237100531804 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.182094841956214 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.28099998531738 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.37995212841782 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.431177720739285 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.57799662753231 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.67708859016623 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.77622676577992 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.875410957444046 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 116.74521980992773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.07391660079772 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.552813761866588 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.27260394303364 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 134.75693786123426 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.471471405518386 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.57097218813783 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.92137727385041 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.77010686792939 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 85.69759698626454 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.96941771514577 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.24259240766975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.35228417534786 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.268710835415405 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.36856157611876 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.468455169964855 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.568391418541886 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.66836991517397 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.768390877511216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.86845389880548 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.96855878029446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.068705323140584 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.16889332841763 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.26912259711889 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.369392930153666 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.46970412835399 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 118.90424785801885 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.67044832316874 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.77088092103457 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.74415071112946 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.97186612022916 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.0724183223325 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.17300999315753 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.27364093290165 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.374310941668334 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.47501981950375 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.57576736636043 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.67655338212447 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.77737766659919 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.87824001951422 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.97914024052337 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.08007812920333 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.18105327482699 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.282065897212235 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.38311558555933 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.484202139150035 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.936557120668866 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.68648503882783 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 93.97327987223869 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.874385189872 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.99018085552666 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.1451814675769 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.97179645806778 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.2941976064904 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.39560690300263 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.49705105361861 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.59852985682843 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.70004311106765 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.29581457105209 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.90317216600179 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.00478756321706 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.106436604497006 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.34339646542887 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.29485414005422 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.54320479847329 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.51336517108993 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.037909647859834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.9818562181734 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.81890474649565 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.81499440558594 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.052644904663346 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.124732776046365 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.226738561577434 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.328775563974716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.43084358055689 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.532942408580865 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.51706341690036 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.7372316876951 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.839421732994914 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.48761455524942 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.04389162017168 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.09502765151587 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.248479882195284 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 93.22457068205861 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 130.2002586359453 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.64514581786216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 121.94019770813517 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.76045775746428 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.86293865753582 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.53158684867046 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.15560551370439 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.17054834419204 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.084352196128 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.375758373497916 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.478404019132356 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.64162366198394 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.68377533938667 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.78650060606891 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.889252005080024 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.99202933231963 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.09483238363581 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.19766095482948 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.300514841651164 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.40339383979675 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.50629774491232 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.60922635260075 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.712179458409544 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.81515685783747 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 99.02700448996832 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.02118371932446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.12423277214364 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.284200676566485 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.33040109848133 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.433519962487324 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.5366616872867 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.01591176177 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.7430128997525 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.846221762661784 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.94945288143483 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.86398096311095 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 94.17022974237784 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.25927643323463 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.36259366520599 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.092353305189157 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.569290969938734 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.672670632132736 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.77607069375743 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.879490949432274 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.982931193728156 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.12039521734347 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.189870826272944 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 97.99411201302587 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.396887947197655 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 126.82090807816374 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.18082695657614 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.707555321082296 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.81114807429702 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.91475896555238 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.01838778904073 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.12203433892492 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.22569840932431 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.98934137092743 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.4330782879997 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.5367934684289 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 99.26704113794051 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.74427414501166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.86335756495134 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.95181995973394 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 115.6134035926001 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.15942926357067 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.26325720845395 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.40540885304205 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 80.09750220562357 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.57483174002203 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.67871946186276 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.78262161212518 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.88653798443138 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.99046837236058 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.09441256947876 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.19837036931406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.30234156537597 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.4063259511412 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.51032332005776 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.61433346556312 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.7183561810479 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 96.19017204880126 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.92643849545365 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.03049768105188 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.13456839334152 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.23865085887908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.34274465430083 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.44684957284188 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.54324391061348 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.6550919521062 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.75922899918888 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.86337634211672 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.96753377401025 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.07170108798434 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.17587807712198 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.2800645345014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.38426025316951 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.47389539477934 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.59267864648467 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.6969009071436 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.80113160111365 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.9053705213644 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.00961746083226 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.44257651820818 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.21813456915271 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.32906668471426 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.21677359818709 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.53096519858823 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.88724996346065 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.73955296256455 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.84385660007368 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.94816639266115 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.0524821331311 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.46993454769665 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.2370925009818 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.36546296973864 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.46980042957983 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.57414280116502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.67848987723983 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.17549935075374 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 121.47313320794987 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.99155725966361 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.09592108092193 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.2002885702407 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 102.78634421380062 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.4090337238272 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 132.26346703910323 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.61779106187757 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.72217378175277 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.79617464984977 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.93094628651015 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.03533565669869 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.13972682896251 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.24411959594002 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.34851353296465 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.45290886730086 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.55730517427135 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.66170224651037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.76609987665066 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.87049785732404 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.97489598116114 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.07929404078871 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.62550817813627 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.29914958073397 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.26063700715437 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.49688148988538 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.60127611796861 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.45698966679606 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.55055709606014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.72237185324613 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.71990266519725 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 133.78940006988174 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.1918611374673 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.33199026730742 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.43636862227869 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.83367813106848 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.64511622337096 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.74948505484768 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.85385029719788 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 136.09444702387412 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.79866417587468 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.13051546007742 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.27127101213017 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.15657379251516 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.47995455392453 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.5842888683217 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.68861793539467 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.7929415479093 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.46101883032438 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 113.33821620525228 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.44577932541404 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 135.43068698543834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.31447053975896 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.722339773902576 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.5993139323131 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.62730921982552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.731574417791 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.768939234480634 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.60814042154186 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.811123869742005 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.04750987321144 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.96909137235316 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.86872586701134 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.811950363446655 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.7332293751178 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.34728480893466 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.57549039903128 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.45692902512806 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.417360119535616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.338150209363405 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.2588448037402 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.179444667028164 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.38409605460845 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.02036326058423 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.94068352239387 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.86091211617534 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.781049809082944 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.70109736880127 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.28994157303816 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.54092516209816 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.46070693369823 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.38040164814444 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.300010075730455 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.21953298725863 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.42177290574358 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.05832534779027 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.977596340842894 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.89678490591852 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.81589181622398 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.73491784543967 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.65386376769658 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.57273052655242 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.73397850526433 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.41022881006418 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.32886205489517 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.247419070062094 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.16590063230977 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.0843075187871 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.002640507060434 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.920900375082425 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.94295750635656 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.757203864180738 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.49604050820642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.552170593735019 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.511130167201017 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.42896767243697 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.346737513784433 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.90082325522452 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.182077328838417 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.3050805406216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.22002380950187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.934599106274774 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.851979375383024 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.238300442235065 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.68655412531841 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.0353318199644 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.52088655275209 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.437963704249086 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.354982584041537 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.23040336064804 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.188848667138558 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.064101103545198 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.02249108191863 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.939230377251576 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.855916112264865 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.772549073033233 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.689130045859926 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.605659817271416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.95787222060949 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.438568902973643 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.76056637475757 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.27128262643123 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.187568195758217 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.105774819804484 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.133428633768037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 147.01588892991447 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.187940576268023 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.515768614541344 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.241391098175963 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.26771814494747 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.293779569402506 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.41161677141515 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.395673408532764 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.37036994457594 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.395368509869503 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 135.69368659093698 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.444567954252943 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.468768725809934 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.49270346239105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.516372115934303 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.539774640659832 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.56291099306993 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.585781131949126 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.34009434114014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.630722615663945 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.652793889481423 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.296870317106743 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.696137340600796 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.717409460577773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.111254381116005 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.75915436316904 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.779627102150993 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.79983334096967 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.819773063514656 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.87066397601163 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.52192664315206 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.87799296704129 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.896866509552368 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.915473489448992 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.41748316388 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.60421822436842 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.80660358777784 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.987235768572933 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.830195172239705 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.021517577671073 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.03825867901277 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.05473326216108 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.07094134292409 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.086882939384694 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.10255807190527 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.117966763122173 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.133109037950266 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.1479849235778 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.162594449468962 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.176937647363335 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.191014551272403 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.204825197483046 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.218369624553826 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.231647873316017 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.8976115388679 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.1708613452772 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.26988596642673 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.282099956239147 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.2940480059588 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.305730170036064 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.31714650518906 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.32829707040255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.339181926927388 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.34980113827933 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.360154770239383 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.0405145142525 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.380065570420435 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.389622881517216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.7011891948264 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.40794169986877 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.77681349775124 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.29301968560496 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.433431608019117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.441398358767383 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.03306721419888 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.45653755918286 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.09710578362023 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.47061830695845 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.477262000352912 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.652353472052935 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.489756524340923 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.495607549050273 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.501194577053976 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 81.08806818578432 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.511577042331187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.62143776539685 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.520904803659878 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.525173460135647 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.62513164806839 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.5856900231105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.0664671054061 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.539616080420966 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.542569361239106 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.26290398812134 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.6703520249439 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.549853592802226 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.55175691795356 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.55339809845581 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.554777276470652 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.44759554461278 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.556750204807457 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.557344250571106 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.55767688472773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.557748260548355 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.9602416307652 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.557365805636213 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.79377680033085 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 147.01332802872525 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.5548406033998 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.553477897645063 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.551854983794296 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.15899216183703 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.54782921346156 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.545426703319677 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.62760606698372 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.067431585475658 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.536662797272985 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.53322330629066 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.5575514377242 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.525568149166794 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.5213528601319 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.516879352503015 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.512147820306723 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.507158459755214 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.501911469244572 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.496407049353202 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.4906454028397 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 85.63514622951129 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.478351251870247 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.47181916381484 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.465030681934962 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.454367703801744 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.450685408856 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.44312903648926 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.435317137821173 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.351044903231184 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.98459256490776 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.41035051772731 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.622826998332213 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.392432605737966 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.383092292921777 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.37349805513582 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.36365012974846 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.353548756272446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.343194176364015 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.34311878758928 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.321726374575018 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.310613646700578 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.299248700401243 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.287631788014068 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.04089989923254 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.26364308496349 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.65538283871879 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.238649598781073 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 117.8401413559168 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.70094610918707 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.7829585589103 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.185656720254773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.81731642122823 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.157661592250705 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.64515061366767 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.12867026479613 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.113801721905926 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.098684968108117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.083320287422527 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.06770796596161 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.051848291926355 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.07721129497457 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.019388049372616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.00278806768255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.892181665192446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.9688498661406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.951512245583018 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.923124473545755 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.91610147866564 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.067911000477324 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.879712053156638 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.401780531708354 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.84234644889828 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.05696648300109 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.60300543921946 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.78447321298874 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.764696829404272 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.50570139406945 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.72441789764752 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.50324267874885 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.37452994160105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.75368642828099 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.640964884172185 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.619500410987797 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.597796121899115 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.1597444854481 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.553669456222135 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.531247764775035 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.03626229387419 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.48568939259996 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.462553409064306 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.97561052169253 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.415569605093342 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.391722493845247 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.367639052536983 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.343319640737686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.08461795474074 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.293974353876827 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.99021900108171 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.243689549472737 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.21819574815249 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.192468229141188 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.16650725872285 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.140313265571212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.11388662700608 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 94.16588066952579 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 94.26588090177164 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.03321464133151 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.005861233363802 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.97827709584483 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.85447912890453 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.4101858102774 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.894144205674063 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.865641059306178 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.83690914771536 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.807948869545857 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.778760625357997 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.74934481762768 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.71970185074266 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.6898321309969 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.618961877509975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.62941406761046 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.598866546062176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.75229718218318 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.53709659268718 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.50587499429862 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.034324517938245 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.93370829778294 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.410868819098685 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.378754334389033 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.346417690897546 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 96.7516061326506 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 111.7237280181981 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.386821862137296 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.214858134941327 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.18141716814615 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.147756643778095 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.11387700183668 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.079778684156253 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.09245993531769 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.0109277980908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.976176122528184 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.941207556864303 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.906022552062492 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.87062156089901 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.83500503796033 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.799173439635744 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.90910564755343 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.72686685140528 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 98.52320500500852 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 110.41396019729724 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.61680541684101 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.4351332233681 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.542368932741645 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.50483337723169 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.99159740043446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.62541230752649 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.39096328575645 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.352587033572597 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.73783431008692 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.53927328978037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.59893820599486 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.19699704791759 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.157580726574192 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.11795784905717 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 100.08417454618987 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.03809438693884 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.07332749548137 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.957410601642035 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.576008151608804 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.87591046068255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.03253801465554 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.50323969013225 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 77.03675654613879 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 130.38889660262492 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.668614835082188 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.62655207548134 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.584289174620196 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.39357060104482 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.499165260832296 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.456305276897307 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.07738752366946 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 118.68572468033742 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.32653942570321 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.19156870951464 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.68598608707048 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.33827153491168 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.128580251794777 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.106341324789707 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.061718186451515 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.016901778932475 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.971892635550677 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.541041729508706 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.09880046561912 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.81285057631221 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.56216005772299 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.743974659288558 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.69782039022952 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.68055000981363 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.6049455246545 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.5582260218287 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.487795822142793 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.63974068992246 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.416945926854662 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.55951245089176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.321829921326625 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.273994821631593 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 105.96682136079443 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.177773220859606 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 105.85338636683942 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.66043373223037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 78.75642424145582 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.98314014803588 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.9340289523446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.884737721658226 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.83526702660713 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.64107232244376 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.735789533385272 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.685783883923474 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.635601067507995 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 83.36518303002867 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.53470624747982 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.483995404378952 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.43310971533018 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.382049764232207 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 104.58080963787336 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.279409418749495 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.227830306947922 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.176079175963498 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.124156724088905 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.42823968097201 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.01980022920137 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.967367375493836 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.914765579501804 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.86199543940055 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.809057554765744 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.755952526563192 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.59072843987542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.649243450280526 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 81.66526989909934 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 81.76893362207497 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.08236676767815 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.67274517015593 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.18417615113127 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.02555086779805 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.270585655716634 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.215842548210038 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.16093899833974 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.105875623409446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.050653042038174 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.96752201439743 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 80.15211082175682 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.884036381415225 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.828183187196327 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.772173899368592 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 83.32166162174552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.31433732464091 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.327673919341535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.54658836074464 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.48980802767877 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.43287537907845 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.37579104907088 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.10202255586723 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.05504810474904 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.20363433073413 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.14594964146143 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.088116460184043 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.030135428490556 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.97200718920007 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.91373238634502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.855311665179958 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.85745876732344 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.738035054944156 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.67918046239576 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.620182544559402 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.561041952675765 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.501759339156873 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.442335481446325 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.32815174008881 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.32340538511255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.263221882826493 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.203238988617056 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.127960767389155 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.08285961277466 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.328908704654976 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.9619331962072 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.20079211325911 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 117.5989088460642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.77952948357965 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 99.43184726370832 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.804988994126234 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.595924859764406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.68713080828662 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.47286352671021 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.626807010548724 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 99.44030160664074 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.30418774209191 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.22518526711278 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.162945558407767 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.1005791466499 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.038086710902412 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.975468931277398 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.91272661965103 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.8498601969716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.786870476912217 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.72375814367975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 88.56210277473214 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.597168379562337 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.533692322120835 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.470096398377795 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.406381297532338 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.342547709770077 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.2785963262443 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 87.10048911648957 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.15034294137646 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.640731290038445 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.989375958007468 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 114.11599319400585 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.49310806948728 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.15778368163707 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.76282786669284 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.69784758141299 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.350919798888736 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 114.54415253578478 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 116.56603219693912 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.43682514143916 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.71743624233376 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.30566312190351 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.239921776911114 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.174074529357686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.108122086948885 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.89196477144642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.975904452694937 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.90964068055101 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.843274552941818 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.776806781828345 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.676916103041545 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.643569161078428 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 115.92018026943254 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.509933530525334 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.442968250214616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.719963638200372 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.30874634384224 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.2414911538927 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.174140764828703 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.10669589665864 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.03915727017694 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.05290020800167 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.887054837798786 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.87323885709779 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.768079623516368 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.700083043865956 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.63199704649936 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.563822499449305 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.495559845620203 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.33833476451931 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.35877355367293 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.290251372606097 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.221644140677686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.152952588157024 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.08417744600234 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.015319445848117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.623884502994 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.865333962955779 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.80825562381976 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.739073521411695 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.669812229170232 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.600472482677764 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.531055018162098 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.461560572475909 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.391989883097606 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.322343688125072 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.252622726262535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.182827736829621 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.112959459734261 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.043018635488535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.97300600518192 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.902922310488405 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.832768439770105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.762544843764834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.09818170455792 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.03847585081564 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.55146401637627 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.480969542393202 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.410409211473619 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.339783769711216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.233725157605077 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.198340540647584 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.127524248124706 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.9511870835574 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.52505540459205 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.91470563792186 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.843645354109539 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.772525946544496 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.701348165804594 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 128.6253328469561 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.896291922883526 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.487472097364067 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.41606833902547 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.77972644282516 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.273097735522496 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.201532397371741 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 98.18636826533066 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.54642896780281 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.986525437646305 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.914755219742501 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.842935670303097 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.77106754549114 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.699151601851502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.809201786886796 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.555179286169261 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.483124429087914 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.411024783090047 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.338881106552256 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.26669415819171 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.194464697072823 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.1160448228685 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.049881274471515 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.99797633486813 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.669502962194425 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.832706290252942 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.760237711105466 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 100.1390554929218 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 100.23613405704953 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.5426118780584 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.469999107943243 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.397352195349084 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.324671902916618 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.25195914494135 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.84165027329775 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.106438528333987 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.033632348052477 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.960796604855778 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.887932062824431 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.815039486259918 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 123.74141469172197 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.669173287797127 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.596201195529053 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 123.96890458878431 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.217066984353513 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.276965792933785 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.304070727544298 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.230981413607955 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.157870952595514 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.084740110633254 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.011589653999044 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.22680469464444 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.865232962492291 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.54813978166655 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.718807010956715 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.645569979737905 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.572317934214082 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.02709295261127 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.42577202137054 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.352479536046728 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 84.10308936077831 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.6039243335908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.132533478322719 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.58096656596229 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.985853280522567 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.912500636780022 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.839140653452935 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.765774098332768 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 137.31486120157015 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.619024343973221 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.545642680436606 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.47225751647859 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 126.12034610039291 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.865125818860378 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.252088700797513 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.178697213835212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.105306065737436 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.031916024297955 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.95852785728663 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.885142332427191 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.811760217397688 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.01162811664136 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.6650092872690765 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.52480429547967 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.518281359843212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.44492780702559 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.371582268755849 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.298245512202475 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.7375003705465 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.151601412426655 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.078295603022132 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 134.24020239260417 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.931720298843257 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.687877122586434 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.785198524253201 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.420133276840616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.638736409397507 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.5655296393918166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 106.88610894922641 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.4191685028575565 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.346015667269879 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.2728823404637994 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.308169475987357 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.126677273037129 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.053607061846731 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.861418132766474 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.944322880185457 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.8345348905638135 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.761559534125436 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.688609952548068 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.6156866053875705 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.542790407544271 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.995762976634476 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.397082510620362 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 108.36202763265895 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 132.79868719495835 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.178743345710569 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.106026052563867 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.033341242416026 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.96068967609212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.888072114090403 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.0021230248703 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.41374490350216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.99909034155187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.664223918723879 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.73642798994628 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.812008237362317 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.780649533021171 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.819382020740948 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.858075968895848 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.61414759146598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.55473633298806 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 4.973923495859191 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.51802733063422 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.050956761148514 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.0894130114008735 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.127828571494918 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.166203135477248 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.204536397720391 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.2428280529252 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.779883444911388 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.883567341844017 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.50399191331542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.2854399862552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.433651561193232 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.471687181578931 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.13506347001116 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.849146168173384 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.72749803213372 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.623389291672298 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.661203216041843 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.698971896277444 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.73669503156532 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.64822302187406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.812003465869241 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.42916977227373 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.8871261197545 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 5.924617030902711 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.77440367839097 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 86.43348533825421 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.036804441380484 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.84585580359607 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.111355418459185 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.148557811182174 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.1857110781531635 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.222814923453159 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.259869051558306 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.786772483421542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.3338269760598624 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.370730183389712 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.407582495399327 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.4443836185623 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.369020132010437 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.517831126282745 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.44106710133839 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.591070366526809 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.808196172596308 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.241443827147954 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.700533622888843 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.736914717676202 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.88896261563718 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.809515181944578 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.809276889981337 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.45052448039672 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.918007156154873 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 6.954061047504547 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.0923588821763 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.18400657065143 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.77281455558138 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.097718331782817 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.97352604831154 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.46860718085985 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.72024912439176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.891285609425598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.276012383131553 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.91192829461322 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.23568097421442 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.382293812652913 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.472078437306166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.452853806100167 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 134.7470497187535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.523175741231983 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.877799417574234 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 125.71050334020117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.628207385293395 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.663096481663304 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.697924388607946 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.35449277456053 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.767395524983565 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.802038128970006 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.836618508924163 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.87113631720476 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.905591278844783 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 7.939983119378242 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.234332343640716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.008576341765663 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.042777177205382 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.03628188989552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.4903942608502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.144993312688642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.29644539122371 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.212812714375852 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 135.7568912858427 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.280369842723447 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.314049381345685 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.347662545019817 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.381209065975563 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.414688676966295 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.448101111278065 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 125.04673383115353 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.64524181105382 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.547932695047203 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.651900310272637 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.614146335237267 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 101.54763886718439 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.516176203883596 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.712950328742453 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.745746259850053 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 119.13427867924204 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.811128419096457 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.385770702787653 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.876229239022921 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.908673498078942 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.941046645032424 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 8.97334842196726 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 119.70331698323093 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.037736836958773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 88.49153933050289 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.101836691108533 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.13377776914257 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.08103302687734 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.197440954725318 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.229162555051973 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.9916751433033 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.86002580873071 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.323884355105372 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 108.37978306002965 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.68115729380216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.417936377157575 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.26284886330006 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.48026222911957 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.511311812426579 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.542285482215364 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.573182991697054 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.60400409468705 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.634748545606927 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.73418060947199 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 122.15358382156687 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.726519539329717 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.695671314977716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.787312466728425 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.817591882372733 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.847792944079105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.877915411199163 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 105.3521482428271 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.937923602218513 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 121.44737721922672 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 9.99761454277934 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 118.2725813290242 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.056986330319562 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.086551949930426 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.116037072425756 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.145441462853833 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.17476488689701 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.204007050097333 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.4243396032988 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.262246966801529 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.291244195242365 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.320159295421059 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.14894211147737 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.377742189955066 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.33826585128548 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.434993814878334 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.463494830514522 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.491912345202552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.520246132474812 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 123.70835810272497 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.323133396228116 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.58618582608987 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 108.47594573791034 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.39100367508422 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.688477982256105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.36155960805975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.743875287995012 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.77144544787909 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.798929652572015 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.826327683023445 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.853639320872029 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.880864348440442 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.908002492340865 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.935053649258991 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.005974022876348 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 10.988893970676715 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.015682706048112 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.165239451281195 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.068996258522832 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.095520650686476 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.121956504677089 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.148303609780685 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.174561755990469 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.200730734005832 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.226810335233973 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.78059098111275 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.02329672318034 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.304510802949396 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.23282428184151 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.355860438696668 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.381399438687632 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.406847621736711 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.432204784984785 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.457470726293375 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.02463511221134 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.053286666312943 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.532719208100326 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.557618203071387 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.582425028254585 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 116.54551303671474 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.631761223160256 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.65629019956537 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.680726167620856 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.705068932513088 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.729318300170847 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.753474077262014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.147501988670523 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.801504090150035 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.82537794301267 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.84915743944528 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.872842389853462 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.577913093155544 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.919927897978413 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.943328080271167 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.82015677513482 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 11.98984236842251 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.362536388113423 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.035973986320835 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.89303629991908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.554918898144386 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.0683795995735 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.127083338131088 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.149619222978979 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.172058119409998 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.194399941906513 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.90319059384288 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.30958880609642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.260840907150355 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.76260945812062 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.304646034564003 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.326401418262117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 123.44496098218616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.369616958135099 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.391076769653468 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.412437713761117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.433699620093899 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.33157282940218 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.475925641905834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.496889420596036 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.51775348793529 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.538517677506471 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.559181823689864 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.57974576165824 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.724246389499 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 112.6626186949851 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 137.673703548122 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.04406567088708 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.49755089046552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.70101584581248 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.720873778449482 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.11379965107776 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.760284992019324 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.779837958541245 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.799288956630152 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.81863783110486 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.681714371213786 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.857028592549186 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.876070173222617 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.89500901769003 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 130.78240158986677 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.932577894390244 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 12.951207626860965 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.75948691632625 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.73253156268847 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.006476219408713 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.024691725280777 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.44853518977634 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.060810826177212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.078714132966448 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.4288473550323 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.17127138657793 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.13179736667639 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.149282411645219 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.166662504132178 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.183937577780037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.89385779147707 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.21817200900715 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.235131093334603 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 132.00071911820456 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.67861138878473 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 98.64096300842104 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.3496562508703 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.50284367319686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.33466328604189 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.350880363326626 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.366990914971234 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.382994812355998 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.398891927704577 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.414682134093185 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.430365305446147 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.44594131654033 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.217242477496653 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.476771361314755 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.32559588258716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.507171283681602 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.522209644972792 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.97784819045414 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.55196256728352 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.56667686016536 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.581282934972203 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.19587276571481 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 119.74908992942969 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.379150245943162 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.63862242574257 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.65268552380009 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.22375926965495 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.34774562234234 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 126.75931401674777 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.707847447220688 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.721364759994787 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.10375176332032 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.51565470808906 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.761259363390502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.12145075921031 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.273973240731591 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.800165742066541 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.812914443761295 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.825552929307664 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.83808109768883 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.850498848765158 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.862806083276135 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.875002702839007 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.15641462373094 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 131.13590788251724 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.910927876628614 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.922681070406215 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.59878196984652 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.94585408390863 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.957273718329526 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.968581982468388 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.979778785884792 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 13.990864039026052 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.0018376532247 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.007282568255166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.02344961456801 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.034087788820658 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.044613978347558 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.055028098928036 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.065330067231033 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.075519800817831 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.085597218142725 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.095562238551574 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 88.8390728329673 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.115154770478542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.48590967921731 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 89.14482726933869 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.139011800994782 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.152987621874745 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.19501477226161 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.00960497584416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.180176677585054 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.007143268935874 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 123.48226517197213 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.206347370165654 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.250112773720645 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.71925944000114 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.18344732641013 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.482240319638038 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.559763735131867 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.637359711977737 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.1518084095972 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.792766411317857 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.87057566267687 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.948454533127908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.026402285211098 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.397039091885613 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.182501480661763 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.260651445382067 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.508632388131655 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.417148406707966 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.01673038445027 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.329100562717112 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.65237530049351 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.730909679828542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 100.60616021120217 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.888161929821727 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 15.966878472758566 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.04565424440773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.124488497391226 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.203380483660844 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.282329454520507 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.361334660612968 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 137.89758335245398 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.51951077789449 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.598680187188855 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.67790282793905 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.757177947643385 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.447086471302395 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.596712398224593 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.995310646254964 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.442551483589442 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.15431435044843 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.39294522723822 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.611090933504283 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 133.096936926858 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.4688184408505 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.55264950703976 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.632452059281064 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.71229801439729 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.792186446068087 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.7798794816366 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.95208852804386 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.032100489493644 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.11215204866582 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.15219243911286 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.272370909371737 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.352536684260635 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.43273900359813 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.300278771962848 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 111.67306827310439 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.673557580563376 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.93184514798346 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.83427199175574 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.914677506136975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.99511420347274 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.075581316037812 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.156078075681954 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 139.57426477949784 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.31715746147475 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.397738549223604 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.47834620726634 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 139.75563607615086 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.63963815299502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 95.79860452444292 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.80102696430714 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.8817559128952 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.962506804778634 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.043278867493044 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.124071328225376 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.204883413809107 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.28571435074521 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.366563365187424 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.44742968297801 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.17336243793581 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 14.780893681029255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.69012470997793 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.90903622640702 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 20.851993704158176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.5027098790386 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.01391330535416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.094890142709616 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.17587730222577 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.56190364798985 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.33787947911472 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.41889294154079 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.499913616245372 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.08430865380846 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.661973489932933 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.743011132068503 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.80264807879908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.882660160930016 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 21.98614536525109 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.067194727138315 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.922963702758196 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.229295615721014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.31034558273224 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.36317957727924 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.29176299169694 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.523504059736275 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.594004650014057 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.715559436033615 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.764111406232495 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.299317888113986 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.95863085917631 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.03964049941966 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.120641753739 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.20163384065325 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.282615978582506 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.36358738585301 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.444547280692312 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.08459672755846 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.60642940561879 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.76897072534637 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.817809123137955 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 16.419190207529724 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.57805772480745 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.010878344258863 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.517881328688997 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.172539079686246 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.291329620181095 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.38702823246857 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.414883225349037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.49562228928535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 89.76578391439469 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.657032246254218 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.737701574655908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.33674048976496 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.43764468843608 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 18.875166329754954 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.06012296810821 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 22.093364561697527 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.22116916209451 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.904372015357204 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.382097345435582 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.462515228274924 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.54290126279983 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.62325466729542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 88.33922093177162 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.78386045981329 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.46303181254667 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.94432618713781 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.024504719317903 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.10464593335975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.18474904833147 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.264813283444354 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.344837858054433 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.42482199166578 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.753726545936416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.5846658147084 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 17.83382910401152 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.577227162094598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.82410873884192 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.903833845367757 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 26.983513052214803 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.063145580330627 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.0139933801602 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.222267485228322 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.30175530497953 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.381193331960795 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.460580788232534 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.539916896097402 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.61920087811118 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.69843195707972 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.777609356072034 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.856732298421775 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.93579984321252 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.014811543516586 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 106.95245404522834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.172663813734253 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.251502832892243 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.330282741418834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.409002764710024 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.48766212847653 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.56626005875502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.644795781917857 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.686562418360552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.801677514083238 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.07049772990598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.958301142828482 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.12851591404637 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.11466049174325 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.32840237626086 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.270749390350915 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.348690494191672 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.42656167432481 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.50436216120091 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.58209118567985 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.65974797904407 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.3825928877065 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.814841799641123 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.89227713045529 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 19.487886658912615 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.04692144292693 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.12412873067059 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.201258418429447 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.984818605040786 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.355281933799958 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.43217423250878 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.4393648513754 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.585716093363914 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.662364129544187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.72690708255888 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.744378907025673 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.891807515919083 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 79.87601926760141 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.71628496592166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.120483839477586 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.196535276198176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.272498446040927 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.34837259105481 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.424156953840736 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.66070673540125 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.575453306017522 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.650963783490255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.78192382528584 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.80170540911324 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.09937697572516 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.952069934773988 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.02710884339769 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.10205117961526 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.176896192004165 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.251643129775445 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.32629124278411 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.400839781516 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.98015424284891 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.572340845774725 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.62388046680466 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.69802322648212 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.77206267424053 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.19425680503511 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.91982865263759 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.99355369433191 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.06717244622352 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.532645974214596 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.21408811042963 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.58881512706465 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.98220287993576 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.43364588853511 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.5066113296758 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.57946529716642 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 89.9049603194901 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.72483571050327 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.79735083473541 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.92593182382093 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.94203709024671 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.72330353527227 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.08625979549162 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.164494181071284 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.230013089921826 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.3017118801493 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.373291125772774 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 122.60309972793488 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.51608806827698 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.68957171819076 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.658398095839416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.729368700924255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.80021540024858 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.87093746991218 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.941534186865454 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.012004828936654 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.08234867481377 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.15256500407631 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.22265309718253 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.29261223548019 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.3624417012151 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.20215704070551 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.60977838391726 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 92.63586885174601 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.64044837103509 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.70961873972823 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.778655148501755 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.8280173892439 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 80.15803559915716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.110816615500156 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.18192741089467 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.69675518057275 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.19002067603688 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.01456941454213 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.32603870447946 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.39383757971108 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.46149542516362 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.529011539244635 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.59638522136287 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.66361577193855 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.697177154556144 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.797644685226416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.86444165388295 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.93109270289336 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 36.99759713781965 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.06395426525839 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.13016325519891 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.19622369200283 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.26213474746353 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.50965194301139 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.393505958797775 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 84.89377817142307 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.71504989709735 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.58942522181085 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.65442555469767 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.5605205628309 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.78396299089074 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.84849873228346 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.91287824978656 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.977100865248296 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.04116590164368 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.10507268308654 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.1688205348327 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.825803415526025 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.295836756018524 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.359103781738526 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.757782317270994 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.485152312876465 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.547932481587395 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.610549029890294 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.78590851832598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 97.4179086406065 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.79741017525319 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.859365600312245 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 23.977674704608866 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 38.982774984942736 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.04422762700793 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.105511359271546 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.16662552605422 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.7960108315349 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.28834254663944 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.34894409529335 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.40937346817627 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.469630015854406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.74349696948602 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.58962204419592 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.64935623234787 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.0169443586105 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.408397030055227 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.8275037645769 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.88653245873873 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 39.94538317827242 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 24.52866215124532 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.06254814341729 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.120861117297764 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.178993573105416 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.23694487829542 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.29471428158952 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.352301393638044 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.30777251265425 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.98749289225595 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.523961981193295 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.46168410835736 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.637478827753014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.69395831826935 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 143.23684682592614 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.806356332171745 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.862273619071686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.91800227060277 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.97354167201314 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.028891209956626 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.084050272496924 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.13901824911598 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.76883962594914 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.93489122925885 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.30276957960652 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.35696713585345 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.410970575006104 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.46477929515055 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.51839269582406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 25.334063259550604 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.62503114420828 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.22579287588482 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.730881035940385 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.783508883937785 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 133.77965304097225 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.914647913158486 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.940196720065146 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.99202546728773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.043652971416535 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 147.01568419536738 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.146301915293094 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.197322191278445 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.24813889665626 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.2987514533573 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.34915928483785 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.39936181610291 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.44935847368883 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 40.015734781680194 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.548731881739 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.598107493041596 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.64727495235462 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.69623369400627 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.74498315389644 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.589591346019187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.84185197987169 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 125.62960025823237 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 104.75195998242529 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.985571494937375 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.033053507196215 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.08032233339968 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.127377422190534 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 88.85027777164241 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.22084419020469 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.2672547748098 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.31344943278111 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 126.35817323726792 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.382335369422606 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.45073242275073 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.49605795825495 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.541164867386684 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.58605261514792 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.6307206682056 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.67516849488989 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 31.307756282390347 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.83502805865974 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.80718532509776 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.93001207462615 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.89408574148828 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 89.00434984895665 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.8050742814076 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.33810588121611 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.06520176526148 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.10741845665176 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.14940918456166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.170319652356355 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.78334693153172 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.274020482796516 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.92348635611711 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.355955536004984 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.39657980572767 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.436974569064844 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.113642306511125 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.83865103622094 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.55677683791686 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.59624860165884 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 108.37907415381991 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.67449568498745 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.71327002566049 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.75181091602893 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.79011787108834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 44.8281904076278 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.64781406496117 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.22652060359544 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 42.733406578297604 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.42932742652992 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.01502002576872 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.05167592833645 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.08809415687995 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.12427416609451 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.248824069845718 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.195917660663625 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 129.5707707426742 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 92.37201953379196 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.30158464396398 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.33632559595567 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.370825098712814 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 77.09426348902156 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.43909794226008 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.47287038056979 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 81.87362771445075 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 77.51113143312662 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.9816283245819 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.60552313557575 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.638074855976896 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 82.39158440133805 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.52007724391777 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.50793496444578 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.49564681934692 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 82.75387642570563 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.11850630610618 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.859039438736175 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.83628417593266 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.91994214337187 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 43.90808777205923 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.97984757677615 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.009425244037885 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.038752322857576 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 33.53242565094024 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.09665306032714 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.12522589640545 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.15354649890578 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 84.14897343434798 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.20942937944152 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.236990850497435 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 27.888357084473085 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.29135185142887 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.318150586095086 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.344694283367446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.3709825505704 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.397014997003666 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.42279123394715 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.44831087466548 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.10369394850692 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.49857883042711 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.52332638195071 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 137.24176252292807 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.62770006951589 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.725314340742585 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 46.82298089129238 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.2448354754051 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.137711417401988 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.11629105952793 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.2141639737852 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.312087993206255 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.410062923277586 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.3547194532298 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 92.53405204853682 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 136.6105953289035 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.80246785636743 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 47.900694418531735 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.92769686165325 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.16100624980103 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.19567177299338 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 83.01582494852077 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 133.58202861978623 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.491091526924265 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.58966217664787 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.83098306572637 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.78694839910808 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.88566357996666 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 48.98442654583394 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.083237100531804 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.182094841956214 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.28099998531738 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.37995212841782 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.431177720739285 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.57799662753231 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.67708859016623 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.77622676577992 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 49.875410957444046 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 116.74521980992773 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.07391660079772 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.552813761866588 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.27260394303364 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 134.75693786123426 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.471471405518386 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.57097218813783 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 45.92137727385041 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.77010686792939 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 85.69759698626454 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 50.96941771514577 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.24259240766975 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.35228417534786 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.268710835415405 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.36856157611876 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.468455169964855 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.568391418541886 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.66836991517397 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.768390877511216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.86845389880548 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.96855878029446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.068705323140584 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.16889332841763 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.26912259711889 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.369392930153666 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.46970412835399 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 118.90424785801885 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.67044832316874 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.77088092103457 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.74415071112946 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 52.97186612022916 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.0724183223325 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.17300999315753 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.27364093290165 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.374310941668334 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.47501981950375 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.57576736636043 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.67655338212447 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.77737766659919 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.87824001951422 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 53.97914024052337 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.08007812920333 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.18105327482699 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.282065897212235 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.38311558555933 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.484202139150035 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.936557120668866 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.68648503882783 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 93.97327987223869 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.874385189872 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.99018085552666 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.1451814675769 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 28.97179645806778 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.2941976064904 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.39560690300263 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.49705105361861 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.59852985682843 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.70004311106765 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.29581457105209 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 55.90317216600179 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.00478756321706 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.106436604497006 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.34339646542887 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 37.29485414005422 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 51.54320479847329 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.51336517108993 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.037909647859834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.9818562181734 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.81890474649565 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 91.81499440558594 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 29.052644904663346 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.124732776046365 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.226738561577434 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.328775563974716 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.43084358055689 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.532942408580865 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.51706341690036 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.7372316876951 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 57.839421732994914 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.48761455524942 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.04389162017168 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.09502765151587 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.248479882195284 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 93.22457068205861 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 130.2002586359453 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 90.64514581786216 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 121.94019770813517 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.76045775746428 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.86293865753582 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.53158684867046 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 54.15560551370439 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.17054834419204 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.084352196128 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.375758373497916 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.478404019132356 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.64162366198394 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.68377533938667 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.78650060606891 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.889252005080024 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.99202933231963 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.09483238363581 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.19766095482948 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.300514841651164 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.40339383979675 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.50629774491232 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.60922635260075 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.712179458409544 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 60.81515685783747 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 99.02700448996832 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.02118371932446 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.12423277214364 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 56.284200676566485 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.33040109848133 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.433519962487324 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.5366616872867 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 142.01591176177 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.7430128997525 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.846221762661784 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 61.94945288143483 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 141.86398096311095 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 94.17022974237784 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.25927643323463 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.36259366520599 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 30.092353305189157 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.569290969938734 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.672670632132736 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.77607069375743 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.879490949432274 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.982931193728156 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 58.12039521734347 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.189870826272944 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 97.99411201302587 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.396887947197655 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 126.82090807816374 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 127.18082695657614 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.707555321082296 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.81114807429702 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 63.91475896555238 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.01838778904073 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.12203433892492 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.22569840932431 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 140.98934137092743 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.4330782879997 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.5367934684289 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 99.26704113794051 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.74427414501166 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 59.86335756495134 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 64.95181995973394 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 115.6134035926001 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.15942926357067 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.26325720845395 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.40540885304205 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 80.09750220562357 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.57483174002203 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.67871946186276 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.78262161212518 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.88653798443138 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.99046837236058 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.09441256947876 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.19837036931406 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.30234156537597 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.4063259511412 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.51032332005776 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.61433346556312 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.7183561810479 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 96.19017204880126 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.92643849545365 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.03049768105188 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.13456839334152 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.23865085887908 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.34274465430083 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.44684957284188 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 62.54324391061348 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.6550919521062 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.75922899918888 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.86337634211672 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 67.96753377401025 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.07170108798434 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.17587807712198 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.2800645345014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.38426025316951 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 32.47389539477934 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.59267864648467 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.6969009071436 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.80113160111365 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 68.9053705213644 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.00961746083226 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.44257651820818 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.21813456915271 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.32906668471426 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 41.21677359818709 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.53096519858823 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 103.88724996346065 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.73955296256455 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.84385660007368 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.94816639266115 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.0524821331311 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.46993454769665 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 65.2370925009818 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.36546296973864 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.46980042957983 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.57414280116502 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.67848987723983 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 138.17549935075374 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 121.47313320794987 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.99155725966361 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.09592108092193 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.2002885702407 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 102.78634421380062 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.4090337238272 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 132.26346703910323 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.61779106187757 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.72217378175277 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 66.79617464984977 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 71.93094628651015 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.03533565669869 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.13972682896251 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.24411959594002 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.34851353296465 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.45290886730086 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.55730517427135 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.66170224651037 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.76609987665066 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.87049785732404 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 72.97489598116114 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.07929404078871 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.62550817813627 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 146.29914958073397 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.26063700715437 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.49688148988538 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 73.60127611796861 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.45698966679606 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.55055709606014 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 107.72237185324613 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 145.71990266519725 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 133.78940006988174 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 69.1918611374673 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.33199026730742 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.43636862227869 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 34.83367813106848 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.64511622337096 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.74948505484768 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 74.85385029719788 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 136.09444702387412 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 144.79866417587468 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 70.13051546007742 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.27127101213017 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.15657379251516 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.47995455392453 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.5842888683217 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.68861793539467 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 75.7929415479093 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 109.46101883032438 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 113.33821620525228 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.44577932541404 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 135.43068698543834 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.31447053975896 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.722339773902576 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 35.5993139323131 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.62730921982552 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 76.731574417791 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The uvw_array does not match the expected values given the antenna positions. The largest discrepancy is 147.01588892991447 meters. This is a fairly common situation but might indicate an error in the antenna positions, the uvws or the phasing.\n",
      " 25%|██▌       | 1/4 [13:42<41:06, 822.04s/it]key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "key noise_seed in extra_keywords is longer than 8 characters. It will be truncated to 8 if written to uvfits or miriad file formats.\n",
      "100%|██████████| 4/4 [55:12<00:00, 828.04s/it]\n"
     ]
    }
   ],
   "source": [
    "lustre = Path(\"/lustre/aoc/projects/hera\")\n",
    "sim_dir = lustre / \"Validation/H6C_IDR2\"\n",
    "h6c_dir = lustre / \"h6c-analysis/IDR2\"\n",
    "daily_ext = \"2459864/zen.*.sum.smooth_calibrated.red_avg.uvh5\"\n",
    "lstbin_ext = \"lstbin-outputs/redavg-smoothcal-inpaint-500ns-lstcal/inpaint/zen.LST.*.000.sum.uvh5\"\n",
    "test_bls = {\n",
    "    \"sim_daily\": [(3,5), (3,32)],\n",
    "    \"sim_lstbin\": [(0,2), (0,29)],\n",
    "    \"data_daily\": [(3,5), (3,32)],\n",
    "    \"data_lstbin\": [(0,2), (0,29)],\n",
    "}\n",
    "base_dir = {\"sim\": sim_dir, \"data\": h6c_dir}\n",
    "ext = {\"daily\": daily_ext, \"lstbin\": lstbin_ext}\n",
    "all_data = dict.fromkeys(test_bls.keys(), None)\n",
    "for name, bls in tqdm.tqdm(test_bls.items()):\n",
    "    base, _ext = name.split(\"_\")\n",
    "    \n",
    "    files = sorted([str(fn) for fn in base_dir[base].glob(ext[_ext])])\n",
    "    all_data[name] = UVData.from_file(files, axis=(\"blt\",None)[int(name==\"sim_lstbin\")], bls=bls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd243e3e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5536248396021803e-27, largest imaginary/real ratio was 4.531283956724393e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.375321359927079e-27, largest imaginary/real ratio was 4.447012080976897e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3775609945446015e-27, largest imaginary/real ratio was 2.1181337473435454e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9338201961196755e-27, largest imaginary/real ratio was 6.838844237842589e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7251581679543906e-25, largest imaginary/real ratio was 3.525142277359384e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.641049872605185e-28, largest imaginary/real ratio was 1.0039611582754108e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.077636483709548e-27, largest imaginary/real ratio was 7.361601897581225e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0530865373015305e-26, largest imaginary/real ratio was 7.859782916916608e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.462512172149835e-27, largest imaginary/real ratio was 2.3681631618707195e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3286753959130204e-27, largest imaginary/real ratio was 1.102091222097727e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9387340713173985e-27, largest imaginary/real ratio was 3.4893865516441793e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.369710416238887e-27, largest imaginary/real ratio was 2.670658777012373e-29.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.889504382470701e-28, largest imaginary/real ratio was 1.036731839551488e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.323737075567562e-27, largest imaginary/real ratio was 1.033843533389759e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1728089891760949e-26, largest imaginary/real ratio was 1.0959956101599414e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2239883740018674e-27, largest imaginary/real ratio was 2.3059599232778338e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4983979973666156e-26, largest imaginary/real ratio was 2.626807001462391e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9487980903619453e-27, largest imaginary/real ratio was 1.5769104522058544e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4908068067241964e-27, largest imaginary/real ratio was 8.200832000486324e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9579609291078697e-25, largest imaginary/real ratio was 4.687114105889112e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.076732665329449e-27, largest imaginary/real ratio was 5.917486847217178e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6892421043713222e-27, largest imaginary/real ratio was 2.28600352804334e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.966458771569056e-27, largest imaginary/real ratio was 3.4459619304538936e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4415718685434017e-25, largest imaginary/real ratio was 1.4591458092938933e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.589285211134924e-28, largest imaginary/real ratio was 1.322127033859203e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3132801526974673e-27, largest imaginary/real ratio was 2.9890266579361584e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3069483073991225e-24, largest imaginary/real ratio was 1.935484228699176e-29.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3675993429091849e-27, largest imaginary/real ratio was 7.552648837938335e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.501707025643206e-28, largest imaginary/real ratio was 8.906809655513063e-32.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.610647785828495e-26, largest imaginary/real ratio was 2.164700746515018e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2624528210324658e-27, largest imaginary/real ratio was 1.2172229420398484e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.236494865080184e-27, largest imaginary/real ratio was 1.8445975569743627e-29.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.216603552858229e-27, largest imaginary/real ratio was 6.161669675777887e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.687609470332964e-27, largest imaginary/real ratio was 5.43817108934546e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.368304739271101e-12, largest imaginary/real ratio was 6.784970182320382e-16.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6471723862260605e-11, largest imaginary/real ratio was 8.732901572272717e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.47273276081275e-11, largest imaginary/real ratio was 9.833025069093588e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5792368743928367e-12, largest imaginary/real ratio was 9.920022828162146e-16.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.539340890776321e-11, largest imaginary/real ratio was 1.307717737129939e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.181757215362804e-11, largest imaginary/real ratio was 9.668083816890205e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.860217481885216e-11, largest imaginary/real ratio was 4.88718000881359e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7452985129186557e-11, largest imaginary/real ratio was 4.141190052404986e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4078999141748818e-09, largest imaginary/real ratio was 6.992835370134233e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4647631660786765e-10, largest imaginary/real ratio was 6.238988460828629e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.310039381127025e-11, largest imaginary/real ratio was 3.686657622850485e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.570795888889754e-11, largest imaginary/real ratio was 1.7272001348762482e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.80619430962863e-11, largest imaginary/real ratio was 4.830810709644625e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.679488403967468e-09, largest imaginary/real ratio was 1.786551515248918e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.928755954556257e-12, largest imaginary/real ratio was 3.066912654593813e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.1740950526072767e-10, largest imaginary/real ratio was 1.8846439291213737e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.457263499769675e-09, largest imaginary/real ratio was 7.292596431306616e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.500812593431371e-11, largest imaginary/real ratio was 1.7077783566937236e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1375503058343168e-09, largest imaginary/real ratio was 2.014047658368097e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.573882298752383e-10, largest imaginary/real ratio was 3.66081340911956e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.649473274556642e-11, largest imaginary/real ratio was 1.010131469408907e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.702198381442611e-10, largest imaginary/real ratio was 6.215440593192352e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.453199930757499e-10, largest imaginary/real ratio was 9.328463047523524e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.326458047558505e-11, largest imaginary/real ratio was 1.4068760295197946e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.5307025292690084e-11, largest imaginary/real ratio was 3.5125841861595555e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1950726448879e-11, largest imaginary/real ratio was 4.9754548058247935e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6585777309810523e-10, largest imaginary/real ratio was 2.1196538756303859e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.762665761226259e-10, largest imaginary/real ratio was 2.07659822670015e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.0643070429702366e-11, largest imaginary/real ratio was 1.2959451625306204e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.825420068895994e-11, largest imaginary/real ratio was 6.938452233836826e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0673892860804761e-09, largest imaginary/real ratio was 3.0352553507504114e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.1143765913638195e-11, largest imaginary/real ratio was 6.769041435821945e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.625327999165833e-11, largest imaginary/real ratio was 1.4063638256005303e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.51816996860264e-10, largest imaginary/real ratio was 3.7161224702301114e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.55491178637399e-10, largest imaginary/real ratio was 6.353841781748065e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.460362402671296e-11, largest imaginary/real ratio was 2.498479366336475e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8895818046301825e-10, largest imaginary/real ratio was 1.0888497383232665e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.708015103585184e-10, largest imaginary/real ratio was 2.3273097365624453e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1480361775759308e-10, largest imaginary/real ratio was 2.3537365087925137e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.048745776478894e-11, largest imaginary/real ratio was 1.5390678188316027e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.439883752754711e-11, largest imaginary/real ratio was 1.723659109071875e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.878938037221942e-11, largest imaginary/real ratio was 1.3286937675615786e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.039336221765662e-11, largest imaginary/real ratio was 1.129723604978787e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.089714984733467e-09, largest imaginary/real ratio was 9.804422422243642e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5194615401300322e-11, largest imaginary/real ratio was 2.0471350825458987e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.409949228611911e-11, largest imaginary/real ratio was 8.470547444433992e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.720477831552761e-10, largest imaginary/real ratio was 4.765627950212998e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.614473641875999e-10, largest imaginary/real ratio was 3.170282050286405e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.175948931453124e-11, largest imaginary/real ratio was 7.168959472991749e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1088643587379993e-09, largest imaginary/real ratio was 4.77824696216925e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2176173758866633e-09, largest imaginary/real ratio was 1.3279274523415836e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.5328552748627985e-11, largest imaginary/real ratio was 4.3697196938834754e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.126299704886408e-10, largest imaginary/real ratio was 8.339233557474865e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.3151558065269e-10, largest imaginary/real ratio was 9.019935832892945e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.817161214034531e-09, largest imaginary/real ratio was 4.1876063447334795e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.8704959601248564e-10, largest imaginary/real ratio was 1.9336365751829506e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1243485490845598e-10, largest imaginary/real ratio was 2.337170980453756e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.111527995354954e-10, largest imaginary/real ratio was 1.2202026444434018e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.997300305700201e-09, largest imaginary/real ratio was 1.5951839288360514e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.105330068815827e-11, largest imaginary/real ratio was 5.1351429718193867e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3959724683316058e-11, largest imaginary/real ratio was 6.277327645979398e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2013263052067183e-10, largest imaginary/real ratio was 1.9796522542584898e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.406046052459035e-10, largest imaginary/real ratio was 1.7519639522868308e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1934512263606893e-08, largest imaginary/real ratio was 3.529995291537033e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9193850426197684e-09, largest imaginary/real ratio was 1.0938392280699687e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0871626391863944e-10, largest imaginary/real ratio was 2.7508562698537125e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.328119466137299e-10, largest imaginary/real ratio was 2.4146211529500524e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9044772893345937e-10, largest imaginary/real ratio was 6.067600694887464e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6072781101855475e-10, largest imaginary/real ratio was 1.175211283291476e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2915720644771444e-10, largest imaginary/real ratio was 7.899650672050611e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1895640679619655e-10, largest imaginary/real ratio was 4.476048322143898e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.07235300772464e-09, largest imaginary/real ratio was 1.1524898236519707e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.255745317308338e-11, largest imaginary/real ratio was 5.441354185034151e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.0309781905547837e-11, largest imaginary/real ratio was 9.60136878688124e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.431546567354668e-09, largest imaginary/real ratio was 1.54504090479364e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.170802614875038e-08, largest imaginary/real ratio was 2.6666069565320966e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7247613620603444e-08, largest imaginary/real ratio was 1.5777600491947342e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2662960286847234e-08, largest imaginary/real ratio was 7.947240098189172e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.204840845761389e-08, largest imaginary/real ratio was 4.781495574802918e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.298838682208291e-08, largest imaginary/real ratio was 3.5241860485068137e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.553020674247349e-09, largest imaginary/real ratio was 6.718778331674725e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.368753768526789e-08, largest imaginary/real ratio was 2.2639624485558975e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.890325149505643e-08, largest imaginary/real ratio was 1.0696068776150964e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4359826245508e-08, largest imaginary/real ratio was 6.777471353792515e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9826026011166667e-08, largest imaginary/real ratio was 4.276248418914967e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8054271813490113e-08, largest imaginary/real ratio was 2.9817563486448503e-12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.602673960071118e-08, largest imaginary/real ratio was 2.6201125627450253e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.992600766889224e-08, largest imaginary/real ratio was 3.005441365776375e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.0698386005284795e-09, largest imaginary/real ratio was 1.530560689028543e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.560539617759986e-08, largest imaginary/real ratio was 2.337653680840617e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6514530525207176e-09, largest imaginary/real ratio was 6.927452020095335e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.758185593875597e-09, largest imaginary/real ratio was 1.6799861709180322e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.704823055276198e-08, largest imaginary/real ratio was 3.1214312392309037e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1629785128359162e-09, largest imaginary/real ratio was 1.8370714259227223e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.906802729838497e-09, largest imaginary/real ratio was 2.249915138401324e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.782770674666688e-10, largest imaginary/real ratio was 4.711795313711259e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.087358684819211e-08, largest imaginary/real ratio was 1.547860475730084e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.95782461099615e-08, largest imaginary/real ratio was 6.823245737026968e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.533305762785164e-08, largest imaginary/real ratio was 1.6561013311304643e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1083279725622613e-08, largest imaginary/real ratio was 1.218145496206218e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.004458016478352e-08, largest imaginary/real ratio was 3.897893024826927e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.316143574003621e-08, largest imaginary/real ratio was 1.0054129005437752e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4323995620774208e-08, largest imaginary/real ratio was 5.3388999126489836e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7601617571232172e-08, largest imaginary/real ratio was 4.128283705124874e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5526319796482605e-08, largest imaginary/real ratio was 2.0988663151490227e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6811277920579054e-09, largest imaginary/real ratio was 6.00729552392132e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.254455882681106e-09, largest imaginary/real ratio was 2.6459657826584355e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4249809691462457e-08, largest imaginary/real ratio was 3.295842326495312e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.533230601873165e-10, largest imaginary/real ratio was 6.887082814524459e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.048990541560928e-09, largest imaginary/real ratio was 2.9795394759703625e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3593037194221786e-08, largest imaginary/real ratio was 3.4790281967344276e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.874620031005117e-08, largest imaginary/real ratio was 1.5927475538995436e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2342084161489137e-08, largest imaginary/real ratio was 3.1428004647205624e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.571141744371024e-09, largest imaginary/real ratio was 3.397499270505473e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4704883106246832e-08, largest imaginary/real ratio was 5.0588355433292445e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0275626948973697e-08, largest imaginary/real ratio was 6.708238784330927e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.014796865800988e-09, largest imaginary/real ratio was 3.071743509783874e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3864709087653903e-08, largest imaginary/real ratio was 9.712367366558547e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.873586642950235e-08, largest imaginary/real ratio was 1.1706627698447285e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4625912073824857e-08, largest imaginary/real ratio was 1.399267463617949e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2076779774875003e-08, largest imaginary/real ratio was 1.0118932762273106e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8081011526158753e-08, largest imaginary/real ratio was 9.232446299042228e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6329322376313286e-08, largest imaginary/real ratio was 2.972207592932359e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.219416344062504e-09, largest imaginary/real ratio was 3.398081494955315e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.330946606955355e-08, largest imaginary/real ratio was 2.4932301273030625e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9489240456891673e-08, largest imaginary/real ratio was 1.4897667743905611e-12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6040290651982402e-08, largest imaginary/real ratio was 4.704288509588146e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6860249262719653e-08, largest imaginary/real ratio was 6.547182095721393e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8032533590235848e-08, largest imaginary/real ratio was 5.497562256478671e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2227268359397126e-08, largest imaginary/real ratio was 5.071883608730699e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1983378255135645e-08, largest imaginary/real ratio was 4.854155664654327e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8019945497592153e-08, largest imaginary/real ratio was 6.718308356254907e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2205039537921847e-08, largest imaginary/real ratio was 1.1376264489009517e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.18291039570764e-09, largest imaginary/real ratio was 3.792757610914287e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.408055728341314e-08, largest imaginary/real ratio was 9.309713502965772e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.48519273264277e-09, largest imaginary/real ratio was 1.3173579483196196e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.720055168996267e-09, largest imaginary/real ratio was 2.356731283650073e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9607450889352906e-08, largest imaginary/real ratio was 1.853322799535522e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.27721792165379e-09, largest imaginary/real ratio was 2.6245204212045967e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.561872501124656e-08, largest imaginary/real ratio was 2.2009311918213455e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9434356796169266e-08, largest imaginary/real ratio was 2.958035972899803e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2222500915394127e-08, largest imaginary/real ratio was 1.0017725012196981e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.321379543049697e-09, largest imaginary/real ratio was 1.9389394971818822e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.819129506215745e-09, largest imaginary/real ratio was 4.374505758018675e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2653990653311038e-08, largest imaginary/real ratio was 1.1446787010746704e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2150052350047793e-08, largest imaginary/real ratio was 3.9157081542633956e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.869575962396475e-09, largest imaginary/real ratio was 7.1038394081361645e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.535881133279892e-09, largest imaginary/real ratio was 4.846872877891023e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.220470029586313e-08, largest imaginary/real ratio was 8.277839144786286e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7374855245658893e-08, largest imaginary/real ratio was 2.8293741398168913e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.62599945556766e-09, largest imaginary/real ratio was 3.938207926346022e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.322261883878588e-09, largest imaginary/real ratio was 7.050721609501232e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.802841425498878e-09, largest imaginary/real ratio was 4.399423807425018e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.920261521077906e-09, largest imaginary/real ratio was 8.421192244801191e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.938702069737874e-08, largest imaginary/real ratio was 1.5087394331420958e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.33521915938403e-09, largest imaginary/real ratio was 3.8862889421906235e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2256155620698152e-08, largest imaginary/real ratio was 2.4739997772036684e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7676065125303255e-08, largest imaginary/real ratio was 2.57630257124868e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.7523292103926345e-09, largest imaginary/real ratio was 1.758049164924108e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6285531696592266e-08, largest imaginary/real ratio was 3.809790532102052e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.72959200966969e-09, largest imaginary/real ratio was 8.139554982597509e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0250643807249518e-08, largest imaginary/real ratio was 1.562244513936079e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.160128135228671e-08, largest imaginary/real ratio was 1.0870753712387828e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.017519554293467e-08, largest imaginary/real ratio was 6.126688694271997e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3559900352073155e-08, largest imaginary/real ratio was 1.4174393381389064e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1556672354254919e-08, largest imaginary/real ratio was 7.709696501214023e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2420359106634802e-08, largest imaginary/real ratio was 4.297152545229306e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.916967188724926e-08, largest imaginary/real ratio was 1.5562614930036803e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0890231940872423e-08, largest imaginary/real ratio was 5.869371105612505e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.9984245679060835e-09, largest imaginary/real ratio was 1.5125580731043172e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.569046665079774e-09, largest imaginary/real ratio was 7.848485115876554e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1799970087573295e-09, largest imaginary/real ratio was 1.2326925339803403e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.784113857070354e-09, largest imaginary/real ratio was 4.71958422306795e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.067978307011445e-08, largest imaginary/real ratio was 7.253501115258151e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.419415461363358e-09, largest imaginary/real ratio was 4.337347795134101e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7823667049290324e-08, largest imaginary/real ratio was 8.710608651664264e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3407760431518731e-08, largest imaginary/real ratio was 1.782946265296856e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.853731549564898e-09, largest imaginary/real ratio was 4.0171993131028364e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0315450343086094e-08, largest imaginary/real ratio was 1.2246610884016781e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.759710215459754e-09, largest imaginary/real ratio was 2.5956190087986667e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1384543351827143e-08, largest imaginary/real ratio was 2.083776417827599e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3918607018301572e-08, largest imaginary/real ratio was 2.2702655611985905e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0891340484430251e-08, largest imaginary/real ratio was 1.2418021512110589e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.59832884764788e-09, largest imaginary/real ratio was 4.57932031644086e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.34712749321625e-09, largest imaginary/real ratio was 8.641427581598463e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.171163371972776e-08, largest imaginary/real ratio was 2.7461188650202967e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2680437745202443e-08, largest imaginary/real ratio was 4.1058684845270163e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.9366096238348975e-09, largest imaginary/real ratio was 4.859559249863406e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.689482126170206e-09, largest imaginary/real ratio was 1.9118857279775378e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2992538329057446e-08, largest imaginary/real ratio was 1.1084996012852898e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4345400134629866e-08, largest imaginary/real ratio was 3.6776320029692343e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4696274790106096e-08, largest imaginary/real ratio was 4.690929708589901e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8214615610482724e-07, largest imaginary/real ratio was 2.1444549677403457e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6616317124086854e-09, largest imaginary/real ratio was 7.674907336200239e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.100791519871483e-08, largest imaginary/real ratio was 7.13509452524505e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7393437819472083e-08, largest imaginary/real ratio was 6.097918857848801e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0136074842447312e-08, largest imaginary/real ratio was 4.0332005008423974e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.470598915451145e-09, largest imaginary/real ratio was 8.40068037571125e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.625523122497023e-09, largest imaginary/real ratio was 2.1028922363010352e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.543425999603534e-08, largest imaginary/real ratio was 2.2322893251154824e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.574230495765677e-08, largest imaginary/real ratio was 5.654089164908293e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.799194635354698e-09, largest imaginary/real ratio was 3.0782162501770337e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.363982366154148e-09, largest imaginary/real ratio was 3.683630300097909e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.168427699988972e-09, largest imaginary/real ratio was 3.2892476763719833e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.232813851539988e-09, largest imaginary/real ratio was 9.496795552655555e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.450836465592161e-09, largest imaginary/real ratio was 4.0070947161438597e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.304491065327759e-09, largest imaginary/real ratio was 1.328512701043959e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.362398599480553e-09, largest imaginary/real ratio was 3.251384117065768e-12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2318325133532574e-08, largest imaginary/real ratio was 1.0250677977232282e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0408953764415837e-08, largest imaginary/real ratio was 2.7007827678748546e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.409198471849834e-09, largest imaginary/real ratio was 1.0729702727441425e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.368231271563367e-08, largest imaginary/real ratio was 7.14911268975828e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.777461441711456e-09, largest imaginary/real ratio was 2.6905241873921937e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.06690518479267e-09, largest imaginary/real ratio was 2.211440125551796e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.778981635769626e-09, largest imaginary/real ratio was 1.960848270054804e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4365099882705167e-08, largest imaginary/real ratio was 2.4720176217727713e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.727244223976614e-09, largest imaginary/real ratio was 1.3146376707402287e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7493259321003315e-08, largest imaginary/real ratio was 3.655887943186897e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.945518367666774e-09, largest imaginary/real ratio was 2.1691141176267303e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.42224974680037e-09, largest imaginary/real ratio was 4.1190106185793417e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.06499657925915e-09, largest imaginary/real ratio was 1.6194444435365324e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.049409744673331e-08, largest imaginary/real ratio was 1.248465502219641e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.2321978112729884e-09, largest imaginary/real ratio was 2.1015304030352195e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7260783774321202e-08, largest imaginary/real ratio was 1.311406715450822e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.384776226026708e-08, largest imaginary/real ratio was 1.7351026475040018e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.061071240971224e-09, largest imaginary/real ratio was 1.8813981431985592e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0631784498452967e-08, largest imaginary/real ratio was 2.4879969842513474e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9469196349979274e-08, largest imaginary/real ratio was 1.3985274259462038e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1966416696836037e-08, largest imaginary/real ratio was 2.9010095328386436e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.241446941892612e-09, largest imaginary/real ratio was 1.5072221054298264e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.142644727495995e-09, largest imaginary/real ratio was 5.243418464408372e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3790598497718947e-09, largest imaginary/real ratio was 1.2033767987004e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.449284833957989e-09, largest imaginary/real ratio was 1.2037157735808014e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.522291882905874e-09, largest imaginary/real ratio was 9.68729804329313e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.997314113316551e-09, largest imaginary/real ratio was 2.975482593492362e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.97483205671467e-09, largest imaginary/real ratio was 1.9563602126806073e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3109705974222586e-08, largest imaginary/real ratio was 9.965840077443212e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.031321287329223e-09, largest imaginary/real ratio was 3.419067350915366e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0992686748102555e-08, largest imaginary/real ratio was 1.004161068807828e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4560894475952538e-08, largest imaginary/real ratio was 3.5348066330667478e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0175258867533527e-08, largest imaginary/real ratio was 1.946202924964163e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.499222349337584e-09, largest imaginary/real ratio was 4.0762138670690654e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5998550398469708e-08, largest imaginary/real ratio was 2.698380607633755e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.871914468074682e-09, largest imaginary/real ratio was 2.1775511937170586e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.176680544243278e-08, largest imaginary/real ratio was 2.0854384938778395e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4669948226941486e-08, largest imaginary/real ratio was 3.277075037838747e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.586233601128956e-09, largest imaginary/real ratio was 2.45848682853209e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5391393425392365e-08, largest imaginary/real ratio was 3.587785780306486e-12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.854137624950736e-09, largest imaginary/real ratio was 1.435176063627195e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0568377647645934e-08, largest imaginary/real ratio was 5.2824235124638696e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.644359724889049e-09, largest imaginary/real ratio was 1.6978529836254763e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0374231681482543e-08, largest imaginary/real ratio was 4.504006702152354e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.044783138841812e-08, largest imaginary/real ratio was 1.3133393777374305e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.7091122615128413e-09, largest imaginary/real ratio was 7.847855787379833e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.424020407697586e-09, largest imaginary/real ratio was 2.524028417569885e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.521433820211869e-08, largest imaginary/real ratio was 1.4724771548244336e-10.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4539321980056183e-08, largest imaginary/real ratio was 1.651389583152516e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8589927830673994e-08, largest imaginary/real ratio was 3.1375986654543946e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6190880556972696e-08, largest imaginary/real ratio was 4.592091465466639e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.052441604933096e-09, largest imaginary/real ratio was 3.679875245811685e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.604185401495287e-08, largest imaginary/real ratio was 1.9818391993329013e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.083657068934776e-09, largest imaginary/real ratio was 1.4076603988127706e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.256384996039017e-09, largest imaginary/real ratio was 1.1136661056212408e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.424298040183535e-09, largest imaginary/real ratio was 1.1692780092259466e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.039250601695278e-09, largest imaginary/real ratio was 6.210627150166696e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0112883991699023e-08, largest imaginary/real ratio was 3.995405373752001e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.081664531947777e-09, largest imaginary/real ratio was 6.915938217719297e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1081309893454442e-08, largest imaginary/real ratio was 7.835188140788522e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.773302257475803e-09, largest imaginary/real ratio was 2.2299138457496224e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.995251101570385e-10, largest imaginary/real ratio was 1.2595375122520057e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.831142462099596e-09, largest imaginary/real ratio was 3.586134114159357e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.475830851824431e-09, largest imaginary/real ratio was 9.85123189454775e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.139664822307188e-09, largest imaginary/real ratio was 1.8417539576409397e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.000286520901251e-08, largest imaginary/real ratio was 6.144435372773797e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.01076792071163e-09, largest imaginary/real ratio was 1.5941294265627417e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.581638554153001e-09, largest imaginary/real ratio was 6.416100415719494e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3867097235022088e-08, largest imaginary/real ratio was 1.4449103430499637e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.047096618795126e-09, largest imaginary/real ratio was 3.9586475354220125e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4142291690140583e-08, largest imaginary/real ratio was 1.3219480334568015e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.930577900978794e-09, largest imaginary/real ratio was 5.944869151555966e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.758951664860101e-08, largest imaginary/real ratio was 1.1986807048998403e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.7285559984592907e-08, largest imaginary/real ratio was 1.1028413364955975e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1931610762833104e-09, largest imaginary/real ratio was 8.419572682026822e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5424401077989555e-08, largest imaginary/real ratio was 7.622512460839026e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.530725976925426e-09, largest imaginary/real ratio was 1.5352294082515874e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6323603185326012e-08, largest imaginary/real ratio was 1.0367826408118997e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.006948475596756e-08, largest imaginary/real ratio was 2.504958400745828e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.7017903820911854e-08, largest imaginary/real ratio was 1.4607556028291576e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8874603079300735e-08, largest imaginary/real ratio was 4.3427287878909393e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3048177424609182e-08, largest imaginary/real ratio was 9.660229975401921e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2428497355795687e-08, largest imaginary/real ratio was 1.4957510538997724e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8761364897608734e-08, largest imaginary/real ratio was 1.2862433084907285e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3123848995082908e-08, largest imaginary/real ratio was 8.21668450404578e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.641070020445113e-08, largest imaginary/real ratio was 5.113977961025743e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2613149691831881e-08, largest imaginary/real ratio was 3.494457918287935e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3794143411246375e-08, largest imaginary/real ratio was 6.860133351092303e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9808326227624583e-09, largest imaginary/real ratio was 7.687911740137219e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.483986195854757e-08, largest imaginary/real ratio was 2.1009919723373345e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.059400680752651e-08, largest imaginary/real ratio was 3.5283152899316407e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.136357672470565e-09, largest imaginary/real ratio was 1.0769721129094026e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5748383886515277e-08, largest imaginary/real ratio was 3.265212988934273e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.515625302427202e-08, largest imaginary/real ratio was 3.157402959145439e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.72040588807666e-09, largest imaginary/real ratio was 2.595271326585126e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.355436549502785e-08, largest imaginary/real ratio was 1.675975854706961e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9046662291193715e-09, largest imaginary/real ratio was 2.467287059260456e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4661518418823689e-08, largest imaginary/real ratio was 7.824324403696146e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8868983292100645e-08, largest imaginary/real ratio was 2.319113769438474e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.405300639816883e-09, largest imaginary/real ratio was 4.651972929206284e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.566421537541739e-09, largest imaginary/real ratio was 5.331987914808434e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.216706022611564e-08, largest imaginary/real ratio was 2.0776876148698297e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.237561608130194e-08, largest imaginary/real ratio was 2.287862347318072e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.500314763148572e-08, largest imaginary/real ratio was 4.598965075960393e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.993254320457382e-08, largest imaginary/real ratio was 5.553297117516623e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.824046973983455e-09, largest imaginary/real ratio was 1.6725783654181534e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.403142788431242e-09, largest imaginary/real ratio was 4.268521929721469e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.819400626810136e-08, largest imaginary/real ratio was 9.897279795617249e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3843998789734544e-08, largest imaginary/real ratio was 6.448722104060824e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.125708444498598e-08, largest imaginary/real ratio was 1.0566805905387723e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.967032266370775e-08, largest imaginary/real ratio was 7.489034458261556e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7573250107612748e-08, largest imaginary/real ratio was 3.453286313368619e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9141841040960057e-08, largest imaginary/real ratio was 1.4315732222041734e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.558739003982189e-09, largest imaginary/real ratio was 4.2928624970677264e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.675110118172012e-09, largest imaginary/real ratio was 4.310988449168237e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.849252370555857e-08, largest imaginary/real ratio was 6.902943255936537e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4662804652215374e-08, largest imaginary/real ratio was 3.812324975783499e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5446823868754266e-09, largest imaginary/real ratio was 7.742339479243311e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.169138479884509e-08, largest imaginary/real ratio was 1.1014109761723164e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8739906272911955e-08, largest imaginary/real ratio was 4.7113762507555794e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.239357197346922e-08, largest imaginary/real ratio was 2.4014863160029936e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.15168660769486e-09, largest imaginary/real ratio was 1.8029390645409416e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9898891890783734e-09, largest imaginary/real ratio was 4.222058514319102e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6325062737814466e-08, largest imaginary/real ratio was 7.474555440076752e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.544611371664656e-08, largest imaginary/real ratio was 1.3705320590200731e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2591009498444472e-08, largest imaginary/real ratio was 1.3882571768081188e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0902331030645206e-08, largest imaginary/real ratio was 2.6433659093634067e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2562960201588194e-08, largest imaginary/real ratio was 2.994466731549797e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.133687460290973e-09, largest imaginary/real ratio was 3.8609792575689394e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6470176479782362e-08, largest imaginary/real ratio was 1.0578195365493028e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5356115976450253e-08, largest imaginary/real ratio was 4.6495349576377234e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.126113605818486e-08, largest imaginary/real ratio was 2.6806449148499737e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.979617693956744e-08, largest imaginary/real ratio was 1.4774622488545787e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3945314801803884e-08, largest imaginary/real ratio was 5.057819130383245e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.4674724598618075e-08, largest imaginary/real ratio was 5.107913719067654e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5460245628424305e-08, largest imaginary/real ratio was 2.2104588024252596e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1152118058020662e-08, largest imaginary/real ratio was 1.0855131720892022e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.36794227977065e-08, largest imaginary/real ratio was 1.918834803565257e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6471463039719826e-09, largest imaginary/real ratio was 1.395049201712944e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.4245137526819286e-08, largest imaginary/real ratio was 3.866125797548469e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.650784551257138e-09, largest imaginary/real ratio was 1.6508670099528338e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0807846432200964e-08, largest imaginary/real ratio was 4.307372480381702e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.919012076802729e-08, largest imaginary/real ratio was 2.998479122130145e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.438455994958522e-09, largest imaginary/real ratio was 7.911747347921776e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5481028942517003e-08, largest imaginary/real ratio was 1.322844679121476e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9865902477213816e-08, largest imaginary/real ratio was 2.3332205241263733e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.118214129634896e-08, largest imaginary/real ratio was 2.8805424451801223e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3833518090386578e-08, largest imaginary/real ratio was 1.1209709985108446e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0808538596671519e-08, largest imaginary/real ratio was 2.0311649887401013e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4782316123235395e-08, largest imaginary/real ratio was 1.6195396397373477e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6092945848499932e-08, largest imaginary/real ratio was 2.0386150400798177e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.161487041000621e-08, largest imaginary/real ratio was 1.5304078626218038e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8264844367351395e-08, largest imaginary/real ratio was 3.9714861143371106e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.370702230237821e-08, largest imaginary/real ratio was 3.3710088877665617e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.033672023802775e-09, largest imaginary/real ratio was 4.4180706384520584e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.586029069311623e-09, largest imaginary/real ratio was 2.1210639552650217e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7965126865528364e-08, largest imaginary/real ratio was 1.8175547468615843e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.432790767878883e-08, largest imaginary/real ratio was 1.4622171624267276e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.083234656809492e-08, largest imaginary/real ratio was 8.850082549847752e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.846606922871027e-09, largest imaginary/real ratio was 8.342860810504293e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.918566552478926e-08, largest imaginary/real ratio was 3.331055860425407e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9311890961201728e-08, largest imaginary/real ratio was 2.9738715744515545e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1765944169472214e-08, largest imaginary/real ratio was 5.5275062134291135e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.628641923848885e-08, largest imaginary/real ratio was 6.94278219413323e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3278465959622518e-08, largest imaginary/real ratio was 7.00698534630729e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.412066477166097e-08, largest imaginary/real ratio was 5.85836314193922e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7300962004433538e-08, largest imaginary/real ratio was 4.5343695483568315e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1156275416459213e-08, largest imaginary/real ratio was 1.1401467594567788e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.55849964521733e-08, largest imaginary/real ratio was 1.1675799430913272e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6229327314709686e-08, largest imaginary/real ratio was 1.9744756513807836e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5544041850227305e-09, largest imaginary/real ratio was 4.052902224997005e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.0987703386821634e-08, largest imaginary/real ratio was 5.861439357883062e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.548032083737067e-08, largest imaginary/real ratio was 6.089149843081799e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2303698180997248e-08, largest imaginary/real ratio was 1.8183796670452235e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8907455933012537e-08, largest imaginary/real ratio was 6.183208825441275e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.160379341264591e-09, largest imaginary/real ratio was 1.0401522108750986e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1417927513794274e-08, largest imaginary/real ratio was 1.5065509785112804e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.318147577708757e-09, largest imaginary/real ratio was 6.729496051518519e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.410130111563859e-08, largest imaginary/real ratio was 8.405372003814411e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.42315735427723e-08, largest imaginary/real ratio was 8.7456974054938e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3760594290954313e-09, largest imaginary/real ratio was 1.8293449243604874e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7694120557962167e-08, largest imaginary/real ratio was 7.615327989894489e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.182267565327225e-08, largest imaginary/real ratio was 3.0348252210779503e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.542352462025048e-08, largest imaginary/real ratio was 1.021925924724065e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6743164252893952e-09, largest imaginary/real ratio was 3.6522660266434426e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.362793526313621e-08, largest imaginary/real ratio was 3.7863879196090534e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.100701977988134e-08, largest imaginary/real ratio was 3.68488603293307e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6284144871592716e-08, largest imaginary/real ratio was 1.8032507240683578e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7045338436422218e-08, largest imaginary/real ratio was 1.2138076997432492e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.265836939070913e-09, largest imaginary/real ratio was 6.771929827316065e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9067255337772877e-08, largest imaginary/real ratio was 5.460814999004667e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4578062315834584e-08, largest imaginary/real ratio was 8.845176898751141e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.51315582292395e-09, largest imaginary/real ratio was 5.109431230113374e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2010797768132527e-08, largest imaginary/real ratio was 1.501243521028279e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4568234371451046e-09, largest imaginary/real ratio was 1.566637736757734e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.399215052460942e-09, largest imaginary/real ratio was 8.42791819717833e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.220921068090849e-09, largest imaginary/real ratio was 1.804438787000001e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.692568776166939e-09, largest imaginary/real ratio was 4.3220167131706604e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6918736566878742e-08, largest imaginary/real ratio was 4.290437623207234e-12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.134194122323265e-09, largest imaginary/real ratio was 2.928429576207592e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.461488371752493e-09, largest imaginary/real ratio was 2.432422663657385e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.578168437711234e-09, largest imaginary/real ratio was 1.1073227557472874e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.186642672360697e-08, largest imaginary/real ratio was 1.2108984640845169e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.4068764024918345e-09, largest imaginary/real ratio was 9.229685137029936e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.051992040533321e-09, largest imaginary/real ratio was 9.077878073894289e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6984858119698312e-09, largest imaginary/real ratio was 7.46295157893088e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0034374312881162e-08, largest imaginary/real ratio was 2.80589625532245e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9535793521066734e-08, largest imaginary/real ratio was 1.140391447329409e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.52464460418158e-08, largest imaginary/real ratio was 7.997372832812222e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.523967898811921e-08, largest imaginary/real ratio was 1.2122743047515992e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.278240222593851e-08, largest imaginary/real ratio was 8.323722052110098e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.14010185868002e-08, largest imaginary/real ratio was 1.2561758833019298e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.821594045343148e-08, largest imaginary/real ratio was 2.5121719360495866e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.992496677872278e-08, largest imaginary/real ratio was 1.2141311583243437e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1731398441840538e-08, largest imaginary/real ratio was 2.899230578255018e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.433267965219073e-08, largest imaginary/real ratio was 1.4571016285254818e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5544202242949754e-08, largest imaginary/real ratio was 4.059640822059109e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3241686033812885e-08, largest imaginary/real ratio was 5.645197430885221e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9796963879734646e-08, largest imaginary/real ratio was 1.4443661651239888e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.183752426064234e-08, largest imaginary/real ratio was 4.109093458386136e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0782024911859236e-08, largest imaginary/real ratio was 1.810237420363601e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1676112413337034e-08, largest imaginary/real ratio was 2.201071874316365e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.977027815630024e-09, largest imaginary/real ratio was 1.2942424416208263e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.109049307391263e-08, largest imaginary/real ratio was 3.722881904460939e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2074546791470023e-08, largest imaginary/real ratio was 2.479015651676275e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.158220663032778e-09, largest imaginary/real ratio was 5.070973877838776e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.806316948483626e-09, largest imaginary/real ratio was 1.2590640479838121e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1657833372269575e-08, largest imaginary/real ratio was 9.202758055063235e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.899378722181904e-09, largest imaginary/real ratio was 1.6963202807797777e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.993789659427668e-09, largest imaginary/real ratio was 2.8477212506917436e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.551811337401981e-10, largest imaginary/real ratio was 1.0144616994265868e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.46337281670452e-09, largest imaginary/real ratio was 2.8516750781275406e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.518787638728781e-09, largest imaginary/real ratio was 4.2424924226584174e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3720710528252448e-08, largest imaginary/real ratio was 4.236023013667213e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.342265559046143e-09, largest imaginary/real ratio was 5.0058357127688575e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.261167096162602e-08, largest imaginary/real ratio was 3.1806355105372787e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6865107150515374e-09, largest imaginary/real ratio was 1.0404664794517281e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.2313422712838535e-09, largest imaginary/real ratio was 8.046083355190289e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.10989832495483e-09, largest imaginary/real ratio was 2.8336135062905787e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.474628954539014e-09, largest imaginary/real ratio was 1.3664826259235568e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.915943253839594e-09, largest imaginary/real ratio was 2.2361629480896443e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0427322601941775e-08, largest imaginary/real ratio was 1.819306078361866e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.405010327075776e-09, largest imaginary/real ratio was 2.788054513281503e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.877719688074239e-09, largest imaginary/real ratio was 2.3552746859553346e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.642239447420255e-09, largest imaginary/real ratio was 1.8219751425738275e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.15128660204596e-09, largest imaginary/real ratio was 4.826188613608501e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.213915936779082e-09, largest imaginary/real ratio was 1.6801456642746227e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.03384999981332e-09, largest imaginary/real ratio was 2.034682092206811e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.728229957975304e-09, largest imaginary/real ratio was 8.021920901542655e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.72418191001961e-09, largest imaginary/real ratio was 1.843952111966989e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.062557009409784e-08, largest imaginary/real ratio was 1.7093098681323794e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.196414337451336e-08, largest imaginary/real ratio was 1.6493583272773534e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5715857489894678e-08, largest imaginary/real ratio was 1.647562488122055e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.510225508980063e-09, largest imaginary/real ratio was 5.030712956120383e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6314337009823724e-08, largest imaginary/real ratio was 1.298793692648377e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.966473219206016e-08, largest imaginary/real ratio was 3.591729727664151e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5939872590782486e-08, largest imaginary/real ratio was 5.848003826435324e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.721754548544802e-08, largest imaginary/real ratio was 1.9079748757633318e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6411404890665147e-09, largest imaginary/real ratio was 3.0363232854999e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4987170246640823e-09, largest imaginary/real ratio was 1.777255761685084e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5148610836159235e-08, largest imaginary/real ratio was 3.08259578833667e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.858667250358277e-09, largest imaginary/real ratio was 7.569807740792125e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.61146923496838e-09, largest imaginary/real ratio was 8.619434885846311e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2860496959186456e-08, largest imaginary/real ratio was 1.9746865762572545e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.323442305482765e-09, largest imaginary/real ratio was 2.4922748396499236e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0228366971872298e-08, largest imaginary/real ratio was 2.112240117365281e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.147263392659533e-09, largest imaginary/real ratio was 2.647647827089209e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3610014544807084e-08, largest imaginary/real ratio was 2.2965820403089835e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.807659625720758e-09, largest imaginary/real ratio was 2.9319822117879825e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.558433378072758e-08, largest imaginary/real ratio was 6.998866630729654e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.221588480166881e-09, largest imaginary/real ratio was 4.6798104682779624e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.0099219583681515e-09, largest imaginary/real ratio was 1.6048264816069488e-09.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0776930906689986e-08, largest imaginary/real ratio was 1.284222100410729e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.291754001159385e-09, largest imaginary/real ratio was 1.0589394377039584e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.610462993740744e-09, largest imaginary/real ratio was 1.824564562233482e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4649689690146086e-08, largest imaginary/real ratio was 1.17076214946886e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.865457985757731e-09, largest imaginary/real ratio was 1.5352370010356653e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9838117213033497e-09, largest imaginary/real ratio was 8.097967995403163e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0248715804828131e-08, largest imaginary/real ratio was 1.1342949536666522e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.827053331272133e-08, largest imaginary/real ratio was 2.9251805440693583e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4145064723348803e-08, largest imaginary/real ratio was 1.2149427964848557e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8078218713883598e-08, largest imaginary/real ratio was 4.355653024755408e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.5888575139334395e-09, largest imaginary/real ratio was 2.7228671351758186e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.1558559997725205e-09, largest imaginary/real ratio was 1.3211727348474819e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.0437063773356105e-09, largest imaginary/real ratio was 2.5738832393746946e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.179980056360107e-08, largest imaginary/real ratio was 2.4331550196604226e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7489095162498178e-08, largest imaginary/real ratio was 2.975966252848654e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.659807682241695e-09, largest imaginary/real ratio was 1.4422840097132046e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3380361300831747e-08, largest imaginary/real ratio was 4.070989332875565e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.232606703051955e-08, largest imaginary/real ratio was 3.190099839034112e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.132540043475333e-09, largest imaginary/real ratio was 9.185896508724966e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0768378493557825e-08, largest imaginary/real ratio was 1.2858860412933644e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.862889530994148e-09, largest imaginary/real ratio was 9.905313686100681e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6209104123954626e-08, largest imaginary/real ratio was 3.681710089330651e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3705288535035892e-08, largest imaginary/real ratio was 4.1882593415127806e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.561998708755561e-09, largest imaginary/real ratio was 6.048467915489631e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7444021893458525e-09, largest imaginary/real ratio was 6.636362505330483e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.469181473060234e-09, largest imaginary/real ratio was 4.423180319009102e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8971752513147203e-09, largest imaginary/real ratio was 2.5558944194150374e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8325165732545072e-08, largest imaginary/real ratio was 2.3473984206655006e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1179524901586336e-09, largest imaginary/real ratio was 8.215229754332428e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.494199886506106e-09, largest imaginary/real ratio was 1.978213754710687e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.7218501592020927e-09, largest imaginary/real ratio was 3.2087267203966074e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9389247148393013e-09, largest imaginary/real ratio was 1.9063289754957366e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3020979801425178e-09, largest imaginary/real ratio was 5.0032757116849805e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.922238360340176e-09, largest imaginary/real ratio was 5.299072693825952e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6968724074900886e-09, largest imaginary/real ratio was 6.3882980110518e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3446086204104445e-08, largest imaginary/real ratio was 2.049383279273489e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.1202291924274865e-09, largest imaginary/real ratio was 4.635542229157361e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3046771791473303e-09, largest imaginary/real ratio was 5.495230047261644e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.44064708636408e-09, largest imaginary/real ratio was 5.9100633859634356e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.920525709734235e-09, largest imaginary/real ratio was 7.785525550305424e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.772010299500278e-09, largest imaginary/real ratio was 2.2516665789879608e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2508887756923634e-08, largest imaginary/real ratio was 1.0518518840370415e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.709403104660571e-09, largest imaginary/real ratio was 5.97079125775577e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.070759323395795e-09, largest imaginary/real ratio was 5.852112541805367e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.842050595008994e-09, largest imaginary/real ratio was 1.775967797934669e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.679497909772029e-09, largest imaginary/real ratio was 5.742612541778689e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.7250651638038266e-09, largest imaginary/real ratio was 3.145499094262807e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.9677273120586085e-09, largest imaginary/real ratio was 1.225758016547357e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0334058112323693e-08, largest imaginary/real ratio was 2.550412692686309e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.030444388316682e-09, largest imaginary/real ratio was 6.420628360609693e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5784902538487042e-08, largest imaginary/real ratio was 3.290330340230157e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3158114107143163e-09, largest imaginary/real ratio was 1.6997389874267016e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7682932091162783e-08, largest imaginary/real ratio was 2.188009057668363e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.704864646750123e-09, largest imaginary/real ratio was 1.9467379524696178e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.813623829690094e-09, largest imaginary/real ratio was 6.187205866848333e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2772799692097983e-09, largest imaginary/real ratio was 1.9979250037178964e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.227691618767156e-09, largest imaginary/real ratio was 4.11579299316403e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2176447613179659e-08, largest imaginary/real ratio was 4.649592932893072e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.97669691711898e-09, largest imaginary/real ratio was 2.7140771563838705e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.090251977588373e-09, largest imaginary/real ratio was 3.132726824919708e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1903053514092347e-08, largest imaginary/real ratio was 2.228625569783543e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.704349531838594e-09, largest imaginary/real ratio was 5.966853996897542e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.902077905901663e-09, largest imaginary/real ratio was 1.3219068809192915e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.7831398155632255e-09, largest imaginary/real ratio was 1.6480073311567279e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5153024968585895e-09, largest imaginary/real ratio was 8.230954766743304e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1386934488398283e-08, largest imaginary/real ratio was 1.3788914448764583e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.15518601297345e-09, largest imaginary/real ratio was 6.196084989267717e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.988246593427071e-09, largest imaginary/real ratio was 7.566925189634248e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0729547666134446e-09, largest imaginary/real ratio was 1.7608874272706353e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.700762268285757e-09, largest imaginary/real ratio was 2.793202481149063e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0558345926930103e-08, largest imaginary/real ratio was 2.774486363130941e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.841987570839623e-08, largest imaginary/real ratio was 3.665617901513761e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.314086414907667e-09, largest imaginary/real ratio was 1.5852004935937665e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.883903564611264e-09, largest imaginary/real ratio was 4.658950803931897e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1840082555485185e-08, largest imaginary/real ratio was 2.4219929379660754e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1106906340674947e-08, largest imaginary/real ratio was 6.551688414943824e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.9432535736569416e-08, largest imaginary/real ratio was 3.028706959594926e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.339588126918441e-09, largest imaginary/real ratio was 9.805416561355148e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.607721333352651e-09, largest imaginary/real ratio was 4.940227051599865e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.76998618469226e-09, largest imaginary/real ratio was 1.785221480427884e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.197936664804057e-09, largest imaginary/real ratio was 8.329757329191952e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.26173395355797e-09, largest imaginary/real ratio was 2.2562908982428026e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.57488752322773e-08, largest imaginary/real ratio was 2.2870800609579756e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.627388274314071e-09, largest imaginary/real ratio was 5.249243455360467e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.668468595812165e-09, largest imaginary/real ratio was 1.1981810104925692e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3270073672063048e-08, largest imaginary/real ratio was 3.762990266077387e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.478493206842442e-09, largest imaginary/real ratio was 3.5008409175717847e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9751538365342242e-09, largest imaginary/real ratio was 5.763401797789591e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7934947834894877e-08, largest imaginary/real ratio was 3.5370409223956713e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.850578848974764e-09, largest imaginary/real ratio was 4.973445919140388e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4534870659024011e-08, largest imaginary/real ratio was 5.043349700371742e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.147467176429986e-08, largest imaginary/real ratio was 8.928529406119686e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0852583405999497e-08, largest imaginary/real ratio was 1.0141859233791235e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.98529967040207e-09, largest imaginary/real ratio was 1.054494089263637e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.234612441174563e-09, largest imaginary/real ratio was 3.0323837288558243e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.502879469180327e-09, largest imaginary/real ratio was 5.152642393133092e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.258801949720946e-09, largest imaginary/real ratio was 4.01864921733742e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.265447497085692e-09, largest imaginary/real ratio was 3.050507709594032e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.232630438272209e-09, largest imaginary/real ratio was 5.313092542697453e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.527115083640915e-09, largest imaginary/real ratio was 2.5768563012003664e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.307098493614828e-08, largest imaginary/real ratio was 3.6222227703575823e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0810606680593997e-09, largest imaginary/real ratio was 2.49382598542524e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1687284400849604e-08, largest imaginary/real ratio was 7.120274305420895e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.607155741209514e-09, largest imaginary/real ratio was 8.894359559786767e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.682344428452109e-09, largest imaginary/real ratio was 2.3852040097121043e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.737572752177554e-09, largest imaginary/real ratio was 1.942841577898716e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9939297345926177e-08, largest imaginary/real ratio was 1.902081941038127e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.4272361135006316e-09, largest imaginary/real ratio was 8.24682181026059e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.88964199610546e-09, largest imaginary/real ratio was 2.071785379066572e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.08838839988287e-09, largest imaginary/real ratio was 8.952627877683007e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1167705917243771e-08, largest imaginary/real ratio was 5.5687191936168987e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.280984228015193e-09, largest imaginary/real ratio was 4.546843021792481e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.281894829674692e-09, largest imaginary/real ratio was 1.7042715917825298e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4344343890357191e-08, largest imaginary/real ratio was 3.739361586372448e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8305731946004527e-09, largest imaginary/real ratio was 1.5903835642325658e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2526283409433847e-09, largest imaginary/real ratio was 3.831939079150645e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1430664486832564e-09, largest imaginary/real ratio was 4.8181462599261467e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.733085922469019e-09, largest imaginary/real ratio was 1.9671633876429962e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.3245744293326085e-09, largest imaginary/real ratio was 3.3190378887446726e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5183786492619378e-09, largest imaginary/real ratio was 5.835949670130726e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.1209132518022745e-09, largest imaginary/real ratio was 6.82656026413021e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.900069040330137e-09, largest imaginary/real ratio was 3.7395244788409485e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.747411610782195e-09, largest imaginary/real ratio was 1.3824368544918622e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4895116980158768e-09, largest imaginary/real ratio was 6.308237425103641e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.384305384923703e-09, largest imaginary/real ratio was 5.83259341173924e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.491613814658033e-09, largest imaginary/real ratio was 9.413656571012554e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.521924635408451e-09, largest imaginary/real ratio was 1.5894195095943067e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.493024150262291e-09, largest imaginary/real ratio was 8.012597325606325e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.876188273716207e-09, largest imaginary/real ratio was 5.988231679802592e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0086415631770742e-08, largest imaginary/real ratio was 1.7028962652485244e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.722592519046071e-09, largest imaginary/real ratio was 1.0971430409061586e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.120715708165654e-09, largest imaginary/real ratio was 5.93212893420968e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.718003556267256e-09, largest imaginary/real ratio was 2.35672087574066e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.433668850657945e-09, largest imaginary/real ratio was 2.016155361960418e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.433020629610834e-09, largest imaginary/real ratio was 1.4943448314120477e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.856163066639988e-09, largest imaginary/real ratio was 4.299374704547665e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.512364085593124e-09, largest imaginary/real ratio was 1.098135522122181e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.7846770618883036e-09, largest imaginary/real ratio was 5.121850078403203e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.584431822887373e-09, largest imaginary/real ratio was 8.5467942549809e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.938586195244949e-09, largest imaginary/real ratio was 8.188673836408771e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.517049220122916e-09, largest imaginary/real ratio was 6.783550211262267e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.705806753572762e-09, largest imaginary/real ratio was 3.018347555608183e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1866419883141541e-09, largest imaginary/real ratio was 1.7760049748809947e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.439796564996986e-09, largest imaginary/real ratio was 2.9001457239585086e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.411266544680933e-09, largest imaginary/real ratio was 5.6371574487051834e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.779063778781636e-09, largest imaginary/real ratio was 1.5490530066117094e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.863785971290149e-09, largest imaginary/real ratio was 2.9283246343906196e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4782051806208196e-09, largest imaginary/real ratio was 7.829223562787171e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2120910705768105e-09, largest imaginary/real ratio was 3.540620614944256e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1571477246186984e-08, largest imaginary/real ratio was 4.834146089982861e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.914356407883332e-09, largest imaginary/real ratio was 1.950908588857341e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.295207041276843e-09, largest imaginary/real ratio was 1.1255240113505895e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2644565632529644e-08, largest imaginary/real ratio was 3.9943122349814e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2653696281864963e-08, largest imaginary/real ratio was 1.3279431083780656e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0102960990808948e-09, largest imaginary/real ratio was 2.2097210678032526e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.051159097237714e-08, largest imaginary/real ratio was 8.230968406349402e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3366984158534063e-09, largest imaginary/real ratio was 1.2097602405954346e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.332476814565406e-09, largest imaginary/real ratio was 4.1978016882588135e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.487633000496209e-09, largest imaginary/real ratio was 1.8919049565174757e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.61594831266535e-09, largest imaginary/real ratio was 4.960673380265127e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.555709171793835e-09, largest imaginary/real ratio was 2.289773729624257e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.397519795260467e-09, largest imaginary/real ratio was 2.18803990987202e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.617974868666511e-09, largest imaginary/real ratio was 5.966941679125532e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.941703616700233e-09, largest imaginary/real ratio was 2.714484328386462e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.897123364050623e-09, largest imaginary/real ratio was 1.8396736302439253e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7407020136726e-08, largest imaginary/real ratio was 5.98300436505924e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0027758078491898e-09, largest imaginary/real ratio was 8.190983144187093e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.923171521739586e-09, largest imaginary/real ratio was 6.028974799971635e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.997479658445344e-09, largest imaginary/real ratio was 1.7184733155588752e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.031920651066417e-09, largest imaginary/real ratio was 1.8405095367966388e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.2403941836473894e-09, largest imaginary/real ratio was 8.452969415974412e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.307213713362764e-09, largest imaginary/real ratio was 5.4882686943815074e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.230439743495373e-09, largest imaginary/real ratio was 8.976749749921265e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.513719478907951e-09, largest imaginary/real ratio was 2.995948397532692e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.269701445785345e-09, largest imaginary/real ratio was 1.5894852196596866e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.812410883735092e-09, largest imaginary/real ratio was 3.627731207120573e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.255887235335762e-09, largest imaginary/real ratio was 6.416949685516211e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.695513471390127e-09, largest imaginary/real ratio was 1.5362975299400004e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2449468689708376e-09, largest imaginary/real ratio was 1.3430896419093155e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.093972214288124e-09, largest imaginary/real ratio was 3.0733430858687824e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.388366385640204e-09, largest imaginary/real ratio was 1.7589099923063695e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.806054967750254e-09, largest imaginary/real ratio was 1.1699705517395874e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.315812626713557e-09, largest imaginary/real ratio was 2.8604885857425644e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.091685003174731e-09, largest imaginary/real ratio was 3.502049282483971e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5086892846064856e-09, largest imaginary/real ratio was 1.196737332434594e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.465460222604539e-09, largest imaginary/real ratio was 7.896742421376553e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1416679805296483e-10, largest imaginary/real ratio was 4.749774900564773e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0068180997559789e-09, largest imaginary/real ratio was 1.1596883159307679e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.513020429781533e-09, largest imaginary/real ratio was 5.841018700339249e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.6547777525374114e-09, largest imaginary/real ratio was 1.9740509433627593e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0810037745961906e-08, largest imaginary/real ratio was 5.2001185753960324e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.265696296727865e-09, largest imaginary/real ratio was 1.2088620767427e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1945443118263105e-09, largest imaginary/real ratio was 2.8465240124594234e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.823634941269522e-09, largest imaginary/real ratio was 1.2015693593257598e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.629043607883005e-09, largest imaginary/real ratio was 3.262369790630582e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.031634436671133e-09, largest imaginary/real ratio was 8.24072585168533e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.840707332590453e-09, largest imaginary/real ratio was 3.0974295536444423e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.861498843918894e-09, largest imaginary/real ratio was 5.993874215016715e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.627078665217537e-09, largest imaginary/real ratio was 8.374696697942869e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.148379523563215e-09, largest imaginary/real ratio was 6.714587072898786e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5111443324301517e-09, largest imaginary/real ratio was 1.1930258943490989e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4795617040123906e-09, largest imaginary/real ratio was 3.152267656265913e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9831634006300976e-09, largest imaginary/real ratio was 3.258461188045863e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.8237298049957166e-09, largest imaginary/real ratio was 1.4706458565687772e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.378922367017902e-09, largest imaginary/real ratio was 4.265947570999216e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.103462112934248e-09, largest imaginary/real ratio was 5.027430379564652e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5570972021601243e-09, largest imaginary/real ratio was 1.6712607347080174e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6750357549208604e-09, largest imaginary/real ratio was 3.0850016175983855e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.48830146419612e-09, largest imaginary/real ratio was 1.5858642920589414e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.827818072063527e-09, largest imaginary/real ratio was 4.9449087107969935e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5626939592548594e-09, largest imaginary/real ratio was 5.156692824938983e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.743744982269319e-09, largest imaginary/real ratio was 1.6284055827743288e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1884896201057968e-08, largest imaginary/real ratio was 2.4252242871080124e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.597989595549294e-09, largest imaginary/real ratio was 2.2276743871449615e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4337907135619718e-09, largest imaginary/real ratio was 3.774029162301942e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5384300678537065e-09, largest imaginary/real ratio was 5.897677843016676e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.861394312456421e-09, largest imaginary/real ratio was 4.8918790740059216e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4274923659956016e-09, largest imaginary/real ratio was 7.196362466261796e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1486986886931315e-09, largest imaginary/real ratio was 7.301414783667498e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5059752300598264e-09, largest imaginary/real ratio was 1.32255119155667e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.677476324647269e-09, largest imaginary/real ratio was 2.8095992750278565e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.550200676012298e-09, largest imaginary/real ratio was 9.761132709806528e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1977890798481073e-09, largest imaginary/real ratio was 8.829007122658442e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1086364588359585e-09, largest imaginary/real ratio was 7.833935390721174e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.647525246192235e-09, largest imaginary/real ratio was 4.2725053820964956e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.638836244817688e-09, largest imaginary/real ratio was 3.699975693289526e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.369049295559524e-09, largest imaginary/real ratio was 1.8550987253540286e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7236556190105918e-09, largest imaginary/real ratio was 9.149868186176531e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.787221380580053e-09, largest imaginary/real ratio was 3.275997845692982e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.076915623135136e-09, largest imaginary/real ratio was 1.507145341943753e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6596198533547445e-08, largest imaginary/real ratio was 9.756337235957454e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.687093398843316e-10, largest imaginary/real ratio was 2.477490921014213e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.27212550360964e-09, largest imaginary/real ratio was 2.461068539034191e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.2169105982794454e-09, largest imaginary/real ratio was 6.91681935723451e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.905402175081353e-09, largest imaginary/real ratio was 5.4712985284817877e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.255038369231235e-09, largest imaginary/real ratio was 1.6010504734724676e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.449010495004946e-09, largest imaginary/real ratio was 4.634758318747295e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0992808044767692e-09, largest imaginary/real ratio was 4.1138626999975637e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5186368740912996e-09, largest imaginary/real ratio was 2.5757301047964804e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.926055379801169e-09, largest imaginary/real ratio was 5.50495475310102e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1374785277564443e-09, largest imaginary/real ratio was 1.0315149029437583e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.478144865612679e-09, largest imaginary/real ratio was 5.382964068527553e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.467214043854953e-09, largest imaginary/real ratio was 1.0552581107770662e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.849857752018496e-09, largest imaginary/real ratio was 3.146105477248177e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.304533659032647e-09, largest imaginary/real ratio was 3.9054198900527344e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.024495606706617e-09, largest imaginary/real ratio was 1.7317217002682719e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8684620833264386e-09, largest imaginary/real ratio was 5.943468211808503e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.960882639849052e-09, largest imaginary/real ratio was 5.286684127170593e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.8681686029403804e-09, largest imaginary/real ratio was 6.226724064094905e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.379671105539624e-09, largest imaginary/real ratio was 5.322985931999736e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3290197978635982e-09, largest imaginary/real ratio was 1.260025131676004e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.413278706966519e-09, largest imaginary/real ratio was 5.55551341692485e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0060478902777726e-09, largest imaginary/real ratio was 1.057701851848075e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.224946160759499e-09, largest imaginary/real ratio was 4.832186057739968e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.423829417746344e-09, largest imaginary/real ratio was 5.237775548925383e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.8442816254041526e-09, largest imaginary/real ratio was 2.030599080036236e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.591527217572634e-09, largest imaginary/real ratio was 4.2555799245474574e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2082161026945992e-09, largest imaginary/real ratio was 4.8954002587267123e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.339551156118801e-09, largest imaginary/real ratio was 6.288985920953748e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.637046654861854e-09, largest imaginary/real ratio was 1.3548975772401544e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.037975697289325e-09, largest imaginary/real ratio was 7.371603364646547e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6278091002046061e-09, largest imaginary/real ratio was 9.923419527129043e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.439022632385178e-09, largest imaginary/real ratio was 9.085784718950275e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5092008593829427e-09, largest imaginary/real ratio was 2.483384670806151e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.572691214134364e-09, largest imaginary/real ratio was 7.502327906940647e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7187529849974245e-09, largest imaginary/real ratio was 3.3269448682388934e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9648625948106425e-09, largest imaginary/real ratio was 1.0286488012427958e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.794829012224608e-10, largest imaginary/real ratio was 6.930975858511355e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.725305065562386e-09, largest imaginary/real ratio was 7.090951359379811e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.754823118003318e-10, largest imaginary/real ratio was 5.805824327323994e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4633857599553494e-10, largest imaginary/real ratio was 6.048919285858091e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1982177072597692e-09, largest imaginary/real ratio was 2.2217926218337378e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7445054184877022e-09, largest imaginary/real ratio was 9.054985745961312e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6994087179570534e-09, largest imaginary/real ratio was 5.298029049215342e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.01575876130056e-09, largest imaginary/real ratio was 1.5307243742431854e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.427008783611707e-09, largest imaginary/real ratio was 5.281981848066953e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0344852642976165e-09, largest imaginary/real ratio was 8.489701618347422e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.026623337140148e-09, largest imaginary/real ratio was 2.1613319281999008e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.624644636113098e-09, largest imaginary/real ratio was 7.085366718398536e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4739147462147466e-09, largest imaginary/real ratio was 6.064405373410713e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6372961783957017e-09, largest imaginary/real ratio was 5.60229304546569e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.849519920071525e-09, largest imaginary/real ratio was 7.5828932861878e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.224650464989639e-09, largest imaginary/real ratio was 5.219957217453452e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4255245662658842e-09, largest imaginary/real ratio was 5.93334945737748e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4644942616054864e-09, largest imaginary/real ratio was 6.213239763085719e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9823002216114957e-09, largest imaginary/real ratio was 1.3060169334422498e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3626460503379185e-09, largest imaginary/real ratio was 5.343063218244188e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.900703692425658e-09, largest imaginary/real ratio was 2.9109925724464867e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.3543017902606915e-09, largest imaginary/real ratio was 7.813574807235275e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.221673575639216e-09, largest imaginary/real ratio was 2.1272305006903812e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5689130511824175e-09, largest imaginary/real ratio was 1.870655723570231e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4065301291043933e-09, largest imaginary/real ratio was 1.3244407949795493e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0335464329615872e-09, largest imaginary/real ratio was 9.012183431383629e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.2878786347463714e-09, largest imaginary/real ratio was 1.2431490057090637e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.484672612527295e-10, largest imaginary/real ratio was 8.718660853604596e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6847743831275619e-09, largest imaginary/real ratio was 8.512252007199686e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.328868288496505e-09, largest imaginary/real ratio was 2.6212721266995454e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6252050220307069e-09, largest imaginary/real ratio was 2.6815351073497442e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.979245615518309e-09, largest imaginary/real ratio was 2.941017966073308e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.25445840948919e-09, largest imaginary/real ratio was 5.597749717889744e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6390330472052516e-09, largest imaginary/real ratio was 6.791962322309715e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.205188593487582e-09, largest imaginary/real ratio was 2.6083923844227e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.258543615629076e-09, largest imaginary/real ratio was 1.408374570818151e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.2756163525672295e-09, largest imaginary/real ratio was 7.47766079861314e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3002111012710564e-09, largest imaginary/real ratio was 5.03585577865576e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.779186183262044e-09, largest imaginary/real ratio was 1.0501190472477613e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.856107629147442e-09, largest imaginary/real ratio was 1.6164391014502379e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.903054179761344e-10, largest imaginary/real ratio was 2.4510053285765508e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4262907998715252e-09, largest imaginary/real ratio was 6.862361588628142e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.263259814143146e-10, largest imaginary/real ratio was 3.926412780835191e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.860634397999203e-09, largest imaginary/real ratio was 8.338251674083983e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.174372149105059e-09, largest imaginary/real ratio was 2.0937644534370873e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1594804960907715e-09, largest imaginary/real ratio was 1.7796938205009504e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.107778980350014e-09, largest imaginary/real ratio was 4.4759370578767857e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.274347770507465e-09, largest imaginary/real ratio was 1.2805759980018412e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.936144714679575e-09, largest imaginary/real ratio was 2.423949054001765e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8048022069974563e-09, largest imaginary/real ratio was 5.3986148210485645e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.462979202930023e-09, largest imaginary/real ratio was 3.787649958287458e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2392047432521497e-08, largest imaginary/real ratio was 1.4649329689041873e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9335275694251738e-09, largest imaginary/real ratio was 2.2248188289106755e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.161878417032085e-09, largest imaginary/real ratio was 1.7179283719974452e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.258895604721018e-09, largest imaginary/real ratio was 1.923400895794971e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.877828045587863e-09, largest imaginary/real ratio was 1.1067652480904128e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9210402781165748e-09, largest imaginary/real ratio was 2.6951929504380253e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.1803004000552766e-09, largest imaginary/real ratio was 1.2790026466904757e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8192547327885856e-09, largest imaginary/real ratio was 1.7947737318077814e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0875106816660896e-09, largest imaginary/real ratio was 6.798566959515993e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.436181115361503e-09, largest imaginary/real ratio was 1.7194190557668562e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6285769060317301e-09, largest imaginary/real ratio was 3.118066406282441e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.118336479929662e-09, largest imaginary/real ratio was 4.102114775119936e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.089620211944457e-08, largest imaginary/real ratio was 8.203494919500306e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.351121596155519e-09, largest imaginary/real ratio was 6.974390455075376e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4604322118203523e-09, largest imaginary/real ratio was 2.1777399906504345e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.50097828927798e-09, largest imaginary/real ratio was 1.96277662898633e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.895471517676075e-09, largest imaginary/real ratio was 8.087199629068883e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.310167245460476e-09, largest imaginary/real ratio was 3.787290100029629e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1071518982711836e-09, largest imaginary/real ratio was 1.9050511903469942e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.802596806506432e-09, largest imaginary/real ratio was 1.262820186887097e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3391830059519128e-09, largest imaginary/real ratio was 1.6515634709789741e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8919591835305746e-09, largest imaginary/real ratio was 1.7467675257272526e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5386030730691958e-09, largest imaginary/real ratio was 3.7120538938388143e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.671099071736095e-10, largest imaginary/real ratio was 2.3846216141067695e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5494551436483183e-09, largest imaginary/real ratio was 3.846579123664547e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5090947939387023e-09, largest imaginary/real ratio was 2.7722311530519685e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2076599514443144e-08, largest imaginary/real ratio was 6.887405232530619e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4920667340885163e-09, largest imaginary/real ratio was 1.0894047982821197e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.455356025562971e-09, largest imaginary/real ratio was 8.540004630113038e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.708709503634709e-09, largest imaginary/real ratio was 8.32663208252789e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.677706600657097e-09, largest imaginary/real ratio was 4.461637727619389e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.731592560749008e-09, largest imaginary/real ratio was 2.027897853840261e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.37380608993216e-09, largest imaginary/real ratio was 1.840542525815287e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.081909546274969e-09, largest imaginary/real ratio was 4.734219773198682e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.260091813198096e-09, largest imaginary/real ratio was 7.05723080080917e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.467165870580902e-09, largest imaginary/real ratio was 5.294100459996948e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0353330727251065e-09, largest imaginary/real ratio was 1.969609345877076e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.045519578855319e-09, largest imaginary/real ratio was 1.4236166275983843e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.574058436881047e-09, largest imaginary/real ratio was 1.546466016398106e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.8690225647517e-09, largest imaginary/real ratio was 2.2878254299167657e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.101536654362375e-09, largest imaginary/real ratio was 2.009071875566199e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.788720271332066e-09, largest imaginary/real ratio was 7.647353485604216e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2197310894682932e-09, largest imaginary/real ratio was 1.532675960865993e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.451659986910087e-09, largest imaginary/real ratio was 2.941714509221123e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.323921123991012e-09, largest imaginary/real ratio was 6.947166798964343e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.711822144374802e-09, largest imaginary/real ratio was 8.881800180613974e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.9071624776289e-09, largest imaginary/real ratio was 1.2889253949946584e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1671001049197763e-09, largest imaginary/real ratio was 5.65027094049577e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.904040377985851e-09, largest imaginary/real ratio was 6.701149771200619e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8879637760204737e-09, largest imaginary/real ratio was 2.1775741455354546e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3180808613027345e-09, largest imaginary/real ratio was 1.9292462360851383e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.005477193718654e-09, largest imaginary/real ratio was 1.5422125425215934e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.999316085540338e-09, largest imaginary/real ratio was 2.3373326918180335e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.155250687387161e-09, largest imaginary/real ratio was 1.3804045775667354e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3828163629247978e-09, largest imaginary/real ratio was 3.1376940733958796e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2452614968520197e-09, largest imaginary/real ratio was 3.320480165311608e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3635758532944295e-09, largest imaginary/real ratio was 7.217703396654261e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0521073669021721e-08, largest imaginary/real ratio was 9.656978633931742e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.116328181526662e-10, largest imaginary/real ratio was 3.296978138228957e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3812007816495112e-09, largest imaginary/real ratio was 9.831676869239097e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.548694258573148e-09, largest imaginary/real ratio was 6.534157053217475e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.446726370293415e-10, largest imaginary/real ratio was 2.0485987955991488e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.924106369309895e-09, largest imaginary/real ratio was 2.457474606134305e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.030141923673118e-10, largest imaginary/real ratio was 3.237425695114943e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.653475814565524e-10, largest imaginary/real ratio was 4.000714093089757e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7350486122064834e-09, largest imaginary/real ratio was 1.378736262768274e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7991001444127755e-09, largest imaginary/real ratio was 9.562681275771559e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.745621901464708e-09, largest imaginary/real ratio was 2.2151555056863758e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8210529707494357e-09, largest imaginary/real ratio was 7.297817421960965e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.421187739199806e-09, largest imaginary/real ratio was 3.870368241944072e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.5112431606659195e-09, largest imaginary/real ratio was 1.4447536835592786e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.686529293226074e-09, largest imaginary/real ratio was 1.2725233653107825e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.198018989310175e-08, largest imaginary/real ratio was 1.8172228460047123e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3696953609343594e-09, largest imaginary/real ratio was 2.9456182400917874e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0974016225753246e-09, largest imaginary/real ratio was 1.6825398291483238e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2313760034806707e-09, largest imaginary/real ratio was 2.4064252067420135e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6363691069138489e-09, largest imaginary/real ratio was 9.943032903552897e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.399511654677464e-10, largest imaginary/real ratio was 3.781633040475278e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.764481748433753e-09, largest imaginary/real ratio was 1.9427646145295477e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.477903146282757e-09, largest imaginary/real ratio was 1.2645945634204299e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.6309453522983926e-10, largest imaginary/real ratio was 3.6078672164426805e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5119525883311003e-09, largest imaginary/real ratio was 9.987316205340222e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0644023689064749e-09, largest imaginary/real ratio was 1.1006905480624071e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6753816689790636e-09, largest imaginary/real ratio was 1.044926963406686e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.00338086531317e-08, largest imaginary/real ratio was 2.299787975907503e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.2989413746030095e-09, largest imaginary/real ratio was 7.229449596587817e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.289392466245972e-09, largest imaginary/real ratio was 2.682667255210495e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.931797854192095e-09, largest imaginary/real ratio was 1.637588628190466e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.8096923617929775e-09, largest imaginary/real ratio was 9.469413906173413e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.699473854850162e-09, largest imaginary/real ratio was 5.954454282387473e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2646835822588556e-09, largest imaginary/real ratio was 9.419255254782655e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.377268353918472e-08, largest imaginary/real ratio was 2.4338618021751017e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.195682327045728e-09, largest imaginary/real ratio was 4.794781369594136e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1240836229388138e-08, largest imaginary/real ratio was 1.994154059193872e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.886139419240033e-09, largest imaginary/real ratio was 3.571258729946804e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.037462809745846e-08, largest imaginary/real ratio was 2.7910584918375856e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.240603705839603e-09, largest imaginary/real ratio was 2.7759180596306245e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.6642412653236735e-09, largest imaginary/real ratio was 1.6102993721618217e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3171937945352166e-09, largest imaginary/real ratio was 5.951112885677403e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.731481886499072e-09, largest imaginary/real ratio was 2.422423870634885e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3746368962352308e-09, largest imaginary/real ratio was 2.9816978517712386e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5122906965284735e-09, largest imaginary/real ratio was 1.854789398553725e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.903823217093775e-09, largest imaginary/real ratio was 1.1457279507946502e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.634262750883099e-09, largest imaginary/real ratio was 3.772254299507093e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6299213930393157e-09, largest imaginary/real ratio was 1.0888450428593331e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.137307240020397e-09, largest imaginary/real ratio was 2.8027085581981504e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.345620498376642e-09, largest imaginary/real ratio was 1.0011579651408662e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.172240021381935e-09, largest imaginary/real ratio was 7.29007556124605e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3291436622149109e-08, largest imaginary/real ratio was 1.6435645597760301e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2286213000986694e-08, largest imaginary/real ratio was 7.457984402459912e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.70781985458052e-09, largest imaginary/real ratio was 5.948527331188997e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.341080830771523e-09, largest imaginary/real ratio was 1.0309577205542136e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.0866379971612375e-09, largest imaginary/real ratio was 4.347681686563711e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9582857666126967e-09, largest imaginary/real ratio was 8.453673677635481e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4991964412538953e-09, largest imaginary/real ratio was 1.1460689985468587e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4695799241065743e-09, largest imaginary/real ratio was 1.2555437554994595e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.284089501800231e-09, largest imaginary/real ratio was 1.5643464603966789e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7620493350349301e-09, largest imaginary/real ratio was 2.911415752022978e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.853056253014669e-09, largest imaginary/real ratio was 7.108395967651132e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2130061133196543e-09, largest imaginary/real ratio was 2.6279017320214083e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9153892516365855e-09, largest imaginary/real ratio was 2.1779098442751313e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.450770982883317e-09, largest imaginary/real ratio was 5.424342078841399e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.782615430982351e-09, largest imaginary/real ratio was 6.597144207482744e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4434904281903815e-09, largest imaginary/real ratio was 2.1722061732244853e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.299392039660662e-09, largest imaginary/real ratio was 1.1593390415383214e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.502924461657699e-10, largest imaginary/real ratio was 1.4218778282453746e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.230789252463654e-09, largest imaginary/real ratio was 1.8386775460822313e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3025080305870543e-09, largest imaginary/real ratio was 1.1219452539077382e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.944819014736915e-09, largest imaginary/real ratio was 5.477444166718039e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.840690104187017e-09, largest imaginary/real ratio was 2.613255495138631e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.916194927814858e-09, largest imaginary/real ratio was 1.093462971943332e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.205937786571165e-09, largest imaginary/real ratio was 4.002661045740862e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.856503627142549e-09, largest imaginary/real ratio was 6.185633330207967e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0941100171177103e-09, largest imaginary/real ratio was 4.0419667240869814e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.9117814357286094e-09, largest imaginary/real ratio was 1.646496487111807e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2957453787884078e-09, largest imaginary/real ratio was 1.5576766027043278e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0209218447842905e-09, largest imaginary/real ratio was 9.132456834232888e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4686129809476213e-09, largest imaginary/real ratio was 3.9841688278969e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.725772519601416e-09, largest imaginary/real ratio was 4.035823411052082e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.562061067530043e-08, largest imaginary/real ratio was 1.369782331126196e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3876563514031676e-09, largest imaginary/real ratio was 9.445738397596837e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5232888556919393e-09, largest imaginary/real ratio was 5.884311135061406e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0516322243985953e-08, largest imaginary/real ratio was 1.163181593560447e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.013017094751025e-09, largest imaginary/real ratio was 3.4647304021487056e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.963245427656405e-09, largest imaginary/real ratio was 1.7071937314014339e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.558446847845899e-09, largest imaginary/real ratio was 2.7731173689486048e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.182569399667886e-09, largest imaginary/real ratio was 1.9872433394289733e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0636919469721287e-09, largest imaginary/real ratio was 1.439562475024186e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.761352826886121e-09, largest imaginary/real ratio was 7.842095131209414e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3055508446027173e-08, largest imaginary/real ratio was 2.330439099053322e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0446645151234147e-09, largest imaginary/real ratio was 5.219140758742886e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0283718528950942e-09, largest imaginary/real ratio was 2.6911099026480477e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.456921205806177e-09, largest imaginary/real ratio was 2.2691422579338965e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.169470926547297e-09, largest imaginary/real ratio was 5.224023993856903e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.514425887965001e-09, largest imaginary/real ratio was 1.0068357203495695e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1883565473311687e-08, largest imaginary/real ratio was 3.321996560805394e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.127888057187925e-09, largest imaginary/real ratio was 1.3207572182249163e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0906084618583839e-08, largest imaginary/real ratio was 3.767652621312164e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0107442299413509e-08, largest imaginary/real ratio was 8.138936301667706e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.920168322615899e-09, largest imaginary/real ratio was 9.376909200810547e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.926791891795795e-09, largest imaginary/real ratio was 3.417326321034571e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.200064889295388e-09, largest imaginary/real ratio was 7.715550290935147e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.608587273369057e-09, largest imaginary/real ratio was 1.383962718370549e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.919114187768821e-09, largest imaginary/real ratio was 4.158915464920159e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4002861237780104e-09, largest imaginary/real ratio was 3.8180665702020504e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.338256054488986e-09, largest imaginary/real ratio was 8.425158368116561e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.481320882527825e-09, largest imaginary/real ratio was 1.3060150996185677e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6323900639370906e-09, largest imaginary/real ratio was 6.198265309682707e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.455079808955627e-09, largest imaginary/real ratio was 5.636334720722057e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.2760248818845386e-09, largest imaginary/real ratio was 4.745268042035649e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.554392950515303e-09, largest imaginary/real ratio was 7.58398241311222e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3734171665418752e-09, largest imaginary/real ratio was 2.3086245373982654e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.888975538441324e-09, largest imaginary/real ratio was 2.713061822216709e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.840514716915944e-09, largest imaginary/real ratio was 4.2654503733707334e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.457853330134188e-09, largest imaginary/real ratio was 7.1417230312646e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.89374193317243e-09, largest imaginary/real ratio was 1.843682491454458e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.946042230138214e-09, largest imaginary/real ratio was 1.854543322255393e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.289284734774039e-10, largest imaginary/real ratio was 2.0955676104306735e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0023928413423066e-08, largest imaginary/real ratio was 1.0019797134952407e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.727093493005955e-09, largest imaginary/real ratio was 1.7069353498848558e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.391623353544586e-09, largest imaginary/real ratio was 6.213872058363055e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.019335011086723e-09, largest imaginary/real ratio was 5.847811701945933e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.95579702900213e-09, largest imaginary/real ratio was 6.043230477892257e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.696037024846573e-09, largest imaginary/real ratio was 1.4622789801521747e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.644271740840442e-09, largest imaginary/real ratio was 6.508793184783407e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.140544330960607e-08, largest imaginary/real ratio was 1.8500105033164171e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.521317889684824e-09, largest imaginary/real ratio was 7.862860821408174e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.837154438613964e-09, largest imaginary/real ratio was 5.421653776108934e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4452758929428704e-09, largest imaginary/real ratio was 5.4950911970734127e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0648875862614388e-08, largest imaginary/real ratio was 3.0791002740430517e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.098764798725754e-09, largest imaginary/real ratio was 1.6391295080529528e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.558482699179243e-09, largest imaginary/real ratio was 3.0581572806118634e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.119687726799927e-09, largest imaginary/real ratio was 1.047809086147382e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.842490426098644e-09, largest imaginary/real ratio was 5.976291129542515e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3473701889131043e-08, largest imaginary/real ratio was 8.032162937372243e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.311133991843025e-09, largest imaginary/real ratio was 3.701217426564845e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.594447823336691e-09, largest imaginary/real ratio was 1.5309441206556345e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.880073807105264e-09, largest imaginary/real ratio was 9.520213872573887e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6925130460549865e-09, largest imaginary/real ratio was 5.350014313440144e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7555984671863864e-09, largest imaginary/real ratio was 1.4415013345067852e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.406333871878789e-09, largest imaginary/real ratio was 4.526377224097633e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.736887127391669e-09, largest imaginary/real ratio was 1.5989075217344444e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.28235512132756e-09, largest imaginary/real ratio was 3.215803542064675e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.955747011677033e-09, largest imaginary/real ratio was 3.714800025375382e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2584921409685097e-08, largest imaginary/real ratio was 4.0540547036061975e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.942978127535324e-09, largest imaginary/real ratio was 6.597504790995171e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.310401120612438e-09, largest imaginary/real ratio was 9.32514355678156e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.934785818602142e-09, largest imaginary/real ratio was 1.471321541064362e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.13127229394495e-09, largest imaginary/real ratio was 1.8142809467448126e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.529752316823456e-09, largest imaginary/real ratio was 1.126093012571196e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.365138027102444e-09, largest imaginary/real ratio was 4.300010543667459e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.429825927574283e-09, largest imaginary/real ratio was 3.286593973943418e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4960907325485647e-09, largest imaginary/real ratio was 4.385585673907936e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1232072614248927e-09, largest imaginary/real ratio was 2.132516626582653e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.534147030789611e-09, largest imaginary/real ratio was 6.541052867028341e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.10854401865383e-09, largest imaginary/real ratio was 5.69316252316713e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.297603284769061e-09, largest imaginary/real ratio was 9.99733784739183e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0470148212499518e-08, largest imaginary/real ratio was 1.7059200241659352e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.740175034674263e-10, largest imaginary/real ratio was 1.8793328838986367e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.925048557392494e-10, largest imaginary/real ratio was 1.3402116227603068e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.189452210457054e-09, largest imaginary/real ratio was 3.728930628069752e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.387880682113184e-09, largest imaginary/real ratio was 4.760343807013906e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5211417940365174e-09, largest imaginary/real ratio was 6.68522478196507e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.627762541252795e-09, largest imaginary/real ratio was 4.433712212305603e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.226143816396528e-09, largest imaginary/real ratio was 2.6679823935835124e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6168666179645482e-09, largest imaginary/real ratio was 1.3140984936691411e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.7977090610962774e-09, largest imaginary/real ratio was 6.9744886266719785e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.726439775433998e-09, largest imaginary/real ratio was 2.608826172585384e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.185396892617042e-09, largest imaginary/real ratio was 1.9122417210024158e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.42337105832397e-09, largest imaginary/real ratio was 8.110634412046173e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.251232198634825e-09, largest imaginary/real ratio was 2.9125251753025095e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.474575929215557e-10, largest imaginary/real ratio was 1.1330016072077264e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.6287906198282785e-09, largest imaginary/real ratio was 1.0806796101078076e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0873342096866286e-09, largest imaginary/real ratio was 1.1040851046143551e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.5922425572991305e-09, largest imaginary/real ratio was 8.580488397700445e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.379207945412609e-09, largest imaginary/real ratio was 4.407446761535493e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4344553163588332e-08, largest imaginary/real ratio was 2.0066982464153814e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.735593115939898e-09, largest imaginary/real ratio was 2.658880660356705e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.647483324885706e-09, largest imaginary/real ratio was 2.858515134864121e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2122413716908406e-08, largest imaginary/real ratio was 2.9113688499610074e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0084869564905973e-09, largest imaginary/real ratio was 3.1643428800323664e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.253957272223708e-09, largest imaginary/real ratio was 2.6687938179448253e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.729813680306637e-09, largest imaginary/real ratio was 4.784668491418485e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8454931689736869e-09, largest imaginary/real ratio was 1.6103297784840964e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.312608014246846e-09, largest imaginary/real ratio was 5.964329471706984e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4294425380674052e-09, largest imaginary/real ratio was 1.5369411656680422e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5190412267874829e-09, largest imaginary/real ratio was 3.781544103229721e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.833137157436501e-09, largest imaginary/real ratio was 1.5014250309582892e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6471103832540664e-09, largest imaginary/real ratio was 1.6065460132841633e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2001859144328566e-09, largest imaginary/real ratio was 8.230360915615245e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.738117631983623e-09, largest imaginary/real ratio was 4.0873041905221456e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5595332884882406e-09, largest imaginary/real ratio was 2.5840646101974248e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.343151682244564e-10, largest imaginary/real ratio was 1.2346615014986144e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5505979736803104e-09, largest imaginary/real ratio was 9.517883459233345e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.248259178895477e-09, largest imaginary/real ratio was 9.066257952510243e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.032387256743879e-09, largest imaginary/real ratio was 1.546695250792187e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5306909318259535e-09, largest imaginary/real ratio was 2.291939197223073e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2295502843753952e-08, largest imaginary/real ratio was 6.246257507112219e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8656690211020067e-09, largest imaginary/real ratio was 3.5998791014099703e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.294993576510732e-09, largest imaginary/real ratio was 1.4268467428295245e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.22652348663076e-09, largest imaginary/real ratio was 1.6653141270034283e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.374965773254312e-09, largest imaginary/real ratio was 1.570800716314921e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8553822442341609e-09, largest imaginary/real ratio was 3.661324220908811e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6782749915621596e-09, largest imaginary/real ratio was 2.68446228421687e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.174218878706871e-09, largest imaginary/real ratio was 3.5786920002564296e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0414256057765363e-09, largest imaginary/real ratio was 1.4962830150737564e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.358116629456107e-10, largest imaginary/real ratio was 4.977393620011374e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.813687648112066e-09, largest imaginary/real ratio was 2.005091343289393e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.640771875684362e-09, largest imaginary/real ratio was 3.3538323486437723e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.482455610567862e-09, largest imaginary/real ratio was 1.455916350447616e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.315907096253476e-09, largest imaginary/real ratio was 6.36358249114825e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.763152192749288e-09, largest imaginary/real ratio was 1.6344495424928684e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.713651610577044e-09, largest imaginary/real ratio was 1.9888008994782878e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.30953879532384e-10, largest imaginary/real ratio was 1.5330824415376571e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.853388212563679e-09, largest imaginary/real ratio was 7.706643484776932e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.482857191359293e-09, largest imaginary/real ratio was 2.4992904901934016e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1092140576251565e-09, largest imaginary/real ratio was 2.310966637177803e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.34068232875937e-08, largest imaginary/real ratio was 1.9978975379917132e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2760182585809677e-08, largest imaginary/real ratio was 2.2200195554786016e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5047785981289993e-09, largest imaginary/real ratio was 5.6424802894704374e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5711040538267742e-09, largest imaginary/real ratio was 9.940291611825345e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.8457401236100955e-09, largest imaginary/real ratio was 4.592742459996742e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.144403161851087e-09, largest imaginary/real ratio was 6.971518340250309e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.870542552191463e-09, largest imaginary/real ratio was 1.930480052945149e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5462202485678273e-09, largest imaginary/real ratio was 2.7762626389961313e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.372853170468714e-09, largest imaginary/real ratio was 6.929676455341376e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.460066201342791e-09, largest imaginary/real ratio was 1.5669825405770095e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5030779133273292e-09, largest imaginary/real ratio was 3.598175227651177e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.232199898759192e-09, largest imaginary/real ratio was 1.2454786849132235e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.245449942513499e-09, largest imaginary/real ratio was 5.032038996514525e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.467767637280895e-09, largest imaginary/real ratio was 8.935398193396374e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.537091008071353e-09, largest imaginary/real ratio was 1.212037873665313e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.776981844082296e-09, largest imaginary/real ratio was 6.094271015460066e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.6608866800622635e-09, largest imaginary/real ratio was 6.905837179175406e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.132797243433202e-08, largest imaginary/real ratio was 2.033811101852209e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.845387798547069e-09, largest imaginary/real ratio was 2.1947176614490769e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.895618801917668e-09, largest imaginary/real ratio was 6.401565280814044e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.727460028617395e-09, largest imaginary/real ratio was 1.8778765949962125e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1297659127662258e-09, largest imaginary/real ratio was 5.185549580178455e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.845920848758841e-09, largest imaginary/real ratio was 1.848127867016765e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5101653353553053e-09, largest imaginary/real ratio was 1.3346581293182994e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6851547126148843e-09, largest imaginary/real ratio was 6.146362110089548e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7207222757481207e-09, largest imaginary/real ratio was 1.0715554434497483e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.05254663109135e-09, largest imaginary/real ratio was 5.220432632639223e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.26273488711258e-09, largest imaginary/real ratio was 1.6585200623341435e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7022455026959273e-09, largest imaginary/real ratio was 1.2621833544819534e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.020713964647917e-09, largest imaginary/real ratio was 5.115064168348989e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.252543643554841e-09, largest imaginary/real ratio was 4.1279073395185463e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.402975073012331e-09, largest imaginary/real ratio was 2.8556936333492094e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.777609169972009e-08, largest imaginary/real ratio was 3.65371000844752e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.870134668048359e-09, largest imaginary/real ratio was 3.386851468034378e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.003489699754117e-09, largest imaginary/real ratio was 5.631496426286681e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.9244149206716345e-09, largest imaginary/real ratio was 3.5849552265631434e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.520343785844472e-10, largest imaginary/real ratio was 1.3954551728153957e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.410443999575035e-09, largest imaginary/real ratio was 9.999923349412293e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.666151006990106e-09, largest imaginary/real ratio was 1.6829339018887467e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.729169195854299e-09, largest imaginary/real ratio was 1.848443828222668e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.160670959907286e-09, largest imaginary/real ratio was 1.1909961040079735e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.589158694666558e-09, largest imaginary/real ratio was 2.940205332852779e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.895183075223188e-09, largest imaginary/real ratio was 7.916234101298644e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.861399974808421e-09, largest imaginary/real ratio was 6.432866794657593e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.440583879077451e-09, largest imaginary/real ratio was 5.5570569194422385e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.700446781546984e-09, largest imaginary/real ratio was 9.095561687988497e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0925399144393286e-08, largest imaginary/real ratio was 5.170309435750602e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.059334021373834e-09, largest imaginary/real ratio was 1.2362840284190527e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.150666934651489e-09, largest imaginary/real ratio was 1.1109796275093325e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.425289645798532e-09, largest imaginary/real ratio was 1.823468679683701e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6289105227279627e-09, largest imaginary/real ratio was 5.13621165390423e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.748733879644572e-09, largest imaginary/real ratio was 1.3146312223660216e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.278374555735375e-09, largest imaginary/real ratio was 7.023684170473083e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.955591845771494e-09, largest imaginary/real ratio was 1.095466925361989e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.7381214448863575e-09, largest imaginary/real ratio was 4.1922485593209615e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5449524963579572e-09, largest imaginary/real ratio was 3.893504653578874e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.322947570389586e-09, largest imaginary/real ratio was 1.4235120253478392e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.8119676965554575e-09, largest imaginary/real ratio was 3.8935034108624836e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0052115019836812e-08, largest imaginary/real ratio was 9.909969511955988e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.21857194982785e-09, largest imaginary/real ratio was 6.5196394319907e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.61394565783587e-09, largest imaginary/real ratio was 2.7248626397512274e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.748027350008564e-09, largest imaginary/real ratio was 1.2446868228249861e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.905197447567625e-09, largest imaginary/real ratio was 7.60333603965319e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1300264807849355e-08, largest imaginary/real ratio was 9.260706552146849e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.1983382670377745e-09, largest imaginary/real ratio was 1.0845006908557436e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.305663515050062e-09, largest imaginary/real ratio was 2.840647060925592e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7083148724817038e-09, largest imaginary/real ratio was 6.868071245875896e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.612921603740028e-09, largest imaginary/real ratio was 1.724845176759422e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0083841923443738e-08, largest imaginary/real ratio was 1.541644837429649e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0470477913332638e-09, largest imaginary/real ratio was 3.443224092628443e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.896582872096181e-09, largest imaginary/real ratio was 1.7249279417324917e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1052754691813138e-08, largest imaginary/real ratio was 9.841793558767869e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5824488053048195e-08, largest imaginary/real ratio was 2.017941176344717e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2172892430264882e-08, largest imaginary/real ratio was 2.3166091560341527e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2127724197010966e-08, largest imaginary/real ratio was 1.652961336509839e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3461479956267023e-09, largest imaginary/real ratio was 4.0325159377487706e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7437603561336552e-08, largest imaginary/real ratio was 7.928562155671775e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.660305057157085e-08, largest imaginary/real ratio was 4.4259970131282644e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.8655855052979955e-09, largest imaginary/real ratio was 6.213674257739308e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1221414932743582e-08, largest imaginary/real ratio was 4.0600366696747903e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.974997674974121e-08, largest imaginary/real ratio was 9.57007937556552e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.3370939191567055e-09, largest imaginary/real ratio was 4.751737629530452e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4421696782345091e-08, largest imaginary/real ratio was 1.7019392513268321e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.170364246394079e-09, largest imaginary/real ratio was 5.072048011726949e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.001043947945969e-09, largest imaginary/real ratio was 1.9236059425093863e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.955794343797904e-10, largest imaginary/real ratio was 2.5393058298061398e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6416626268812107e-08, largest imaginary/real ratio was 2.511709048767633e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.134382490178251e-08, largest imaginary/real ratio was 7.217739901770845e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.639671059112641e-09, largest imaginary/real ratio was 3.266100013096492e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6189338224906513e-08, largest imaginary/real ratio was 6.954177770567392e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.492158194770812e-09, largest imaginary/real ratio was 3.9060047524808405e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.578097848501377e-09, largest imaginary/real ratio was 3.7865641447815783e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4906471943919092e-09, largest imaginary/real ratio was 4.581303762607249e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.818352706803902e-09, largest imaginary/real ratio was 1.2994834535446257e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.505028540485881e-09, largest imaginary/real ratio was 8.76131515789184e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.029944152184394e-09, largest imaginary/real ratio was 2.627835097040408e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2844499696719815e-08, largest imaginary/real ratio was 1.3270234558631607e-12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.928614185466626e-08, largest imaginary/real ratio was 1.3297234711721557e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3776466788534615e-09, largest imaginary/real ratio was 1.134163323596753e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9707757222969547e-08, largest imaginary/real ratio was 1.737159756183834e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.876317018100896e-09, largest imaginary/real ratio was 9.49656015248584e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4651036393077349e-08, largest imaginary/real ratio was 4.1806760506016853e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.63931920309206e-09, largest imaginary/real ratio was 1.6561928443475565e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1769412345227485e-08, largest imaginary/real ratio was 1.1618626243849389e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4587578157972643e-08, largest imaginary/real ratio was 9.020205315313499e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.24004249143816e-09, largest imaginary/real ratio was 7.890799670413199e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1245142808006557e-08, largest imaginary/real ratio was 7.672819333548547e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.149683344350884e-09, largest imaginary/real ratio was 2.813751128336799e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.810743488113014e-10, largest imaginary/real ratio was 6.4786412934019876e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3367603474066067e-08, largest imaginary/real ratio was 6.967413489663211e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5306230959047757e-09, largest imaginary/real ratio was 3.435450710458773e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.793423183247795e-08, largest imaginary/real ratio was 4.9786589633873325e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4010771781725963e-08, largest imaginary/real ratio was 1.3743654221360918e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1201591148982073e-09, largest imaginary/real ratio was 2.2832041929451362e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.809684174126307e-09, largest imaginary/real ratio was 2.7078081027700543e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.531734362521696e-09, largest imaginary/real ratio was 2.5427211933184366e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.476858987848895e-09, largest imaginary/real ratio was 8.020139093949258e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.692403900673346e-09, largest imaginary/real ratio was 1.8019186138028927e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4561564983411884e-09, largest imaginary/real ratio was 2.1971150191017872e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.880658179291087e-09, largest imaginary/real ratio was 9.435887178408537e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.099462346830849e-09, largest imaginary/real ratio was 2.356359214503929e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.237265487955898e-09, largest imaginary/real ratio was 6.093813749935582e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.152893512707305e-08, largest imaginary/real ratio was 4.805966797673221e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2673418741649255e-09, largest imaginary/real ratio was 1.221765625459522e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.437712737399804e-09, largest imaginary/real ratio was 2.2854260686716964e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5667632099330935e-08, largest imaginary/real ratio was 4.996502340188931e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4636558249085512e-08, largest imaginary/real ratio was 1.1900549478301485e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.935828034999688e-09, largest imaginary/real ratio was 8.530356735348693e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2803840722889281e-08, largest imaginary/real ratio was 1.1681973864470155e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.081544710386503e-08, largest imaginary/real ratio was 3.698145320020051e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.823181021644448e-09, largest imaginary/real ratio was 2.3831176983985143e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2660047589223966e-09, largest imaginary/real ratio was 6.606164692301201e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1038264698579742e-09, largest imaginary/real ratio was 2.1406959213103208e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.657682670787435e-09, largest imaginary/real ratio was 5.837242641180854e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.724048055652594e-09, largest imaginary/real ratio was 9.172010324418761e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.102916871229836e-09, largest imaginary/real ratio was 9.223420173357405e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.770902354637185e-09, largest imaginary/real ratio was 1.0277321969476854e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.885499937228753e-09, largest imaginary/real ratio was 1.7472544912019285e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.061937321683268e-09, largest imaginary/real ratio was 3.531692943400626e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7030130895816696e-09, largest imaginary/real ratio was 1.0929504352212503e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.686091825611073e-09, largest imaginary/real ratio was 3.7849842376881896e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.961464919754843e-09, largest imaginary/real ratio was 8.788549309709664e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.624977394210528e-09, largest imaginary/real ratio was 5.970148876803364e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0688191638732626e-09, largest imaginary/real ratio was 8.994604031896465e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.198696827426045e-09, largest imaginary/real ratio was 2.5201205507640795e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.8700347522467585e-09, largest imaginary/real ratio was 4.730248887447066e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.792470022388389e-09, largest imaginary/real ratio was 6.722994320113531e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1382074034104197e-09, largest imaginary/real ratio was 2.1929866543195922e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1738438023365089e-08, largest imaginary/real ratio was 6.580618296583788e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.804878564001313e-09, largest imaginary/real ratio was 1.007257079539991e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.357225309576992e-09, largest imaginary/real ratio was 1.5453708215956473e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.511616471007291e-09, largest imaginary/real ratio was 1.6612034058569024e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.050063173086682e-09, largest imaginary/real ratio was 7.058985889372832e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.13687651547886e-09, largest imaginary/real ratio was 2.448072675893296e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.986192315551668e-09, largest imaginary/real ratio was 2.6298126388769936e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3609662444617122e-08, largest imaginary/real ratio was 2.3052537564585144e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.722180962302648e-09, largest imaginary/real ratio was 1.0520115540240844e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.454188190214006e-09, largest imaginary/real ratio was 7.644495680112428e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6581011721738138e-08, largest imaginary/real ratio was 1.5914707905919737e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0295060383351946e-08, largest imaginary/real ratio was 3.850415108717211e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.047456048545165e-09, largest imaginary/real ratio was 4.971581602865165e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.718032105552347e-09, largest imaginary/real ratio was 6.699622018455517e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.388883759244389e-09, largest imaginary/real ratio was 1.5817905156657352e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0922108126343841e-08, largest imaginary/real ratio was 2.7341775421537738e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.1753476425058595e-09, largest imaginary/real ratio was 7.562464885023066e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.038937743429952e-09, largest imaginary/real ratio was 3.4424082785474076e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.45234187052708e-09, largest imaginary/real ratio was 3.5193683892824406e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.8526936346794535e-09, largest imaginary/real ratio was 1.2983276696080255e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.526599398859405e-09, largest imaginary/real ratio was 1.2061399935193758e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.123846723070091e-09, largest imaginary/real ratio was 1.4590922992123286e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.513214704487075e-09, largest imaginary/real ratio was 1.8207038560393702e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.681076628851585e-09, largest imaginary/real ratio was 6.536980110009002e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4827528352746114e-09, largest imaginary/real ratio was 1.8570180749686356e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.859907233376106e-09, largest imaginary/real ratio was 4.191844932780196e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.540177068028243e-10, largest imaginary/real ratio was 3.962384625676887e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.859547424285715e-09, largest imaginary/real ratio was 3.6796727049259236e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.489133693337831e-09, largest imaginary/real ratio was 7.743314813547808e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1847371844935997e-08, largest imaginary/real ratio was 2.9768321419473564e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2562083679901322e-09, largest imaginary/real ratio was 2.0053220568327676e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2472622958200963e-09, largest imaginary/real ratio was 1.212914351841036e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1151156139760276e-08, largest imaginary/real ratio was 3.050554329311367e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.741111138375079e-09, largest imaginary/real ratio was 9.891700499706325e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.306377194502313e-09, largest imaginary/real ratio was 4.6488254221639475e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.94829102694419e-09, largest imaginary/real ratio was 1.0798657091524244e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3298957668746051e-08, largest imaginary/real ratio was 3.7086738979468987e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4408967201582847e-09, largest imaginary/real ratio was 1.620884008173952e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.407877823769459e-09, largest imaginary/real ratio was 2.0614528943159236e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.185448672628144e-10, largest imaginary/real ratio was 1.9681355177369056e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3768683102254106e-08, largest imaginary/real ratio was 2.2259072157756526e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0991880952321096e-08, largest imaginary/real ratio was 8.433648778792344e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0896126283848704e-08, largest imaginary/real ratio was 2.791257595003508e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.608630336578804e-09, largest imaginary/real ratio was 1.4268827270146214e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.96223438478472e-10, largest imaginary/real ratio was 8.301609904337913e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.102670461885902e-09, largest imaginary/real ratio was 2.8639753697458523e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0132660632898865e-08, largest imaginary/real ratio was 1.7456484220690673e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.937654380182334e-09, largest imaginary/real ratio was 1.691676978563975e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.799744156179287e-09, largest imaginary/real ratio was 4.2422379520353895e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.274436501232861e-09, largest imaginary/real ratio was 3.3352449235597088e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1780353842608368e-08, largest imaginary/real ratio was 2.5209006930654096e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.563163884764987e-09, largest imaginary/real ratio was 4.4530293158788597e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.042716055142586e-09, largest imaginary/real ratio was 6.340519837247348e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.270485192636281e-09, largest imaginary/real ratio was 1.769526952355952e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6158641041162705e-08, largest imaginary/real ratio was 8.901841812901127e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3418367063834942e-09, largest imaginary/real ratio was 3.973734528214334e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.495146214971516e-09, largest imaginary/real ratio was 6.653179619452637e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0823247576335615e-09, largest imaginary/real ratio was 4.0057345460202535e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.027865992419365e-09, largest imaginary/real ratio was 4.733604662564403e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.6703824832561925e-09, largest imaginary/real ratio was 1.211758391808036e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3447865750003758e-09, largest imaginary/real ratio was 3.1678869886478106e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3556031255430535e-09, largest imaginary/real ratio was 4.9472325879342236e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.707872606362172e-09, largest imaginary/real ratio was 7.491796913569805e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.719210483447604e-09, largest imaginary/real ratio was 3.3065926584495053e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.64446659709095e-09, largest imaginary/real ratio was 5.589148733221097e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.172948388352924e-09, largest imaginary/real ratio was 4.762621528339827e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6796180234273908e-09, largest imaginary/real ratio was 1.9232732997387927e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.056705122727186e-09, largest imaginary/real ratio was 8.864872599524869e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0764688563627149e-08, largest imaginary/real ratio was 3.397513141739098e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3391496964213607e-09, largest imaginary/real ratio was 4.947062646159937e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.580815711500299e-09, largest imaginary/real ratio was 2.2503699586133297e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.601173693626175e-09, largest imaginary/real ratio was 8.907108622805185e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.13367073897986e-09, largest imaginary/real ratio was 1.9667867845932182e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.5661068117710244e-09, largest imaginary/real ratio was 5.6547140587575116e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4745436444128617e-08, largest imaginary/real ratio was 1.925041199663169e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6304568028787438e-08, largest imaginary/real ratio was 1.1599116134692412e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.814890987223146e-09, largest imaginary/real ratio was 1.5610911417338817e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.010903321266682e-09, largest imaginary/real ratio was 2.6212853516401295e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5567323280271698e-08, largest imaginary/real ratio was 6.117710186721888e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5747880485219632e-08, largest imaginary/real ratio was 1.2448855819621638e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.238665512872907e-09, largest imaginary/real ratio was 5.621271777479716e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.021506920747471e-09, largest imaginary/real ratio was 1.1212389621702138e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.117979812249994e-08, largest imaginary/real ratio was 9.089808790304728e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.211998983740791e-09, largest imaginary/real ratio was 4.6544631010241506e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3923258093350396e-08, largest imaginary/real ratio was 5.5671114958708274e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.538746960497081e-09, largest imaginary/real ratio was 2.0411474228474945e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5057079833721875e-09, largest imaginary/real ratio was 2.610791439291301e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.796953789166234e-09, largest imaginary/real ratio was 7.56515844827279e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.228786270626428e-08, largest imaginary/real ratio was 2.1929486815925345e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7144115064187893e-09, largest imaginary/real ratio was 2.652820755541065e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.662973460633701e-09, largest imaginary/real ratio was 5.096724588314936e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3213331774026779e-08, largest imaginary/real ratio was 2.491352683977771e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.574937193342025e-09, largest imaginary/real ratio was 1.4337310227950297e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.436073115897468e-09, largest imaginary/real ratio was 7.381502087819541e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6801509152382554e-08, largest imaginary/real ratio was 5.695352297883832e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.6387286311526405e-09, largest imaginary/real ratio was 1.2674702454413002e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.552815919481197e-09, largest imaginary/real ratio was 1.999592371774419e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.8283780028043946e-09, largest imaginary/real ratio was 6.556787153931019e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.091646017727137e-08, largest imaginary/real ratio was 1.9221154482919454e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.042397863226118e-09, largest imaginary/real ratio was 2.077120755517522e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.928655452198283e-09, largest imaginary/real ratio was 1.3404082037584353e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2496466631844953e-08, largest imaginary/real ratio was 7.309132348605119e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8894286762532003e-09, largest imaginary/real ratio was 4.4207859368551627e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.377868676166789e-09, largest imaginary/real ratio was 3.061573014785146e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5804735287854286e-09, largest imaginary/real ratio was 1.579336393367558e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2519643900435432e-09, largest imaginary/real ratio was 6.42292828694918e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.040510415574102e-09, largest imaginary/real ratio was 6.051077799003944e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.022637596787094e-09, largest imaginary/real ratio was 1.3078190004328037e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0254603131651884e-09, largest imaginary/real ratio was 3.40134234878098e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.599855817044623e-09, largest imaginary/real ratio was 3.965491517507581e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.8190869984953e-09, largest imaginary/real ratio was 2.8422280170177915e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.104105989273479e-09, largest imaginary/real ratio was 7.836232940408154e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.965012778447154e-09, largest imaginary/real ratio was 2.386190525951286e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.331689950278305e-09, largest imaginary/real ratio was 1.4721567918493252e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.055551968286126e-09, largest imaginary/real ratio was 7.91284895882358e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.548509601391076e-09, largest imaginary/real ratio was 1.1897395231248222e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.100396520281043e-09, largest imaginary/real ratio was 1.1316927678032309e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6519849858097446e-09, largest imaginary/real ratio was 2.827270303291861e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3458627807506643e-09, largest imaginary/real ratio was 1.0139219196385292e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.2879071199432637e-09, largest imaginary/real ratio was 9.907590354621168e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.440068369072212e-10, largest imaginary/real ratio was 7.351570507291904e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6388319467447586e-08, largest imaginary/real ratio was 7.766202352496541e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8215002490733437e-08, largest imaginary/real ratio was 6.892744616996385e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.016248040623658e-09, largest imaginary/real ratio was 1.6562754956200586e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.435835192720118e-09, largest imaginary/real ratio was 1.1948533722197573e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2232728664256577e-08, largest imaginary/real ratio was 4.010262625199382e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.405908097391209e-09, largest imaginary/real ratio was 4.4841297318076115e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.247271993719954e-09, largest imaginary/real ratio was 5.594479307842797e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.815920531163121e-09, largest imaginary/real ratio was 8.349503170817885e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.376881422690457e-09, largest imaginary/real ratio was 1.4626164427141736e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0854117406768913e-08, largest imaginary/real ratio was 1.1530312005682453e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4579862411425926e-09, largest imaginary/real ratio was 6.269458215960103e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.41276520839979e-09, largest imaginary/real ratio was 2.8265899019247143e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7467440084776177e-08, largest imaginary/real ratio was 2.207618254347336e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.841267235063712e-09, largest imaginary/real ratio was 9.667274934368189e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.414232944466769e-09, largest imaginary/real ratio was 1.213614651637148e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1279347277715989e-08, largest imaginary/real ratio was 8.26884743582417e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.640896918112921e-08, largest imaginary/real ratio was 3.988898319296157e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.650691442139182e-09, largest imaginary/real ratio was 5.388745985725879e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2477432853215272e-08, largest imaginary/real ratio was 1.6996242385242955e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.319941036590345e-09, largest imaginary/real ratio was 6.133634238254625e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5595108272984753e-09, largest imaginary/real ratio was 3.227355827123373e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.133582160414925e-09, largest imaginary/real ratio was 7.829992125693129e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.828054722312902e-09, largest imaginary/real ratio was 1.945197786190169e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.4973482273520494e-09, largest imaginary/real ratio was 1.345739558710109e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0991792967417363e-08, largest imaginary/real ratio was 7.719172348917242e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.283857362064302e-09, largest imaginary/real ratio was 5.503796386508928e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.664898327041735e-09, largest imaginary/real ratio was 8.868361637474861e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4583861032864991e-08, largest imaginary/real ratio was 1.3962615633768943e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1668010050401127e-09, largest imaginary/real ratio was 2.1975250739675318e-12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.666733395289945e-09, largest imaginary/real ratio was 9.81695481755854e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.130855373674757e-09, largest imaginary/real ratio was 2.027813558448882e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3481826026614591e-08, largest imaginary/real ratio was 1.3336274371767197e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0036411153363863e-08, largest imaginary/real ratio was 8.690526400497939e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.714477844063909e-09, largest imaginary/real ratio was 3.7208428110247686e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0489487127715037e-08, largest imaginary/real ratio was 1.5565261389307026e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.192448819062537e-09, largest imaginary/real ratio was 2.0743423799740874e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6005611969643328e-09, largest imaginary/real ratio was 2.6835214846065484e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.278131717283927e-09, largest imaginary/real ratio was 5.114089904046779e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.666522584497499e-09, largest imaginary/real ratio was 1.6350717790202555e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.649592081569545e-09, largest imaginary/real ratio was 2.5820206385094964e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.783509299352243e-10, largest imaginary/real ratio was 5.182576004076231e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.110126405139843e-09, largest imaginary/real ratio was 1.8014645497109303e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.160494925921699e-08, largest imaginary/real ratio was 2.1733944999091366e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.576009126390219e-09, largest imaginary/real ratio was 2.0447397746110265e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.0396527383335094e-09, largest imaginary/real ratio was 3.4825473516975266e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.905572459514453e-09, largest imaginary/real ratio was 8.814038761866225e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.901233119643838e-09, largest imaginary/real ratio was 3.542410054220786e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.112297338164443e-09, largest imaginary/real ratio was 9.527907749080146e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.457583933223047e-09, largest imaginary/real ratio was 3.2563251754429387e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.597464042702073e-09, largest imaginary/real ratio was 9.042749801289239e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2134459925999705e-08, largest imaginary/real ratio was 5.204866477734908e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.525761461407943e-09, largest imaginary/real ratio was 3.281564510858906e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0906939540677383e-08, largest imaginary/real ratio was 1.2323404039436375e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5321920967946183e-08, largest imaginary/real ratio was 3.7293246379742094e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.995077131892147e-09, largest imaginary/real ratio was 4.2167978668143416e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.635354273116062e-09, largest imaginary/real ratio was 3.894163432616113e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.2741990983584e-09, largest imaginary/real ratio was 2.3616486209981796e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6625080137287707e-09, largest imaginary/real ratio was 9.404564112519707e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.486280593689532e-09, largest imaginary/real ratio was 7.574336705040468e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.0692408001630416e-09, largest imaginary/real ratio was 2.0492151164582827e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0232034620096864e-08, largest imaginary/real ratio was 2.011330341399506e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.1287813054580895e-09, largest imaginary/real ratio was 1.9536659017950606e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.7665046265262766e-09, largest imaginary/real ratio was 1.0482406610651863e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.660674876473899e-09, largest imaginary/real ratio was 8.05408023868147e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.778425479965505e-09, largest imaginary/real ratio was 1.846872208465343e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.659126710237045e-09, largest imaginary/real ratio was 2.0065991831247212e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.08861887226859e-09, largest imaginary/real ratio was 4.711781818425697e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0275039817052007e-09, largest imaginary/real ratio was 3.250678498202227e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.596922805838489e-09, largest imaginary/real ratio was 2.0775801331336423e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.27507205373954e-08, largest imaginary/real ratio was 4.0116356568049074e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.734504778267802e-09, largest imaginary/real ratio was 2.9383859500223304e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.8362259341185255e-09, largest imaginary/real ratio was 3.97791509089821e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.171468151821006e-09, largest imaginary/real ratio was 3.38227967901652e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.501060811258553e-09, largest imaginary/real ratio was 4.409341716836483e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.226893335434632e-09, largest imaginary/real ratio was 5.223191167259708e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1377383906982278e-08, largest imaginary/real ratio was 1.0022665723382515e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.389548982529457e-09, largest imaginary/real ratio was 5.70914586486933e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.086712800741074e-09, largest imaginary/real ratio was 4.747596972645942e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.394674300627452e-09, largest imaginary/real ratio was 1.2341423742955565e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.565405838041568e-09, largest imaginary/real ratio was 4.009593364065011e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5941715355237847e-09, largest imaginary/real ratio was 2.487725183542645e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.905957138307044e-09, largest imaginary/real ratio was 3.0251394518060344e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.8357740411641265e-09, largest imaginary/real ratio was 1.612860496871928e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1194809861980564e-09, largest imaginary/real ratio was 1.6386923615740739e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.3378656514978675e-09, largest imaginary/real ratio was 7.017262866242421e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.206713531518989e-09, largest imaginary/real ratio was 1.570493416442389e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.540453033934722e-09, largest imaginary/real ratio was 1.3188729196830492e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.283792164870959e-09, largest imaginary/real ratio was 2.0938995782907654e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.300517100620401e-08, largest imaginary/real ratio was 5.187482398235664e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2217217922176708e-09, largest imaginary/real ratio was 2.0256519577949578e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.069130527294344e-08, largest imaginary/real ratio was 6.197266059369192e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.241259852227886e-09, largest imaginary/real ratio was 9.018343712879237e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.833181155001327e-09, largest imaginary/real ratio was 1.3166074418485538e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.1735634684192965e-09, largest imaginary/real ratio was 2.4266760606399152e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.971647409939405e-09, largest imaginary/real ratio was 5.401623654808755e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.201325894332622e-09, largest imaginary/real ratio was 5.868124981246904e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5756097940398814e-09, largest imaginary/real ratio was 2.649641193573091e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.588719935959691e-09, largest imaginary/real ratio was 4.12592935595195e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.308858587088141e-09, largest imaginary/real ratio was 1.635318862863543e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1268315272055875e-09, largest imaginary/real ratio was 2.736803532943387e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.484591401130734e-09, largest imaginary/real ratio was 2.7050085968519003e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.7084129077321218e-09, largest imaginary/real ratio was 1.0608291036144619e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.449823428325595e-09, largest imaginary/real ratio was 7.977484210157116e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7251071858585606e-09, largest imaginary/real ratio was 6.933483994860182e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.222154822464305e-09, largest imaginary/real ratio was 9.398698236022216e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1880409014440117e-09, largest imaginary/real ratio was 3.3316229151834176e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.596285696997625e-09, largest imaginary/real ratio was 5.953178206828356e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.899326030860722e-09, largest imaginary/real ratio was 3.957429664625885e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.3382439700525384e-09, largest imaginary/real ratio was 1.0756553528295087e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.957356842645238e-09, largest imaginary/real ratio was 8.830277257702568e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4423804370827618e-09, largest imaginary/real ratio was 3.0527036023499246e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.289421922954966e-09, largest imaginary/real ratio was 1.7139859417345873e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4758806441446632e-09, largest imaginary/real ratio was 9.171644874566819e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3735796150347216e-09, largest imaginary/real ratio was 4.74225613855445e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.263833319142498e-09, largest imaginary/real ratio was 1.145242108335843e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.005182098721543e-10, largest imaginary/real ratio was 1.9924821777109303e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.414102862287418e-09, largest imaginary/real ratio was 9.935578065812038e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.855111113691579e-09, largest imaginary/real ratio was 1.4285154032419913e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.406756631007013e-09, largest imaginary/real ratio was 9.047110682098284e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7967743676012002e-09, largest imaginary/real ratio was 1.1167113517088638e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2776621732597605e-09, largest imaginary/real ratio was 7.477879850635395e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.096159475944777e-09, largest imaginary/real ratio was 5.2064990106682845e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6099988795229888e-09, largest imaginary/real ratio was 4.1245312031640943e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.642193674653043e-09, largest imaginary/real ratio was 1.1217034397054428e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4282567487016796e-09, largest imaginary/real ratio was 5.099343175428558e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.919597326155467e-09, largest imaginary/real ratio was 4.734136337545243e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.046247387350687e-09, largest imaginary/real ratio was 1.0976391176242991e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7005666557278904e-08, largest imaginary/real ratio was 4.736846418077095e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.592084911269475e-09, largest imaginary/real ratio was 5.819492539431783e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.884755207436728e-09, largest imaginary/real ratio was 1.0432527381078591e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.783211236644596e-09, largest imaginary/real ratio was 1.7191378872982702e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8685192464661253e-09, largest imaginary/real ratio was 9.67340272669338e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.524267736561736e-09, largest imaginary/real ratio was 4.2809717790373994e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.866815074580575e-09, largest imaginary/real ratio was 1.099652220810286e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6230093483158334e-09, largest imaginary/real ratio was 2.541475973748085e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2786345251329297e-09, largest imaginary/real ratio was 1.0335190501062243e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.603386234885702e-09, largest imaginary/real ratio was 2.5900530731515684e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.849419346442216e-09, largest imaginary/real ratio was 1.1144246804526183e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1476206804426927e-09, largest imaginary/real ratio was 5.66473774262687e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.809942222716785e-09, largest imaginary/real ratio was 2.2408702657008614e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2143131776143863e-09, largest imaginary/real ratio was 3.052504315716065e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.758711124445524e-09, largest imaginary/real ratio was 2.5465710656443884e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0450182187427375e-09, largest imaginary/real ratio was 5.067459307125405e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6177247907577464e-09, largest imaginary/real ratio was 5.784887537201122e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.2891621781712094e-09, largest imaginary/real ratio was 1.0308012819884436e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.148737507510319e-09, largest imaginary/real ratio was 1.1550178206349512e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.383625180231046e-09, largest imaginary/real ratio was 5.773973281432053e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5270139990949717e-09, largest imaginary/real ratio was 2.2240726088452184e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.967678771533123e-10, largest imaginary/real ratio was 2.9937701751773755e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.950801621203674e-09, largest imaginary/real ratio was 8.842478000190492e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1303439012068133e-09, largest imaginary/real ratio was 6.255580554461445e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8520202272938962e-09, largest imaginary/real ratio was 2.3252192442712443e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.25011323415443e-09, largest imaginary/real ratio was 4.767262341630007e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.29122677999756e-10, largest imaginary/real ratio was 3.736169957034042e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.95629607877809e-09, largest imaginary/real ratio was 4.022613186699353e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.465773702002188e-09, largest imaginary/real ratio was 4.6557168703755e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.045904936767557e-09, largest imaginary/real ratio was 1.5646887682094524e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.683852704349406e-09, largest imaginary/real ratio was 9.403468366188994e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9802645073126943e-09, largest imaginary/real ratio was 2.1457757047643056e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3346574019315244e-09, largest imaginary/real ratio was 1.403265067661898e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.540009774485351e-09, largest imaginary/real ratio was 1.1165619393659956e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.975214740713377e-09, largest imaginary/real ratio was 6.608917669509989e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.491750846421689e-09, largest imaginary/real ratio was 1.112574583928585e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6224697769957384e-09, largest imaginary/real ratio was 1.877586988077905e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2601344610055628e-08, largest imaginary/real ratio was 3.5236284882204973e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.2859678867909043e-09, largest imaginary/real ratio was 2.193172061976954e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.241983855202551e-09, largest imaginary/real ratio was 2.1681078022768186e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.895051026397498e-10, largest imaginary/real ratio was 3.988625540010123e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.481208261495495e-09, largest imaginary/real ratio was 2.6217014019500347e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4519087544941777e-09, largest imaginary/real ratio was 1.479557432288407e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0747136707060497e-09, largest imaginary/real ratio was 4.713253395853303e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3238732946940672e-09, largest imaginary/real ratio was 4.1605158952838155e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5326191766398194e-09, largest imaginary/real ratio was 1.5485948539195145e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9808964013320856e-09, largest imaginary/real ratio was 9.492098534812954e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4225542798865215e-09, largest imaginary/real ratio was 9.285098164615947e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.094425508760757e-09, largest imaginary/real ratio was 3.2508957293210677e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3180706966205499e-09, largest imaginary/real ratio was 7.619840070294517e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2153077901880391e-09, largest imaginary/real ratio was 9.46985805148596e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7875525648213681e-09, largest imaginary/real ratio was 1.1090469260161337e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6765727148016033e-09, largest imaginary/real ratio was 3.714271152486017e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.216343407226337e-09, largest imaginary/real ratio was 7.831610362218123e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.261031482947952e-09, largest imaginary/real ratio was 3.5027720876165184e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3687066287985323e-09, largest imaginary/real ratio was 8.56480856840572e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6315806323933242e-09, largest imaginary/real ratio was 3.2823885838176537e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5545295136546335e-09, largest imaginary/real ratio was 8.60181125838085e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3062399297502215e-09, largest imaginary/real ratio was 1.782912468041937e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.069678232640967e-09, largest imaginary/real ratio was 4.5489430685816946e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.633908826738357e-09, largest imaginary/real ratio was 8.144429601017943e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.412684052760628e-09, largest imaginary/real ratio was 1.0829942819013184e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7283759418420147e-09, largest imaginary/real ratio was 2.463361335249453e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.939971412141525e-09, largest imaginary/real ratio was 2.478212201238215e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.166956964665199e-09, largest imaginary/real ratio was 1.2023714309867425e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0824975165945766e-08, largest imaginary/real ratio was 2.832584104947181e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.445629306220688e-09, largest imaginary/real ratio was 5.553153441277249e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8590667299476254e-09, largest imaginary/real ratio was 1.521686453913986e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9925121741098685e-09, largest imaginary/real ratio was 6.181359006647097e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.197057161870009e-09, largest imaginary/real ratio was 2.275043851727771e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3988619914494678e-09, largest imaginary/real ratio was 3.1387152276170434e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4251522162162295e-09, largest imaginary/real ratio was 9.669864481948036e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.023384029841499e-09, largest imaginary/real ratio was 9.042418356989623e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.125722401379594e-10, largest imaginary/real ratio was 4.226444631031447e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.565415042407133e-09, largest imaginary/real ratio was 1.8089216811110127e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1780291392390927e-09, largest imaginary/real ratio was 3.182717507670609e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.434294575695322e-09, largest imaginary/real ratio was 8.072129572250314e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2899781869917703e-09, largest imaginary/real ratio was 2.814814745532037e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.839774232371672e-09, largest imaginary/real ratio was 5.63276155366404e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.623854121620788e-09, largest imaginary/real ratio was 1.3037661745432425e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.358133614764116e-09, largest imaginary/real ratio was 1.5138329852353061e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5989079259543246e-09, largest imaginary/real ratio was 1.8283967748758756e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.596339866814019e-09, largest imaginary/real ratio was 2.3670651419611546e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.519501515040389e-09, largest imaginary/real ratio was 1.4632598735989235e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.109587140099213e-09, largest imaginary/real ratio was 2.9223145407638285e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0026315530425193e-09, largest imaginary/real ratio was 7.863325868982396e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.047650096499366e-09, largest imaginary/real ratio was 1.910101517222009e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2110117642620545e-09, largest imaginary/real ratio was 3.442594374931615e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.2209413764991285e-09, largest imaginary/real ratio was 2.6283388774657905e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.462155887686797e-09, largest imaginary/real ratio was 1.2865475605687883e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.428061549090342e-10, largest imaginary/real ratio was 1.1202443408856266e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.998523070975345e-09, largest imaginary/real ratio was 4.400306331693298e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.747783310173321e-09, largest imaginary/real ratio was 2.1759643156822985e-10.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.781155152654636e-09, largest imaginary/real ratio was 1.4897525110937746e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.372798640316069e-09, largest imaginary/real ratio was 2.613733802398795e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.650408490067144e-09, largest imaginary/real ratio was 3.0490052228967426e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1479629503380054e-09, largest imaginary/real ratio was 5.72953277426665e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.451099189658057e-09, largest imaginary/real ratio was 1.5349512361105784e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0202530900376492e-09, largest imaginary/real ratio was 1.90646348522296e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.962877374369252e-09, largest imaginary/real ratio was 2.6890531318383283e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.915082445217239e-10, largest imaginary/real ratio was 4.1996739723685127e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6656047332258804e-09, largest imaginary/real ratio was 1.5943853016208221e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.587459001520404e-09, largest imaginary/real ratio was 2.2932448487669395e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0986888817617238e-09, largest imaginary/real ratio was 1.3009064717884827e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7850350393978406e-09, largest imaginary/real ratio was 1.4708320746407047e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.452166635989942e-09, largest imaginary/real ratio was 6.624220662259676e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.84005019204171e-09, largest imaginary/real ratio was 1.5438585123341945e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1015072362033176e-09, largest imaginary/real ratio was 4.0360803778249037e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.303675083967652e-09, largest imaginary/real ratio was 6.479966181342461e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8594646166453159e-09, largest imaginary/real ratio was 4.640593868903645e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6272749177953909e-09, largest imaginary/real ratio was 5.747618206071778e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.43917576006952e-09, largest imaginary/real ratio was 7.105405214478853e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.9036817844143455e-09, largest imaginary/real ratio was 5.410428198899717e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.857752530589371e-10, largest imaginary/real ratio was 3.6151217166800496e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.707533382660988e-09, largest imaginary/real ratio was 1.0714461811041214e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0193124578925644e-09, largest imaginary/real ratio was 3.8871946244193704e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1387880968370243e-09, largest imaginary/real ratio was 1.2359637204097644e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4179697420992907e-09, largest imaginary/real ratio was 1.1441141618098952e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5051080723633437e-09, largest imaginary/real ratio was 1.5108758748973106e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.294192198010445e-09, largest imaginary/real ratio was 2.380487330738937e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.127774718960802e-09, largest imaginary/real ratio was 1.6864238934532882e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.770045489630393e-09, largest imaginary/real ratio was 1.0621793999425389e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7688801652485202e-09, largest imaginary/real ratio was 2.6020649110187973e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.1873453547469124e-09, largest imaginary/real ratio was 2.7534827088401822e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.281521798284017e-09, largest imaginary/real ratio was 2.1797586033339983e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5340977850907968e-09, largest imaginary/real ratio was 2.1023737501945635e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.694290659481509e-09, largest imaginary/real ratio was 1.5348582944474582e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.081394565361544e-09, largest imaginary/real ratio was 1.0517900327572026e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2697826800822061e-09, largest imaginary/real ratio was 1.0498779985312371e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.7482365588948386e-09, largest imaginary/real ratio was 5.256014115217822e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4961734707264787e-09, largest imaginary/real ratio was 4.130979731788267e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.506097422688723e-10, largest imaginary/real ratio was 5.3789752595197485e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7945030896287666e-09, largest imaginary/real ratio was 9.358266614679172e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3582280192907144e-09, largest imaginary/real ratio was 1.8877962504624925e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3424080011708296e-09, largest imaginary/real ratio was 3.9836998678790324e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.5919287554938765e-09, largest imaginary/real ratio was 1.5640079750955166e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0945044503600297e-09, largest imaginary/real ratio was 1.5610860699302895e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6872323945641474e-09, largest imaginary/real ratio was 9.733973808083571e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.893380269369505e-09, largest imaginary/real ratio was 7.947428994463724e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5069919797141973e-09, largest imaginary/real ratio was 3.0201349190786554e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.220480666251587e-09, largest imaginary/real ratio was 4.016267299857367e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1771690488541368e-09, largest imaginary/real ratio was 3.8504983220528815e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.905374222146217e-09, largest imaginary/real ratio was 1.1137057696537296e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.295070968457999e-09, largest imaginary/real ratio was 2.919294559391574e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6994098648147996e-09, largest imaginary/real ratio was 5.867839270805378e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.331055957737974e-09, largest imaginary/real ratio was 3.1031326073329305e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.148823149813355e-10, largest imaginary/real ratio was 1.6266847131742007e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.610037050146814e-09, largest imaginary/real ratio was 2.5173264902780963e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.83148588480575e-10, largest imaginary/real ratio was 1.5878075246407687e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6040947808686865e-09, largest imaginary/real ratio was 1.0635045623503327e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5426611468773427e-09, largest imaginary/real ratio was 5.978422013121137e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.471375071469557e-09, largest imaginary/real ratio was 1.36282614144186e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6724332789029355e-09, largest imaginary/real ratio was 1.6586062425592347e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5935725864458716e-09, largest imaginary/real ratio was 7.08936714030424e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.109048409728553e-09, largest imaginary/real ratio was 5.942311073571752e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.987472939787038e-09, largest imaginary/real ratio was 1.1614528066223525e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7112344177659417e-09, largest imaginary/real ratio was 1.223024796352076e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3782681158884234e-09, largest imaginary/real ratio was 1.183655487621362e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.038638469313592e-09, largest imaginary/real ratio was 1.0020182529343877e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.464322531966439e-09, largest imaginary/real ratio was 6.446203889243839e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.2966024529495095e-09, largest imaginary/real ratio was 1.919966668863101e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.157187162709952e-09, largest imaginary/real ratio was 6.984432126074388e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.691849387563824e-09, largest imaginary/real ratio was 1.0118662090719275e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.650373080228922e-09, largest imaginary/real ratio was 3.808522706916844e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2640492711694206e-09, largest imaginary/real ratio was 1.4643675340598866e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.327152412624417e-09, largest imaginary/real ratio was 9.1710679735252e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.819577418071293e-09, largest imaginary/real ratio was 4.227967256722071e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8440449524579735e-09, largest imaginary/real ratio was 1.5154180810584845e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.677863975086713e-10, largest imaginary/real ratio was 6.155649820653447e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2762465931089793e-09, largest imaginary/real ratio was 1.1043351151723521e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.806836105070342e-09, largest imaginary/real ratio was 3.181379140738351e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.224657877824703e-09, largest imaginary/real ratio was 2.5898204910358446e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.418130706745838e-09, largest imaginary/real ratio was 1.6626167204111181e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.427783945198812e-09, largest imaginary/real ratio was 2.25221947627394e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.615214211400768e-09, largest imaginary/real ratio was 1.1928361514468005e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.601231674057399e-09, largest imaginary/real ratio was 5.24812143802892e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5064677039774823e-09, largest imaginary/real ratio was 1.5346761788121513e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.561892622181297e-09, largest imaginary/real ratio was 7.98847765609602e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5230891097314353e-09, largest imaginary/real ratio was 4.5036705522021664e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.592904895320192e-09, largest imaginary/real ratio was 2.3792994716406308e-12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7903451332318465e-09, largest imaginary/real ratio was 7.339680082031541e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4125253595606758e-09, largest imaginary/real ratio was 6.688497261267836e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6842186911444045e-09, largest imaginary/real ratio was 1.0701753896278308e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0478363097381254e-09, largest imaginary/real ratio was 4.0732760306188827e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9676950951820837e-09, largest imaginary/real ratio was 8.392472691171639e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.437119216234621e-09, largest imaginary/real ratio was 7.967588969837373e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.622629575238166e-09, largest imaginary/real ratio was 1.9304606689182635e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.130980963139491e-09, largest imaginary/real ratio was 5.515078186903448e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.299997836423855e-09, largest imaginary/real ratio was 1.639478038771074e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.725549690022002e-09, largest imaginary/real ratio was 3.302481510686919e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8467402692135534e-09, largest imaginary/real ratio was 3.649334341694615e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.672591973413906e-09, largest imaginary/real ratio was 1.922246170004906e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9322904879222448e-09, largest imaginary/real ratio was 3.62150369604071e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0990406436857802e-09, largest imaginary/real ratio was 8.481718505353992e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.720867345922548e-09, largest imaginary/real ratio was 6.755447336711655e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.155058156721452e-09, largest imaginary/real ratio was 2.0773447778984054e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9048783722558677e-09, largest imaginary/real ratio was 9.011470748460335e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.029144426810894e-09, largest imaginary/real ratio was 3.485701339662788e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.034938568145848e-09, largest imaginary/real ratio was 2.5278462436498813e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.089519439529426e-09, largest imaginary/real ratio was 9.118421036612992e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6383220470026745e-09, largest imaginary/real ratio was 8.144962704688683e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.729861854704912e-10, largest imaginary/real ratio was 5.748054386431358e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1615111983422786e-09, largest imaginary/real ratio was 2.1542577731621535e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7327886004072363e-09, largest imaginary/real ratio was 4.25045428124098e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3670453333083797e-09, largest imaginary/real ratio was 1.3567667610424498e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.159830837404506e-09, largest imaginary/real ratio was 3.9927959730545235e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.803763180782199e-10, largest imaginary/real ratio was 1.6728614777512585e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.077633974243574e-10, largest imaginary/real ratio was 5.087407447602138e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6719365729585624e-09, largest imaginary/real ratio was 2.401534743022611e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3067884884773978e-09, largest imaginary/real ratio was 1.3132020859108787e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.795792030013569e-09, largest imaginary/real ratio was 1.137203332453557e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3693826832929257e-09, largest imaginary/real ratio was 2.35943250843598e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.21795563693354e-10, largest imaginary/real ratio was 7.890742335872631e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7625692830633877e-09, largest imaginary/real ratio was 2.1306135578759327e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4954780909127772e-09, largest imaginary/real ratio was 7.968119072758239e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.6002167179824773e-09, largest imaginary/real ratio was 3.168752608803487e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2315843210300312e-09, largest imaginary/real ratio was 1.0696987640745075e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7413658895689672e-09, largest imaginary/real ratio was 3.1730459268004617e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.082801888124816e-09, largest imaginary/real ratio was 4.562191565279628e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5294052163947917e-09, largest imaginary/real ratio was 8.339828247098268e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.998624023655457e-10, largest imaginary/real ratio was 1.1794839899445743e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.626530048750956e-10, largest imaginary/real ratio was 7.523593088853837e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.80857853263767e-09, largest imaginary/real ratio was 2.6362334405099502e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.476540202041827e-09, largest imaginary/real ratio was 2.2722327469893818e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0076996782161426e-09, largest imaginary/real ratio was 4.513425865694784e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5097040043777793e-09, largest imaginary/real ratio was 3.5216827942527e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2534122111360926e-09, largest imaginary/real ratio was 1.6870455960805952e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7635060565604738e-09, largest imaginary/real ratio was 2.5093890965806946e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5959253692416684e-09, largest imaginary/real ratio was 8.919523639266955e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.787586262577443e-10, largest imaginary/real ratio was 1.3764707148426073e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.706592826503516e-09, largest imaginary/real ratio was 6.46640470919979e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2886406626149414e-09, largest imaginary/real ratio was 1.0763779487803164e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.2413414328439932e-09, largest imaginary/real ratio was 3.592240760127227e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5557916261968215e-09, largest imaginary/real ratio was 7.566328370626982e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.1772456332358188e-09, largest imaginary/real ratio was 6.798657840631513e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.524692763887682e-09, largest imaginary/real ratio was 3.977686979690827e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2614319930706376e-09, largest imaginary/real ratio was 4.595974485437779e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.14212015462023e-09, largest imaginary/real ratio was 8.88236207309666e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.676609946935883e-10, largest imaginary/real ratio was 5.218157583583229e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8784478220515854e-09, largest imaginary/real ratio was 3.4066667198894655e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.88213972134819e-09, largest imaginary/real ratio was 7.385927608720305e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.07620347968606e-10, largest imaginary/real ratio was 5.154165417388405e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9748781289660777e-09, largest imaginary/real ratio was 1.7827259536841545e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0227493132574574e-09, largest imaginary/real ratio was 6.810397677679661e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9021532063784735e-09, largest imaginary/real ratio was 2.744268685195431e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.932632215386407e-10, largest imaginary/real ratio was 1.0569997380089312e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.170765430141411e-11, largest imaginary/real ratio was 1.067594719431776e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.505391172482112e-09, largest imaginary/real ratio was 3.1102369376954317e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.420574012573233e-10, largest imaginary/real ratio was 3.089113733549767e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2742941271856542e-09, largest imaginary/real ratio was 6.605086915541454e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9132862918522065e-09, largest imaginary/real ratio was 4.006828630200961e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8842917850247693e-09, largest imaginary/real ratio was 2.712438453365076e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.783270989615556e-09, largest imaginary/real ratio was 7.240083497496358e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2149921988824859e-09, largest imaginary/real ratio was 2.0811994896494298e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5938216090031753e-09, largest imaginary/real ratio was 1.2770989823733798e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.371618098418593e-09, largest imaginary/real ratio was 2.6936760369025563e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.4762282930180713e-09, largest imaginary/real ratio was 4.683063157543544e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.308799141815648e-09, largest imaginary/real ratio was 7.015304006192406e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.086479972638481e-10, largest imaginary/real ratio was 8.286824865203719e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.002965226956278e-09, largest imaginary/real ratio was 1.2378427264008595e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.0370635328156185e-09, largest imaginary/real ratio was 1.2519322274073979e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3580076953905457e-09, largest imaginary/real ratio was 3.46000702968677e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.294691614928598e-10, largest imaginary/real ratio was 3.3189438488781e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5106980203162792e-09, largest imaginary/real ratio was 2.636293609463132e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.049531303037361e-09, largest imaginary/real ratio was 4.558975666982637e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6347787016326178e-09, largest imaginary/real ratio was 6.166375206748612e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8624575248500456e-09, largest imaginary/real ratio was 7.133720685686801e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.058767087472354e-09, largest imaginary/real ratio was 7.320711340063334e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9845483674725347e-09, largest imaginary/real ratio was 2.8798121320941137e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.058927492457572e-09, largest imaginary/real ratio was 2.0790494006331238e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0868552537615765e-08, largest imaginary/real ratio was 3.203204775099201e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.436698003828251e-09, largest imaginary/real ratio was 7.163835647421368e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.131762815964181e-09, largest imaginary/real ratio was 4.956692159361112e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.979288771612795e-09, largest imaginary/real ratio was 5.2366293813959575e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.55418232019405e-09, largest imaginary/real ratio was 2.1640249176335382e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.9656613158226605e-09, largest imaginary/real ratio was 5.955246510977905e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.89644195726254e-09, largest imaginary/real ratio was 1.0792343410829376e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9709049897335606e-09, largest imaginary/real ratio was 2.4114944222547864e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3482810167930625e-09, largest imaginary/real ratio was 6.887309826754078e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2629835202411187e-09, largest imaginary/real ratio was 1.4597091685715109e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.382822855652575e-09, largest imaginary/real ratio was 5.924308714172813e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.102686887435805e-09, largest imaginary/real ratio was 7.856446365002542e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.526894984842047e-09, largest imaginary/real ratio was 6.844495985856185e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.923283795638446e-09, largest imaginary/real ratio was 3.140126710768481e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.6488909934216915e-09, largest imaginary/real ratio was 1.0926708369234574e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5654084671560078e-09, largest imaginary/real ratio was 1.6827578025920588e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7590360847585065e-09, largest imaginary/real ratio was 1.896118582186565e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.3714429539644057e-09, largest imaginary/real ratio was 1.329572260186661e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.69218784360925e-09, largest imaginary/real ratio was 4.516433663851293e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4825203783527798e-09, largest imaginary/real ratio was 1.7591294862361597e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.329865817977811e-09, largest imaginary/real ratio was 1.3497271705254353e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2194313647661382e-09, largest imaginary/real ratio was 3.2093707716764224e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.074058054604688e-09, largest imaginary/real ratio was 1.1573414323888431e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5551224579859992e-09, largest imaginary/real ratio was 5.547289590546686e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6079340445569926e-09, largest imaginary/real ratio was 6.05459450655034e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.27996744879838e-09, largest imaginary/real ratio was 4.411528105234006e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3059600317805803e-09, largest imaginary/real ratio was 8.704807235083404e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.129796492113553e-09, largest imaginary/real ratio was 4.670869210391386e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3871387962870229e-09, largest imaginary/real ratio was 4.997536401887784e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.599528547118424e-09, largest imaginary/real ratio was 5.2725706948332485e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.793827740766407e-09, largest imaginary/real ratio was 7.849705897884434e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.311681287179738e-09, largest imaginary/real ratio was 4.192082343105789e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.575452297891224e-09, largest imaginary/real ratio was 8.656191257762684e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.994027406534608e-09, largest imaginary/real ratio was 4.375513806187425e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.848863016884709e-09, largest imaginary/real ratio was 1.9525109372146896e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1811808938214858e-09, largest imaginary/real ratio was 5.298356410558619e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7174710256748428e-08, largest imaginary/real ratio was 8.927129126607352e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.748169574423315e-09, largest imaginary/real ratio was 7.356756447415955e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.758070488489929e-09, largest imaginary/real ratio was 1.3261286894909978e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.146870167135578e-09, largest imaginary/real ratio was 7.36489283655342e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8013449012243027e-09, largest imaginary/real ratio was 8.410772069614117e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.861799271770469e-09, largest imaginary/real ratio was 1.2675530240034391e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4285119175288655e-09, largest imaginary/real ratio was 4.926334221263287e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.849076001531646e-09, largest imaginary/real ratio was 1.2623548534253803e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3105943153634793e-09, largest imaginary/real ratio was 1.6689384952302694e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.9793925529380296e-09, largest imaginary/real ratio was 1.647976853993093e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.878008356481483e-09, largest imaginary/real ratio was 2.0327473498600014e-10.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7144293279429724e-09, largest imaginary/real ratio was 1.2442298423603204e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.322960443779712e-09, largest imaginary/real ratio was 3.1823186983878054e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0692392602797922e-09, largest imaginary/real ratio was 3.5672045663489443e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0997236229829862e-08, largest imaginary/real ratio was 2.7199238467455e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2236159939622693e-09, largest imaginary/real ratio was 7.41670741841008e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.1146049569318444e-09, largest imaginary/real ratio was 6.146206550679714e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.330443407070393e-08, largest imaginary/real ratio was 2.09950819853499e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.371098651932915e-09, largest imaginary/real ratio was 1.050550321598925e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.268593856364961e-09, largest imaginary/real ratio was 4.103204110076588e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.7066785883837362e-09, largest imaginary/real ratio was 9.036932629418455e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0846487848898056e-09, largest imaginary/real ratio was 7.92964223598726e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2386395962822974e-09, largest imaginary/real ratio was 2.8682740436934774e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.204172133863748e-09, largest imaginary/real ratio was 1.5945272292458222e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8159787279636431e-09, largest imaginary/real ratio was 1.01337766018089e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.165099651059081e-10, largest imaginary/real ratio was 1.1243545767989946e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.683295182887835e-09, largest imaginary/real ratio was 2.1287818863291667e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8695883394319633e-09, largest imaginary/real ratio was 2.8747461509382103e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.6931923941249955e-09, largest imaginary/real ratio was 2.2202218389088265e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.900831789362841e-09, largest imaginary/real ratio was 4.7173879093701603e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.880752274023205e-09, largest imaginary/real ratio was 4.733751177529272e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5218868367783153e-09, largest imaginary/real ratio was 3.3582604683352285e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.166963142891691e-09, largest imaginary/real ratio was 6.883787951184828e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.524873076461207e-08, largest imaginary/real ratio was 1.5605366086236903e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.2564271621722837e-09, largest imaginary/real ratio was 7.053095828484453e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5606340873164384e-09, largest imaginary/real ratio was 1.7587353066881566e-14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.3265849305892807e-09, largest imaginary/real ratio was 2.997123635131881e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.413918901781356e-09, largest imaginary/real ratio was 3.332993971331066e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3715164461927262e-09, largest imaginary/real ratio was 1.261670422201887e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.791502569394658e-09, largest imaginary/real ratio was 8.651194975875352e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.7252361399940825e-09, largest imaginary/real ratio was 1.4509511250320738e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.180955818333583e-09, largest imaginary/real ratio was 2.7414559519548957e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.5387146508473505e-09, largest imaginary/real ratio was 7.105112378478715e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.32047358782135e-09, largest imaginary/real ratio was 8.080290991653599e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5791671491175914e-09, largest imaginary/real ratio was 2.747416168036103e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.761633845940946e-09, largest imaginary/real ratio was 2.871670867658945e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.984864301129229e-09, largest imaginary/real ratio was 3.1165968168240304e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.756835776846232e-09, largest imaginary/real ratio was 4.0316854018808355e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.421032842909554e-09, largest imaginary/real ratio was 7.660989786109749e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.107511865812946e-09, largest imaginary/real ratio was 1.9188017446042795e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.182479242637475e-09, largest imaginary/real ratio was 6.941535639640349e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9007364096207253e-09, largest imaginary/real ratio was 1.0928405174297395e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9824117776328808e-08, largest imaginary/real ratio was 3.643498325881577e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.822942524974596e-10, largest imaginary/real ratio was 4.797573965886263e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6029532187023786e-09, largest imaginary/real ratio was 3.5176368595451366e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.253589218159727e-09, largest imaginary/real ratio was 2.0234174917792467e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.762686007858615e-09, largest imaginary/real ratio was 3.324597486429663e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.884987991125088e-09, largest imaginary/real ratio was 5.6186363375777366e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.009285384134717e-09, largest imaginary/real ratio was 6.039594400007495e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.439349742265983e-09, largest imaginary/real ratio was 2.627245690692635e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.733965498508419e-10, largest imaginary/real ratio was 9.02313870864296e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.0665647184769584e-09, largest imaginary/real ratio was 4.111736906892906e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7634663998906573e-09, largest imaginary/real ratio was 2.2965322946060605e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.584932606711215e-09, largest imaginary/real ratio was 7.414535323698216e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.007291321825853e-09, largest imaginary/real ratio was 4.6843610630509864e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8258848608477235e-09, largest imaginary/real ratio was 1.0353085929759027e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.609727228867042e-09, largest imaginary/real ratio was 1.8790851520560687e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.925070364656695e-09, largest imaginary/real ratio was 2.0018671465005403e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.272018754554811e-09, largest imaginary/real ratio was 1.700196619996444e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.8419845478396605e-09, largest imaginary/real ratio was 1.0643817293424362e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9459372387871594e-09, largest imaginary/real ratio was 5.860697549525382e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.180410328471657e-09, largest imaginary/real ratio was 4.830554135337215e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4670777444614256e-09, largest imaginary/real ratio was 9.311485230375395e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.85419343025601e-09, largest imaginary/real ratio was 3.1019017410348426e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4112313958049089e-08, largest imaginary/real ratio was 9.103574455539679e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8297700326715176e-08, largest imaginary/real ratio was 4.662479203559956e-12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0600323316722144e-08, largest imaginary/real ratio was 3.582777404808206e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.049637219273211e-09, largest imaginary/real ratio was 2.5610192801152645e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5173642230086563e-09, largest imaginary/real ratio was 1.1772501356157681e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.8989378433266955e-09, largest imaginary/real ratio was 6.285573941569473e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.083675423067704e-09, largest imaginary/real ratio was 2.5567139765495845e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1936420168246912e-08, largest imaginary/real ratio was 8.581040159358584e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.056192204561809e-09, largest imaginary/real ratio was 3.389882170029352e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4606724200160315e-09, largest imaginary/real ratio was 7.835372288853216e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5970284577325663e-09, largest imaginary/real ratio was 3.221327430563296e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.063041100128326e-09, largest imaginary/real ratio was 4.252139403559972e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.565952119453977e-09, largest imaginary/real ratio was 1.1687214098368975e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.464555668926123e-09, largest imaginary/real ratio was 4.1953815496672236e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6820327266092735e-09, largest imaginary/real ratio was 3.5062090561183283e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.191654686895815e-09, largest imaginary/real ratio was 5.634784254512062e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.367061148685744e-09, largest imaginary/real ratio was 2.953733997422817e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2962639042819657e-08, largest imaginary/real ratio was 1.710438231389982e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.277141891872102e-09, largest imaginary/real ratio was 1.8445405967480263e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3142914644874942e-08, largest imaginary/real ratio was 3.748213929175881e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.328679890911526e-09, largest imaginary/real ratio was 3.636489429860878e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.040333762061534e-09, largest imaginary/real ratio was 2.8582387874994516e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.964045088282208e-09, largest imaginary/real ratio was 6.853119916125543e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8216947048164054e-08, largest imaginary/real ratio was 2.1039316236744895e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.078807811266483e-09, largest imaginary/real ratio was 1.487494467317988e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.343519677282506e-09, largest imaginary/real ratio was 1.225005740424577e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.653019600752232e-09, largest imaginary/real ratio was 2.15219250586548e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5408324349919781e-09, largest imaginary/real ratio was 1.4786167545969392e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.928872877697712e-09, largest imaginary/real ratio was 5.830885150581413e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.491365674017419e-09, largest imaginary/real ratio was 2.0554156868128963e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.230355307062909e-09, largest imaginary/real ratio was 7.181123931612218e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.9934918403986755e-09, largest imaginary/real ratio was 8.832881940178635e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.541774242413926e-09, largest imaginary/real ratio was 1.948288805203974e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6539321487634793e-08, largest imaginary/real ratio was 7.52637879416507e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.923103037829788e-09, largest imaginary/real ratio was 7.47285719224634e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.878214653031201e-09, largest imaginary/real ratio was 4.4314435592287517e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.860733706272556e-09, largest imaginary/real ratio was 3.153075190117332e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.851608029665981e-09, largest imaginary/real ratio was 1.6202812707133396e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.838555586439065e-08, largest imaginary/real ratio was 1.1801863410836268e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5164113512581547e-08, largest imaginary/real ratio was 5.1627216186337964e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2307180933385005e-08, largest imaginary/real ratio was 1.2572812886351804e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3873802710633427e-08, largest imaginary/real ratio was 7.065926752476837e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7241468851954583e-09, largest imaginary/real ratio was 8.443982120668498e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.462810767900124e-09, largest imaginary/real ratio was 9.607207955861558e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.1425217511337394e-09, largest imaginary/real ratio was 1.0426747441657312e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.478134722647389e-08, largest imaginary/real ratio was 1.3773043892613396e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.342898115575589e-09, largest imaginary/real ratio was 2.971555365992507e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.326729279474813e-08, largest imaginary/real ratio was 1.0895048712296091e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.589199847817973e-09, largest imaginary/real ratio was 1.608333148784462e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.766652079231449e-09, largest imaginary/real ratio was 8.861794232180759e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.173109085706515e-09, largest imaginary/real ratio was 8.325888615155445e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.2074719857722796e-08, largest imaginary/real ratio was 5.995991730277073e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.5880571247204078e-08, largest imaginary/real ratio was 5.813425335696318e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9584835908470427e-08, largest imaginary/real ratio was 4.656149965291126e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.335889899891393e-09, largest imaginary/real ratio was 1.1223225248692978e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.252904248479156e-09, largest imaginary/real ratio was 1.8586883024461334e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.854046810724296e-09, largest imaginary/real ratio was 1.7425314498516245e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3828858255597025e-08, largest imaginary/real ratio was 3.4709177273941713e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.742483583706012e-09, largest imaginary/real ratio was 1.1938883924547516e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.9645607354414365e-09, largest imaginary/real ratio was 1.0098441436224781e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 9.08044710910425e-09, largest imaginary/real ratio was 2.884804793430227e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.534614007348421e-09, largest imaginary/real ratio was 2.0882377043154528e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.216497012587542e-09, largest imaginary/real ratio was 1.723330330915239e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2432480110174273e-09, largest imaginary/real ratio was 1.8707551067743928e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.6812898591734597e-09, largest imaginary/real ratio was 2.2291197765411493e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.985195458678969e-09, largest imaginary/real ratio was 3.5241576347951226e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.6652212152083784e-09, largest imaginary/real ratio was 5.1926069930414024e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.1584035492343576e-09, largest imaginary/real ratio was 7.27047841132234e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.496956713287865e-09, largest imaginary/real ratio was 9.149822763612708e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.456216005518979e-09, largest imaginary/real ratio was 1.609003383430548e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.143768098158061e-09, largest imaginary/real ratio was 7.808731468126779e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.683360720412553e-09, largest imaginary/real ratio was 3.8633502892809494e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.7022848796686907e-09, largest imaginary/real ratio was 1.0187226696598956e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9442901758996575e-09, largest imaginary/real ratio was 1.0787123853712831e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.246224002037108e-09, largest imaginary/real ratio was 2.4096309928560903e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.4648641674364243e-09, largest imaginary/real ratio was 3.988699727533314e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.178763801404985e-09, largest imaginary/real ratio was 5.714948741284176e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.201927098017274e-09, largest imaginary/real ratio was 2.3628693691549338e-11.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.9728272609304534e-09, largest imaginary/real ratio was 6.658116623152475e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9555302259812122e-09, largest imaginary/real ratio was 1.0520641103573377e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8829193865029858e-09, largest imaginary/real ratio was 2.5795244942894658e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3387133507207755e-09, largest imaginary/real ratio was 3.705527164440421e-13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.2184777287244654e-09, largest imaginary/real ratio was 1.0131265934051434e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.9380147283859e-09, largest imaginary/real ratio was 4.088243044773802e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.0979700531270516e-09, largest imaginary/real ratio was 2.9919853566366122e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.7062429137005129e-09, largest imaginary/real ratio was 1.2126177530976192e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.2456420904066777e-09, largest imaginary/real ratio was 5.875624333739772e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.667190919989676e-09, largest imaginary/real ratio was 1.894681449207004e-12.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.289807839280804e-09, largest imaginary/real ratio was 1.7968791972408376e-14.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.3701528673399925e-12, largest imaginary/real ratio was 3.7448408449172068e-16.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.8454868450799952e-11, largest imaginary/real ratio was 6.208637476033176e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.594920578963951e-13, largest imaginary/real ratio was 2.5460348575030886e-17.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.616982820753328e-13, largest imaginary/real ratio was 3.289289154024166e-17.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.8619351686683694e-10, largest imaginary/real ratio was 3.84980018088e-13.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0197847903198969e-13, largest imaginary/real ratio was 7.312608151741506e-18.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 7.905622007328509e-10, largest imaginary/real ratio was 4.644195609607561e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.4896156581104848e-10, largest imaginary/real ratio was 1.7024928773359095e-15.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.337421663256177e-12, largest imaginary/real ratio was 1.17278919302852e-16.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.948702332467367e-28, largest imaginary/real ratio was 1.163795992954106e-32.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.5533425958783491e-27, largest imaginary/real ratio was 1.5896404291689628e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.564718841695301e-29, largest imaginary/real ratio was 7.353431710852399e-34.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.780895060778725e-29, largest imaginary/real ratio was 1.6492068466630052e-33.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.0192445784642e-29, largest imaginary/real ratio was 1.3838508756606565e-33.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.364880656369414e-27, largest imaginary/real ratio was 1.8440135221770185e-30.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.171611439620445e-29, largest imaginary/real ratio was 3.0614493530139884e-33.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.579116366039801e-29, largest imaginary/real ratio was 6.410009259510224e-34.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.650669008953811e-25, largest imaginary/real ratio was 4.377180415337931e-29.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.343572050879368e-29, largest imaginary/real ratio was 3.8118360782862463e-34.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.355016882575332e-29, largest imaginary/real ratio was 7.880262346899775e-34.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 2.450528837284126e-28, largest imaginary/real ratio was 6.206593875074649e-32.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 6.628289171600746e-29, largest imaginary/real ratio was 3.285950157602122e-33.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 8.519971738378379e-29, largest imaginary/real ratio was 7.69403663891507e-34.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0193736815011474e-26, largest imaginary/real ratio was 6.4381156192705775e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 4.850422501375296e-29, largest imaginary/real ratio was 1.417331194919293e-33.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 3.7807132545693055e-29, largest imaginary/real ratio was 7.548104679237182e-34.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 1.0340345582934346e-26, largest imaginary/real ratio was 2.427377226292923e-31.\n",
      "Fixing auto-correlations to be be real-only, after some imaginary values were detected in data_array. Largest imaginary component was 5.181876182780056e-28, largest imaginary/real ratio was 1.1931930760358112e-32.\n"
     ]
    }
   ],
   "source": [
    "auto_files = sorted(h6c_dir.glob(lstbin_ext.replace(\"000\", \"autos\")))\n",
    "auto_uvdata = UVData.from_file(auto_files, axis=\"blt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fee246a9",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'redavg_ext' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# test_file = list(sim_dir.glob(f\"{jd}/*sum.smooth_calibrated.red_avg.uvh5\"))[0]\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m test_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(sim_dir\u001b[38;5;241m.\u001b[39mglob(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mredavg_ext\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/zen.LST.*.000.sum.uvh5\u001b[39m\u001b[38;5;124m\"\u001b[39m))[:\u001b[38;5;241m2\u001b[39m]\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# test_file = list(h6c_dir.glob(f\"{jd}/*sum.smooth_calibrated.red_avg.uvh5\"))[0]\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# test_file = list(h6c_dir.glob(f\"{redavg_ext}/zen.LST.*.000.sum.uvh5\"))[0]\u001b[39;00m\n\u001b[1;32m      5\u001b[0m uvd \u001b[38;5;241m=\u001b[39m UVData\u001b[38;5;241m.\u001b[39mfrom_file(test_file)\u001b[38;5;66;03m#, bls=test_bls['sim_lstbin'], axis='blt')\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'redavg_ext' is not defined"
     ]
    }
   ],
   "source": [
    "# test_file = list(sim_dir.glob(f\"{jd}/*sum.smooth_calibrated.red_avg.uvh5\"))[0]\n",
    "test_file = sorted(sim_dir.glob(f\"{redavg_ext}/zen.LST.*.000.sum.uvh5\"))[:2]\n",
    "# test_file = list(h6c_dir.glob(f\"{jd}/*sum.smooth_calibrated.red_avg.uvh5\"))[0]\n",
    "# test_file = list(h6c_dir.glob(f\"{redavg_ext}/zen.LST.*.000.sum.uvh5\"))[0]\n",
    "uvd = UVData.from_file(test_file)#, bls=test_bls['sim_lstbin'], axis='blt')\n",
    "uvd.get_antpairs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a9a7c82",
   "metadata": {},
   "outputs": [],
   "source": [
    "def frdly(vis):\n",
    "    n_times, n_freqs = vis.shape\n",
    "    taper = uvtools.dspec.gen_window(\n",
    "        \"bh\", n_times, sym=False\n",
    "    )[:,None] * uvtools.dspec.gen_window(\"bh\", n_freqs, sym=False)[None,:]\n",
    "    return np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(vis*taper)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00dd3736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f0312d99250>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmax=100\n",
    "plt.imshow(all_data['sim_daily'].get_data(3,5,'ee').real, aspect='auto', vmin=-vmax, vmax=vmax)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "26850fb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_lsts = all_data['data_daily'].lst_array.copy()\n",
    "daily_lsts[daily_lsts<daily_lsts[0]] += 2 * np.pi\n",
    "daily_lsts = np.unique(daily_lsts)\n",
    "freqs_MHz = all_data['data_daily'].freq_array / 1e6\n",
    "\n",
    "lstbin_lsts = all_data['data_lstbin'].lst_array.copy()\n",
    "lstbin_lsts[lstbin_lsts<lstbin_lsts[0]] += 2 * np.pi\n",
    "lstbin_lsts = np.unique(lstbin_lsts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0df1fd0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "_spws = \"50.1~62.2,63.3~73.5,74.6~85.4,108.0~116.1,117.3~124.4,125.3~136.2,138.3~148.2,150.1~159.2,159.3~169.9,171.9~181.1,181.4~196.4,198.5~208.4,212.3~220.6,224.4~231.1\"\n",
    "spws = []\n",
    "for spw in _spws.split(\",\"):\n",
    "    f1, f2 = spw.split(\"~\")\n",
    "    spws.append((float(f1), float(f2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "21482682",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_start = 6.5\n",
    "lst_end = 7.75\n",
    "spw = 8\n",
    "freq_start, freq_end = spws[spw]\n",
    "# freq_start = 120\n",
    "# freq_end = 170\n",
    "\n",
    "daily_lst_select = np.argwhere((daily_lsts>=lst_start) & (daily_lsts<=lst_end)).flatten()\n",
    "lstbin_lst_select = np.argwhere((lstbin_lsts>=lst_start) & (lstbin_lsts<=lst_end)).flatten()\n",
    "freq_select = np.argwhere((freqs_MHz>=freq_start) & (freqs_MHz<=freq_end)).flatten()\n",
    "daily_select = (slice(daily_lst_select[0], daily_lst_select[-1]), slice(freq_select[0], freq_select[-1]))\n",
    "lstbin_select = (slice(lstbin_lst_select[0], lstbin_lst_select[-1]), slice(freq_select[0], freq_select[-1]))\n",
    "# daily_select = (daily_lst_select, freq_select)\n",
    "# lstbin_select = (lstbin_lst_select, freq_select)\n",
    "\n",
    "daily_times = all_data['data_daily'].time_array\n",
    "daily_dt = (daily_times[1] - daily_times[0]) * 24 * 3600\n",
    "lstbin_times = all_data['data_lstbin'].time_array\n",
    "lstbin_dt = (lstbin_times[1] - lstbin_times[0]) * 24 * 3600\n",
    "daily_frates = np.fft.fftshift(\n",
    "    np.fft.fftfreq(daily_lst_select.size, daily_dt)\n",
    ") * 1e3  # mHz\n",
    "lstbin_frates = np.fft.fftshift(\n",
    "    np.fft.fftfreq(lstbin_lst_select.size, lstbin_dt)\n",
    ") * 1e3  # mHz\n",
    "delays = np.fft.fftshift(\n",
    "    np.fft.fftfreq(freq_select.size, freqs_MHz[1]-freqs_MHz[0])\n",
    ") * 1e3  # ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b699056",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f03193e5590>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_extent = (delays[0], delays[-1], daily_frates[-1], daily_frates[0])\n",
    "lstbin_extent = (delays[0], delays[-1], lstbin_frates[-1], lstbin_frates[0])\n",
    "extents = {\"daily\": daily_extent, \"lstbin\": lstbin_extent}\n",
    "selects = {\"daily\": daily_select, \"lstbin\": lstbin_select}\n",
    "xlim = (-1000, 1000)\n",
    "ylim = (-5, 5)\n",
    "vmax = 1e6\n",
    "vmin = vmax * 1e-5\n",
    "norm = plt.cm.colors.LogNorm(vmin=vmin, vmax=vmax)\n",
    "cmap = plt.cm.turbo\n",
    "smap = plt.cm.ScalarMappable(norm=norm, cmap=cmap)\n",
    "label_fontdict = dict(fontsize=12, fontweight='bold')\n",
    "bbox = dict(boxstyle=\"round\", alpha=1, ec=\"k\", fc=\"w\")\n",
    "\n",
    "fig, axes = plt.subplots(4, 4, figsize=(10,8), dpi=150, gridspec_kw=dict(hspace=0.03, wspace=0.03))\n",
    "for col_block, dset in enumerate((\"data\", \"sim\")):\n",
    "    for row, name in enumerate((\"daily\", \"lstbin\")):\n",
    "        extent = extents[name]\n",
    "        select = selects[name]\n",
    "#         if f\"{dset}_{name}\" == \"sim_lstbin\": continue\n",
    "        for row_block, bl in enumerate(test_bls[f\"{dset}_{name}\"]):\n",
    "            for col, pol in enumerate((\"ee\", \"nn\")):\n",
    "                ax = axes[2*row_block+row, 2*col_block+col]\n",
    "                vis = np.abs(frdly(all_data[f\"{dset}_{name}\"].get_data(tuple(bl)+(pol,))[select]))\n",
    "#                 vis /= vis.max()\n",
    "                ax.imshow(vis, aspect='auto', extent=extent, norm=norm, cmap=cmap)\n",
    "                ax.set_xlim(*xlim)\n",
    "                ax.set_ylim(*ylim)\n",
    "                ax.text(-850, 4.5, pol, bbox=bbox, ha=\"left\", va=\"top\")\n",
    "                ax.text(850, 4.5, name, bbox=bbox, ha=\"right\", va=\"top\")\n",
    "\n",
    "for ax in axes[:-1,:].ravel():\n",
    "    ax.xaxis.set_visible(False)\n",
    "for ax in axes[:,1:].ravel():\n",
    "    ax.yaxis.set_visible(False)\n",
    "    \n",
    "fig.tight_layout()\n",
    "fig.colorbar(smap, ax=axes, aspect=17, pad=0.03, extend=\"min\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fdceeec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qr18/h9K4o0JvnfAZyCWLxDqhyEHo4RwpRfbl2SBZ/CzDSQAU+Hwj1T+bV21u5sihAShDNzimjSJW+va/CMMpAPhvNNvrvqwtvDe1LuM0Rv35zg/S1wd6OFU1kgU6VySKbC71bDpvSGzDUM9FXtu7fhVTlF4sePQLtnFN2vgp6aWdtLrpRBKALVz71Ui3xXkceOCBMGXKFLjrrrsAAODiiy+GiRMnwmGHHdZy3Zo1a+DUU0+FtWvXAgDAXnvtBeecc05huiNHjoTp06enAu6sWbPg0EMPhY985CMt123atAk++clPwtKlSwEAYODAgXDxxRejlc8UFXqjoYE34OYNGJImlpCilMtgGmIi5BQpfcsnQWSUYAMVTH0hWBzQIrhCIVAfkCbR27bShkCHl9X5Oc7BeaPL9Tlmnz70+WBhUCfRCaYSxTVXJIpZdarfLDb9vKhZJD0rzb7jI6Zm06EgL22b/mUz7xP2W/YxUlI/c8F1LpZYbok2KbXh7LPPhu9///uV13z605/u9d/Xr1+fe/2sWbPgscceg9WrV0N3dzd84AMfgGnTpsExxxwDgwcPhkWLFsG1114LixcvBgCArq4umDNnDgwcOLDUjssuuwzuuece+POf/wxbtmyBj33sY3DqqafChz/8Ydh1113h+eefh29/+9uwaNGi9J6rr74ahg8fXlUN6KjQGxNVg6zpJppSMMagbGIMudGv2yfzptShDJHhG9cymChLeegbtvdsEVShGHzzAKJ2VdGWHykvVjHWAsRliU58BaAVCCUKoib4HmBaV9G1CCn9viuilzllz0bs/cfAftKxUkp/rCLU3NppYSIovw5QOpJNmzbBhg0bSq/ZvHkzbN682TjNUaNGwZ133gknnHACvPbaa7BhwwaYPXt2GtIhS58+fWDmzJlwwgknVKa7yy67wE9/+lM46qij4KWXXoKenh645ZZb4JZbbsm9/vOf/3yplzAlKvTWCW4hsj0/DnEpbxLHFAxs6zBSETSEEJi3CBXnJRoKhAVQAsn2xRfHgsrXeyZNByM0gGcaWGUBsLKFJa4t9TPWyYKtKZybT4HiLZkAEVK8oRBcpYtRlIJCH+FlzxKr2C6JgH09ypdHJtStXFjzUF69uK6LXOuYu21Mni/usCzS5zfB9NQppgIzhx56KCxatAjOO+88uPPOO3OF4gMPPBBmzpwJhx56qHG6+++/PyxatAguvPBCuPXWW+Gtt97qdc348ePhyiuvhClTpniVwYdGIkBpeeONN+COO+6ABQsWwPPPPw8rVqyARqMBe+21F4wcORIOO+wweP/73w+HHnoo7LDDDmj5rl27Fm644Qa455574H//939h9erVMGjQIBgxYgR84AMfgKlTp8IBBxyAlp8LI0eOhOXLl8MOfQbDv4w4K5whvt2EspuFEhoIyyTgsQxDJ77xxRQZOQkl0PrWF4ao7lr2kAeQCRNkMcc41o07pgiKaTehmOxVv1ybOyniG/dmVpK4I6UNuIlUwGg+15zrzVqJrDGVxWR+MFkj1D2mOjYSBV4MiMr1wNI5sGFLN4wYMQKWLVtGkkcsNPWf4Xv1gT/9fhhLnu98zwr4fyu31Lb+X3nlFVi4cCEsW7YMNm7cCMOHD4dJkybB2LFjvdJdu3Yt/OpXv4KXXnoJ3nzzTRg2bBj8wz/8Q3ANEUCA0Dt37ly48MIL4dVXX628dv78+XDyySej5HvbbbfBOeecA6+99lrhNY1GA8455xz42te+Vhmvg4oWofftZ1ffQPrpdMC0pYgUyHXA+viFEFFjFTE54egDUgT0QCIt6XNWNDahxfbleYakvlwiFwYkblw9bCKpL4zNI5UIKFlko+67oYRVoXXe3veljmkAeM9pURmjElRjstUEKaF5OIih7bI2Yn7FikUnvxxsQ4Xe7TT1n2F7dcEfmYTef3jPClixskfrv0YEC92QJAl86lOfguuvv77lvw8fPhz23ntvGDhwILz88svw/PPPW8XjMOEb3/gGzJgxo1e+++23H2zYsAEWLVoE69atgyRJ4Fvf+hYsXrwY7rzzTugTw8EjeRMGlsjTaOCJF7bpmC6cMEQRhDKybDCoxbvQIq2UTZoUkbQdrvYhLD/Zc1I1Dkg5nCyDZFEiFGhihc3G2zZPaTFqsTeNGGKir02hN6acgmokIqopNuMatjgZjdjpYWc0Zayik8TRPKS2Y9MurPVJ6HLm5S90zAWA8PWlKErUBBN6zzvvvBaR9+STT4YvfelL8O53v7vluvXr18MDDzwAt9xyC/Tv398734ceegguvPDC9Pfb3/52+O53vwuTJ09uyXPmzJnwxS9+EbZs2QJ33303XHLJJfDVr37VO/8gYMbqwYrLazJ5uaSdXSy6ii8OCxtUkYZCWJMUTxWDTvdOFiQ+s8SZdSFgeAVy0dak/SVvXgzIFTBCnmBtkDe66GLbhliCJLpY7Cv0EopAgp6TaEQ7CzujKVNoOl3opKBufc+0PBReq6HnNolI7l+SbVOs0Ri9iitBhN57770Xrr322vT3NddcAxdccEHutQMGDIDjjjsOjjvuOJS8v/CFL0DPto38zjvvDA899BCMHj26V54XXXQR7LbbbvCpT30KAAC+/vWvw6c//Wl4+9vfjmKHOCg9gdsJ5cXnaQeKeBMiJmlp2h5lIusf8Xuq2oImDHKIqAK9YU2g8x5GTDfb933FLar+7bhBsxJ8TEUPr9ix9vWLIlpRibdUG2fnw2eYhCvPcosVIqXZRfVVVV0FTmntp9gTaxu62F0H4dUWSe0rxRYpdiiKggK70Ltp0yaYPn16+vuMM84oFHmx+d///V94/PHH098XX3xxL5E3y9lnnw3XX389PPHEE7Bhwwb4r//6L5g1axaHqflQvc3lFrwwQ0Aw0mg0+D+vthE9pQhZGEItU58MEruVAnIPUpqy0NY/QtqhvLjrKJIVCTronkJ2dWdXBkbR0yUvKaJshe0u4mp2rCATZyk3udIETZuy2ozTzXKqYGAGZj0lidZ7ETbjqc/aoRMF0zJi649S7ZVql8LClgg1E0UG7ELvnXfeCUuWLAEAgP79+8PXvvY1trwfeOCBlt8f//jHK+/5xCc+AU888QQAACxYsABmzpwp1wMEwDHcgUdoB9cg93l1iP0JeMiB0XSh6CMkOX+azuRVLDm2K5UIi+L1TSsuBo8FG5EAm1jY2sDc4DXLF/gUexIv3K0J06QLjiKgk6jKIN7alsW3vyD2Yad2KLmH5pA5YeJrO1LWmtb9UIjdnQbGSyub+bmuoiZHufQZqaaOdSSkTIlHH29UjBE+aYOM6lGU2sAu9Gbj8p544omwxx57sOX9t7/9Lf37kCFDYO+99668JxszeNmyZfDkk0/Ce97zHhL7UPAJnO8jwLAcPsbj3WokhFGLpdRe2QGEWyuBkULslBIyJAd/4Zrw+ZMUmxjsxFcuUETegIdesXrmGoprXsKeaT1IFGvT+yxtIxInCtuBc8MaUpDl3pgLEQK8qasIWHe42q0u/VwiWrf4BKxTL+GUAGn2KIpSDKvQu2nTJnjwwQfT3x/60Ic4s4e///3v6d932mkno3uGDBnS8vvpp5+WLfQ2cQmP0NVwEAxdBE9HoaZ9s5cnsBmm7SWs2dSRqUhGJS6b2mKRtnPdUQr1kr1gIzw4LoiYyiEoE3jIOom8NnZQire2AlpRmhbpkIRKkCzYpvfjtDm6ZyvTQXUk+WIR0hbdNPdGUt9Q8NH2jRttvxQb0ZPUE1apLQkAcLnbyHOjUXxhFXr/8Ic/wIYNG9LfBx98MAAAPPbYY/Dd734XHn74YVi+fDkMGDAAhg8fDkcccQRMnTo1vc6XrLi7du1ao3vWrFnT8vuZZ55BsYUEDI9FykPZKDw6S9K0Fudcy0nhqUshJjukX1iHmMJqXh6W6YsSYpEESxKxVZh3rjdIwm0Qj1ybDQJmqAMpYRNiEHGxXgxQtfX2m+zvaeLrLRty008aU1fwpluFlt5Q1YlvSDJFwUb7mRHShFNp9hhB0de0/yoKG6xC76JFi9K/d3V1wYgRI2D69OkwZ86cluvWr18Pb7zxBjzzzDPw7W9/Gz760Y/CDTfcAIMHD/bKf+TIkenf16xZA0uXLq0M3/DHP/6x5XczvnAQJBy2ZWqDU+iIEhHKIj0n0Q9TWC3LX4iAC2BRT7airqdwG0S0RRBAnYXZEOJrVR1zLsQcxDTUeLgedqQY2kMW83Zr4nbXG+SD5j2aVz8m9W3TzsJDJFjVpW1ZJIm1JLF0BW8MJW1aJdlSF1jnwg5sP0lrEcUaLvGyzBs2SgGVCqnPi1S7ImGL+toqjrAKva+++mr696FDh8K0adPg9ttvB4Ctm6AJEybAHnvsAatWrWrxnJ0/fz789a9/hYcffhh23HFH5/zf9773tfy+/fbb4T/+4z9K77nttttafpt6ApdxzTXXwDXXXGN07YoVK7zzK6RMmLIR26hjr1LG/6WIbesSNsMlnyyuHrpCwilUiruBDvDy8qqV7D3bXHQFPOzLW6x1td3nEAoCMXVbwm7pWuZlbL+VwEoYhsJKHKXvD6zethLDIfimT70hl7SZlGRLO5JtU6KnSnhrEe1q0BcxP9/3Td8m35gEUlG21qDPsqL1pSjBYBV633jjjfTvq1atSkXeD37wg/Df//3fMGrUqPTf//rXv8JnPvMZuPfeewEA4Mknn4Rzzz0Xvve97znnP2nSJNh///3hhRdeAACAK664Aj72sY/BPvvsk3v9jTfeCL/5zW9a/lt3d7dz/k3WrFkDy5cv906nEAxRLDswF4lw2B6hjuIomkjIFYLBJh/LtvT20qXy8i0hSPu13Eb0IiGgiFqGOG/YLIi2+R3mFUhk8/TMLCxzDGESCq+n97gl87YN6WmL+vKgRgc0YeWhm1d0qAUzzPwVdzq5noOUvdGApA9yvrb7NR0v6dC6rT1b1KFXcYRV6M3G520yefJkuOuuu6BPnz4t/33MmDHw05/+FCZPngwPPPAAAADMnTsXPv/5z8PYsWOd8u/q6oIvf/nL8IlPfAIAAF5//XU4/PDD4frrr4ejjz46vW79+vVw7bXXwsUXX9wrjc2bNzvlnWXIkCEwYsQIo2tXrFgBPaaiGuVBakaHheELulaf8WMIgqFj6PrGzgUQ5zXtfnib7UIyI6pY1DmJ8MkBlZBMXB+oh0n5CmlZmIVhd+9gId62aR4MIRMoQmO03mh3PeeLAI5+4gLV5jLEpjXyjXInCmedWOZaYvPsYaxT8/LDXP9GPpYYE0s5y+z0afdYyq8oSkfDKvQOGjSo5XdXVxfMnj27l8jbpF+/fjB79myYMGECJEkCPT09MHfuXLjiiiucbfj4xz8ODz30EHznO98BAIClS5fCMcccAyNGjID99tsPNmzYAIsWLYI333wTAAD23Xdf2H///eHnP/85AADsvPPOznk3mTFjBsyYMcPo2pEjR273/uU6FA07vq5luqjibsjP58vyNmhLu3pAiKOLmL5XnF0MTESksvYJ4Y1LsHEVK6oWQbV45hRiqyhrZ+x+RxXjlsPzFzzbQGI8ZK+XCYQbyxDlEZC+ioUVqJhRX9rXaDG0dZ1eLtUB23pz2RdQto22u6IoNYdV6G0/TO2QQw6Bfffdt/SecePGwaRJk+C3v/0tAAA89NBD3nbMnj0bRowYAZdffjls3LgRAACWL1/eK5zCpEmT4LbbboP/7//7/9L/9ra3vc07f1JsJ1IsMRFB2Cv3Ug0YfqHMBm5v3V5pEraJR3iGpkDjJfiSb8LzXzBxQCYiZvERZjkXwMgCcmXdYvcrhkPceueJeXiWwMPItt9kfw932A0JHrdVNoTc0DLkzSrYqjjQWZi2d+iX27bUoR9LKgN2KIM6ehlLsUMJg1H7ax/JQ/CJL4pwWIXeoUOHtvyeOHGi0X0TJ05Mhd4XX3zR245GowFf/OIX4YwzzoA5c+bAfffdB3/5y1/g73//O+y+++7wzne+E0477TQ47bTToF+/frBkyZL03r333ts7f2eoDkhrgnwwl5PI5+K1nCe2UoR8KMvPMl3ruhFyaJotLIKmFGIQVh1tFNGOGGIOk6hpjG+9Sgp/UJ6o230hYiWHFm4x6h/1RQBOWmK9ZyWMbT7Ebn9d8GmHECKx9ht+qGLBK+EI/SJI+4miKCWwCr3jxo1r+b3rrrsa3bfbbrulf3/99dfR7Hn7298Ol19+OVx++eWF1/z973+Hv/zlL+nvSZMmoeWPRuVhVjQiYVAhN/c6kzjCJde4hnnADsOQpkscjqGJqZhCKAj3giNMAAD9IolYWC3rV+yerS2ZM7SfdBG2NO0AXsAlkB1G1g6HcEvZbi51LzUsgme+YoXbKrjrVTfiShXaR+JH21AGXO3QXHub7LsaDe0fijMJAGxh8nSO7LsUxQBWoXfChAktv/MOZ8tj/fr16d8HDBiAalMVCxcubBFTDjroINb8t5P4CbrUAm4RmDF0MUIt+NpAcNJzIV1dduKqy0LCpk65xFcTMBZNSOVB8YB0/RzedPKXenAb5+KXUnwmEr3QvGs5PWqx7JAYz1agp6xvOuyCbUwb3phsrRvUh2YV5UGRj6LkUZfxxfR5cS0vxdesob78URRFYYRV6B0yZAgccMAB8NRTTwEAwOLFi43uy1635557kthWxK233pr+/b3vfW9lTGEyEqD9fD+9jSjkgYu3LLctBCKul2jO4UErdbFCJCo7C2cUYohU8TWLVK/WMpiFK9KwFqFfaIQO48DVlqG9hon6kJeIK2FukGCDVDqxbrjKHEvdcolapph6NypbqaqvWOoq8kM5FUU6nD5mSr1gFXoBAP71X/81FXoffPBB2LRpE/Tr16/w+g0bNsDChQvT3wcffDC5jU2ef/55+PGPf5z+Pvvss9nyzoXSK5cqTm3LPYQ2udiDMHI6C7lFIm5WlJEWUoHYo5dEMPP2OCX2Yraxj2OxW1VeYZ9rk8cOlrDBkCDUYtYDaciQAGESiPsIucethD4uwQYA3HiLUspkQl55YrK/U5HWRtLskY7Wlx1Vc6GqYX7k1a/WqaJEDbvQe/rpp8Pll18OGzduhFWrVsGNN94In/rUpwqvnzNnDrz66qvp7xNPPJHDTNiwYQOcffbZsGnTJgDYGl946tSpLHkXQSrecgi3APSCMtKkhBq+IkuVKNv8dxNxx1Xg9RRsScU1pxiYgjxDOQ/XaiegCIvSJyRsejjDk0gob8g4zqY2hMw3gG0soRRiixtsSye8AMKkbuVRFKV+qOhIi9avWLhi9Cr1g13o3WeffeDTn/40fPOb3wQAgM997nMwbtw4OPzww3tde//998NFF12U/h4/fjycdNJJva5bsmQJjB49Ov196aWXwmWXXVZow/e//3045ZRToH///rn/vmrVKjjzzDPh4YcfBoCtIsZ1111XeD0bJoOw64FiLWkQC7iEh57ZQCbmloEpJIUWbLEFCReR09YGZ4HDsa4Dia/ObSv0cLoUCYKEBBuKiCVsR4h8iPIiFWZZvPiF9mepdimKouRhMheomNa5mK4VtI8oioIEu9ALAPCVr3wF7r//fvjTn/4E3d3d8P73vx9OP/10+NCHPgR77LEHrFq1Cu666y6YN28e9GzzWhwwYADccsst0IUglJ199tlw3nnnwZQpU+Cwww6D0aNHQ79+/WDFihXwwAMPwA9/+EN444030utnz56dK0SzYyqQOocTcLgPQ1i2tCOIQCsYL8HWK4aj57OIIZBYHe4kwF6oaC+S8BVMXqohhBlp3uUh4a7/urV3BjLxVpp4KcUeKXZIQ4UBpYyy/qF9gh6XeSJ7j7aRkkX7g6IoyAQRenfeeWf42c9+BkcffTS88MILsGXLFpg7dy7MnTu38Pof/vCHMHHiRDQbXn/9dbjpppvgpptuKrxm0KBBMGvWLJg2bRpavl6YCpyukwWFaAvgbE+nCrqsnracoQLQ4oVa2Owo2LCLsQBugmzk3pCsYmqniEmSQyHkUCWqNizmD5awB00k9ieJNgHg2ZUkcsuIiW0/bl5PLRRwxMjUOJzmhKiLrkZcbWDzLNmWq/16fTGDD2X7YWIi4Gu7Kw4kwBe6QXto/Qgi9AIA7L333vD000/DJZdcAtdff32LB22Tvn37wimnnAKXX355S2gGX0466SS499574bXXXsv990GDBsGHP/xh+OpXvwr77LMPWr5eJFA8SfgKtJ6TT1BBNlCcWkychV1XUQNLKEWJy4pz+BdbqAIJYmwoD25bYhVkYrXbB4Yyu4qwzuJtzO0o1fbQdoXOXxrcwgFHfiqGyCa29uG0Fysv33Waqx22+UrrC671hlEOaXWhKIoCAYVeAICBAwfC1VdfDV/96lfhoYcegsWLF8Pq1athyJAhMGrUKDj88MNhyJAhlemMGjXKSmz8wQ9+AD09PfCHP/wB/vKXv8CqVatg7dq1MHToUHj7298Ohx9+OAwcONCnaAQkxYIuwgTjLda6Cq6hyLMXUfytFB5Dxrc1zdtlUx1CuJUs1mILE9zhLiSky0kdytAGq5crBxLbSKJNpsRsuwQoPsVW0UBRcOCY/7CfV1dPYdfrTWwIhY8dFF8HhBSTTSmyUUqbKpY0oCfhWqfperBuBBV6m/Tv3x+OPvpo1jy7urrggAMOgAMOOIA1Xx+SLVShFSITaTHwEHWtvUeNhVXGA79My5BnE5WXrc31Nu3nJFgzellz2ueKIDGodsIlNYLajo1OLLMtWkc46OZZ6RRCeZrGlic2lGInxn0UgmmIMpsQQ3+KwUZFUVgQIfQqhmzZEtoCebhuVnt6rMVeMoEXoFjkpfK+NRWVc/KPUsAtuxZzUUjpicydHjCKqio6xYu2HS8S6zvviyCJdirycf26TPsbPSogxUe2zfLWc6HalDJf7adKzeCK0avUDxV6FXkevTZiX3NTULTIt0gruKcupqiLmGduvZjWFbdwiyVMYh7GRrQB9RJhuTbFuvlWOolO7e+dWm4Fn0ajXOzl7msuwjOHjaZ26bOpNKmLACpJsFYURRGMCr2xkCSQOHr0ZoU6qoPTnA/D8s8YIQkbj1MEL12TNH3F3KqT7I1DNyB441J4ubrWmQ2E3rkonrIcz5zkTaJk22LGZ46oapOQB3dSQNEH61ZHAHzPKmXd1WG84exbtvVlalvI56OoTO02SXqGqW3xfS7Ug1uxRaqomyTaL5s4jztC2zYwW0DOAfJKXKjQGxOOk1tiMnB6CE92QqnnYFV5yJlZ+oU2m9QDlrdsVVlM8mnLA13AjU28DXRoWqVwK+Uwt1jyxkbj+uaTN6dQtnud+pQr2MILZ5262t5+X5nNkkSyLFLtkkod66uOZfKFq0507lCkE1sf1fFMUWqPCr0xkViGWLA53MtERC4QS5pewkYio21sXE9B0sgmH3GXOtyCi2cuhfBeKUpj1TNhjFtMr1vMBR23CCxV9KRcJOuCNp8QfUGqNw4XUrxbXQ+BxHiWJD6Ppt6adQSrjJLDKkgKp+BCbCISNp3wHGLQ6f0kJrJ9OkS7meapz56iRIsKvXWmShi2EYKLkuCI1errpdvLljxxlNhLtyp910PPuDxzferW9H5f0bqCXOE2xOFpWKI4tQ2S8LWXqrx1WABzl4G6b3e6kNwEq8/XoY+bQF3OTqhHyWW08SrPu56a2OZkRSac/Vzay/nYniETe6leZkkeq5UWEgDoSXj6tvaK+qFCb11BEC7NvGGJDtvKSVeEdy6RZ+7WpB2FSIw2oPTIlRLrlsoD2FcExyBkfOJOxqeepCy0fT0Z1SuEDq0zvA0/Zl3GGHoiFkzqD2N+ktZO0uzJUof1gKQD/mzy9e0XlP1KWp+18ZCV+hWDj11Vh5NnaV4jrQ0VRUFFhd664BtaADy9c5HDBfSyheMQNEIRd2vyjsKhiZDrI0pihp+ouq4iDRGxbilCIlBsJBg3J0kdNnpCaNgsxjHotMNuTMczTM9f3Sy1Ero+YvfOpUrf5ZkO3ZZ5hLCJWjiSPN5K7AOYL1tsr9evJXChEB256lZiG0q0SfFiCwieHxTRqNAbE336ON2G6plbeIiZfRiISrt6CbEWefh45zocgpafPJGHbsh4uZ7hJIw8cAXF6fXywhV2AJtosbbqkbMMTy6Z9nZoUC/KXduderMcEuzwDlixa0NThzKY0kllbdKJZcaCU9yXPFdLIWRfxsib+jP7mPqQjkuKoigkqNAbDQ1zj1AA3JAKLh64uemYeoR6CrrGnqdunr+VZS37dx/vXOzYrpiCKpW4S+2JixFOAcN2S7xFW//w3NVgekBx2IuBgyBt2pbkgnCvDC0/K7Xtk5zl4YjbK8Fbsg4b5jqUYRsNxnjRJvMspz1NrMIrlUBhe5Ftvnl5l1nSM0AZmicmMRIbKV8FUIYe6OT2VRREtiSxbIIUaajQGwsNwP2EH8rT8zrYrDBRZI/cNN08sRc/r1514uOdi+GVC+AXdgE7Bu02nA894/JUNsVlkZpzD6knrYS5X5jncjCSxL09DARin35EJhK322R7cAiHoCHlcDaqskoShQDk2ROQ5lzIIbCGEHFN4LYru/6oyhvbNixRWxT6PMvDN66+yT26rlMURYkeFXpjwiF0g50XsO1n8ATCrU84BZt8oKRuOEJdVF3DcfCZ5fXOnrnSwitQ2wMeohy1cKuLcDp8hEusdi8QjE36I4oYbLsB5RB7i8YHV5GHW/iIRWgRYKdUsTNqBLSrDY0tyMKVTd4BRe0ignsnlyYeKDZu7HA8k5htg22v9gMZRDY3xEoCDehh8upJNBZw7VChNyLEiLa26VsLa/6irvPBZ7n5lNiD7RVLHebA9zA003worgNw89hF8swFIPLOlRBGgTsdXzp1gUkQqxjV27fOcX2xbO2gU9BVeAVxbdKxSGwHz/lUoqe4KGE4FFLWSZxwto2UfmAb3ooqDxek1KGiKKSo0BsTRQsobG9XKmHW1g6wFLcxwlYgioLb80LwcjW5B9s7typNotAPweIMt2Et6voKttI2B9LsyUJpm88CmPrgs6I+5iEAox0O5xujEXvj4SOCxLYJCmwvueAUW3tIx7U+Jc8JMVLDdmj0JM5ib21CaYSKj6vwIs2zmrM/YZ7BoSgKGSr0xkKjYR66wUlEJPTozcuOQqDDDKdgcj2FcI5gG4qIW3UdtRduGYiHn7GEWAi5mNeNhD2SDxUror0/Wgi/aB69vqJtXr272uYrGGDa4nsvEyzetxHUQymx2+8LV/kp5i0NE8CGtEMIxVJ2cBnFs6Z9OizU4iiF93BRmiEcIjqYLRpSQXFEhd6Y6LKP0VuenuNbd0rhsompaOuaB4ZQ6TNZYRxOtg2rha6vaEsaPoQ+di6Ao7hr661LvZARslDCDGVhKziyxZ2tzITQK5UItHrBSAezzooEhpgPQvPIg01wiaTfW8Pdv0ON63k2hpxjJAnKmLZg1amKLCllYxyWCNyeB4m4HGIuqVlfCIqJUC99noxhrlIUxQoVemPCcHFhJcS23uh2XxE2Yq2LDc4iMqIwTCgyOy0mXQVkzLanChFieK+X+GjSZWsg4trUUZko2Pw3DMHXNQ0WMZc6D9dDyRxDNniHa5Ak8JqImZiHvkUiGjuLvFI2o1LssCG0RxYmoW3hEBKkHKzIKZpwljmwGET1ossnPIUoyvqCCnl2hB4vuSnzRlfQ2ZLwHMam1A8VeiOi4SKc2kAtzGLch7W4wvQeRZzkUA9Ds73WBiFxdLOweOoC1GJxbCLsNeuTSoD1EYdZxN2WDBGFwjxc00aI2VtZl5LEK9uNu03eksTrDNF75UrZAEuxQzFDSnvFLjhLittJRZKw550dl6MSfSNZoypMYIQq05jUiiIWFXpjoQFuQmx6P+IA65sW5qKIOnwCQj5knrk+9ziJovImaW9P0qYg5vMOpUaLF6xQDCZCbNE1JjZUXUMiBGOFaKDa1HscyFZKUV3bLu5NrvcVNTE+aab4LFqKaAUg37PZFil2mFDWv33nV9NnR+A8LpoQnmuY8Vo5YxOHCg0QYA0WlbibpQaOCrWAa61QtW7l+FpN8aZHY/QqjqjQGw0Nfu9ZAL6DtLjuJ0rLe9FXZQuGrWVpcInRnmmhCJEYjvFUi5fQnzoKEE2C28C58GUM1wDgUbcm9/nUm43Ai9k+IUJWVODlwUttH0X6ZC8+wo9lANA6t5bNs1z2YubTLI9PmrEKZpxgvIRqT4sDCaEqFHPUM5MPasG9mX5Vm2J49GKlrSgKOir0xkQfQ4WKY3CtQR5snrZU6UqIUUx13zZECLs1XKzY1it2GIbgYi4Vgrx1SUVdn+ubuIpDWJ4wgfqgtZAr4VmRFL7DFJN5jkNclSI4Y5FXr9n/VrfyZuESLjm9cEMQINSCLdF67GIjvJ1qhVBHptx01TOcjQQAtqB4KJnlpdQLFXpjQkJsVub8yBdb1J6yEtJrwi1sI5QjK1Y5i77tIpntfGmy6cJqM+y370XJOAovRW0QhXDLYSPToWro9U0dpzeL9Di7numixtSVGCJEsg11Fhml0MkhIqQKGLGJLgJtUmFXIUVgn/eibuVRlJqiQm9McHzeDxEveCgnnsCCZyUUbeZrt6PXBlac2FyoXopyxfELfNItq6AbQqjCypMqXi4weuq2ZIr46TAlvofmIZSvff50En5DtLHEfExRgVce2TahWlN2sugcM3njR+iwVdv6kqj9j4ppuGh9KorSYajQGwsNgMQ0dAOZDUiTZB0mW0kH0pkg0XM5B1KRF6BVgKMUfWM/qdsVm3LXRTQmFHUBHIXdEGInl6ApIEQDqtduLIQYbzjrWeOJ0lHVjq7rIc51lPaDchjrx0eMzY7dokTdPHRMsiNEvWgbKQxsSQLrP0q0qNAbE1I9ViUgzX6MBaS0MhGHMCAXedtpCnQU82ezrqS1ITUcYlDEom0TL8/oWERcrLx929vXyzeHpKvB66nre6+E9E0IKaB32lgtAelCWxPqviGt70mzpw3TsTdPyBUv7rYjvC16gWmvhDmpitjapx1x9kuzR1HiRoXemOi0xaYLsS3i2pHcBoS2sYu8WarEPR8h2HWhWlUfLulye9tiHZqFAZGA6x3KgrIufNP2Fd1CxtZ1SA/FS5eyPQkEa6/0OtGrWcEh9nUaJpxrH9u8iF7uRye2YiJ5jY8BZliOutdVGT5ljy1mt1JBA3qYDmNTob1+qNAbDQ2AqtAN3BuvWBZr2YkNa6PcSZMlQ1l9BbOgQnEVeSJj1ZxNIRg5e3NaXt8sWwTxbrOQxh8O6cmJPS9IFrc50ufKgyovrPRCx11VtuPaFmV1HXJ9J3E+l2hTO4EPBq6VgBtDe/vSCWXEJJb6isVORVHIUaG3Tmi8smow7abwuvQl1nbxxFSkcxKEKYRGJvHSiC7AtUfiQWRYYOUfMoSCCQLi4FKnX+nBG7KvUeUd+vkxoashT+z1WVvZlIVbKKPML4a1iIZD8yIKYTfmdojZdm60rpSakiQAWxKe/h3DElGxQ4XemKCYyFzSjHkkkPyZnC9JYt82HbY44hYKy4Tl4KJlky2hDXBESv0B8NjiKn5J8gglrieR4Rck9FMJNthALSDZ9pOeJI4DwzCJYW0g2UbTZy5AGaIQaH2R1jek2RMKrYetmD6DUr+6UBQlClTojQkpE6QUO5RWMNoltkPEJAgYMYi5LmAcgkWZvmk6pn0Zu61CeCWGCqsQuJ97CbzUtrf3PylfesQ8NvnSqRtkyfO6VNuE2BWdOCuk3pyJ3X4bOqmsVWAIsK5plt3bnl9s44HixRa2GL1K3VChNyJExyH1oJEkQcoWtQgXC51Qx51QRhua9SGlXmzs4BBnYwtzQGgvigdubIR68VBmB/XhjrZIGTua1HTtVYrEMku0KQexoixX/VGOcZH0gVJ8y4B54FkMSHye6hRmx5Y69zVFqRkq9MZCA6oPcJJMSdzOUAJ2e742wi+2zWJEZykiBDWUIS5irxtupIp9dTvUqwoEW9iEW0n1hkXIMknbuEmzhxqp5cW0K9u/icorVmCtQmr722JbjrqUu4k0IZ2CWJ8xKurWhxVFqRUq9Co8cIrUjodBcQjOYgRdG7htllhH0mySKo66IK1uAeTYFMgOVk9bKXWdR7ttnIc3Sa4X6WC9mItlEx+DnQY2RiHUSqxriTZhUNdyVRHDc9BpdGpfVETQk8Ts6aeERIXemIhtogm1UbUZDx1F4SrEC7qU9kk+mKoOSO9bNkguizDbRIQ5EFYnpVDYGlP56wDHmqcTvPAQiUKMbSemuo/JVl9iKKtJf+eK1xo7MbS3gou2uaIERYXemDAZMCVtRGPwUMqKwoiib5V3MLsQHOqQpjwkCFY2SOy3WTrdPuJxRoTAioX0vpKHhJAGMdabDbFsxnzjCrenIQxxIqrguiIlpnLHZKsUsJ8zac+tDZ3cf3zK7rMm6OQ6V6xJgO8wtpqvdDsSFXrrBscEIm2DhbEJJ/LsBSAUdbHSpRYx9ICr8ERkv72wGtg73XVck9omUu0KgU8sb856lL5xpLAPMU1xAiuA/DYtI2bbs9SlHCGQ+ExJow79i/L8Csn1I9k2odT1QHlFkYwKvUo+MW32fb2uCEXeYMQaPsHVbskHZ+Utbhifr1p5pALIa+uYxkoFB5MNi+RNjWTbtiFSfDVFWv1Ks6cd6fZR4tPPy+b2mJ4fl/anmHfr3A/LysblvFPn+u0QsMRa8eENhbEl0WdHcUOF3ogoGmBJBszYwkRIsmUb3u3icr/LPVI9bjHbNKiwKkgQj5lI+0MllB4xmHnbkLWzjpu7WMsXyNZgoq30tpFuX5NY7CwjJuHTFJ8yYYc84u4jMfdJabYX2cN5GGmH4COUUomjehC5otQXFXprQLA3bLb5Sjscx8GT12uykhhewFbkDS3YIqTt5eFal8WKtGexrjT7WtmGPGS9hfCOlrRZlGRLFia7WAVYzrqW2q5FcNtbR9HTBsr6jvXzdBVqlQxJo6FtJIyYQx/EbHt4GtDDFKMXQNupbqjQGxOmz7ljKAKXgdhK+KQ4NIn5sJxsHUV3oFo7NoInZ0gFaYdphRLisPOVKsRyhZbAPj3bhhDhMzDEnDy7fdP1mTMw4dp4IOXDIspS1wlHnce8ocRqY6y1Vsx16Ytr2Tu5zmqIClQdSN5e2yfEH5dGJykMoWuZ9XFTFFRU6I0JpkHcScCMRZRyrEN2T17bezg8c33us7iXNKZsiHAaGPlWEWMcXm7heYvgOsI40K09DQ7Rmlp8xAiREJmgm4Vc3K2D8OqSvkSP1jq81LBBok1KISp4KiLhElG58vEhBhsVRWFFhV4lxUvMxPIgwRJ/LAXdoCEZTO7n8L61TQsxHydxl+uFBKUgiSXGSfXWpUBiWX03wRhlykuDcnPOLZYlid+hPVh14ZkOmXgrWSgMJRJx91HMcnLG7q6JiKdipKIgg+3d6pOvdJr1EqPtWXog/jLUjC2JNojihgq9ih9SRBdKT11uL1CuGLJU125DtHjrez+2t2QnxsztQdotdAlYAEmo6zyRgzJeJKdnr4+AwyTwkgi4kgVW37Qp+0xIwS9E3sR5qoAqCO7pjkrUEzBtK45o2+VTl3qpSzkURVGhtxNAjyXL5TFaBVcYBpc4wGXXhvTiDCXucvYZjLxCHNhGHt5BUgAvRKSVS4LwzE1PgiPcYQhKTCEenIRdCYI1ZrqdIPAzEr2gij30ZYf2DhxWRaD1rnQSpmOwhJf7SkeQAEAPU/Bi7dX1Q4XemkFyQFiouKYATmIuS4xhSi9d07QZw02QCbiYoSGkedk6eQoziZYYh/PUfaHrKrqYtqGLICxVCMIS/Exi8gYUg43EXYo2ChGfWFqICaK+H524GqPw5lrHfQr+O+XcE1t/UOjgWuNon8OF4/DJRqM8H842jWUtrv1cUdhRobdGeIu83J6ISJqWc7kpvVvLREfKfKlDLFDbY3o9V1gIjHuL8BF1Qy3sDPJNGGxrcCwYi8qBlXd7+5sIv5RxeDkPtbLdUPngcD+bty71PdLEWt97SyATbKULq3XbPEs5PDEW8YQS6jryibnOQd2erU7Bp92q+lc2bSlibsh+SvyVqLIVjdGruKJCb0SgeesyHbCVC6WHLseEYyswUsYALrmPTLzl8JbmOHhOinDLtPDhEF85sSkPuiiMLba6hnrgOPgJUyikEjVDHY5mmy+luM1VBuz7M6AItRx7obqJPzGWh8PmGOuFm1jiUbusfyjLVrP1WK2ganffPuhqF+ZhtNpvFSU6VOitG6GEL0/v3FIxl8GrldxDlFBMJT/0jDqURCjvZwDaUAkEi6K6Cba9yOsL1HFAfXFdQHOGbqD21qUUPR2uF+uRS+2NK0i4beIs4HI7sEjxJBUIhgiP4aiA6bVdZY+UMucRItwIVVlYkPbMSbPHhpj7QUja2xzLCaiqL8Xc15SULeI/LVKkokJvTHDEbq2CK2ZuE0qRl8I71+X6iuvIhVybe6SEVrD2EkYSdAmfLRYR16VdbAQn0/SL0kQWdVG8eTmFXIy8q+7BquMAHq3WAi5nWAvTvEJ7SXvc6yUwUe5TQj7nBEQXN9gAaWXisEdamX2QVJaoRefYKesHVOGu6tjegp4nY2K0WVE6HBV6YwJrsvPQvFhFW9f7pAi4lteWCrrYC52q9Kr+3fXgMyoxl8oz16Peg3jfYh9IZytKMXvfksbqDbmopfIupfISJfaGNRZ4Ke1wFvwRbQodKsFXrI3wk3tJAhcK6hjUWbgsjXz6CMN5srE9kx0jTFO1i2/YAoUOynMjFEXxRoXe2InpQDOfNKQcGOZYXvSYuZSxf7MU2c0RLxpApIC79XahC0gMoTXb5r4eusiQH8LG4b0rMVZrWV6BDnsrFHcphVmq0AoxeN5iCYAReNeyC0UxiKu2dYI1B2osVFq4+55LfgzicEgkhwHJYmJnVKK1ab1TxXCOqa5cUVGXgQb0JFz1rO1ZN1TojYUEjBdDQUVbn3SqRCVKQVZaXFzq603s9+0PGP0pK6CZiL5MC6t2wTGo8GvSljaiVKyiLcWCk0rAdbm+CZXAGDJuLpc4iB1eganNgwq37XDGibaAXLyVKtaG2mjHsMGPwUZOpApPUp8tE5hEatfxzWY/2Lw2No/pIFQdcib1WVMUpaNQoTcirARcCSIddQiFqnuRJtpCURdzIqeKqWvaBpx9i/IAtEA0Rcsggi+DMEvuSWuKk+DluIukKDO2h26AOLl5WMXOxfLI9W0f7DAWocMqNLHt7kK95MnEBi5RScqY2Q6n5y5nHZja2cmfgXd6rNSypafruJC9T+DSFnscbaYXlWdvGRL2ynVF6waFBPgOY9MWqx8q9MYElRhoirQDtizvr/TC5QpDgX2vRO/iLD7CLsfnaoR5VAmkUkNAeAm75J51TMItdjkwDxIL6FWJ4plL6WkMQBtWwaMOrTfdLl1dgMctukgbwuMvhFArURyWaFMekl5YUH0Kbgv2+oK6DjDyLbODehyhTl+QkNw+xpsKv7ZzA7mg3G6P0DV5lMQydyhKjVGhNyYoJiAKj0+XezzKZh1GgVqM9RJFAwjFPvdie+cGWGSZiJqmgqytQCrGU7YIDPtchVlTqIUtqgPMqNIruNbK69Y2T5+0KNqBOj5vBhbxth3qA+TacBZu6yjQSh+zBcEWVrCARijNRkofybODa40Vug4oRO7QIqDteMooDIsTcF0J1W+l1ocigp4k5vg2SkhU6I0JaYImUVpBDi7DzC8PDq9b6rRrGHLBFDGCLJcdsYuzRWCGueDyBMWIhStQeM4FO1SF4z1Wm1ZOr1us+7fBGiIiS+jxlCn/0EJnpxOq/oMJzCZghq6QHArCV+TOux/Ls5gLwR7GNnOPWFEYi7qXT1GUYKjQGw0JXxgE6ti2LnlQTIScoi1GvlRpYYZW4Iz/FwKXhb5vHZVBLcgC+NmLVVYsgTZUWTAPLkNIHyUNLE9cBvFWpHDLJjYSl90EyrIyi8Yq3ipVcPURVkE5ZMx6rHUidhliW7+a0rk+HTzUtd8oZGwBXXgobqjQGxPEQq91CASkfFHudb3ftczSvG1NwfTKxT4AL7SXlykc4qoPIYVZyoPhQpbL835r4bYsT84Nt2t7hhCet0EaSoFT7A/lbVsEVygPRmoj2jIcyMmCyXpMUlkx1swe2PZf0Z7GZWCMFxTr6qZdtmljOAzYErF4G31YCGn2hEDrQFGCoEJvTFgMlGxxa7EG71Detb75h5y8QodSELJZrySEMBuybkzyptgshxRTCevbSaTNUmUb9cFkJnDFIeYSO20f+VgE3CJchzj1tKVFkigZA9z1RSnUlpUlkEDs+4wEF4qlikVUYx1meSmWwULF46TRkCX2ur4QkEbs9itKB6JCbywkAI0tBLNqaIEz1OFjGPdXEUqILSpXLMJsk9Ces0LiaOYSUqgN+QlnBcairDTvRGdBM6BIXDePW4/7UcRbrOFO6AuTLGJE2ZoJsVX9UJT4IYFQ7d+eb2DPYBOCi7wAtOOTxGeDWyAUKty6jltF46GOg47URbCOjAT4DmPTlq0fKvTGBPbgGtqLth0BcYWDe8lmoWjvEGJvaMG2SagDwJqYbCqFiHFRe8hi3cOVbkix1iNNUsG2HeY2R/e69R0CJfdfQ7zEXWGCrEv/oBAXbO1ov74OgkctDnXK9m+hoi/ny5kgonK2H0nrJ1wCm888Rbh1wp6Pg3n8ShdKq+xq2h+b05CidDAq9MaE7eQQ0luWKq0sFKKs1AnYBEmTr6+4G9KbFkNU4BJYidvcW5QFoAtNwNXfMUUmpvYlCQ/QToThAkR43goS601hE3kECboU4oI0JNpki1jx1hWMZyCwWGwj1OaNLa7jDZpAbPtccPVBG7u4n4uieTGQ70ztxgVqTOpL6zQYnXIY28svvww33XQT3H///fC///u/8Nprr0FPTw/ssssuMG7cOPjnf/5n+OQnPwmjR4+2SvePf/wj3HjjjXDffffBsmXLYOPGjTBixAiYNGkSnH766XDssccSlSg8KvTGBEXohnasxeQae8DGAPdGDdM7l1vMxfScDOxhhyLAetrgfL3rPSaE8BYmTEdUPNcmAsJeoNYL9QcHwl7eNGENnyBIwDWlKRSQPIPNJDttyYJUbhVxKmg+b5HGAm5HRNiIMrg9NSX2f0FbQYDt47bIsSJvTpFop6IwMnPmTLj44ovhrbfe6vVvK1euhJUrV8KDDz4I//f//l+YMWMGfPWrX4V+/fqVprl582a45JJL4KqrroKeNr3qhRdegBdeeAFuvfVWOP744+HGG2+EoUOHopZJAir0RkOCOxFgC7RCJqlEiB0NLgGWKxyDi8BL7Z1JfYBU2bXY3l7Un+9LFtVtESQse4tAXFFNBIXwEFVnnC/KnEVtZDvKECLIRuNp6msmleBbZFfo5RHW2blC+odIESlL0fMsLDyEeCHXFKx+afoJvfT+JwAxB7NJsKEIybYpANBgi9Hrv6hx46KLLoKrrrqq5b8NHz4c3vGOd0Cj0YDFixfD0qVLAQBgy5YtcPXVV8PixYvhhz/8YaneMn36dLjhhhvS3/369YMJEybA4MGD4bnnnoNXX30VAADuvvtuOOqoo+DRRx+FwYMHE5QwHCr0xkICZuIs04AtRVCVSl79sIm/prh651IJuNTCrc31Dm1l7WXLIVh6i2pMomonCrHCvIdtIRdbJIaGYMijTsKuFEEODari+KbbvtyQsjwzLZcUeysQIRgV0S7elj3bFM+9h3icHfOiFn1t+0fR+NhomKVVNb5y9lfb+ZrRA7hsHmJ7pk3bNAT64kAJyCOPPNIi8u6///4wZ84cOOKII1qu+93vfgfTp0+HJ598EgAAfvSjH8FNN90EZ555Zm66c+bMaRF5p0yZArNnz4YRI0YAAMCmTZvguuuugwsuuAA2b94MixYtgunTp8O8efOQSxgWFXojIuEKkyDsbb8oPBbISZK4ib1oh4EVrMR80qcSErEFWYPrnMMhYHr+cnjO1sm7EyAOMZYw7aBimrSwB9j3I+Ms4grwtK2daOuCydIoRDVJa5qk7f9rDsaz4S04mY4Rwtf3WC+6gh/oZgOVwCZRXBQU4oH9xY1J/4jhfByFlwRgC5dHb4Au861vfSv9+8477wwPPPAADB8+vNd1kyZNgl/96lfwj//4j/C3v/0NAAD++7//O1foXbduHVx66aXp7yOPPBIWLFgAffr0Sf9bv3794LOf/SwMHDgQzjrrLAAA+MEPfgCf+9znYOLEiVjFC44KvTERcoGWEM3ODa7PERBA2Gw7ib224RlMPXVN0qQWHvPSJxRHjcVcDPHW9DrqsA2I96OJPViPvYB4sb541Wno4VOi+Ofdx5HsqEKAeAtQQwG3ZsWJkvY2EKw3mIg9zWeE4llhF5vKxh3hInCWqD1/26GM2SrB6zeguBvUCz+00Cqh7RWlgocffjj9++mnn54r8jbZeeed4bOf/Sz8x3/8BwAA/P73v4eNGzdC//79W66bO3curFy5EgC2fk09e/bsFpE3y7Rp0+C73/0uPP7445AkCVx11VVw++23+xZLDCr0xoSN2Cp1AG+feKgEZFNshOaexGhzbi3kum4eygTdEGIklmcrp3BLYZfAeLvsh3oJ8hpuIkLQwhBmJZSjiIC2sYY8MIVAzBXRj6WhVYIPxhKy0fb/PiAvaU0FIJvnTXRohyoiEXmjFXil9g0fr19B3rntiHgWy8YOCfZxY9JfOrBaOp1XXnkl/fu73vWuyuuz1yRJAqtXr+4lDi9YsCD9+xFHHAHjx48vTXP69Onw+OOPAwDAz372M9iwYQPssMMORvZLh13oXbJkCYwePdorjcWLF8OoUaNwDAKAF198EebOnQt33303LF26FLq7u2H48OHw7ne/G0477TT48Ic/DH37BtbEEwDYwjCrumxMixaIeWlxvaUGwPcWNqybZnxeY8G3ymPXxEOX0zuXMaxCpZBLUe6i6zG8gS2uRRV0bB6FqnylxtMtQvqBZ7Hkh4woz1kXkQNJxI1SuI3Q5FK4P6mNAQltTFjlFM+dmIOf2olExDUhaUQg9krsA3l4hwuxuJZZFG4+3yKfR4DiOUeqvRh0geiXA1JJAKCHaUIO0fsGDx4Mr7/+OgAAbNy4sfL6DRs2pH9vNBqw8847t/x7d3c3LFy4MP197LHHVqZ53HHHtdz/4IMPwuTJkyvvi4HoPHr79u3bq1F9mDVrFnzhC19o6TgAW8XfF198Ef7nf/4HDj74YJg3bx7su+++aPnak/CET8BcEOalheXhlJ0Mcz//51F3jMVcY2/QjN2U3p6uHrrI5S0Ucn0EXKswF3yidTuFG80QYQ1MYhiHEqSq6iOUXREJdCK9XavgDG3AkFeUgm47NShCLnXeXMdMRCEgmogUe23HN+HCcHM+Ey/4UlPUz2zXqxz91XRdi7zNNZl3WZ9XSWNDiBjOlet5FisUQbz3ve+FX/ziFwAAsHDhQjj33HNLr3/ooYfSv//TP/0TDBo0qOXfn3nmGdi0aVP6+5BDDqm0Ya+99oJRo0bBkiVLAABg0aJFKvS6MnDgQKvK6+npgV/+8pfp78mTJ8Muu+yCYsvll18Ol1xySfq7q6sLJkyYALvuuiv8+c9/hhUrVgAAwG9+8xs44ogj4IknnoBhw4ah5C0KFwHZVUgtW0BieG82SXrsbLRYCHuLu1geukXXYYqXDmJvrnjL5QXMKNySxVYlCnPRxMpuqvclWKIXsXgWpUBahJCYsJT4iqkUn3OLJHLzK8FsH+yNMGU8TknUsEhViBN4swgXb12omp9ZheAQbS8hNIBAD03Rz6GENuPOq52yPiO46ULCdhhbAD7zmc+kQu+CBQvgV7/6Fbz//e/Pvfbpp5+G6667Lv194YUX9rrm2Wefbfk9ZswYIzvGjBmTCr3tacQMu9C75557ws9//nPj6++9994WofeMM85AseMXv/hFy4l8hxxyCMydOxf2339/ANgqMM+fPx/OOuss6O7uhmXLlsFHP/pReOSRR1DydwJroYax4W+Kwy6CL8aBXaXXG9pkaEehsOvipVuciVv6PvFgHWPqGsXFtcnL5bp2G7A9jbG8bQnjNVuLTa7rBCpRK5ZDswA6QiQ1IXqBswIx5RNiRlTYtp3vRtd1wy5ZhCgjUrPzEC0EURC5yBuF5y5n+BfTdDgO4xIYviE7j4t51iXYQWWDQLFfiYcpU6bAueeeC9deey309PTAcccdB5/73OfgE5/4BOy3337QaDRg8eLF8OMf/xiuvPJKWLduHQAAzJgxA0499dRe6TXFWoCtUQBMHTT33nvv3DRiR3zohptuuin9+y677AJTpkzxTjNJEvjCF76QxlIdO3Ys3HfffbDjjjum13R1dcEpp5wCu+22Gxx99NEAAPDoo4/CHXfcASeddJK3DUHBCqlgK/JSHVKVZ4dledBCMOSJui4iLlc8WFvvXNf4rdjhFByEZHJh1FE4b0LqaUvsJWwCmjArSHQVIwzWFe7qddkHaReQAfdG2jY/KZ5cvfIOlzUnYgSfPCIXYl0RLeBK7i+morKkMnSiGBii/kPkWbQf6cQ2J6KH+fPCFStWwMiRI42unTFjBsyYMcMrv29+85uw3377weWXXw6vvPIKXHHFFXDFFVfkXjt27Fi46KKL4Mwzz8z997Vr16Z/32mnnaDLxNkOAIYMGZKbRuyIFnrXrFkDd9xxR/r74x//OMopePfccw/84Q9/SH/PmjWrReTNctRRR8Epp5wCt99+OwAAXHnllfELvRhQhEUoFAkN8srJgzR+rk1avqKurbjqGx8XI/32axhCKhiLbqZd1yNGMXl4BMr4zRV4rzfqEA9Vxb16ou2qUCFJdMmS1+eFmuqKaJEXYPucGLngK1q4NUF6P7GBw8NYqJAX7HmnyjfGfim0byhm9PT0wPLly42uXbNmDUqe5557LhxwwAFw9tlnw3PPPZd7zR577AHnnHMOnHzyyYXpdHd3p38fMGCAcf4DBw7MTSN2RAu98+fPh7feeiv9jRW2YcGCBenfR48eDcccc0zp9dOnT0+F3ieeeAKWLVtm/KYjCNahETxiv/gIeHUJsVD1b9hCbsW/Owu4ttf5hpbwbC+v8Aoeh705iYhYNrlcuw0pAi2KCKuCHB6hvJJj3LwoSgj0UclFvIALEL14W4YIYTevD7jOaab31U1IdMVkXcso+AUfD6jEddND8zpY3K8zCTRgC9mBKb3zAtj6VbtpyIOsJ6wrS5cuhbPOOqslVOsee+wB++23H/Tr1w+WLl0KL774Irz88svw7//+73D55ZfD3Llz4YMf/GCvtDZv3pz+vW9fc5kze232MLfYES30ZsM2jBs3Dg466CCUdO++++7075MnT670/Pznf/5nGDRoELz55pvp/dOnT0exxQrq8ArUsXMBvMTdSg/d3IPJBBx85pO+ySf/LvFqpZQ551ojITCvWbni9FbZ4ZCX8/XgIeBaPO9W4qwkEVZDK8ilDm3DubHslEO8lO3UpHmpBZii+Sm48AOQ763rspZHFIyLBFrbtYTN9WSiMMU8IqHf5NFeVql2BhIDm+MA6XMfss5RnCQMReMimnseFXxrzbBhw2DZsmUseS1ZsgTe9773pR7E48ePh29961u9DmR77rnn4MILL4S7774bXnnlFTjxxBPhrrvuguOOO67luuwX+uvXrze2I3vtoEGDXIoiErFC74svvthy8BmWN+/LL78MK1euTH8fcsghlff07dsXDjzwQHjwwQcBAGDRokUotqBRJZ5SxcY1zd/QDuNQC1svLsijxBbPGKpb0/cQObE9cz1CC3iV11NIrhQNiUMreIV4wBTRM3CItVvzcXm+7W+xS1+e4MccDkspQITHmBSkbuoVXGrYzK5fcZgKNiIE3SxVomxgL1+X+a1jxmJpfamJNLsECn0s40B2LJPQJtw2CGx3JW6mTp2airxjx46FX//617Dzzjv3um7cuHHwk5/8BE455RSYP38+bN68GT75yU/CX//61xZhdvDgwenfs1EBqmge8taeRuyIFXpvvvnm9LC0rq4uOP3001HSffbZZ1t+jxkzxui+MWPGpEJvexosNBoAffq43Wd9j7CQCgB4nrmu4RXK7vUNvQAloq5LmXxDKpSlgR2jtqhZEQXVXFt8QykYCcoGebTj4OmDGo/WJCnph7VRIFCMtkLChqSNWggHLv1CYFt0HNoERogTUKuocZgETESPve19rq4euxJs8KF9DS1AAEwajfjGrNgwdcQR0B/qBPdhbFz8+te/hocffjj9feWVV+aKvE0ajQZ885vfhDvvvBM2btwIq1atgttuuw2mTZuWXrP77runf+/u7obu7m4j4TbrBLrbbrvZFkUsIoXeJEng5ptvTn8fddRRMGLECJS0lyxZ0vJ77733Nrove117GrZcc801cM011xhdu2LFiu0/sBY8eUKugcDEEgPXJk3fQ84w8vERcavS8bXNNzZwwTVSDj0jEXA9rzeeiw36hHE9+w4LgURM63ULtp3NDUHsIq4JocuYs/mSsm5lFz2o2qITN7g1LbKKFQaoqFuKaDG3DJ/xMZbnJhY7heA6HpKFdJHQfiFsUPFWQSYbk7dfv35w7LHHVt6z1157wXvf+970q/+FCxe2CL1jx45tuX7p0qUwYcKEynRfeuml9O/jxo2rvD4WRAq9Dz/8MCxevDj9jRW2AQBg7dq1Lb/L3hxkyQabbk/DljVr1hifZtiCiaethUegcagElzAJJvebXoN9uJjN9UUHclGEw8DwzKUOqwDgJ+AaXuNkB0boj1xbzJM17aul5XPd5yAKSCgiXGhxMUtunxVknyuxLLxN+kKgzVOzr4sWRiRsLF3BMD2v+0RcJVWwxJqMERV3jSEb16S8INVnQxZl+wLPdYqrpy7Z+Gna9yn7aIh4zRqfNxg9TIexcZPVwoYOHQoDBgwwuu/tb397+vesJy7A1hi/WZ5++ulKoXfTpk3wpz/9qTCNmBEp9GYPYRsyZAicdNJJaGl3d3e3/DbtVAMHDixMw5YhQ4YYeyivWLECenq2jqqNPkjhCzAPKLPOG1m0RfD8dfa4Nc3f9npKD2DgD2WA6gGM0A7WIib2oWVll0gQa31tqOd6RCYh6xp7sV/V78gPdGr9LVr4jYkG+Iuyzfsbbb9rREeLupEIuFVjAsdXCsHHJUyBt2593qY8NvUoqZ4CH7RmS8eMq7b141Mv2XWnir6KBzvssEP6d9d4ull9DgBg3333hZEjR6aHyT3yyCNw6qmnlqb3+9//viXNww8/3NgW6YgTetetWwfz589Pf3/sYx/r1Yg+bN68ueV3375mVZC9btOmTV42zJgxA2bMmGF07ciRI7e/8fD1rG2CcSgZgH08UapP5stERqLwBV7XEoaUEB1WwTdfi+vYvXDzYAhN4LSxpPTEIRQeUWMRB6BjNhwRElw8aYe7r0grfxEB7BT93EYikHJg8gybzJc+Y4G4cQQTyc9BSFzrhbM+IxfjxI3BdQj9gEXemj/y/iaRLVJinSEzbNiw9O+vv/46LF68GEaPHl1535NPPpn+Pc9xcsqUKTB79mwAAJg/fz7MnDkT+vfvX5jevHnz0r+/853vND6/KwbE+V7dcccdLaERMMM2AADsuOOOLb/Xr19vdF/2uuzpfmw0D2PL+9NotP7p6ir/k722T1frn65G8Z/2a9vzrbKj6vr2e3L+PenT1ftPV2P7n7Z/q8zLxKZsG5heW1aPpmkV/Em6urb+aTR6/YEuMPvjkT8A5ObdYkdaD21/yvp3Xj2WXZO5Lmm0/imkqD1yKCxTGY3MH6PrK8rcYo9FOfPSN8ynBdP+VNW+OVT1Iaf6F4xtecXXg0ObO8PgzWv8TFGDXdbE4A8hjSSR/2dLT+Ef6EnK/zSpuo7iDyONxO4Pt02214e0O0p81hFKWMTt9MtpH5/F4fsMWO63xNBj+EdRDGn3nL322msr7/nRj37UEk/3iCOO6HXNmWeemf599erVcN111xWmt2zZspZIAtl764A4j95sZY8ZMwbe9773oabffvLeW2+91Uv8zSPr0m1yeh8JRYO+rWdtWVpV/4ZxfQm5HrjGIpuFHS42F9UzgX1Wgo6vl6yRyJhzjc3iEbmOCgUZh2fBqq5tknfoYyweugyLflGCZCSUbWiy9WlSt2ybI8xFfYANnZewI3EDCiDGO1fkBj0LllAqxKtWmkgpzR5RSH82JGNSdxjrjxjbKDKRTfwc0U4Ie036MpddXRBdH1Nkc8ghh8D48ePh2WefBQCAmTNnwrve9S74P//n/+Re/+tf/xo+9alPpb/33HNPOP7443tdd+CBB8KUKVPgrrvuAgCAiy++GCZOnAiHHXZYy3Vr1qyBU089NXUw3WuvveCcc85BKZsURAm9y5cvh/vvvz/9je3NCwCw++67t/xesWIF7LbbbpX3ZYM9m1xPQt8+9vcgCy5Wh5DZ2EAh5koJLQEEIRXK8jcWS+XZBEATdmFruib9sOrfcZ4nY29cLGIQd7m0YYo1santJXnb1J8YIde0X5lsDtrLj1RGMsEpay9HewTeE6P0OSGiqDRUFPUkNsFI2Y5L21G8SI6hD9VEZHOZS6rWRyjzUwx9oB1sm2vSx+pEAgA9TJ+bcT8BXV1dMGvWLDjuuONgy5YtkCQJTJs2DW6++WY45ZRTYP/994d+/frB0qVL4e6774b58+fDli1b0vv/67/+q/Ar+1mzZsFjjz0Gq1evhu7ubvjABz4A06ZNg2OOOQYGDx4MixYtgmuvvRYWL16c2jJnzhzUcLESECX0fv/7399+8FijAVOnTkXPY+zYsS2/ly5dCu9617sq78u6iY8bNw7dLiMsFzfWoqzr4onaA1hgOVhF0qp/M6SXzUzxcrfnb5gfQNSeuaxxc4kEXBaP3FBOvyGdjZt5+4Z4Q2qfys0RppBrg3SRt1dGBe2BuQlrAPoqvI7ibVmb++6VOlKQjVH8UOLBZS7rxD5ZEwHOdc5hebnNMY/bwp23xtxVmDn66KNh7ty5cPbZZ6dhUh966CF46KGHCu/p27cvXHXVVfBv//ZvhdeMGjUK7rzzTjjhhBPgtddegw0bNsDs2bPT2L1Z+vTpAzNnzoQTTjjBv0DCECX0ZsM2HHnkkbDPPvug57HffvtB375900PZnn76afjgBz9Yed9TTz2V/n38+PHodlXSgK0xZ42uJfK6tYHK89fn+gzeh4WZQNQOpSKPZPEWwK1f9MozL7yHQ0Je/YcwP4c+yBYqoYMjMmTr2HvTUVSPHHsZio1DVZ+13Sg069rTVurDl7ZmQtRoHsmyfhKbHdMFiL5Jo7hNaynUYrU10jOnKFZofzPDdW8iTKQrW6uKDeVA9LWRU95VcK3vhPWrutOTRBZo25J/+7d/g/e85z1w+eWXw49//GPYuHFj7nVdXV3woQ99CL74xS/CgQceWJnuoYceCosWLYLzzjsP7rzzzlT7y3LggQfCzJkz4dBDD/Uuh0TECL1PPPEEPPfcc+lvirANAAD9+/eHgw46CB599FEAAHjkkUcq71m5ciX85S9/SX+3B4/mobH18DAKOERZ3/tKcBK5iqrS+/Nzt/tRxFtPG2zuNRY6LfuWdVvaFlVq3FxPqhbIXkJwB4u7WZw2IQL3Le19gXRzVbUZCLyxsxb+IugDQTbLiAJvLcVYE0KLHOiHAJqekKYTjAgw2t/VucEmb0kxSyUToRBXtE4ln9Ow+36osZSLCPuWIp/x48fDrbfeCt3d3fDb3/4WXnjhBXj99dcBAGDnnXeGMWPGwHvf+15429veZpXuiBEj4Ec/+hG88sorsHDhQli2bBls3LgRhg8fDpMmTer1pX/dECP0Zr15Bw0aBB/5yEfI8jrxxBNTofe+++6DVatWwZ577ll4/bx589K/v+1tbwsk9IKdaBbwQLV2nEUmH12b+mC2NlA8KrEFXaq4uAD0oRWqyEsK81BALnGX8CUtWn1XrTF1n74VTicP6oU/1ULe024yEdDHLsdb6dtQxuaQpM2kbXx9qFNZXAhdfu5Y2yb4zt2hysESq1xIG4WiQ0Q21heWps9bmU2U9oZ+udEhfU4yWzpoozV48GD4l3/5F/iXf/kX1HSHDh1Kqi1KRYTQu3HjRrjtttvS3yeffDIMHjyYLL9PfOIT8KUvfQk2bNgAmzZtgq997Wvw9a9/Pffa7u5u+OY3v5n+Pu2006Bfv35ktpWCeahZAajesT4whZ8g/eTdpV4Yyu0Vp9Ax9EJtPHUR8weArQsoIrG3uVAmD+vQvr7snPXIdnwjOXBsajAX60ybMBaPW6v0/W4na2ckUZfcm1aSSCPJliwUm2rMOcbUPuovTbHsCCGQhLSJA5O2ieFLZKzDSF3TD4TpPGWytkSb80zS0XjPNHSB2L6qKEo5IoTen/zkJ/Daa6+lv13CNixZsgRGjx6d/r700kvhsssuy7125MiRMH369FTAnTVrFhx66KG9lP5NmzbBJz/5SVi6dCkAAAwcOBAuvvhia9uwSGxCN1AuojCEI0mirBShGuF+J/GSS7zNgtWUsQq8WbILKIS+yBavt9AAi2upTA20dhcRYw5jQU5YDvaYuARFcWrnWMTYKrj6OOezZPPMhDqg0IcQtkgpvxQ7svjaJF0klW5fEbbt4rJ2k9gf2/BZQ5KtgajWtSFjk0tYL1aB1F9FrI2jpQE9qBvX8ryUeiFC6M2Gbdhnn33gyCOPJM/zsssug3vuuQf+/Oc/w5YtW+BjH/sYnHrqqfDhD38Ydt11V3j++efh29/+NixatCi95+qrr4bhw4eT25ZLAwD6EE90yKDGncUikEhtg/d4jnDgWRanRZ+vCdwHpRHYkULU14MLuiZEYGIVYheoAjx0o/AARTz4LqTA26R9fENpA8o+HvL5oRBVIhBqOhm2L1mqsJn3fQRGE3zWIJT9XZogHMLzvSZgegHbpolCCME3dEgGE8qeCYs+Hnw8VpQOJbjQ+/LLL8M999yT/p46dSo0GAaEXXbZBX7605/CUUcdBS+99BL09PTALbfcArfcckvu9Z///OfhnHPOIberFCmxZAF4FmicEwNyXmhiI7Joa0qQSVmKuNsEqw6IwjOI2dCWIVQjtYHllGiuTaGlvSzeo651aHN+D0toDPq6tRZ56yDgdphgYorYF1BCyKsf1rlSUr+VZEsWqXYp1lQ9W83nUfS4JdE2jPjBVFTta/T5VpTgBBd6b731Vti8eXP6e+rUqWx577///rBo0SK48MIL4dZbb4W33nqr1zXjx4+HK6+8EqZMmcJmVxHGi1RqEZZ6sSxUuGL5cgJB2CXdzFAl7WlzmeDh3G7NhRN2eIYsCM+qzcJZtChsgejNQhmUC1+HOkETctFPmXa7zatfEB1ehinGijyMDkDMhi7acUFhwbV/1GXeVJRQ5D1D4sZrm+dcou3cNgmZ9zuBnkTa5w9KLAQXes8//3w4//zzvdMZNWoUJA6D3Nve9ja4/vrr4Rvf+Ab86le/gpdeegnefPNNGDZsGPzDP/wDHHDAAd62oVH1nIdcjHIeCEZNIC/aJmG8aSnS5CmHuFANgvFdWEtarBc9JyT2UC1oiesu2Gf9UuLiAqAKuCRCa3s/9ukTNTt5W5wQ0MTWLmqvLIkCRY3mUmn9UIVnhQJp/VyxhHseKNIkVABWFDEEF3qlsNNOO8GJJ54Y2owSGgA2h7G1IUpYDSykFhHV4jm0qYHriqw/Y3rxmlC2IBL4AlfSRsBd+MO1wwmkekQXHhnCKDShEeTL0yQRajmfCcy8GJ4DtvHCJ5+qsR6jDNT1IGhcTuEQrTuUKMI2KflQxm72JNufqsZul74naf1YSix2+pDXfljl9tm76JDWiwQAepgqpgN6fsehQm9EOIlbQkXVMsQuXoWa5QxzPYt62SC1jzXJWyjV/aTuKkIJtEyLfjFhFCIRbU0QGepAsNdtNBvxJjGIsIodVO0hfc53ILbnVezanhMJL5oNwG6rqPqq5Li4PsRmr6Io3qjQGwsNKBVtWRdQodZq7XNUjGtG33ai9FxCRpSw2w7FggezfjlOi+/EE6YZFroivUY9b5cg5JIfDOfszUxkWADR1ik0S6duHqWVm1tE4/76xZdse8ViswBUnFUwSBqNuMTeOqD1XRu2iN5QK5JRoTciEqneuSYLQYyYdkKLzwrhorv284huWFrhFGd9RYEAC1axwiKAnBi4nl63XnVM0SeEhgPh2iCX5hNDGIZOJlS9SXtpmr23yDZpNhOgAq0iDco+qSJyDpxjgNa/oohEhd6YiHnhts12bjExT0iovaCp5IOxEKF6BjkWSS62Y9uFkB65AJuFo12ovlT2td1RxLVuH8o6julgvm2wblgx82pPy3a80Y2ifChe6puma4oEwVvAWr19HFHhV6kzrv3ba74N9RWFRHR8oSMB6EmYYu8J7mKKGyr0RoQKlPZonSmoSF5oVWFpO6ugSgWbF6Lf7SG8aU0Ictgb9zNGJNxat6nkscXFNsnlaUdi6BmJMdVN21Q3/eKwHY98hWGs0DGKQon4kBJcthU9e5LrRlGUSlTojQldBNUfnVQ7nloIrC6QenbSJd2OhHi2WZz7k0BvbiMIRTunto3VQxcTiUKqL2VCLEZ5q9KQKAQ36VRBuEbl8RWGVbRVYkG02Bv6OTIJhaMoilhU6K0Lkhf9TVw2P6blqstG0uatakTxTqkxEbOwvLs7SoiV1leQzAm2qOcKh5CFuqzY6Qc4hMwZCc+HBBvKqMvc3A52uWJYw5kQWphwwUfMEFDeIlGV24u3Y7F9dus6JkZMp/T9qEJVKCk9+nmy4ogKvTHBuBFon/RQNs+U9se2SbJd6GFOsBHETqQQU9kFWuxYhRIFHY7Qwuyf8tPlh9IHpXnaxhKzliXessBnFBMVKOjogTjWMTFu9m1sFlA+LNGpFuJVDM+ELa5lsh1/qevO1B5fO3TecaJo7RQknrGiKEFQoTcWGmEXbbVYMIKgiapq4RN6YROonkR6y8bgISik3sQ8XwBhwh00kXAgkA+SxVtJfYzaltDzgKJkkbgOlGiTA3VZY6PTPgbWUfgtg/JLSF8k5aNzZS+wxxST9ETtAWpCAgA9wDM/aOvVDxV6Y6IT14HYzmsxvMnkXrB02sQcorwCqrhOnrGmiPSg5UifYQxB6U8hxx7J455uWjsLqeJVCPGxBoKneNGWor9RjllSnw8OOrnsNmg9mUP4rIof+xSlw1ChNxIScBtAo3+7Rj1nGFYPa91zxiUW0j/EHBiVmwd9Fk1EPa8EQi2qx7aEunKxgUnEY+9LMQriZYSMwdpFkD8Ref1MN3sVcIgSUtpAih0MiOz3WH0tbzzqKvi70pnYnDGihKPoWfVdc+gYQESDMUavwDlM8UKF3pojcuFZArs40awegmybdU9Wpuyk6jpBI5yoyhZuAS1WJ04yZZD3YwZvWZZ2jekT/gDCm3U/Cr2hCp1/ERJEUwk2ECB6jVHXjWeoOhfa1qL7YBkx9U8qWznbrmh+wrKBev6LtZ+3282+z8upN2wbQpcRg5jGI0VRjFChVxFFdsEuyrvREZIyYAsGnjY2XzQGj2uKvW7DaDtiQZZUjI3h8KpA4hnJc01Z39LG0pqKntjY9jMMwas9z2hFNACZ8T0l1SejLVH3Ixsk9DHJhOwH1Hl3Sh/nWk9Q1qet97GtLaH6AkfbmJZN2rozYnoSnVgUN1TojQnJawhCj1hTvMSXBsj06hUUnsFaVESJ4emfRB7eQh2ygCvae5ZqsebZt8lfBElapEqypYmKtU6Y9NvmvBFCHItSkItpD9Rsf5t6jqhNxPafmPpIFVLrmIuq8kucL0OCHVKhzv1PUtkk2eJC7PYrSg1QoVfBoWo853AObDT8xV4AVFu9xajm5sRWVEFe6DoJkY2G56f5ltmReE/7pSnqMLAIvGejP+BLQv4AKsIyQfWyIZuuWOHMFKkCm8R6RbApmv4itV8UEUu9SkJDjMhG62k7NnO5b72V5cXZJj55SVjnKopSiQq9EWETi5stbqop7bYT2Ve1yTHamJclYRsy00d8FiDWFPYj7Ene1bHA6TAsf9vZwlRg1zNCnwoWUoUzX5SwHf5JSAezL3i/qCMAK856MPEtNjGtHY56Y26baIRYAJn9J5b6i8XOQLCdLeSI6RpPYjnE7f8ymNYXexkaDT7bQoSkiCYfgQ+UAPgOY1Pqhgq9NcV2TOCfVCv+3ccxV6BY4GST7WFrHuWmjfXqdht6O1oIvEHCVPikYykqRhf2oAMOXstD2lhGicSyYtnUSJIwAp9tP3YV9mISteoaDkOKKCuxL0i0yRPd99MSc/3GbHsTyWWQbJuiKEoWFXojokh88p10WEVewrzIhAKuEAIu4pJhXkZtHOgwtNL6Cu1961InPvXo0Aec+1ssh3/V6cA1paNgFXdDCn1JgiemBRblgnvc+rRjaNurkG5fG7UWdLpyCkd8eGwhebZUUWarS3om6WLhYx82PuV1LQdVHdvYw9nXfdvb1dZsvqGebcWbBAB6mDydtZfUDxV6I8Jn0Rn8Ux7JAm8M3sMW+QTzRq3AuK4sFyTOfdu23E5hIuxvKaKy/kIIk8yidh4dLchilz0yISYEtRJvI/3Mk12MDSWiR9o+tqAKqk1xIxZhQ5LoJ8mWKqhsjakOMAhRXgl1LMEGUzBsjaW8kZipKLGgQm/NCS7wNskO3qE/5Q8l7DIcqEYq8lJ5NhdsyND6LqZXrqM4iS5GcnsaVxFL7N9OFoVNoK4fgUIymmgo4dN5wbH/SMVZrrrHKIOgZ0CcxyqmGCFI2AjuJa6Uri9iax/TtRJ1uXzWbJLrPK9c2PYW1Z1LPh3t7NAhaIxexRUVemtOc2wIKvh65o0yiSEkYW0HYSgGAIs2df603+02jFAMtII1bTsGC6XA0T8LYF1o6qJWPoSbODEbRAxxkbosxOk7t0VMwixSWlHu0wSJpe2IGQcooCga17QptFkSG8NsyuBar671lMjp+1LswIajXJh51LUdFEXxR4XeGiPGm9eC2oRhKBPRMOPqWqa59VqLdLdhVTclYi6Ll66VrXbZWvcR6usJ49fSxbyOZGCitLPOC3OCsonZyNgIlEI+uxdRd5IOeWOqD1JhN4AAK6IfhST24sdiP0U/SxK68nPXayztqNSbSJbxdUE9ehVXVOiNCBbhlnnwDuWtaydc2qefgumha5oW5eFxFnHvnPorhTDKGW6B+h4u72JbpIq3dbZLiJDoSzABSeqhVwhpO9Uph5etb9lQvaDQkipGkEes13Mmpxh8dLqwXSe0LemwWcuEagep68CY0UdKUaJAhV6llQbQC6eBMLIRw0OyIB8yD930HvtbALZuALHbL2joBYs2JPXQxX4mDMpF+hzGGkPXZnPBNY65bniy9uXZKkhIa8dLaJL+qb/jfegiN1U9VdlJ1G/IxVgiIda7XWPfRAsV1urqFNW+3qprORWBNBrG632sfmm9v7AYj4psjPELWUVRFBV66w6FbkIqJtnf4mwP8eFopGJneo+JHT4HyPW+l7xchOELjOuCy5tXyuFuVVDn55p+0QK+ShANTdMmW0GE+npLvAUtTGFS8EFZrLFsqdrcI10y4clDqI3Cw1WoYGpCx4mNAduKtK6r5k/MclO8vJSM9BfLhhj1P8SyhhhbOm48K0AF7zBo6AbFFRV6Y4L6QHSKRUeIQ9CymAhp1AKuYz7b7zO7jCIEQ2lZqQVcADovViG2uxDssDeJSCuThE1pqE//bfAVeLHs4/bE5RBuGfug1d4jlChbBHU1BRgL2PeCEsa7WOgC0pj6VliNQ4htXFV+Kf2J6wuSqrqV0l8wkNK23Ehbo3qieqOixIUKvTWDxdNPyoFnWaR55zrksfUeu8uN6xPrgDTq0AXUC9sIBN7ohVwpdlDDvXGREh6giRSxFiFd6zriPpTNqUz+2ebiGe5ArCetRI9lgPgEEjaRLEJqXDdGz3Wfrf8XQ6g3EdS4v3jTXGdj1RHV3iO28bsJ5jMaax0IQj16FVdU6I2EBhAsjhjWWk42+064DnnSxpS1S9o9FAWheG1iU9k1SIsokQekZQnpgVG1mKrJZ4LRg1Q/qAKu62ZJ8KFkpOItAHs83zIK9wCSvGgpuop3HwmXtzGRiD3BDlXsFLCqV4DGGlVfKTNVQF3WmdL1Pva4GMk4awTGPiSmZ1RRlEJU6K0ryAsQtji4LljYJtUzFwAv9AJ6DKUiuwiFQ9JD0nzvjfVzuuzCTT1qqpF6MFkVIT1trQ49IagDn7K72GN5D7pTRoGQS9a/XJJ19kR3ug3djhYYxQCRYphAk1wwqduYvE7zylNof03aUARal24YH94mu4Jtxwiq8vSyo06itQIJAPQwDTbxzHqKKSr0xgTRE0geA7cI5IV0J4i4vdO1NIQyLECIkAW+fcjlfuJYe0mjQb/JdF1w6udcMjxpTRHidWpVZ6Z1EvjwNWch0tLLVkSoA+56kvYSwILS9rIpVtlQG2jolC68YBK0rHlZ27777qC2UvxhfbEhvWsWVUWb3U7PGMVROMKf9ZhemilK3VChNxaSwIOli7DlaW9thNsqKoRdlnrAKE9zI83p8dpo+Nnuen+ZaBCrx68JghaUYha33F6kRGl51Sd36IeQMWsthFrvPsoskqJ60VKHzqgAdXwIMdQY5BlkDBQy7NaZ9uewkYDWu2KPTYQ5KespRJz3aaQx1wnT9oFQXrDqW1LrJyRJgy9Gr8YCrh0q9MZEKAEJUdR0Divg7InqmF8bbrGG3TIXJ+x69DuSlxMYaQp/w0y56HZtk6g3ApTefILEWgCEdnKpK/R4rsXpkaxDObxsbW/hFLMFedCK8GAuIYh4z4Du7+SibRMG9DBo3MTYbxDrPKY1a3CvV9eqiv0ZUZSao0JvnZEQGqEIa2ESMW+gC6VQnqfDTZhtyBiLNkgYBhtMFoCu9tgIHAFe3rAsfmOMEeYt4AiK6RvCyxhFpDa4yOOAsfw8C9Ij9eqx8QqmSzvFsr+IigHchmTbTKitqIf83CpKC0jr9Cy1fRYRIBPBY6lz7LPRYxWl4zFbUToSFXpjAkkIQ5mgqUU5rnAKnF63WbDqzzcdSd66zYVO6DfbnMQoiGLAvahlzE9UDF+rT+YCfvIvKRwCqWBveQNFbOMCWLyULWDZ+LqEWOQYSlxE0aq1TCRCq4prShGl6+5I+rd4DPdEdXpOnfZz0srPuG1ynZuDey1HDFvoBqV2qNAbEWRvUFG9Rs0uYx3wPd/0e9U75eFnPiB4kpIdGla2iJDm9YtNWfkkv/GXbBsEij9rgiAv4tx1JOLm2bkNGMMRkK2lGbxsARzqWLIHbRakLFDbN7Cw1KssfWSPwaX49KGqNYHwuanj8FjD+Ty/vvsnqrmBIzxEme298vcd19r3W9IE+Jz9IKeG5tPeWTt7pROimgkOiFQpWFFwUaE3JlBik/onYYq3COgh0EYhimOmzRgCINhb2U54GyxhU4pkg7hP0bgPDCNMm2xjUrApC36wFbe3sWt+SC8FuEVx8mfVR8ejHkakCbY2SBtjQ0BQB6Gcp6KPCWtCoD4b1CGuZO3KZRdb35Im7LaTtY8g3EcVWO1tJd5TYVOWThjbCFGPXsUVFXpjImScWh8IJ9PKCY2jjBIEyC5gE3urRAGyfqWb2hbECak2YHrJOn1yTlN36GsxpE0Tuzdtr3TsEvKuR0YPbrTnEG0D6OP2hmNDE6d2JBQKWPdKwsdn3TcWgF0vZcuhtryM2kTActOKBoi3mVwYEzAWVPYtCfsYD5zaUKoo7blnDjm2F7aD0KpWlLqjQm9EkAqzjG82SRdVUj1ubQlwSBcWWaEhhPdvu9CBZYNYYZUrzq+U8nOLhlU4bhZQ+lMgIbYd1DqusoWwv4cIfeCcdzsh480SbZhj9natlZBap7L4UCmWOdyDbYNEQttcsQSM4VntBDG6FxZrd5HipitcAjTjAYYd8bUCEQnwefRqM9UPFXpjYeuTzpIV64BMfqiboGErYvHWFQniKLoN1MJq6DqjjvvKhediWZJn5PZ0/RMmPRDMFI74swBk7RBClC3Cuj1R4y6jJbWdkIcCciDJRkm2IJD3XHK96G0kSe3qUxHAtu4bxdiGTEPiA5UznkhsG6N9vM9awFSP2JaHoJ24onQUKvRGhIg3YlzCqSSBFqAjRVoRcHmrmsAtwMYSXqAMAs8E0pcHrklLE6cEvdwIKYqS9JWQnrNFOHuVe+QZ+oUUUB6eR5QuddrESHhxawuXzTHWjSnBzmXAgqtpKKpJerei/IiScqnnajfVc478jJnUnZemIDXsRU1JJL5NUKJAhd46wr0oi3kRqAIuDRwCraSNlTShr0lsQmsegsWooEIZl1esCYTVzO3J69ymyM8a+boe7dBFlGTcQQtfgpQOI9GLi5Gb3ykklA3lEg5DKhbVJFo8tzEtUhE9+LzVTqNhJrwi9pvaHQSoKEovVOiNhsR+gJe8kKhCBdgwhPSg9fb84/LcsbwhwJtvaQdEVecjKEZsO7a2ETxD5IKOk5eqLM9q1D7QgaJtlN6xNiDZwSquSqk7ZhLidUiDeH1pY7+vLUV5oZYxZD/EzFvQlohyHPEWkW1M46rTkH2QoYzGYihmv2HSCJprCxV8FYUfFXpjYouOkgoxzc2ByUaFXHwSHksyi4cwFOTzcsK2EyW6ZiESD5zbT5oXrA2eyaP3EYfnj92jR/LLjCzkojRx+pZE9YVCIKiFV24klYfKFkllpMJazJb07JGGPCguKLonMVWdStruZstIZFeQQ8xs5z7PvpM0VOx1pUfU4KXEhAq9NUf050ERIvpTSYyFPamXngAxxjmWJXOc0Vji87raSbwJlSa+xuJhTSImInjHsoqyyM8ev6AsPD0HRMy7AkwA6AwBzwoh7cICx3Keoz6RyhHiWUDzlKaoZ4N6DTmWWu1HpfZDV7sc+zz2+sFLaG00UMReRVH4UKE3IqIXbU3tl7CpK6DZBk6LpRAbNBJvUQbBVpKHrEtyjjY4LYIwy+vZR6XHgJVyOFc7kj3OTYghNEER0QuxDHkEF1rlLglyESvGRlaPSht1ab+I4/JSPNtk4rGwerSdR8j3vAyeukHyamZJcjigYRvGrlcIo0cVcsURFXpjgnLgdNnIUdgTekPZhvcGl3rDZ2NfqAPDnD6rZvTI5PikmrJfW/QxKZ6uKMJRjN6tTYLEbXa4iWE8FrV+DW0LeZ8OXUCgDVkiVWB1RUBzxY6IPl8ClpjVLGf0DiEAwbxdQ+E7bhUKxVxdnyycQe8CkPVvk7rCytq0XTzzM11baTgFRakvKvQqW5GyOLS1w9lzkmkF1AN+Yq/TifOBPz0uELGs69y3GAb5BRe/HPoGZz16PSfo4jBueoV4irAsdhKPX2LEV9aQDfRZiBWdBJklUqwNXD/ZfuMjdIjtfyEJ7AWJ3SZ1bWPXfp/WR0W1BBXIfQU9pDHT2bOYssu11Q1G/3Zua5esvUIlIKVTgct6r1IczmsnKTpDhCRiFuVKbKjQGxM+g2RNF3+QJE5l8wrBAIDvqWtiR6jDfAyFr8K6dM3XorxWZbOtR8u25hZH2QX0XBscbyTybOU7uCoSz1wTYpgiBAlupAhuC5FCbBPB9eZDmTAhNRxNKVT7fQy769iHaqCvUI+9rumjCMRFWXO/dCAc21lEZMP6EucpnKXIDJ/uL+X5r6sOoSiCUaE3FhLY6h2ah8nkTDaJMQzciHmwC7vOn8rb3Wcs/lgKa7n1FcgLtrCMJmk7th+liMoZRkFSGAIvW5gWivQxZ4nT587Hgyg90JhNFi2uUiCwS0g5xAjVDkZPPCMEtnsU5NV1o+DvdSdyj+wWGh4CJGM9oMUYNoWgbKbt6CUIhxJcCTyDi9bIGgaCBo3Rq7iiQm8dyE6yddoQWoudBAMhl8BbcD2FgGtVT2WXGqZjPT/Z2GfQPqXlRewylfVqmJfTfI7sGeu9pkB9OYOWlIwNbwAbohRTmwg2vTYCrOA6loL3V0AeYM0t+Ykjp4eZRoeSnfOMhBOt6+2U1UWEIlTSaLiJixR9osAM9gPqMMtmWbXO7QFQbTdH/0QOVaPCrqLIRoVehZ/QHrpcMXNLrrUSryhEXA/vWDLhtqBdnNq44hY0sTs3bbvrXUVajvjCft63HvcGzkOsMCrULImwibDaJt6Ifd5Col7/tcVmXo31QKUyu4PZyih+ombRNj4GiydM4BlaRN78TeI57FAmsjAfIUJ41OzFiKIorajQGwsNoPHWpd5gWcVZ9bTFtH5KPTwZRbcmPoeX5V2CKdYSxrPFiOmLFVrBqD6sBHfjS1sJEYe56h7iIYJV5AkoatTG+9MHCk8cIUIVVz8m+4zfB2QvocJ0FRRs5wlXYU6/NuWjsq5t2oJaxBPcL6z7uoQvdJgFuUaSBIkZi72G6iUcm5bJeTx0DA/hWte+/SIv316H41kkp8KxEwk02A5jS3TRVTtU6K07Tt6QdA+69cbUZWJH8rrNghFCwdvbttd1HuEeOAVc45AF7p+nUoc7oBDGS9PEfgQ90+OMJ1xqB6dY6mK76UJW11IpRX3L2fulJM06EKxsNtnWt/q9sf4snyhvjvsUQajIYkTSECxICbHLK3wBgKiXDk4Qh2Dwrl9KmmW3NE/sM6UoNUeF3pggDfrv60nIIOC2U+gRiujRinF4GYDlJhlLLDRIxzXGrUHSWOERML1tqcJOkIi2lJ7Nlumn+VALrYIFBVMP+7wFOrY4V7UJqIPQGbQM8VefIpSQG16XvKMXeUPYT9XGvmWReECeQAGIqs97P/sh2r8oKY7DywBIvWzJwkMgfOGSrV+SmMBSXzLGPt9QkDAexiZwPFb8UKE3Jgg2vqibaYzQCS7XbcN5HDQWCd0Ez63XeQiGVffm1DtFDFp0j2QoKK9Be1R/pujWGVA+fyy4Bl24skzOS6QlXmOIEyY9zOEoi7j6UsjwWd9ziYohvVTbiV6YVHAJ8Sl+kdATum+Gzj+PBogXF4rGtOg+Ww8g/KEIliYgiKsAxetkLwE4a5un6NuSLIdXdVEWRfdL6OeKoqSo0FtDvEQAVzGIMEQE5eFWznFwAfA8b6vSdPW6zU3f4/6cS4LFts2x16qfEFzLGd7AWbRF2uiFFq05UMFIDrab5E5tu1Bigkm+VW2CZTtlHVD2K42DiwB2XWD3JSmCLwU+dSW83UyeMRFCbh6C7CIPSUCZdBeity+Snc11uKgwDxG8uIkRSU2sxIUKvRFhJa5wCbbUh5dhi7YA+YtKCtHWMt32NvMRobG9b3uVGTNUgk+sYQCy2JEoYTjy0jX2fPfLJ81PSHxdX2oraLiUq2zR51NPQheTsbR9+2af226X/EwFCoyyUAj2GAIL9uFkLnVVdA9Vn8IQ58URcvyKra5cyNZve3kllb9pC2N/kPRVQwu+npvIkIqThGVFEXmJQBHQMZvD04NZURQ8VOiNiT7YXnR26Xkt+n3CI2SxtaEiPaoQAM7hFJA9bnunU/KPFW3kWldmYq/nv6d54Qrcabq2L04wBGWk9FvzcrvPCkkbPgdiEDdsTDQtTyOxTFjpBUrfkRo7T2heIZ7X2OuMMl1rpNihVBNbW1lNlHjZUj9bJEIyc13ZrHtRRGFfHdTBeapQHMZ+uQ9u+4iWeqXos7qmRKNHK1JxRIXeaGjQe8+WYXko2fa8c+5DFmt754mctk8MXF/Bz1e8bVLQfl4hJUzT8RB6SerZxdvdVhj2CW1gnZd7Vr55uyBGYIiYughmtojylKIihEe2SZ6dUPcKP2VeoorSRMeflKQReC5sAFt7oHn+BphXm3sNkgPe0kwQ0vYhdP6KopSiQm8sNAASbI/ebbB4FDrk4S3YMsS2tUrb2CvVMIMc8dY1VELI+LY+LwOCedoifpprDdNmmFT4q/FhYqafYItGaECw0GK0+DYkfWYR0pBef0pYqPtHfacduaEDsIil7QIcgFkEaT9oz5/hMDcbUENDlJlgEgKH4oC3NJG8DM1u9fYCLspfURQxqNAbEcEPQMKMY0sQb9iqfjA8NLG8bZsYeE0XpmdQdm+vW4vrfOPbUsSxtX5+MD1rQ3rpliFccA0t7GHiWpYgm3Yp/UKY4MzZH2sj1tSlHEr8tPdFIcMcBrUZL6RQg/pszlcsfQP7WfINtRDBwW95+xxU8Zeg+F4xlmvwTIUiqdNmSGFFhd6YcAyfAICwQfXZ+FMItU0qbsGKbbs9PcMLEb1tre3wCJXQmod/3fkKtpTi/db07e8RJ9pyxtrGRIodWJB5tNCkW4UI4UCK4NxEmPAsFq0mNPKeQzFjeEg46sC2H2u71AeXthQ67lWNFyLmegJI4wDb9A+LpIPHAm6nwHaTulXvX0UJjwq9EYEVT7UFl3ilGbCFOK7Yprl16SCkY7ZJblpIou3W9PGEdKPFCIbXtEP65m1il51V2i350KxuvDb7pvfarH1rvIgz+ixWcvldnC9sH8eYN4vCBNyo6xLA63NSpRUVdQugjEftci2AzpedTqTisMiwH4Reqb2yop7/icNcoMYCBkCzz20/rwNjHj26EFAcUaE3FhoASV9PVTYnTRtYYvm25Gd4oaVAmy+oEntFCguNAGD55rgtXWdBnlJcNa47SwM8hFp6b173W808zN3Tryvc6y2UjReR90nLbYT1Qrb5FCbw1pKYqph4Q67UgLLwDzpfdjY1GC+o1zfWczmVPRk7bPcz3sIwZpmy5bDY05WKwoSewIqi8KJCb0xQesQ63ua0KHAMQUHh0Uzi/YkRTsIiT8oDyQBwROX8dB1vzLmPU7z1zju1IWDeFIS0hSL2opCFal4bsx6yAhC8LsgOnrEZBxhFYdbYitQwemZ5E4ONih9O40TB3xUlC3XfiGR8imLeaoDHS21mYbg08Zz/5nBAHJo3cJpgNjPktDsQ9UlQXFGhNxISAEhKBFJnwYcyXEE7juIayyfq3qJlVRAsw3SKRFsUUTBPMTK5zzFDa0HZNR/iPky4eCcVaiPZkJrWQenmgaKsFGmifRLndp/zBgyrLijPRqH+/LR9nGFYeSeNSDbNilIXIpk3lQ6EMpQIIpjrWvaX2k0Q80U/SL2AVFB2+XKyT/MvSLZkhWMdUxUlGCr0RkTSJ3+0DHHQmn+eTPfkUDrpYgiqVR62Nk5kROEyqD/bjyI8AmUYCSpiFZ0RqMWhZD5lwDjlGUNU9yEm784ymL17oxZ7Y7ZdKC79Qfr4LoYEOl6Y4I7P3qmHhZFj2o6C65f9pXaagMW1QuqvuV/08iCuKrdh0i6Hyin5JACQME3gQrqyggi70LtkyRIYPXq0VxqLFy+GUaNGedvy5ptvwlNPPQW/+93v4Le//S387ne/gz//+c+QbBskzzjjDJg7d653Pig0AHoKhF7X9ELd7/1209f71mUCwgp3gJR+a16WN1AKnNyCLdHcx+JFTohu6vEI/hk9U7512EyzlCHAN3RFz3OQNqtBP4kdHd+Jwe7jkbUX97hSh7knakL1T6YvfEwJ/sVTGRa2kYaWcCmrPt+KEozoPHr79u0LO++8s3c6Rx99NDzwwAOwZcsWBKuYQHpDZjQJUHoOUr3po/aUtcyjNT+efJzyasnX7mbUDad3+AwcM3pRp1i6UuxoQuEtS7SoJIsTW5mowz2eHn/kG2+P+HgAgYWB7BhJKPqKFT/q4pWtWGPaJ8XMd3lItq0DofIeltwHMcZ21vmaCmGiYcg+U9mGpF/zlSTutU5zDyWhKAoO7ELvwIEDYfLkycbX9/T0wC9/+cv09+TJk2GXXXbxtuPPf/5zXCIvNKCnL/FoGWoiqQLRLpSJHC2EhG2++A3EKUCXwbLAYshDzOZCih1VcMXWZdoItbd/sA2Yp5DK4sXsIRiKqGdiz97gYTYqM972/7GKDIo1YuY3H2z6a6TlrUU7FVCHspnOX6ZlDV0nrHOQxPZnDKuFgVV7+TgdiWysOOEK3aDUD3ahd88994Sf//znxtffe++9LULvGWecgWrPoEGD4J/+6Z9g0qRJMGnSJLjmmmvgqaeeQs0Di7LD2IxgHickeXtWgT6Geoqy5GN6KI9hDKR6/rYTup6U7bi2BVHsQZaNUTZvqWIc8uF06PUa8KjjaLy0yp4t5DI06yT4HMSASfuHqAepdinbwRo7tB15iL2eY7Yf5VnxLT/zXI/VXiReyBH3JUWRiPjQDTfddFP691122QWmTJmCku43vvENeMc73gETJkyAPn36pP/9+uuvR0kfnQZRyAOCQTXopM9wumks3qeSxWJMVLjlod0r3+vAB4nUrDgpDuUiExnrWseWRCPiulDTT2u5Ee/RXUCt2qhZt3UqE9SsjRQ62vdTdVvzAe2zYDw2h3geI/NC7nR6tLIVR0QLvWvWrIE77rgj/f3xj38cdthhB5S0TzrpJJR0OEEXeusiivoQwD7SOonZa9sHKXYw4X2goQHtQm70wq4A80sX/tLtQ8kAyMopTfAqIhY7K6lLOYQSsp+ImddDI6weat0uDGuajsV17dZ+n7bRdgzqVOLz2pDy8krXD4rCgmihd/78+fDWW2+lv7HDNsREAgBJHxVmc4nR5gzB6zx0/h5wCJ6dClbdtgvEvuk6C8622RIsREsPUKHoypZlYAk10Z4H4WfG3naXHlLiljjFeB9EFCQU7RUB6wLJ1KBuRLevrqtkt48tlu1ZNp/Uql58aDRkvbQ1XI9IaL9GArUYwzmJ3c9GCYdooTcbtmHcuHFw0EEHBbQmPC4evRIGdRSqyuG9ofe8v0NQYdWDDj28CLPPsHoVx+x9ipC+qE2MA/TeyQ0xq++kEVDsNUVGVSmudNjUX5u1M0D0wm2t2gJAxEvnQhoM9S11LrDcZ4rql71CqwWywwBR9aYoNUes0Pviiy/CI488kv7uZG9eAHCO0VsksnB/hk0uELZ4ypmXTYVLhQ3BCy+AGoRmyEJ0CFQQYhBtGerHuwwR9m9RmzVJtgSmql2oD7vB3Cgb5yE53iQBHHVfnHlNKhGJRlIzccjGo9Fl3JVeV9LtK4Lbbo85N9bnRZcZioKLWKH35ptvhmTbxqyrqwtOP/30wBaFpwcxdEMS7UxbjdSyUW/aYz28RWkjUPtgvPQIJhYTZsvyvDCMDbEfsOYdViKvfwsXf5tlFjFmE4XccMrbFmRbuTbRHPkY5SFzSQUA8QoaLQgXdsUcQGzzHAuuUqv6tAhJVItnQdkOY3varjGq+pqINUtdSAAStkUHTzYKHyKF3iRJ4Oabb05/H3XUUTBixIiAFgkh4OZCB20LhC/CqO2obV+h8mgSVF/oQq2QsonpkwZ2BLE1UP2EKSt+pmL6lyvS7ZdunxIMKS/QKxEu5pZhUsdixOBAcK7vpewllPqA3ae0jyqKDEQKvQ8//DAsXrw4/V23sA3XXHMNXHPNNUbXrlixIv17Tx8qi3rTvrBjHbRj39SVHbhEjYC6q62QXFSudnuCiWbUO166pEUJZZxhgL3DEaCYUYiodjGB2UM3uvppJ3b7K/BpH92obiPSegjefgzCbvAyuiLIbq1DRVFk0+Dz6NWBpXaIFHqzh7ANGTIETjrppIDW4LNmzRpYvny53U0NAHCI0etKkv5PJCDZaroxLBtzs2l4jc1MsbmiEiuQYrWhlrnMHsa6NQ294CwImwrdDti2KUr7Bez3aP2PuO9hhYNxeWad6shXXLF8NiSJBG71hW6GKJzjzGJS8zr2pe4es6RjhMwi5xJ0rIyontqRNMeg0skxikPiMwe61Kttftp2ioKKOKF33bp1MH/+/PT3xz72MRg4cGBAi/AZMmSIcSiKFStWQE9PDwBYHMbGISI0L4lJJDSgcFFlUU600BeYE15J/kFEtjyY+hK6/R7piTwALSaPVlO82gjPDGck2NCGTb3wxDkWWEkFiOhTeUi1CxPdSDrDLjoJFW+LqEP4gmDCYlxN3QuRgqxEm2ypQxm2YXsOBsn+oM0EzoPSFXc6YWmm0CBO6L3jjjtg7dq16e+6hW0AAJgxYwbMmDHD6NqRI0em3r/sQm8JzY0q+uLG1fY2O6RtpJMGhP3MGtHzz6TNvYVtorry6heujrChBSiE7MU8T552xCAsY8NXZkGF9kRMf+eGclMmpU4T0M1nGzJFKh6jRJWd0BZR5WynU8tdhGCbyYVBJYWtrrEPQlQUT9544w244447YMGCBfD888/DihUroNFowF577QUjR46Eww47DN7//vfDoYceCjvssENlen/84x/hxhtvhPvuuw+WLVsGGzduhBEjRsCkSZPg9NNPh2OPPZahVGEQJ/RmwzaMGTMG3ve+9wW0RhbGg77j3GAjSFkvnhAPskI5GMLxcxIvAYDiM2uLMBGFthPYlbXFWfSVsLCQ6q3LHKrEC+Z2RC2ThD4IAoVHFXW9KRqvxbU1Fq571qr6UN3BCVECGKN4xO+VzJONVbkEtb2sfhjagN7UXljNK17NP/d33qshpO2cR2R1XDf4YvSGZ+7cuXDhhRfCq6++2uvf1q5dC3/+85/hgQcegP/8z/+E+fPnw8knn1yY1ubNm+GSSy6Bq666Kv06vskLL7wAL7zwAtx6661w/PHHw4033ghDhw5FL09oRAm9y5cvh/vvvz/9XUdvXi98YvQaiaOOA4nJhGE8+VQnhhJ3r+E2mbp65lbm5Vqm5n0I4neujYihLJwXFhweqVLEFYovtSIQc8ltjDUsSEgEi7m+9ZwdC23Wz5jtW6u+Qgnz/iZxXBtwI2rfV2eh1gSJYm47jPVWhxAWtogTZIWZY4WH7SLHB0vw4paX5IGUhaJgkSQJfOpTn4Lrr7++5b8PHz4c9t57bxg4cCC8/PLL8Pzzz8PmzZuN0pw+fTrccMMN6e9+/frBhAkTYPDgwfDcc8+lYvLdd98NRx11FDz66KMwePBgvEIJQJTQ+/3vfz9V3BuNBkydOjWwRXJIGp6hGxAnv16bIMTPPnotlhxFZGvB2GbWcxAuxb08zTHI9IC5rf/BLE1vkNKs3LxblCd4KIYMzoIEYRFiD48gTuQJ2N8wDqekwDcOurg2VoLjKv6Lw1RwqhpXkIQrUXUZc3xbAWOsNQQ2q6BKh6hn1ZYIbKeqX64zYKLuH4p4zjvvvBaR9+STT4YvfelL8O53v7vluvXr18MDDzwAt9xyC/Tv378wvTlz5rSIvFOmTIHZs2enZ2Rt2rQJrrvuOrjgggtg8+bNsGjRIpg+fTrMmzcPuWRhESX0ZsM2HHnkkbDPPvsEtEYexoMs8SehTh61rhtzRzE2sZjFGkliN+lZxidumMYELAjDYF1PJnlY1quRl1NFmk6fKjv0Nad+XrXnFRKGQaKwC0BT56R5U8EoypaV23dBblunotpAaQHtcFBmdFNpgI/ohSyYBWkvhjxj8aItwtl+Vg9g3wkrm5jn/YSIP+gOMcSeD5zrlzrMM73KUIMyKW1EsGbz4d5774Vrr702/X3NNdfABRdckHvtgAED4LjjjoPjjjuuML1169bBpZdemv4+8sgjYcGCBdCnT5/0v/Xr1w8++9nPwsCBA+Gss84CAIAf/OAH8LnPfQ4mTpzoWyQxiBF6n3jiCXjuuefS3xq2IQcTj17Xzb9n7FnjidUipixAW7pV1zte277IrBT1HETS7WkbpptJ2zmeE2K4AithoNAT1j+NIqyFC6uyu8T4sLuc0n7vvEyRaFMeRGKsbRlcNhimecQg5ClutPebqrbG6At12AyLp66Hj0kXY31xjXjGZTObiEnlroh3fSeGlADwdxKKiobOV1Vo/SiS2LRpE0yfPj39fcYZZxSKvKbMnTsXVq5cCQBbIwTMnj27ReTNMm3aNPjud78Ljz/+OCRJAldddRXcfvvtXvlLQozQm/XmHTRoEHzkIx8JaI1MjAZnDM9ZAGvRJte2ghASZCEPbNJu2pvreWoWPsLYE9hC3HYRtgs3/lXCvoXtVh7GBXVLcfgQhydpsz9QePW6vCAhz4sg7zJ4YgjTZ2Ib21PFWMWEqjlDN20ZpH3WbYi4NpRmD8j2rLW2jdwepgaMUcQntBnr5S3JeFCUpu86RNBYIW4ctSV2+zHQOuhFAsB2GFuIbcmdd94JS5YsAQCA/v37w9e+9jXvNBcsWJD+/YgjjoDx48eXXj99+nR4/PHHAQDgZz/7GWzYsAF22GEHbzskIELo3bhxI9x2223p75NPPrl2wZAxMInRa71oKHqqMUS5Rn76VgeaEQnDpWKoYTxak8V0pRhcIoZiCqqFMXYd8jAWbA3zyBXIDPuIlbhG5cWL6b3rOcuiiYdS7CgicJxkFWnrSfQbRWoiElI7oi3r9sk5UT6sfcHZw5e5wyJnxypOEsLd16nzq0xf4jgp0aZ2sMNhuKavKJGTjct74oknwh577OGVXnd3NyxcuDD9feyxx1bekw0D0d3dDQ8++CBMnjzZyw4piBB6f/KTn8Brr72W/nYJ27BkyRIYPXp0+vvSSy+Fyy67DMM8ORgM/FYi6rY0sYTU3HSKbDb1AK7Is1f+humWfv5q4klr7G27/cJc8bAkDATm6d/N8roKqlW4ho/AOCDNuJ4sypo0GvntRemxyeEN6ngfSj8UEjJBUYpoJJ0iENIXsjb1GFE5yOucsS5i/8zeWbgN1N+867sufSNGL2VMJNrmY5OJ4w4llvmJ7htKMASdAY7Kpk2b4MEHH0x/f+hDH/JO85lnnoFNmzalvw855JDKe/baay8YNWpU6lm8aNEiFXoxyYZt2GeffeDII48kz/P73/8+nH322b3++8aNG1uuyXoaN7n33nvh8MMPJ7WvF418j95KcdUyhqyph6eV57BpzFbPydwphEHBvUUYH6yWm0f+jamgmNMORt6zpt65RZjcXyGSuorSRvdhxKAsuSZoSAYA+m9lMD2OfWj2f6T6VoFXqUL0piki79gmaPUZX9FTRPWpuohrJiDmLyaerAWo9R+DN28Tz7SD99t2KNvRZU1UZ69pANrySetbihIZf/jDH2DDhg3p74MPPhgAAB577DH47ne/Cw8//DAsX74cBgwYAMOHD4cjjjgCpk6dml6Xx7PPPtvye8yYMUa2jBkzJhV629OImeBC78svvwz33HNP+nvq1KnQYNgAbdmypaVz5dHT05N7TU9PD5VZ1lTGfKXK1+CaKqHVRGCuFFbz8qgQSVtsy96LHSrAkFzvUQNhFSCnHC5hEhAoTNOgbgu9jQ3s9BF5KcIzYISR4Ig77JxPYf58qqsYgRfDDt0ooMN/2FTYRhR/krsHtRZpkKmTN28RaAJtLIJsER55SLcPg2DeyY5OIEHzb5oh4PluQZo9FYirP1fqUo6awRWjt8mKFStg5MiRRtfOmDEDZsyY4ZTPokWL0r93dXXBiBEjYPr06TBnzpyW69avXw9vvPEGPPPMM/Dtb38bPvrRj8INN9yQG+a1KdYCAPTt2xeGDRtmZMvee++dm0bsBBd6b731Vti8eXP6e+rUqQGtEY7Jc24QrxXAQJirSBfrk39v8uwgFDx9vUfz0yxSIqvT6lUOA/G8NIxDy4Xm9hTWp6s3rovIa3SPiZpvmJ/l/V5pWqbrlU9Lnm43ihBjJdhQBZeNNVi8i9pICfLGbT5rscVRFdWeAOTPiLjyNnGwiz2ebDsI2QdvD8f8Y3nOY61f9nQt0quNJywiwftZKDq13AoLPT09sHz5cqNr16xZ45zPq6++mv596NChMG3aNLj99tsBAKDRaMCECRNgjz32gFWrVsEzzzyTXjt//nz461//Cg8//DDsuOOOLWmuXbs2/ftOO+0EXV0GB1wBwJAhQ3LTiJ3gQu/5558P559/vnc6o0aNgsRClDjzzDPhzDPP9M6XEyMPVcPBvyyWr1WsXYc8jNI33Lz28vgtq4cCEdyo7nLssfJiLRVJtyea69lbYMv2+wvsqRBuK8XudptN27Lk+sp7Su6rTMNIGC+IvWsARSxgKwzStY8LHDhsBQYxCLqhsakjYRsIURs5QoFL8qfZVahXsQVC+jObWCtdQBfQHmTPj1TPZQF13oJ00T2LkLpT0ZkelzHaZH8T/EWdIp6uri5jT9isQGrLG2+8kf591apVqcj7wQ9+EP77v/8bRo0alf77X//6V/jMZz4D9957LwAAPPnkk3DuuefC9773vZY0u7u7078PGDDA2JaBAwfmphE7wYVeBQdrz12AQu9P47RMQh64hlQwDKdQ6NFqKlYn7mJy6YFuebYYhTFobEur7Nv+8jTyE85JxjaNnHyNDrWzuccHLI/ewnvx7MBOx7kOixZ8FvUU3HvX4GWIUgHjuh9lg9gJh4lJFx+Y8qlDSAL2jXXAvitVAOQP6YKXVMy2U+ZhtHb3TKMSgrrqZAG5iqhFyjrONQoPzIPCsGHDYNmyZeT55IVHnTx5Mtx1113Qp0+flv8+ZswY+OlPfwqTJ0+GBx54AAAA5s6dC5///Odh7Nix6XXZKAF9+5rLnNlrs4e5xY4KvTXHygsRoEKEzFxeFC6g101t14X4VJnpk/dCz9peiYJ/PZimgRqHNZM9d97U+HrKWofrMLmoOl3OOLvBBd0skmzhxGSt51I3ll+E+NAweLlWfLNQgTdWD1rp3pYmIOVBukFWAdOKmPqNDZIPLeNKl7O/5h1gTZ6nbfmE6nLRCYaRmVtIXcqRR6eu25VCBg0a1PK7q6sLZs+e3UvkbdKvXz+YPXs2TJgwAZIkgZ6eHpg7dy5cccUV6TXZUA7r1683tiV7bbtdMaNCb0xUTQAVMVkrk2/3XC3JwyiEAUBvsapCvDIKp1DiIWoTFqLl3yrqzubQs9IyVAkqaf0aePbm5GsTvsE6TnGm3Jhe2baEEByNwlykFyOk154uo6BbeJvBOELaNrEtEkMt2KV7gpriudGU6KkFUEO7SDza6uEBy+LdajouShsXYvU6NiVyT3RR84GHLZLFdZtxzm4vIKnxALevmjoWYaRXA2J7jmNb5nPBeN41K+2HqR1yyCGw7777lt4zbtw4mDRpEvz2t78FAICHHnqoMM233nrL2JZ169YV2hUzKvTWCU9RyEh8cvFGzRN7cw1otSW9vEq8zNzrFUO3/b9lk88Tb23DSmDD6SEdMs82CuvVqE0KYvRWCLXYHsw+h9B1LAYvfYIjaVENARf5SBtN6XFrJYkI0j6ZJxcbSpIXtblt4mCTUTkiFWJr5Ykcq0d/FTF7PQscA3zGRKd76/BCw4aG0LG/kxD6IluRydChQ1t+T5w40ei+iRMnpkLviy++2PJvu+++e/r37u5u6O7uNhJuV65cmf59t912M7IjBlTorSMeYpxR+IG29I1irprYhCgiOgmsyJ/J97re4fP/ysPDctrCysPaFts6shVNXUTWSpuq1N+K9E2xeNFSKlZb2GQcLqQljwbZ62HylxvtdNiiUMwmxmvDimhHE4Q0pXnahhA8SMRZyYKhhH7jA2He0l+ySMxfzPjcTiz9pAy2NmTIKEQ/CdQ3xT4TWWKwUelcEuBzamF2nhk3blzL71133dXovqwQ+/rrr7f8WzZeLwDA0qVLYcKECZVpvvTSS4V2xYwKvYobJYJhZSxfSiEyY0PomKJWB8UV/LutSIkdS7YXFe2TW+aSUBsAntc374Fiu7IL90JvXkzvbMM+XCrSOgq+hSb1qjPDla2DIGwc1kUBAM6NMm1GEgVcKfF2ue1wFiukitUA8sIrSBawi6ipKF1Fpx5uVQcRNmgYAuqsO+2ZsEWgjVHUm6JEQrsAm3c4Wx7ZeLoDBgxo+bfx48e3/H766acrhd5NmzbBn/70p8I0YkaF3ogommAoxBTjEAgU4QsKPIZtPVWxxV5r4bbIhvZ2zPt301i93B69BmnY1pNRTOOc61vuab+v5YbsvW6hG5r5You9Run0MsQ+GSfPX4Dt4iBhXN8Y8B1H/L1FeSoSpb0iFkolxiO1Ejq4hfHAz3dob94mUp8byvxFjO0SbMhQV+/uKtjEWI5sAtSjiGfJFkabo6wfFzDLafIFLRfq8OFNUtOHYMiQIXDAAQfAU089BQAAixcvNrove92ee+7Z8m/77rsvjBw5EpYtWwYAAI888giceuqppen9/ve/b4nRe/jhhxvZEQMq9NaAwgPD0gv8088VOgzStQ5fUHINupduuxjnIs4hhDKo+nebQxgA7OrJSRgWNGm7xJU2Es/Ti3vnl15ms5CqEMZbbkNK1ySv3ORzPYyRFhmRnihQWG+BvI1Y13xYTR9IlEOvq4r00EQP7jJLEj5DeHLbUANvWykiuS3i97uC7AvmDSulDiTMXVzoM7kVSbaERFI9SLJFEce//uu/pkLvgw8+CJs2bYJ+/foVXr9hwwZYuHBh+vvggw/udc2UKVNg9uzZAAAwf/58mDlzJvTv378wzXnz5qV/f+c73wljxoyxLodUukIboJjTSMr/5IKgq7B+fl2kvZWV0fV6DAGzctNvdz3LwrTt3yoXaY22exoFf9rSzC078j1G9ueVAbZugio3QhX55v2pTMegzxgvnIvawiK/ovxpPh1u1OsPAkX9qOyPWV0j/cG02SH/pMstv2TbvW55Nor/NMr/2LZF0lXwx6GszT9Y7Wzc3l0I+Tc8yt5mB1rfb/7pavuDnb5reUvaoOqPVxth2Ov4h6LuvcYDy/EB7Y+BLSHqKh1LQuXdZgdKn7MhZJ0DXpmt68fQNvK65CB0v67zH6XjOP3001MRdtWqVXDjjTeWXj9nzhx49dVX098nnnhir2vOPPPM9O+rV6+G6667rjC9ZcuWwU033ZR7bx1Qj96IKFtw+IRXsEovNcYxjSp7mmWsiNOam34m7eb1Vt6qjnVVhpNHM0W+FRjXL0BxGbJpmLZDTppJw6Ddcu5J/9nk3hYby1cXqeevySIkMdsYFF5SUCZbKsOFmGBYFixM+iymPY22PsoOQb7oZUFKz8gukvqwTBTRBooyk/RVxzTRbPFJB7k+yMYCwjGGZfwSsgFn91AVUu5KBNjJPo8KKHMewdYT7UixAwDfFkllc0BMH3FEz94IQI3rfJ999oFPf/rT8M1vfhMAAD73uc/BuHHjcsMn3H///XDRRRelv8ePHw8nnXRSr+sOPPBAmDJlCtx1110AAHDxxRfDxIkT4bDDDmu5bs2aNXDqqafC2rVrAQBgr732gnPOOQetbBJQobcGuIZVsEqvIm3jgd8xdIOtTU5CtWud5YiH6c920TJvgi8RrLemsfUm0zi9zXzTf3YR3Iug7BvZZKrEXgCrlwK5dpQttmzq34FCAdxQTDZNvwijNnFdjDpWE/fi1zg/AYtylLrxSEOCgOwl8ljc6lVWrnwQ8m/ibIdAUZR0DJFml4BxKQurCCus7AAQxCabfmUTzosdge0pSoyTZEsZsdhpiai+IACtDwWbr3zlK3D//ffDn/70J+ju7ob3v//9cPrpp8OHPvQh2GOPPWDVqlVw1113wbx586CnpwcAth7Cdsstt0BXV35wglmzZsFjjz0Gq1evhu7ubvjABz4A06ZNg2OOOQYGDx4MixYtgmuvvTaN99vV1QVz5syBgQMHspWbAxV6I8JooeapRUl7U0dW5nah09VD2TZGr2FeeWQ3UiaiY5C2LBGdfbx0e12eJ55XlNfIjtL7Ww2qPNTNwiZTGklOHqYgeQu3g+I9LAxxC9nQXpftONvjeCO3gBrS05arrcuuD9j/QwrPUoXiKoLFYm1HRcJSQs4rEsYoV8TNx2VIslWSLUhE1RcUBYm6HsbWZOedd4af/exncPTRR8MLL7wAW7Zsgblz58LcuXMLr//hD38IEydOLExz1KhRcOedd8IJJ5wAr732GmzYsAFmz56dxu7N0qdPH5g5cyaccMIJWEUSgwq9dcFRvLIWvVw9NrFEUip7DDxcc71MK+4z8kx1IGk0WoVGAq/eXGG0xHu56hojT1tL0bbl/qp5sOBT/cK6KUljazrVE2/D5cE0aJPcvEw9oF1wtClKGMsj9bPrkCEPWu1wuAnJFvGhF6rSYuhbqOIydl4mkPVbpkGE66UG5RcgRVnWYV6pQxm2EetLEDHUtIy1eE59MNx3oKerKDVj7733hqeffhouueQSuP766+GNN97odU3fvn3hlFNOgcsvvxxGjx5dmeahhx4KixYtgvPOOw/uvPNO2Lx5c69rDjzwQJg5cyYceuihGMUQhwq9MUEdP9YiDy9PW07vWZP8XOwpucf7YLxc8dqv8X28WQvFalPR18XT1mLxZBRfOC9NixitxmJyr7TNbugl2lvkkf4k3ohJ8/Z3QeymhNtDFt0O3vy88+YSHAWIk6FDlHg/cyh9BPMTBrykWNLFwNC2mL1GORE7D/kisVwSbXKAu8/krfeC9ttY2zFWuxVZ1GD/ZcLAgQPh6quvhq9+9avw0EMPweLFi2H16tUwZMgQGDVqFBx++OEwZMgQqzRHjBgBP/rRj+CVV16BhQsXwrJly2Djxo0wfPhwmDRpEowdO5aoNDJQobfGoMbNLUrPQ/C1Eo58BjkTQRIR3xAB+d65+auFKq/ePNusPXTBoExl6WDlUZRmTrpGnq8WnkymC1xrQXhbHi5ChJO3cFu+FRm0Xl5mIuciJJKFM6lXnxTvOU87OD1CvfP2tSGEN6uE+gIIE7YgctGRTVSJZDx1JUpRNUabs1CtFer2tY1gCsvf4fVShNGXgh1Kpz9Lij39+/eHo48+GjXNoUOHwkc+8hHUNGNAhd6agSWeok5SOQIkRuzd0jQQRW60uuAQnA3yKAzpYLA4Nzrkq0yENfDUtRZVDdNN/0rhwds7G2Oc1kCO4rB/xnhpYR5ul0edDgeSKrIB8HnUkoud3N7MVUiso175yfAoF5fHNiQI9NjUZtPeCeVgW5AISr+A2vTbJnUrjyBcRFuTa0L0wYbh3s0lTYUTfeAVN1TojQiMeLreaXN54VaAMtFwCd0W+aKSNy8Yer4WHrBFHIbBZtNSGYM4i0FdFKWdm5yJZ7MhTt3BSVh1yYgOMQcIFSA1fm4VaHZze+ti5c0p2HKLwyT9xWUwwbcDO91Yn992yMtB/aWG7GF+KzHY6EId+mcnoHUYnLJ+HHMfp7A95vpQlE5Dhd46YbkorxR+ELxujfMySBPDC9jYhCoR0TBMQu79Jhsr43AH2y808pC0FGy3pptzf8vF5ff3SqPIppL0CvOuuKWUhrv4mRi0v40d6V8JxVjnxRm2dz8GLsK+b5YhF7chRdc8fNNDtidEHFoOYViU175HekHFSk+CPPdSNtKNgr93CCpoOGJTby6huhRjJPfhovWuZJsVRVFiQoXemKAKMyBUhEXxYGYUosnSNRRCrURfIqHSJFxEaVJ5oR/KsA2x4HiAmdHhbggIc7rdikc5MYXrJIToELuARJmHR9r8IQYc8yuCO9SB7W2S+y1xv6c+lFIkUu2yxKXtpH4yTU5dylSXcjhQy35pQaeXX1GMEbk5VGJAhd5YSBhEXMvr2cVRivASZfkh0Ovws1JDwN67oUL0LRV8jQVkA5vAQaTNydcqfi445OcocKd2EU+2TtVnW2eMcC3ko9kwhLJTgkAayivZJZwKRliRwCE0SJ4JSbYUEfsLFAaiGS8NsCpLjcqt8FCnZ0URAEd/kiYK6jOkKMFQobcuuA7sWMKuIHHVVsyzEmOb6YN5HiRir+H1RYJFrgBcNRlbhHvYnk9FmmX5Un3WVRRSw/ReSeEVLPpg1HTy59Mg30sxhKhIEuOZ2dM4D5S6lBi6RNDzFMQbPXY6tdzbKGt3ivUsdj+TEqM/1PNTVv6OfaZjIvY2Str+n4JG2/9jw6AzKAVoHSqOqNAbE4QHobF4C9vmw2AT5QF3zhh47BZeX0aL56xFmAfbfLJZOgqjzt7BFpQu7m1iCluCuuGKffErFFEbvxJbyOxkKD+6UBtIiJTkOdtEkic2Zz618xzuEESNtw5QiLLNdQJW2rHXsS+dXv7o6TShK2R/Ldp/eX6xqSgKPyr0xgS1sIqYr3fe1IIwM4UHs3FS4IlcJbgYC8GW+ZZBvSivbAfJMS7ThJDSwcS1bLZlCbAIFrNRZLYDVZCNxCtUglAa8wFmwQ7bo0DKc9+GmPGoE/D82sk2H6u2lbgOUOTgM07YHCTtmqYp1OMd13NEUQ4K23VckYVO+IojKvTWCGcRkTvsApP3cDvGdgf6NN/pwC/X8AMWaaEJPUaxZHlWF66exqKo07wfsWeqDyThB9qJMGapBKHVFIkevc5fX3AQo1jvS2hPqBACjaT6pyZkaCEuEbqT0L6+FYlzGxVS7TLBwZnGOy9bdBxSlCCo0BsRIsMr2BA4TIKxV23eRIZoC8un+5gCMEbapklzCF9NDLPCEp/by8YlatcJ1v5hgwSzAtkgOXZr1F6xxOlLj/tsCrugWzNB3BhJL4wke99x2abLBzqkPXuYNPtNLGWU/KxLgXMs0HEnCLpdVFxRoTcmfL+gxx4oKAcepLTRymwbN7eEoo1psNit6r1QCpW4KFa0VKLo42SnzdscHEmIl4AnSZRyhEXAZInFjJBIBM9jcEzqyGeNIakNuDzYXM4KkFRPWVQo4IXTy9IGqf2ziNjsDUEdvlBUFIUEFXo7CBFxYk1BmrhMNpnW9YEZ8yr7T6EWNDH1i4ioak8J9Z1no49doj6XlkRM3rZ18lj0yENMX/a0I3g5QuefRZItIWB8Hljmt05vz220t41TGDDp2PQnSWVFdBJRlELa+7z2NUVRQIXeWiIm9IL0fEJguwBlrIvggkCHkTRA1oZkG7H1Awp7bcZQMfUlxQ4MkMoSvG3qFsKgrmESiAneDwPAVWbX9a71eQmCic1eJ2wE0+y/U37d5pIHBmX5dUJfkIRJfXP3D+0D9UPbVHFEhd6IsF7MSRoYAthSi8VvB24QJVH7DXrsXgBE7ROk3eve1zKIea5iCV3gSgfVsyti+mIZ6hVYCEX7uYTX4uxHtVjb+kBV1zGMBQA6BlBSVLdFfYOrz2ibK4rigAq9Cg8B4lVFsYHrJKraQ0hsUHYklSemEAPbKNr06vOPRMT1KLoPdKgALLpNqpBgewgbYvF0rILofAVuqO3AEpKx7KyNsO37Yp3DO7nTEfKMt6Bt2dkkDb7JR8okp6ChQm9MSB3spdrVacQ8PgsNY8BBdPOqMHud6q99zBJWJmNcP1ENTHR9vh3p9hPHYrUVXsS3t3T7QlOX+sGYKzqApIEjrpqkEcNZBmTYeurXuS6U2rVvrZ9dRYkEFXoVf8oOTuNeWNdlQ0JJBHXEKgxEUB9liBdRJFKXOqtLOXyR7h3rMqcJduAQO+ZItUuJjw4XKaQ841iis3io5hHFHK1fJzri+QyM1rHiigq9ynawBxLXhaKQBaYzEdhfy4N7SpCyaSlEun0uUJZJFz31oqivSGln375c91jA2ERSFso6N93YSYsNW5vP9SPpg6WErkMk8voUZ/9ozz9Y3yxzqjG5N0tN+gYqAUIMxk7wcVpRlFJU6FW2E/vCNgL7g2/GBXuJtRC6nrJIsoUR1zZkESgEtIkucBkQ0M6YBB//MfApQ9UzI6h+QrdVkSdjSLuo8taxlAgfYVA4nH1RVP90/UrSpAzqVbyVDqoHUX1bKUbbSXFEhV6lHjBuftg2WnXwAnNJX9Bmv0noTX8lguxLTLwiMOwNvPCR3CeoF+++Ze+0zYXkvsIKo8eUrxdg2bV6LgouIcrZaWNQJ8Pe1tQvtKjKE+szEavdxOgYpyiKCr0xgfVGNibqtqHyzIfMTop0ies0qrowpFYbe4kvEcqSC7Eoxj51e1t60vuRi30SP2WPBmmfpDK0EcXzTP2CQ/uuGypolGDapyKrQ9FtnlfnScHflWI6vJ5E9nGJNimKUogKvXUjAZlibyzeqUR2RiFKIqYlLXSDbqCVKoLElsTul9L7uccmQZ9hD6i96xUj2sMxaJ/GgboeRQou2EQW5iG6Q9oiq18ytA5KEd2nJdtWd3SxoDiiQm9MSHnOYxFtm0gQDYUcTNeRYjgmMdjYCQhecErrx6I2Dh0U+y4apL4cFkjRs2TzzEsbH5RqJBy6xwJX30Qqs0m7iK9fypdwksrewYjqg+1Itk1RFG9U6FW2I1FUBJBpl00ahIvn0rJI8tClJraXD4oXoppC+EKZMkwCCaIaNwfJ7S297mqAzgNKEa7jpk2fEi0aCUR83boeriadGGwkRvyzKt0+ZTvaVoojKvTWjVhi2kqP3+pzf8G9JBtEjzTFbVilxi/OIq3OYodhY+yD0ULd1RbBCzffg6xIoTroRkr5fPAtg45vlWA+B+LmYMUL1/YUM7b6EmE5oojNL5W6lGMbNn2BZeyuUf3WZoxTlMhQobduNAdThEmIdCLLG/Qd8vOy0eTeEPUYg+BZRoR1JjavWHBdxFHUZSwLyvayC7e7/ZlEWbhTlFl4PYom5rqLcFyObfMbfG0RGbG1by6CyhBlfcZocxGCy8LdN1ryE1wvIYjyOZWO1qniiAq9dUKiuOuYnrMdTAIhtRDJuqGSEBqDurydFMYCiaCHirWDbYtheqZtjbKwLcpL6AIP5TCcZpmRXvwVpqUoijfYG/hOmUujBrONPPtPsAPYOmlOEVxWsQJiBxy0J7buFUUpRYXemOA88COEMEbtnZsDpyhJtqkJYY9EMXcb0W8ebewnXHxh1SOpCFoE1uf9SIKwVx0I3UR4t2vV/QLLjIW0TZOkw6acbeGs09jnGCGI+1QaCWnPNwDUejwlpxMOERVmvwr6rYgcUxQ+tP0VR1TojQiJh5uxed5a3mNll+Q4txEI2NhpSYtlLBrOcgWOp+vlYexiO0EsTufFukCxF6VMlZk4pu1Ip26mJJXb1xaxsROx7ZJgAyOS+miUCJxD2om6jQXOZ8HytEBUm1e1YSBbRdVRE4k2KYrSCxV6Y4Jgkc4q1HrcRy3cSgyVQC7WUtrhCkZeEW9mRYIY97s0/arLCvJ3PjAN81AvAxvQwhwY5scF+ue0jnXpM06J3EiZgml7DcZOqrb0ngcl9LGsDdhfSbimq/Agof9VYPuMBR+3Q+dfhFS72gjeflkk2dKGyLWNjvOKEgUq9NYYdhEXKQ3KUA9SREvycBYcMYEp61LDPLQQdEFM7eXgKWA6C42Yh6EZeEt5H2ZWJ28SxzTLbAn6jAjeJFpRl3IUEXjDLGq+wRy3lXyK6hir7mzbUKjHog2ixEGpCAn/lUV8u0Xg8V6G+PpV/BC1eFBiQoXeiAgm3HJ/hm9wD+mYRy2SBg7BwRIzmSKdNjpp3uMsq/WCEVMwdcRbRFVSyOrOIV3yOMB1IEQZYx17Q8QLz94asD+izyGd8GxREaruImqzKOZwKq94KgKPP2zzuctYF7ptPIjiWVEUJQgq9MZEQMG2HUpxU0Lc2yCH2XHF461jLN66rHQEqNfeomnzfooYkpZhHqzj+rp2I8P7nLopQ9cOJahK8hb2ureo39ZkWOoFV7nCD4e9kdCmDvUSRexjpSOpy/INAGSMD1mk2eODvqzKJTs21+pZUlK0XRVXVOiNCTGiWoA8q5AS99bnHpB7uF0Wr3bV2aocivrxfBBNb+9lOsXhJJZiLGr8WEoxtzRB87wLk7C5n/gRda4fSrsQhG6TckUXg5KJwnqJrfxcax7quOk5ZPuiir4KJl7x96Ug1dayZ5XZZptxg6Xtfb5GwxgDBSz3TetZx3xFiRMVemsGl5Apwes2C/VhbS73+QmiTPdsgzYUhtQVsBkcCxzSKmomjlAQdo9UTk9Rl8PZCg4FkySsWoFkS7DyE9YlxjPakgaFrZFuxmKYIoyGT25xgrveXL6UqBFUawGO+nR+YVsnqD7956qzOreNBELFzkbC9dmNfo9TdxLQMUZxRoXeWGgAJF34yUr0us1CHpagJja54O49zDsTdMKbZJZPrzxdspztMhRIsSi1k1hUNK6jkF6z2IKlDQFCVwTfYHDmH7qsoSGcK0L3o3TIpvhiwNgI5Lx9cW1vR8EvdB/wIWbb0TANDSW1rgR551IT9bkLzGveNAvBeyXJtilKnVGht+ZIOrQMgDk0gWM6MQq5aO0caDXV6YsAugOw8CrWKe4tkfCJbgOW5zBRO3IIq2h1ilwH4uL4QiSbTpewKLFTUeYg8wxSnth9jkw4doFasHVFQt1kEfq5eC4cz5q09glJXcLfZIhinjXFZbyt66FydVhrKEokqNAbESybFGrxNsQhZ22wHF7maSdZWyOsnDpdlPUlBlG3CUuoBs7Yq1TCp2XaKHlTxAxmLLOUg+lCHUQnDinliMgrV0w4B1M8vhwhnfdD1FGM6xhJfamKBPzrOKbySgXpgFsqaiXmmiAs7KGiKPVGhd6YkOIJKj3GbexxiglWPtJiKteKQBvm3oefGRjCEoyrmZfF9VQCbHvaFpuevKoqzRvRQ87JexoDl7azuR7CCuhZRB4Kp7jVb6D5p6oPiXgpGsijWHpYMKUASXVpakuzb3LHWK3Dy4+Q7Z0IGSM7HJe1kLabosSBCr0RgT6wFqWHmA97qAbH+/Drlk8NQLFdJ+1qAgo8/l6H5g0sNf4qWvxbbE/Y2D15i64TKt5KEYrF5tuJYHgPEtBAsMt7fsfqh5Z2YC2BovCaFtj3WpBuny9UeyOTtYRP3qH7bWhi75c1aT8VbeXTcZ7vChoq9MYEY5zaIljCHnjeSxuX2H60FTGJSrAhdpgOrQk9oScNBpGz5D7UvAuukRA2wLecmKIwaj352OGTP8dzw/xsco4FJvOUrz1Rfv7PJbQW5IPZB7zq39UOz/oT3efSTLb9v9R1VlEdUtjL5Q1rko+AUHHo6ds+D9L6ZJ0EK2l1qyiK0oYKvTFhOKnQhRRgvm8bUg4aEyHYZpFmjy1SPJWE5Isq6hh0VpT88sIuUMSOLUtXQqxal7x8xG5kIZ7Ti5tNZGcaH0K/mMGCoxyh6iqIwAnA9nLQNB9WL9v0YoNrAsVlRl/TUfbvkKIsBVwv5rgOtOJaj3f6uj8knM8LRRiSmOu+0xEnQCixoEJvRLDG0w2QtnuYB//ZV8QYKsEGTuq+yWiDM/yCc75l11raT+YV7BtSgCvUgkE+ZOJz27WUIjdLKAUuQdmEGMcOBETMkY4U1h91mbblG1XdMXh2t4OanEVdR/XiwdfWmPogJtRhXSTXK0X/llxeamIsO4PNZeOZhLWLonQyKvTGhMmAjTSos2xMiGYA2tANhGmHgPOzRgkg2EInINk3Brl3JLd3rsN1qCEMXNK2TN9bdPWpE0pPZQ7BN4cQsZRNCb7JQRQ4g5cFgV5DLJN45lp3xlMC5ufyXB7M2dswBfE8+4Wt27L9ge0lgPTwErZgeennpaN1FIyoXoop+WTakDXcY52pwfpLCYMKvbHQgPChGwC8dntBJnApk4YUO7iRMDlJFXcLHgj0vJDEORJBTZB3bmEeDnl5ic8V91J553LERrYlRCxlVkEz1PgoYVwuIsBn/M5rE+KwBFV90dpum3bH8DpFPsCNtJ0AWNdpKmh54vMSq6Z1r30KwrRtqC9GAqJ9TVHiQIXeiCgdWBF3p7UXZHWC4iMvhisVBPmgPFYhV0TYHqGmeVjmZ5W3R5tgipmcMYYxPHRJBGJqT2mPvMgFW8liqSkBvDcrwarXgJ/wtwz5GGkjHNSGIgoHXju1l9drahVUFtYlQlmfkbQ2lmRLDuKFLun2CQGlHbWuFWrqsN5UgqBCb0wYruo7auKKxc5OQLgnHL3wU9wZyYSt0B65DvfF4pnL4eGK3SbegjCVcOyQvlc+pvikzTDeNRK3TJKG2cTomv7WTMzzYQE7LIMHjQSc1iaFUwhCiAnb5yjXFq46JQyJkZbLpSxVdjl6TJuWg1xcJPZQt0K4lyTqSwcKqtpSmr0eiKt7RVEUIajQGwkJOE5mdZgA61AGaiS/7SOyzUsAkhgP1/J68kOwBAm3hflY5ksZUsE4fcO0KtML9SLAMX3nfAKEbyjDSxxlgstGUXWRYwqZEE0R67ZAjCWLW7stz0JzfETUqnwLM3W8L3tv1WXIL3dIwkkgeHAXZuvbn0ILiNn8ifOKXkAUaH/0dSoZrVtFUdpQoTcmWA5IY8hDwcdng0RBCM89hBUk9ibQ5frQsXDJbPDJpyIvtHAPPm0SyrsaU4A2zdMlXYf0ve7JQCpIShlz6wTSOsS23Y2FYUfPTZc0XbyDUQ5xQzqwz2paNq1XX29jxHUueggGwjU4i8jmKu4LhDQ0ig3MddSxYmyHlDto+3ZIHWNTh8NwlTCo0BsTHThA2kxIOhASE8Izl2BFQupRSClwI1wnTsDFEBU5vWgN0hIV8sExf7J+EkqwlfhVgZJLwhXXPWdqMe1fVp7CGAKkpRclSigAJBGYRCxrT5PqUDrkw+RaTPAN04EkxLdkHdseA/lFBSsq3oZBD1z0o45lUpQao0JvjajlpGIBV/lFbu69vWFRrACASDxzYxNvK64VcYCZY17Owiq2J2tBmqEOMcOIM2yUlm/61rZ4dEbisZdkbJc4X/hCONe6toHTYWOO5TDpw05hI1w/i08q/r0oOVfhT5rAJs2ebZCuST36bxFooUOw7EIuH8seQYVEcwLbH6z+pLSbFDuUauq4jlRYUKE3IsgnJYpP7GpIVTuwCcHIn5ObgONJaHpQEEJeAMV1gOnhaZNmxTVcXrdONmCJq5516lNHJKInc5s6PxskntYOxnB5jNsgdE6T8GLRO/YqFQ4HWjUh8xzeZhO5d7BL+ALuT/dDeMT2ujCbgY01nvcCkQdtDKEdsIT3vHQkiVOetkQv1NriUV4RdcVpg4TyIiGi7RSlQ1GhNyYkDZYSTv8NBPnG2yd9ak+/yvzzO0b0B5nZpO/7uT2lB7Fl+ig2Y3re+tSt5DoyTYvcu9rVndLttq15ut8bcp6QIMByUVbWoJs4L6/cbUlg229pU/OZsxJ8sQ8AQzz8q6U+be53EIWdvLhdbUASL9EOQ3NMR2wsWSK7yMpbd5E3gH1187jmyEt8P1Jw6KD1poKLCr0KLxEPVmwHDzGkaycM2q0kWDxSOcVUDNEPsR/45uFdR4FE0Mr0EEVSNo9h4jHFWrgN/aKoiRQ7MJBsG4D3ZpSi7kkO8GpCEc+218XleeaRfVYrRV+H9K3Sw0rXxQbL+L+oQoeriGpxX1F/QxWtS9Iie8EB4O89jWhTqIPxohPeYhDYJdQpgg3sfUNCvSmKEhR2oXfJkiUwevRorzQWL14Mo0aN8kpj48aN8Nhjj8H9998Pv/vd7+CZZ56B1atXw5YtW2CXXXaB/fffH973vvfBJz/5SXjHO97hlRcaoQdtjA0ddcwyArw2shifh1J5qlqsOqR55AIgCWpc4qghPp6paF6tDF6zuWkR1TGF8IoWP5dStLVMf2se9llg29COFDG5trjUA/FahOy0ew+7vT7DtxQQi571QgHYpL6q8g4hZEj0imQOpxAqfIO3SB7oXm6P5OiE2yYhPL5D1xVS/mxtHrq+AGTYkEWaPUIQ7cygiCY6j96+ffvCzjvv7Hz/+vXr4ZxzzoEFCxbAG2+8kXvNypUrYeXKlbBw4UK44oorYNq0aXDNNdfATjvt5JxvLcD+TA4bR1GVPN4lYZrVn3q7z5pixTgEO7jj4GKIpFztgVE3TuVlFt1Jnnsq4ZbqZQ9CfiT5N5GwuJVggwshX6oybtay/a1yukP0hKUUmJ0OcqtMNPN3ovi+1t6irh60NnkUge0JTN3nJYVqYBYDncsgVVDGQorHscA0oyknFjF4ZiuKwg670Dtw4ECYPHmy8fU9PT3wy1/+Mv09efJk2GWXXZzz7+7uhhtuuKHXf997771h+PDhMGDAAFiyZAksWbIEAACSJIHrr78ennzySfjVr37lJTL74utYyk57pgKEUQAEIYJZGLYXQatbGz2macn1ZJ+yM3ieOqdvmY+zzQwetFyH0bGFeSARx4V724YYJz3zd0rPZaKLVcitgqJctgKe6/3NZIRuMqntsgrjkJsAojEO6Vl7izoIpe3jE7lnN4X2Lt0DMfDzR+FdKmpMkdKnIvZgDRWigzU9S0T0cXVBVRQRsAu9e+65J/z85z83vv7ee+9tEXrPOOMMNFsOOuggmDZtGnzwgx+EESNGtPzbU089Beeeey48+uijAADw5JNPwqc+9Sm4/fbb0fLnxHfIDTpvIM4X1CKgaxpmdpm1Aqpg5uu1aZOXxXVe3q2u6Vvk4dUGxOIwR/8IJoZXpWmQRq5oyz0G5RHz1wpca/6kfvsLEZu2JlVeoTb3F5FJlyyEQ1s+tljHWfUQvsteIjmHcmDoUxxir3N+Lv3Ywz6UZ7jE+zmvPwb9BJ/akzb2F0bcnrjM4S6wEOmVS9yXgvTVui2a6kYCfB1Du0LtEB+64aabbkr/vssuu8CUKVO80ms0GvCBD3wALr/8cjjkkEMKrzvggAPggQcegMmTJ8MDDzwAAAA//OEP4aKLLoIDDjjAywYnGuDvTeOBxxd228nebGOj7eYpe6tLXdh6ImPbQRU7t+xaDgHTMC8RHrkc3sJt15GFd7C43+Y6HzFctLetVdo4aYUOt0CVpu4f/KGqQ+99g6cwh41z2IDKhBltMLiP3cOXs409FprO/dn35UVZ0pjpUXtVU3tEQxixMmaRN0TICqp0oz2cTUq/I15MVdnrmr1tPeiSUVFwES30rlmzBu64447098c//nHYYYcdvNLcbbfd4L777jO6tl+/fvCd73wHxo4dm/63BQsWhBF6Ewg6AqLNWcQeZuSxV7FEIKzwCgAk4lhp/pSepAEEXGoPV8z0e6VFbZNpHjbpenkx27j4mV+6NW289FgOLyRMi0Wg7YQVvTSRIYP3YUx1IrSHYEl6KPF6XQXUnOsledmxfKbO1Tc48pFadsf8quA+uM0EUaEGMIYWaaEiuBwfUdrRbxHEOX9Le7Y7jk5YLyskiBZ658+fD2+99Vb6GzNsgyn7778/7L///vDCCy8AAMBzzz3HboMEvDx6EQU+0vi6FEIOhncuspDrnI+rNymH6ETpReoilFJ7tRIL6TyhJ3IuxBZnqdP1yIP8pQY1EmyQjm8dSf8sVohXL7dgbe2565XX9k5EckhbmpHhZQV9OgrRQUBfbYHTc1mayMsdvsAFSTYyhqbJI7jQR1Sv/uUK62krmphtV5SaIFrozYZtGDduHBx00EFB7Nh1113Tv69ZsyaIDVQ4j8MMHrFZQsWDLRbjCMIreIlmpta454Pp8ViZrq89FKJ1APG2Mm3M/uGQfqGHrTDv15DxpNshF2kDCbAixGcHgm6kAuaN5tFb1u4M5eP0KAstdjTHWxLB1/BtPq83M256lGmU2uppE2k9hLBNMkbhU+jN4D4wUOzLEwnhIpgXNzG80FB4iXV9rYRHrND74osvwiOPPJL+DuHN2+Rvf/tb+vc99tgjmB3YkHnn+ly/DQ7h0j6f8hrzEh5NrynLi8Iz1Sb9AlwEYidR2VWIdfVULkuDWKh2CuFgcZ2PgEsaqsIhDd970BdYxAs29gWhT34CNhi+9eW1KcNoK4/8yTxTq9JDzI/SuxY9TUMxdWvejpkTe/5ZvSDwFra3/x39kC7OsAQW7W6VF7NXPoddEsMtACDbJUhUFRd6wTMPvzjQNVHTBKyrFEWRgVih9+abb4Zkm9jQ1dUFp59+ehA7fvOb38CKFSvS32UHuEnHaOxnEHK5vOtYwyv0StPwv0lJn6odsb1UkURhOg9X+3tK7zVJy6jdDS4quIQiPIJPPZHk7ZmmddpUhM7fFkn2uupqhmWQ6vkW5DT3vDrj+Eya01vPWki0VVztLve5j/ugMxJBkVs4ohKSi64L9am+pPAGZSC8dCA73I44LalzTy6h+lPwxZtgr2pFUaJFpNCbJAncfPPN6e+jjjoKRowYEcSWL3/5y+nfBwwYACeddFIQOwAIxnJibzjredNThPQRdtHim5Zcw+4ZirBucfEcdRbx2q6xFXBRvD9NRWEKL2CDdLemLcPbtjQ9jmfFI/0qSGOBIyFgX8IO6YbVtj4dPCFNsSqno5iW3mIrYBTl3Sthx3/Ly4ZxI5vXTk75W3hKbs03wQvJgJQMpbdfkBcMHFAI2kaJ5fy3UCFKmNM1y3zb/1MLiRRltBxLWm7l/loD+z6g9vKmXUix9HnTIkgeN5VyOnC9r+AgUuh9+OGHYfHixenvUGEbbrnlFvj5z3+e/v7MZz4Dw4YN8073mmuugWuuucbo2qw3sTVShFwkD14z8ax6JsMSIivTREgf84Atq7SRPGdL/3tRuraepUwevXThJNyEW9f2IPNopXwJVJI+2jqd4jnChjovyjAECJsMrLqW6uEU4jNaVi8iznoPIXY6iUgFN2E9i1QCEbdwQ9xPqYRnjkPKTNOo1YFpEsVoCpuw2hIhTy88xiS0ePLtiXoiYh0hwQZFUcQiUujNHsI2ZMiQIF60ixYtgunTp6e/3/GOd8Bll12GkvaaNWtg+fLlKGnlQiTweseg9c3HcFZFFbKwBE9XkZjKKxHLG9a0XEhir/U1Bmlbl72yLHkuYRV5mNhhkVZheh55oHnoF0CdPlq+Pkh/K+813hTfnDQaOGXHcnjcZot33E0Ee7w3i+31yrXxc/GQs6gzsYesOedheCPhJ/NN6MVr+3uc86P2DucqP4NdRXNdob0eXrDkuH5NkJeU1E/mS9Jvb0uc8AUIaSCn6X8AJs2iS0VeR2K0OTAN4NunaPPUD3FC77p162D+/Pnp74997GMwcOBAVhtWrlwJJ554Iqxbtw4AAHbYYQe49dZbYaeddkJJf8iQIcahKFasWAE9PT1bfyB5xubB5Z3r65Xr7TlL7eVYVWbEwdpGHGU9vM1HSLW9N+eePPDKX3ETsv1UIUWoDuETJwZ75leFUexjpVc9OX+ijiw8UB7kZWtDO9SCFNuBT8KEMr6Yq7Zua3aXuxDUSxerfJyexNKeKQ7vTikhAooINVYLqOMm4oRpieoQoTIWY4zp4MRuv6JEijih94477oC1a9emv7nDNrzxxhtw7LHHwpIlSwAAoE+fPjBv3jw48MAD0fKYMWMGzJgxw+jakSNHmnn/+n7mbZCG7XXlHqWOIRZMbWEV/+zvqQI1rIJBOUkESYf6tW0TaxG9NK2Sf0S22/Ue6+s88qESg53zQMivDDTRVqr2W6eFdlUdc30a6ig8c8ZNJBNNQ3/WbZGWv1eYSR4GF0nzpOWoQ8bQBSIPECMqP7fYF7MHo4hwDIj15z1f5SbqYojDPRaI6HM5kISSKM2QMS8qsn2vDuXhRuq+QhGPOKE3G7ZhzJgx8L73vY8t7zfffBOOP/54+MMf/gAAAI1GA773ve/BRz7yETYbymj0eNzMKeICVM6C7AeTVaVBKcpVpVOSJsWhXqQhFRC8gDFDKaCKt4GFW5+Dz3xfRnAIsmTesQEWSCEOSqvceFDaZC2smhvj5P3bnryJBpe5x3oTV1ScgnS4PIlJP9F3ENudNsjUopyxsObyrbz9LSwvDRw/+yftT8I8v53Sx3ieE7N0nMcry7EqLz/rPG0gSJfkwEnX8TL0C7LCTLb9v21fQrYNraxVCXku0rzWC7Zgr91CC60qWioKG6KE3uXLl8P999+f/ub05l2/fj2ceOKJ8Nhjj6X/7Vvf+lawg+CsQPIotfPgcxRyPbwugx14hiXoShBVTQRVLA/gorTaKBWeK+ssu9qyzMs4j4J/KLsvlHDr6+lbBOLCzPXwOfP0cdKJDddyo2xSbPO2yDPbX7xDPhjaYSUMeNRfXpsV5usozuXB4eHqnJ8QYXd72oQhGCLwUJQabzaICIsJgj0h64DMqxHb67R5q8EcZS2YMzzrAEwvBn2E8FChKDAyRVgwYq45WUV9JR60zRRHRAm93//+99N4tI1GA6ZOncqS76ZNm+Dkk09uEZmvvvpq+MxnPsOSvzHIXrkAFRNUIK/c0rQ9RFkMcawyrYI0rQVG23LYir5YgqeNnVYidMXFtsKzi1Dt6KGLKiZjp2GQTu90HR4Q6zzss7CGIw+TRbogz1oAZs8UZ2ELwTDOT/2x0qW2OdRn5dQil1W9EQq5LtdnEBNT1iFtjHvZ4khX5RdAiGJrmwrEfD4fwhPW9X6iOmMPGWCKyZqmxO6q9Z/UA9lqR1E9a/UpSrSIEnqzYRuOPPJI2Geffcjz3LJlC3ziE5+Au+++O/1vX/nKV+DCCy8kz9saH6HHR7RF8HgNlj6a6G1xbVG+yB69vh7AKKEJyu7vlV9g8RZTuDVIx8dTHDWmNggWaz0WkCLX7qE9Ozy9oVzq1PuzYSO7EtbDraw8bYUQSgRA6acFaXCIXU59ixBR4q4vwryzrdNPb3LLyydNK298T6FNPM7P9tb/Rw/bUJqpw31WX7iYXecVfojSUxvhyxT7snkWiHGxyZWV8ziiKEpUiBF6n3jiCXjuuefS3xwhE3p6euCMM86AH//4x+l/u+iii+BLX/oSed62NMDGY483rALVoWkUIRVy00X2ZM37d+/D1RzqAl1oTAr+nnt/1k3QIx+b+0zubfEars7HywOdsBwU4Q+8FpiMC0SRAi8B6J9rEokBaJvpypB69IIclcgULJYoaUgDmrQxhE70fuLpWcp5CB3L5/yI5REfgkGQfZwHsIl6seUptHvPUdR1UTQ3h2q3qrVC4L5BFpKoiLyEI1+IcpovaiyJlMi7mxIQMUJv1pt30KBB5AegJUkC06dPh3nz5qX/7fzzz4crrriCNF8fGj2m301aJowpdPp6KRrYY5Q2sTdsbhoG91kJvgXXooUGsEwnV1w0be/QYnpBelhir89BbBiiLXW8XY7D2DqWSoHTL3kOj1sAhM8vDbw8yzzTe4l7Dh5KVmEsLDyUnIQGDC9CmzQsvcGtymTRFtab+Jx+Y/oFA1dIEKf25/LUtWz3ynvK7su71OaZK8qbO7+y/BFFu15JWM4FafkcXvLZ5CU6rmjGNop1DKl47NDXfdYLxmXxaQvCNSGnnpiEVrslUTGo2vRJrVVFwUWE0Ltx40a47bbb0t8nn3wyDB48mDTPCy64AK6//vr09/Tp0+Eb3/gGaZ5eJADQ43Yrmueip5eqDS6ejuihDAzy9g1nUJmezfWW7eEdSsFFvK26L/NvbHF3cyARbg3So3gZIslTt25vpYMcZAbgJGygCUGYHoXCVvUivFQxBT5CEYK9rkzfc2N68yLVLYfXqlPoDk9PZVckeHhJD2FRBrrtAb5sZ6t/hxd+Njh7l7q+LLGpa8vyoh9Y1+tmj3sBqsvDuL4UMITJQcKArihKLiKE3p/85Cfw2muvpb9dwjYsWbIERo8enf6+9NJL4bLLLsu99ktf+hLMmjUr/X3mmWfCt7/9bes8peAsStl4jLqk75JuQdqYgmqZOIpSdpM0kTxhi64pFRu5xVQXj1jbe3wE3HY86tMrX4v0qNN1zqPDYD3ILAuGtydR3ughJ7JZ24h41B6RGG3gcT/qgWcCROvcdK3up+sbbAeqcXyq7Smk2yDtU/lgh3MhekADOIr6ZTTt80xTtN7DbJt/XNn2BB2MMF27OXrEu0AmFEf84kZRFIUKEUJvNmzDPvvsA0ceeSRZXk888QT853/+Z/q7q6sLli9fDscdd5zR/XvuuWeLvZw0TDx6Cb0c8xApFFJ4mub8u03Z8T1/21eRRdcZ5ld2bd71LnWM7M1cmI9hvkaet0xet1bpOubh59nboWqvhXjkWr9cn1yX2UfhFWokRhB7W5mCIcaJ8AYmxsgW3/pxEjsJRV5HOLx4UQ8WIxSFvEUuBFsACMQ3D1sK0zVMxzmMg6kdNmxLm/KlsPc4iG0bgbdsll7lpfSG9Wx3q1uo1knU3sKYeSnFaN3mo/WiOBJc6H355ZfhnnvuSX9PnToVGoQHrqxbt67ld09PD/zyl780vn+fffbBNskYI6EXgFa4zUnfWWxzFWRN7XD23iS83lbANbrH7FoRh8r55lGal5twW4TzC4Gye4tweGbdhGH+1UJoj2A/DxKTxrbPgFS84/rs2zBdAH8hWYwnb4Unc3BRllBc9AmpQCnyWmPjvehoT/B+UIaPbZQvfQKQNzdxhbgxTqcIbqGx9OKyjO3yLUwGs60wcFnXEHvOltaHTXqmdjKKq+TrSIL0JY55iqJ0FsGF3ltvvRU2b96c/p46dWpAa4RDJQZReAGbiH7YXqEuQiOGDUjet4V5YnvCIrS3uxjrdl/vdDzr1Dq/6mts0/TOh1C0DS3OYkIuMrS3g4Eo6R3egMsjlNrLEFPk5fKI9E3H8FrfUAa2+VOIsZjPHWU/KM60PL0oN/IVZaq8t4qCtJ2/KCgDS1zNJpFJkzweqeDP2FnroQqHPgUg8Pn0qQuDsqAdxMa1/vMZizAgzrtO62gUtD7cSBj7krZR7Qgu9J5//vlw/vnne6czatQoSAyEjyOPPNLoOokEP2yKQyA0FC1dvS1xyl21ujTMt+Q+spjEJdf7iuI2efW+r+AiTBHWtV0sbHFK1waLsQvFBtc0hG2wWGLpOnj2OttlsUFyElg4RFOsdsD0KLXIw99L2cEOU29UAaEhsMKBoB6uVoSHpypAGI9l1BjNJoToJ9aJVfw7d6iHKmy8y13SRUqbMu66EY6CPsU6TEwdmEJYV8EOo3XBJ1yNgo/WvaKwEFzoVQxJoDp0g4cQLNLb1sCO3HR8hVsf79CC+63LaivcVtxTeB+FMA4GYnhJWr4xcCnjUlN63DpvSiQumCht8txckHiWAWxvb0dhylr0tfSGoRKSnTD2ajVRTIvTk3pwGICDpzlBe1CKFrnlsw6rIVfkbUnCVAhDFCIr+w+y6EklSpKGFOh1s+X1bXlhCofoHpRF9eKbdibdUB6K6PFq86B8cRoSwjZrqQuJZc9iUA8i209RFMUDFXpjwiBGL5oIGECEdRUVneK12ngaY3m/EnnExnCIGbk3umU69qEY3FbL3hsjiSJuaAhFyOCeSyZ2WItltpnLSp8sjXYshKzc9il6VplFaBSo8isbz3LybCSJvdibl4epIIr0Eslp/HC4xys/V9HTZ05CDimQB5mHcGCBO0tuGbHXChjtbZtX0T9zvAxA6NcSPtWPzuNYEMbtp4KwOTXoFyLQelQcUaE3IowPYwNHYc3WQ5LIG5QlVALGPZiCoq0wiiU+g5twm5+O232F9zraYZ02dziENF/EtDoJ4kW2m0hjflNoEbkUKpEX61N9Y09gw8woQ1T4CrwhBHrKkA/O4REcxN52CLxRuWNflyZn6x3uAkYdWgr9LtjM0WgHjvngIfBXETy2bjtUXsdtaVMLriwexo40CMa6dkSvYTgQ1N6KoihlqNAbEUZCr61YW3Kft+eki6dtzn1sh4URiplkh5ch1XEergK/URoW6bqHNHBfjUn3xKXayHTCAp6kjEliLPZWijK+mhaiNyF1f2D5NN+R4B63CBv24GVwTNu5X/iWoX1cFeLZbAKL2Atg7bnuna4NDuFTqmAT0G1ACDNQBc08WfHv0oTpAhqEXxWVwfZiAtnDuo7YtIVrfXHkoUSCtq/iiAq9sZAANLaYX04hdKIemEUlqhKlS+6tiuDF7JundTqeEw+Wp63k2LaxLb447Q110jq6IGIpSjnF8cT2ZDVMV8Sp5hV5UNlgFYvRos2s+5+pqJBzXVEZvD79zvlM2bpPV/Y784GoRRTGFs1K2pXsc3KPMZjDm68QKk/aKkzCp/gkb/lcsMAgUDuvBQSJtaTzU9ZWpra3ahNB7cAJlziaRjYhbHtp+wcWe4SVWVFiR4XeSGiAoUdvEyMRL+cibq9WSkHSVgwlFG6330vsbWuZHkXIBMwDy+xj6Vpej4C0xVhIUBa9yAtnv0+s8Yyh9OKthCokg2XaFB68RoIjlVBumb5VHgjXkQlqvnmgeCsjFc7WFpPxPictVu9RSs9JUxO4hM8AnsC5+NpBEM7DF/ZYtyYwejlyvJxBQ3A4CRRcvdgp6oErTAiXsI9lg0KG7jMVV1TojYhGT5mLUcE9CEKq8yf8Of+OKSz6hBZwF6vdhVoWL1vLdKkPJZMi1AadJCVN0MSbbxGfwraRZ5P5Bs7kgcxPzPszS4/YjdYxBHM8Qa02ucabsO2GGAt1Vuk7eKvmbNqs+6iFjVZpZjGNb2mwCfUWcCw3uoX5eQiSJnOxlRjsKOAap2Vwb7TClKOwHRKjgxQ5MX3hwOhhHCpEQSm2X0R4QNKHVZCT+QLBBkm2NAn5NYOiELJkyRJ45zvfCevWrUv/26WXXgqXXXaZcRp//OMf4cYbb4T77rsPli1bBhs3boQRI0bApEmT4PTTT4djjz2WwHIZqNAbEaUevRTioqsAbJiObwxgPOHUsCBtl1GJmCz1kqZlP5NLEW+ziF6QSNsoEcHhDWkK9YFq/nkR50OdvkUelWKbGG9jOzu8vYQZvG3b4eqrUmif2629gDHLjjVHlYrfBmZQtCeGSE41h5eEUKFCxCFoRem4vMQKCdJLBJM2YYlpHLo+qWD0tFaQ0faQTwe30ac//ekWkdeGzZs3wyWXXAJXXXUV9PS0imgvvPACvPDCC3DrrbfC8ccfDzfeeCMMHToUw2RRqNAbEUYxen28aAvuz7UFwTOXXKjF8o61HGAx4tpSe9p65ZXmyZCHVDDKE1hACX7wmtdn2z752t/sl5/bbRTCI3rYgJa0XVyQbdLHTzeo8GnqwYf4nBp5ahOMC86xnk3rqCUtxAJIHOddbdpmB/dcnLZtqDUAZ76B6riJ0XgW21rMYQzIg7pN0NMPvT6zJbZ+hQT1CwRF6WRuueUW+MUvfuF8//Tp0+GGG25If/fr1w8mTJgAgwcPhueeew5effVVAAC4++674aijjoJHH30UBg8e7G23JFTojYWkwqO37doqfMIelN5vkJ6rB623LRVpUgji7um7rQwkeNuKX9TY2MfhjUcMu6AbSsBtR7iHrlNelN6vTul7NJgAj1jMg/lQDhcsCjvBKfZ6hFNwpTJkhmHoja1p4U5A3sIxxnyDgUUdWqfh6WXMCdsBXa74DKnC6hqLpAHxC4mxeVIrRtT1mUNF60hxYPXq1XDBBRcAAMD48ePh73//O/y///f/jO+fM2dOi8g7ZcoUmD17NowYMQIAADZt2gTXXXcdXHDBBbB582ZYtGgRTJ8+HebNm4dbkMCo0BsRRh692euJhEvXQ9y23mueD533K0IahWnbJeQbpgELUYsVSbZETAgvVFfsBUl8A1k+sa/KqwjKEAQOeTiDKVQjenphiD+o8XB7JWJqhOFlmfSc4hF75F2Gk1159qAK4+WVL86D2AYsb1sJc7ZD3xeJqR9ERC+bvWhE0GYmVL3MsgCzPjqmH0UCVtuWtWstnqcOoxPb7IILLoDVq1cDAMB3vvMdmDp1qvG969atg0svvTT9feSRR8KCBQugT58+6X/r168ffPazn4WBAwfCWWedBQAAP/jBD+Bzn/scTJw4EakU4VGhNyJMPHp9whgUp1lxgbRwD0Z55CeEl777rRJsULbh+ZksF7Eu1jlj6TrZgFyv1J/Nc4i8zsJWxW3eHrFcz0CVB24bVge8+YqWDnVhM994eQKbkEnfyy7Gua9UCBY4Lrc8v3VaI2B4LEeEdZGQXxax4dlHpa2NpAk20uwRRcSe5NjtytVPtD8qAAD33nsv3HLLLQAA8MlPfhIOP/xwq/vnzp0LK1euBACARqMBs2fPbhF5s0ybNg2++93vwuOPPw5JksBVV10Ft99+u18BBKFCbywkAF1bbHY+dslTf/KPFXNWgghKNhGxTaTxzqSo3lMuCNo0eG9gQn+y3sSjTUk2cU7xaSPKzyJvzuetbFy1FkktzbYSYx1xzsNFwCIKF0B1CNL2i00NafvJPKXFfPBSI0lEzWN52Iw7RuuZ9NkTXnAbKNehNaqmdGyoKhPls1qj+gyCfukQJ51efkw6qC7XrVsHn/70pwEAYPfdd4err77aOo0FCxakfz/iiCNg/PjxpddPnz4dHn/8cQAA+NnPfgYbNmyAHXbYwTpfiajQGxGFHr2OA4DT5shBJHTLhyEPx7ywiVl4paZWGzMPQhygZJSvK5zxcwGiDn0QKgQH9eFq1fk73IQQxsE5b8s8UPLBCFtAHEM2b24mKXepEZ75tScnZMpGiQstEOM1kfULHeEFl0LLGFWTNVjIpq/KW1Lc6JCCeEiy5RYSUk8aOn4qHHzpS1+CxYsXAwDAf/3Xf8Fuu+1mdX93dzcsXLgw/X3sscdW3nPccce13P/ggw/C5MmTrfKVigq9kdAAgIaFRy/6RoQxXm5x2sImGWHmiMXrcBHG+IiFRtAkiyomWcbNlCTgSvDKDekdLcUzu4nXM+XoDYbWB7jj9np4ETfzMQHdKzaEQOgYlqEIrDoxsUXqgZZSBOdgCCm/tPAAmBh7gFO1RY3rNkVIPwYAWbZwEnO5Y7ZdMaND2vj3v/89zJo1CwC2euKeccYZ1mk888wzsGnTpvT3IYccUnnPXnvtBaNGjYIlS5YAAMCiRYtU6FX46bI8jK0UZy9gjB2afxImmMQ0VuxIulxusrjW0UOHTPB1TLZeXqHM3rdVhBBVCcoiTeAFQHqOLL1MpYkk5F6ShIJwk8oyhPAwo44ZyOSFzQJ2eyukOPePWNoNsT9Sh4kzscEEtNjlkUHy8pGBjn/pRYHWqcLI5s2b4ayzzoItW7ZA//794Tvf+Y5TOs8++2zL7zFjxhjdN2bMmFTobU8jZlTojYUEKrxSXFdDbrfZ4C246mRTjrAFVy6ENlIdDuUKp5jIdZgZ+aKeW0jF0DM5n7saPj+k9ecRZ7Jqw4gWdkCAMFlYlhjm3HbbuWwWON+qyGGGz7OLXcdJA+J4zhwwquYAIWOokP4iqJJQ9Sut/zOFrZD2EkHnD/l0Qht9/etfh6effhoAAL7whS/AuHHjnNJpirUAAH379oVhw4YZ3bf33nvnphE7KvRGRNdm29fquPmjh06w/NzcK80qBG7ejEn4PFqrRPtCj1/PT5zLaCSJW/ktxCCUxVbI+KWhxF2kMoe2JUahuwnZ2CAxbEHLjUBSr6EPBLWqj4prpWwenNo4hO01Fuc6AU4v8CqkPHskGIS+sSp/wLoqKket2q9OZfGBqh480nXqZ5yhVgKFiVRoWbFiBYwcOdLo2hkzZsCMGTO88vvrX/8KX/7ylwEA4B3veAdcfPHFzmmtXbs2/ftOO+0EXV1mnyIPGTIkN43YUaE3GhKrGL2OWcgghB0UeXJsIrbl4SLCGwlAlhujPCG4l/hLEEPSSexlbB8MqMRddBGTO9wFA2WLbRS7CeOq2owNzqIw1WYE3DfU4jzmkPq3i0Ai+dkCcGjjUOWR1J+At13Z4ypzIKw9WWBqAxEenwhlNS4H4RxImrZvPoo8rM6+oTNDiZeenh5Yvny50bVr1qzxzm/69Onw1ltvAQDAt7/9bRgwYIBzWt3d3enfbdIZOHBgbhqxo0JvLCQOIRCkHV7GiYSTgk0P9KmiLI2yJq70qKjuH72EH4fPjhs9lrF9uZqOyOOPVei07OehRd2YPWOzSD2cyTsb6pjXWSgPDQP/DQx6GwecjkvrQsBUaU0dRUcHotikZ+o5CnvrTie1gfSyUtonvewuEIf7MKaOdcuEzgEOVITuRM8LALq6uoxDHmQ9YV248cYb4f777wcAgNNOOw2OOuoor/Q2b96c/r1vX3OZM3tt9jC32FGhNyIaPRVPetW/x0SXbwBIFw9Xvyx70WjgHKLjenCOZ2iMUrHHUjhovqQwEnwtBVhqT16sT6X9D99yT8DvMDbm/Kqoq5grCKeXQBQE9OiKeUNi1XdDlTNrI6YNxJ7iiiUR1nMnj/2KYorX1zbEecQ47qBBGcKwk+u1gxg2bBgsW7aMPJ+XX34ZLrzwQgAA2GWXXeCaa67xTnPHHXdM/75+/Xrj+7LXDho0yNsOKajQGxEN2xi9UnBZNDuGqfA6bMNBXC7Nz0RsbhNL8hY1xieoS4h9ixHawQJj+ygF2JL03Q9mc7vRKD+qkK3eQjaKGYWY2td8Bk02GHXxUHYBI2Y7uVgc6ZTZC+tQNjRmoCIwHmJLMkKePac1geJFFM9PGTH2DynempLx6ZftdSb1CxOlBRRnIdM0tV3kUtO2+fd//3d47bXXAADgyiuvhD322MM7zcGDB6d/b4aDMGHdunW5acSOCr2R0AADj14OmOI+uQoADZ/P8dvEZaMJM/ujQijO99bNqRwD8Tc3TVPPX4u4u+1iTmW7EB66lofxp+YldhXVb2H7W3ojuB001FQb7W7OlqUwX4I24vYYdsqGYMFMDtaQL6EsOaAf8FlDkkbDqB/41CWLd3Zk5I4XrtWE3M3RhJKQzY4pWimtEA2rXiISJjpt2KN1ho/AOpUmousaTyni17/+Ndx+++0AAHDIIYfA2WefjZLu7rvvnv69u7sburu7jYTblStXpn/fbbfdUGyRgAq9sZCA+WFsHh6SJnZY3+KwiWwkiXusU2v3pyIbCtIvSr5diO8qF2yLhbi2C0vqz0jYSy9o3lTw33slXpFeERibMAsvZav+xeh1yuHNa39Am+X1PnkR2JAF+/M/EcIuFbrWjhanjZJpX06914k7SF2erbo9R7GWp/Tl99bOht2n9WWIPBFJMUfSFzgq/hFjWr0axzga6jj2rlq1Kv37r3/9a+jqMhevvvzlL8OXv/zl9PfixYth1KhRAAAwduzYlmuXLl0KEyZMqEzzpZdeSv8+btw4Y1uko0JvRJh49CYNANhCbwsAGAtSjfYR3/RZ3nab7eLCViQ2FoYbzfTb7jcVfgFaxF9zT92SCxu900ML9ZBznZEHLUYoCcN7qUTeUm9YqsVR6IPVMPNg8ea2u77WQq4wYlyUssQN5EKafdLsccR3DAnVbzpp7KMSkazS5ahvyc9UnfobtiOEZCr6VK9nwPKFokKP1Rxjumd0SdsV7SuKJ+PHj2/5/fTTT1cKvZs2bYI//elPhWnEjAq9MWGw0HQaiF3f0mbssdpINIVoU6G4y6xQLaJf2S29Pt3PlsNAwGxLq6rOW+qmJzE6aM74k/8kMQ71wOn9uTVDszRyD2ozbD/jOMJlzWpTTmMB2iJNAPdnkBjjlwftWDx/XDiH0WDEub4R8ux08uKHm91Y8N+1XmtHsGfFJkRT3u3cdgsfZ8khrG+vcyi4vmxh6m9oa10XEMto7DzCiNGYoXOcfAxC+pW2tbaxDGrYDv369YOdd97Z+Po1a9ZAsk2v2WGHHWDAgAHpv2W9gffdd18YOXJkepjcI488Aqeeempp2r///e9bYvQefvjhxnZJR4XeiKjy6HX+rCaNB+p2O0DORFFgi7GnajYdww14kSDcq15KYtQWeW0U1q2B92ov8aasHXM8fqs9dM3iufZKrypWq0E9+X7KVXkQG9aCF8MTV4gnr3s+9rdI2HBgEouoGYudVgguk1d9Cy6XUhNi62Oe9lLPOzGH8qGeG9jmHsJ417HHsEZtg9jGDsUbF6/e0kvq9LWTIobjjz8e3njjDePrR40aBX/7298AAOCiiy6Cyy67rPDaKVOmwOzZswEAYP78+TBz5kzo379/4fXz5s1L//7Od74TxowZY2yXdFTojYUEygVCyAmR0MTAg7SZh7VwZxpnFgCg0TCeBLaLkVXfEmUMaArCbeJhpXhbImgWpVEpHuekZSTcNtvYIMRDr3RyvHuNcRRCjeqlTATP8+Ztv7/lhnwbyOPnkcay8mg3Yrw9TGUWq7MQvPCOYlMQgY1Y9Vj2nEfRVkLBEA1j+CIBCwl9zdmGDmkjbwS0cSUBbcR+1rG9nzmfUVsbJYwfdULrU4mNM888MxV6V69eDddddx2ce+65udcuW7YMbrrpppZ764QKvRHhMtgmDagUiFvyALPQAlvTbpgthBxEW6eDy5r3Zz2ASzxGC0M2GIm27uKiseBb0Q65Gz8X0TCmjUmBcGzUHgXe10YLYAPBOS9Nqo25U7qWZWi51PWtP+YCkbuf2tpuFGrFxRA5sC34A9VTFBsawoNsoih/hGDVa23bR+iLTidsnRkUJYeWZ52wr7CPKQ7rqrqvOxSlitrO/UQceOCBMGXKFLjrrrsAAODiiy+GiRMnwmGHHdZy3Zo1a+DUU0+FtWvXAgDAXnvtBeeccw67vZSo0BsNidMGr5GA8SLaKLQAQCpAGoc5MF2w5Im2zZ9GYlyOyJkX9iFH/K30TAXoJRSWHc6R6y1sIDD2iucLUCr4uoi9ve7JK0aRaFcpcLoJ4I0egzAOFnaQQhGn1zD8RmqC6YuQ0jxNMjJPzvlFlPHF9umzQmSf+I0YIShlj/iEb13cg6j+aIyKeHZ4PKNRCKa91sSJ37ONcK4GSfqRIzY2PvXaSjICx3+fZ5eyXeqwXqhLt1XCM2vWLHjsscdg9erV0N3dDR/4wAdg2rRpcMwxx8DgwYNh0aJFcO2118LixYsBYGuc3zlz5sDAgQMDW46LCr0xYXXqb9ZDteS+Ei9agIJJKSsE54iQeQJoYZiE9Kb2G8yEr96iZbFYnGLg8Vsag9ZV8HQRKE3DbrTT63Tc3sJ56YKjwPvVtOwAFW2e55VrKvYW5G8sMhu2Q2EdGfZhq0P60v9o8IznlLEsL6+FJbGoXoeFKRsB6ip4+3iKtMHtBxC5US1CRH3VgZrWo0TxiKXP+pYb+2WT60HIDunXjgoniLrQUhYV+MVQpz6mMKL9xppRo0bBnXfeCSeccAK89tprsGHDBpg9e3Ya0iFLnz59YObMmXDCCScEsJQWFXpjouhQsjxxzETwzF5XcE12UioUfQ3EyEoRrsjz1aAchZ/Jm4Qx6AGj8A4uoq2Ld2svcdHVq9cgX6OYf1RCX0F7NzJ93CVur5GXdTYNzBAMhmlm03bzxDXo12355CZjmoTrAqMT9wwCFmMxesGSbn4I0xa9aZNsW0SUzSm2kMeSJwa1vwuqCqN1kFDQxyBB7VJFVbvl1k3pGtHPnuips8CvKErHceihh8KiRYvgvPPOgzvvvBM2b97c65oDDzwQZs6cCYceemgAC+lRobcOVIiVRsKQhaAKwLwgMtgc5dpjsqmq8CAt3JiZOI6WhX8oSSdXAKz0oi7xbG650NKzF6C32Gvs0Zr1OjEQ+dvvtwnn4IqFd29lUs10DNq4KF1z8dXRu78kb1eK42fjpB8rwUVAAZu20jpgMI+sDQJVLaboGAU1K27Q9pMmYpV+hcJrrNE4Ia3+LCF/sctFy4t95KSll10QnS6Kk4cqU5QsOjbBkiVLnO4bMWIE/OhHP4JXXnkFFi5cCMuWLYONGzfC8OHDYdKkSTB27FhcQ4WhQm9dqApHYCj8mdIiEFrG9DU6NCtPtDT45Mpa7Kyot9ID23w8en0m/AIvXyPv0xxB3+izfxNvVV/v35z7GwVe7C0CsGloiKI4zEibvGLPcvN0bRePZp7GjisEx3jBtSNScU2ylyx5nyH14kVIXGCXKhpro4Sifn3nNuo8LLB5gcrRL9rtcXrGqOvO6GWvXCWnQfV1FjFW66mQ2Hz1xYVte9v4D0Tan0Iida3s5BlPlJeiUDF06FD4yEc+EtoMdlTorRumC/iqcA5YC1bXGLN5YNrVjqtnrwsSJrqK8gSZjDHylFC3NliGe2jHdQFGKhD7QrlZFiDaBl3sE+SNEyoCIY0M3mJsgDYKJrSGdDJ1PLyzLD0yOOrJMA/fOmts8brdIiPDyxj7PuVXQg1hKmTpwcgSQPqaigKbNXAjAcMXAc7muMHxQtUp/Bi2ITnEthcow8JZxAXOZ0yq2C2dOnVnhRcVeutE0QLWdFNgsXkwWrC0iby9Fp22Ixda3Fl7wbjwcLXci1vvAyiID1t637ZLy8wsCOdgFJPVMDZzoTeF6ef6jWZ6ZXHRLDxrt6Vp5OlbmYzFiiOtD7M+uC0Dh/Qt7vGgcLElYTUhQIz1hrgIkr11TXES5Vyd00N7qyLVqZjQDYhmiClTRLDVme98kMjzcjUWuT3MJg85ZUgjSYLM6cZtLvjRp6i2GERr+8QJ0/ZBql3YZMuJ0c6ezieKoshChd460dzMti8yTcM2GAqgrouGXmKp7VtKi7izAIYhDJr3lgkBXc10C8I49Eq7aUjWpoKwAZX3Na8vsQ/+//bOPNyuokr769zMJCSEOSRhVCYHZAwokiAotChog6DIEBChFW0RZ/gQRUGRRwS1QQEFVGg1NLQCokHmwRZQMIgERYlAvGEmISGX3OTu74/kHM45dw81rFW1ap/39zw+3hP2rlo17qp3r72KjA9tezX/5h9mgu/w+yrsMbmuQgQurOOSUA0mgk5rE+b0dai9OExkvvEx3ghILcKK2rsmqPMmiOxNaQS3t6206Critcz57SJfUqJpMqJu3EUibz7lqBuxedrFtmHhiBIRpbuxeLk7zBQJj+kAz2IugbqwzRNaT2QpGZtHzj6iV3Dfo8qlHQTOdubYi/RYvwNAIxB6U8JmEcbgxWv1QOMM0VAEpwjtcVCbr4eKdYxiMvTSJTISfJvpGcUzJuqoK+PDw0zeMpcItqvzKl8l2HpJt/7JRgwe9h/M8hh2CffJ7VzJBYzNFR0lZQkifDBkwS7IavVqDdUv2MNT8KZXSiJet6Z1EmuzLtVmTulKedqGfllo+vLZOXm/hNk8mjnKV/W8Z57zh62jAq1bWLIQmvOCebhLTdmahc41SD4bfdKWEqCjis9pLA3SB/UMHIHQW0dMFhIF1xg/MCyE3dKFjdXn7V0zncuCqeoezk/uCuJXGXmwGn6Ok+ulS1R8QF5JiIdi0bbtwgLv6dzbbDZgDpu1qg2YjyDquuEJ8ckmaxxALB6qUVBHKXjBhgzFED3tNmTDaMglHlQorjqwtYyqtYZD2lH2xSFehtvg0LdcBIWGdG1bFsO6DAXrOFNK58WAXYL1BXER3Q4KTM8tMcG4DKG2kX7RKy4kK1gPlaJsmm1H1Yu+Naj2UAYAeAOhtw7YPtgDvVE2WnB0P6CKbhEMKdHBEFWKvVYHyFjET8qN59udRklaRvGJC+L6Nu/Po9Tzt6QeCkXo3Ewq/ntHwmb3dCyoLfqG9Yao3QtZamPT8R958gDleLWlojby2lgqCzNgvaGJ7IkaREz1EVHXYGOn9SFFvqxSNJh8ECqH+EbdIP5/KR0hr/IvCfnJtJPw3ArjwGNDEBhCaFiLhq5dnNnTmOWFe6JisuaY50G8mU33lCCt+ayHaVC4F/PoEvUDQm9KMB+q5jTJV3ilGD3IbfO1iG9qdBBbVbpFi8q2xWOhKMuAkZBcICBbfeJjIhD0NcrTzFlU5sYhZHp8ZA2qXoDnZWXjKdyKW2xqVME/e/QNqxh/JtnoXfurIuomiTtrhd4jRjC0gbONDKJpFWrCobh+7aDFfkdCjfFQn2gPaw/ubB0F6vx1gAWGL6JNGXYugQOlNji0N0tPtAmNZXpv8z871lUQgbjsSzfGL2BEv9JKKJQFB5L7pkJiP6+UtQ3EXQB6Cwi9qcMRY9fyk0Lrh7TLg8UiD6cHl20ZchZ7kosVZ6/hwgSb6XbdauMB3I7hoW+tPGw3DIaHw+Vi4plsguQnm62L3NNvEXshG4uUyx3Sq9UUJgHMyb4AAmsV3vUaoAiavbUKSdDkUkzft8dqq8qXvQEFaMessgaxzwkNhtAZRgf8WuDyAnyYDabZer4At6GRZR5tz7judcXxK60QobvyM67474HEvaK2654Lg71A0IAyD2PjA76BHjIKt46q23oNQOhNhwZlLgtVh3ucHqYcDw0Xjwjuh5XFQs1kseJ6YJhT+qUX5ufn/Mlk1SbM43NPJ2G4HR+ReJgxa/5PalFUF4+Omi4OUo2/2sTbfiEBlqVeI/c5FiFPuAxJCsPaEahScQGj45kv1yeMwk4VYbku6czX4CKXuczTq9jqCzMiozWw84GDtsX37ZKO4iPrutcVx5cU7aJwnuibhGjsgenckqRg60qEokLEBQA0gdBbR1ISdzuMaH4yX52w94Msy/LzqVqIWS6+rMMwDEugOv2OpFziIlfk7bS5cRWIqkJFGBmQn4BPn1F1qnke0HPyCSR0sQvCjAKrfKgF2eQ1C6qqhFTpA/uAGK0DNoXEnYxjsWYSlctjPPjY6OM93GFDdxo283DOuttalGX68skm71w7iOTm9VZZDa/L+0+W9eS0v3F8SdH6J8/5uOh+JwE4kPez8S0h2k8DkcyWirkPIqJoqQnSAkJvKjRI7NTmYCKvx8Na5GFUJPaWkbf4Mji8bVjWHJ+n5S0uOd+o+8ZwdGkzw3iAbqd/lyAxtgKLQN11knosTRZSiblaB+/UMrxCVQjbJi2QRhSDMQeUo26Tu4pEXhA22gegq5icMQgujOJdO9YHqBbY4zperF9uc3kKu9QZozjsNH5MP2U3sUUgjIq4KNyOp9dwFUG8ggOEJhBvPwkim8F9aKq6ZyUAwAgIvTXG64GnbFIvehCpePh0L7wMFlcsn6d5fIrnK55ICsUmeInJeUQ82Z3vNPH0CCZEBcgnqHdniuWpgaDK2l81eQP3KLntGVkoyKQHt8lBn0Xhj4psK1nzGIWvcqX11ZGjh6Dl102F/7ktHacwEpYvmr089ozODjCzx/rMhzwKwopZ32uCpVd6kBARpcb4Je/iVewtDguXqYpooT+ULcBtviZQsY8GAIgAobcmqAi9UJiPX0aiDyHGRW8LF0EjL54XtxDA4ZGzBmPbpPvYsI1GeisWX/Goqi1i1Ymqz9q7ETBNtLwhP8tPQTDV0LcUmMCN1rkkKHl1EHCt5Ds2WNZL7XVg0uZF81Of+7xo9TLZssxFNhXmaeFdGizUlaE47HT4kkUYtby8jLLIS5rBK7s8U7t7gnmSKppWG0PCnsDcThplWcUMn4bYvIARfJ0FXIHQmxBBH1pKNnQqHly2mx4XTIQchsVXcPGtw9PFvO6sN2IV9/Uyseskdv7RYpcylzvKQi922zFlH70P+hIgBIkJhR6dsRAKZzUMxpeklRn54CAUWx8YVpBvLh7rmrIx63ToG0OM4dI+UHSrhTdwFR1tZTonrBkjTp9nS8ybjWJ7WEJBdOTlcI9pGt2XSfQdUxinpmCHwGWkSuB2Qpn93nG5nTItStgzXQAACxB66wD3w8Z2Q5PrAcPlOcqSTC5ui8pIT69Go3PT5GpHRAGfQyRQJzSATuoYj1SDSJhiqIYyAgqX6jwhJL4iccnDFCUvfXOxCLnju6FtMAggkuGYVt9v3+6F48Oi3b3CTTjEJW5QZv3i2/qwN8czEJxEaEM7nEKYWQjCPnOl8fjqrsO2+mI/RIpD0OV+2egSAphr3jB52REyBjDTC5rCWxmfg7ltIPDCIDRRvPCBPahn4AiE3pSQflBo3tAJEOXtpytcCxYNohUoRJ0oZYu2/sVgjhpv0DqKo1rqlgi2FMF84JM4RfFlfat0jajstAZoeVXytqu1eNmOjSdv5Zc0Xbdax1p1+GpqiKzE3mbdSx2E251PR1I+48Hgk3CWMyzany+hPOWJyucXj4Pj7PtgdwaW99vk4xlqREuoPhNR2EkMDvz4kDxkUjsqvpoFAIgBobfX4diQ9ZBAnLwQpwFNIkYoeqTIsnFqw1eiqvEeatxoG5+xQm8oIckTx00nPInPhH2+MDK4TjwGq4XI5hriaPVFRYnmXWsnxHqFj7DxeLedGwxjBjt5UFqKv6YUeggbhaLIS88g02Z/teqLxpea2+Exlr3Fb8WhBZzGvWWf5SJPDK4Uf12Hi2N5nA9zbP2DW76VhGgfQ9shCMdF1V4EJAWE3jqjZtMHrMQTY48ThTO/QpPKUOOt6YqS2J1NRBcj2tqKw54EREy1Y0SrXRERayuptcSq4f8kI1YbfOrfaIj0qYblbj1zndPbRDkvMbNKADY+iNVeJHT+/N80v+Z8W9EXrOIDt/5DdyLV5pjQyLLyz8atvUPz/z237k36oqP3r9MhcUSr2z7U3oYxJEJhuoxFYfNaT3XrKFSv3VjXcw1oZBB7AUgRCL0p0UMPFVFiCAQ2WSoUMIKIPcpEyybq3qTW2bMzovAZTNBUOL6j2ZTX3lxengmI6KyEOkTHl0AHag7bhJf1hz6SeRlLhunmxCm13lRXPT+7BDlrMdNWRHH4JN9LTLARAE3nhpwxZexNySj8tudp7U1o6szelo5zyIcqckRhpxAMHi8b2OMAE/m1tc10Z9yW5Ymyxgp3iSLRNf5EDoBzfBFiA0tIjTIUe5WDwCjcNoA0gNCbEjE3Ir2Cgqqoy6FIQQVS6TrTOEYCCFmsfVFDHXLZkKCIqNYzt5scL88WIQ4yS5W2euP3NGqP3+mblFtMzkoa5kJmN9mQRb4SonDOAVXsn8JbxGCtFDB8xUWLA7miepFZxAH2rrNWQmb5SMYadhZ9q8hbb1b2xc7fVvZUjVPL2NPDki+6nUl8N07XtCtIPBsZBNWiWL8sAnAAwbeJiPAb0P4qXPd08AQGIB4QeuuKjXeD0CeLwQlUhOCCiYAYKyLCctSLTxqKxDexPhK7jm1J2UM35TkxZdt9qVPZHTeKlX3fZwO6SvCT1T4PAdXLy8/s3qzRsJvTbEThdrsc2s9KlLKMnfxc9AAAkqNJREFUwWp9wJiv92+J8DvcjpJ0irDpv93t7SD8Wo0XQ2HWO66oYV7iMTytPdE98mWet5y977nJq8IcmzjWpdYhX8qoeqlhMdeyx/1laFPW+PpSYUQCoO6rSAB6CAi9dUbjhjeySWwCXKqesC7ljxSLNIoHokSenGkyCacidVvX0AexQ4ponMfBcKRiy5piEyvT8nCtbgrnD8c6aG1yi+a3vE2867ioegle8t+sP4m2CRFgU6clwmgRuWERTOe23E/uDevCRaCw8Nw3WS8NK7ePR3ZVm1qEe2iZ4+Kdax2bl9ETOCd/MU/s7j5aIfwWkTXIfs6wPPjNOdSDUSaW1wuFX3B+gZEHo+czezgI32WX4/i0JdjBcEUoFJbrCMRy4AqEXuBHwMlHVPhzFHPEJ1/bMivwiPVqJ22esI51EkSkjiXaF8EliMYWNlPzmgbhKPViFPSiNcFlrupj+pqnJeq51UFl3FPbslV6d5UIpR6hJZw/3S07JMxE1DUUfp3FuKHM+dAtY2w/Ubb4as059q/LmHWIO+4knmXkFh+16HA3WyryL1sb57aFcP9y8r61nM+cQz0Y2dKdt0daeWlrFOx8XmwYPjNE4gMTyYXv6E6GSyB2hXupi6UzAKxA6E0Jrk83IkykYsKXp7jkLdRyl8smPU9h1rlNYnuDhvQO1vb5v2t/D/nCIFAejdietu1A2NULq8C6xivORZRw6SO2sfZty+rzDOkzFEeJzIXg9mvJbN7O3bDaeAZ32+Uj5nfdby38msSEbabpaqfrwW8WMX5Xp23pQdrERvC1EOKc4s46eE7nYhH+weqQPEcxiS2WqKMQl9vvbJ/njsIwqxhr6RlelLeTAMztsesgJLusq1n6WhEuLz4cnoGssYPLEBTfWT2zgTzYYgBHIPTWkQATgpdwKyTQOIm2WoQ8i8UGi2huk4ZLfpaLJ6cyhRBvJcXEEOKrR18REVK5xou0sFpX4bbiM1i1+GxEBDxtG6sMhcESjMRiW6FJst8O+4TdIK9mGW3awDKsBMuha+0bdw+P3o77HWIBF8YAdvX0NbTPOvxBWdsP+7Te0YO0zCZbodbC87JUdPPtG55Y16WhpyaLYOcgPNqs2Z3Cjjg838RCUHjEofa2x8VJyOVRwtjXWOcM5qHJHiaiCG4xv+jWuq53AQAQeutEioeEqTkULEIcVeP2iimOSQi2EiKzTV8MXZ+MfctZfA3p0azUe78FFrWvsipwXTiLIYLeXqb9ofAQGjchuVFW94X1lH9PqWjs66FqQlEepi8W8uywOSTW+BP+AqGs/TlXtmk38abtvtb0ejK0r0m3nTa2Na+3GI/G4Q9yDnpz9swuvLiZsOn15nXjfMCWTX06hHdowh5rtyPxiqQ4P/e2/RTfpV0YXmzm7VFYxF+H+aHIno5kOcNDDMvc8X7HECMdSXu97HW/dbUxFf/Zcp8kIgw7eGSDhMC2BTgCoTchnITcyJ83B/OyjRCb1ro9JL1oQ4Qz4LLfp0+WpSvtpVxBqQgr7b1r3V+Yy6/FM76IIaYA1MCfPiH3l/aNoImAbb1xbEvTVDCwESs7srIU4VaVXd+1YeYQhYd58xmK1lXzjks4iKJrDeIBO4u+RbbZvgwo8fo1CvNAVCz4muRnUW+rbcv/91xxKa+tS/qeU6gHW8HQYlw5eXbajvWOsdH27wZTpHNoDA/y1ozen+I7Cr6lSReladknbW1wFlm5QoSQn/NMpf15abuEhbClIe/IVNqPy7IOFC6CEzEPZACAOiD0pkJG7OKM6EFiPg9l13tDxW6VEhRDhW/ovlcqlIEScdPaCzbEi4aq66Vt5lg0+4in0ov2WF67HqfYa6XBLSSsWmWSqVvaNiKypZjagXHXH+7daISpQOwQ8qB9Phwm+pqKqza22YiJNnFgq8prIK5WimU+sX6L7Cq6flh4geI6zooEwjLbKvLLvabq2uZ/NhVFDYQ2a+9fkym345N0CyG8w65uGwzyraLI07hqfmlrY6vQJZX90TipNZm7r0VbtjjkWXmJjRjss68a1nftbi/sQxzrF5eQRDbL+2byrqZavaCRz8M5fIk2T0uT8QG/h+QQP/gd1BYIvQmhVpiVSlNauBX3mrS7nD3sgcl9povcCN6/xgKtVJvnXeuyKZAWjl0EV4Hx7iSusnsWJ7KCNdA6o9AYrhJlpjsZTvG6rS9ZCc3tIrLNfVJexk0KBUNDsc1WlDN+CWZ02eqs+nLSNfEANhFhcz1rLURyl5AKFoe+DRPIXOLomoq+FnGJvQ6ic2lLY/vabq263PKQt9Xpy8SqtSkn6yf9ti8IbMT99mRt+3kV7clZe+bm28ImRld9bl+SjnE7enoIswnDuRd7rK0sx3hHtqY2hhCuuvNgfmfdcCyEzTiz//LSLR8AQD2B0FsXQniwOQk5blmxhhVg8V60v4WtDKbp+AqjGjxqTW3Iu07KM9km7UJB3XEg2HjrxAjf4CushvK8jRzCRgQJr+DMUSglMhOvc4RkIioti8sLhIZtyIS88Vkm/nJ5bpqGMnAJY+B5cNiwLIayYg9gV8G3+5pWZoZ15GKPJcab5SEyFtw4YN3EW4mmmdX1TqKvwdxmfWp883JjIcpO3F5tU04yLs1k61XtEfO3IwvLudbYs9qwDlj7tIfIZyrAGocs8aEj1rV7MmIiscVBhy6wHYZHxPaSwDq97uRzx7dbWtb5RATCMwDhgdCbDJnZwzeCF20epQ+Y2N7DJWVUE3c3hGhrYQ+Ld62vWNqNZHiDytAKBgNFSqQtjQXM6NEaSjhOxQuXYwPHKc6aegUXiatl5Hlv2lDogZrT1o0+9pAXzfFk7A2cJ151j3Eur18boWwoM/do9Yy/6oWJ8C0gwnph2A5G3qMRYh5ae7Vy4ti3rEM8WHj45pFbP7aCL5Hdi5bu/1Rxq7kHpKXwzCT+lmHcBw1FVy5hqrTdq2AShK0FSuFwTl7idekNlm1mOWc5hZPwhXOLqsnDuZvAjw+zfaQucVoFGYWrFlR/7YDQmxJlD1QGrcR5kcUl3DKGRtAVezeQSBpKuA0p2LLnVdBpDO9nE2SHJWw4gEN67boKsIq8Z6PF6i0geGy0voZ9O9oIr808urHyQDWwrylWWwrCpu1v5AG8alXxJtVEBK46PCzvOiI/sTfvmrw8DEXY5nOj9EA3i/RK7zcSswrqxvR+03KXhUogMhPVTMeEhbe4lVdr0eFtZbbZevpa3tcUcaxi+jax9PptYh0b1jbkQ+s+dy/g0qwaBfkVZtDuHW+RkUM4COMXDy6PZSsB1kL4H3YRjz3WwqrUuslSQBb3yBVckzVCK5cG6FqBdqHaOAAABxB6E6JhcpK4LT4PXa2hHGIKtr5hEbjEWm7vb5NrQ9hjGwIhb6PnW5bcfCrs4szTxwPWYTMR72CzRDx926kQ/ELUZYcXq4tgW9a/8ryDXbxxbYXApk2m3smWHoFZlpl5/xYJiM2+WhXuoSqPqkPKXA51k7jWlLx2NhW5TftIUd3YiG+GQmel4OuKa1iL9n+umFtaNrcP76rhZBuixPE+J6+97nlHMr4vkb33m4cXcOltVeK4rR1FDNnb16DM2mtYVBwm6mg3Vo9h26QKvZi9zSlnVcYb/qCMnDEY9BCprvYNVu4SOsqv5WsWzUB8zkVbGA6QDhB6U0LaS9UEbi/abiS9ijk8R12uN7i2ULTlFoiLrvMRsV2vI8oX8wzu9/aslfCgrapDrhAFlmKts7goLbTGWLhIL7Rt68wxHIDVAWVGdhTtQhm+9eX4JNXWDlsvJtP6zLvO1pu3jDJx08Wr18qz2twjMzder0keHNcVYeL5XFa/3GPKVVC1ifdseg8ViNS2n/ebzNkmh+8VXdd+i21zWByMVbZWLRQebR5Xrl7AuWkVlcHcHK84wbbjwtJr2HffwHZ4WxOD4rrYnIX0Nu3KSlJs7ehbMb/mii0ymyCw5tUgZgMA9AKhNyW0etCW4evhWoWvQOpii+nnwLHF2yaVAmQAb1tX8dZIgPb38q3Ex9uWafFrLNhyC7QcY9X2cKw64hHj1VnYdRVZXeP6Gqcff3dSWqcm9pm2p28+TThDOJRh+sm5qdhrE0ahSCRUFtNYzLPXhZgCdTcuoR4chExjccPBE97rs//WxVWZmCflPZ7JIYxAd/5WmdnMaTn/ZtnnjD3ZTXFZpgiJw0SOc0yopVYjjJhqNN4VhQzLheOFd3sZ16SnTswGMqCdgSPBhd4FCxbQFlts4ZXGY489RptvvrlXGvPnz6e77rqL7r//fnrooYfon//8Jz3zzDO0fPlyGj9+PG244Ya044470jvf+U469NBDaa211vLKz5uMvOLwegu3Zfc7ewwKee8KibzBDiTj+sxfUsAN4XWbm6ifmGyVl0mevmmTYo/bJkWxI4uuc/3vGuE6gGsNLJ64LhsGaeF2WH6e5WTy4PXy1C3Mi6FPmOYnIZ4XXWd4v7Enr0Wavja1YPEer07DWnwZInMBy9WTsixJ17Rs7CZyEyUdytsubliLQI59xCkWbRG23sFCYSCGJbMmGy8vQd/+W7WsYRaCi2AV9vMwnRoDviDXIooPu8V0qat4iZlxC9HKhW0I0ADoIDmP3pEjR9KkSZO80rj++uvpXe96V+F/X7JkCS1ZsoQeffRRmjNnDn3uc5+jb3/723TYYYd55etLQ9On1FYHTTnM+IIeuZWCbWiPW6Lh9SkRJqGVl7tYau5V2l2eCKESXPJ3Tb+Zjc+iXHtMWkmh1lU8y6szw7TYwx/Y4itKhRZvO/KuEvUZBXKpMAyrLza0oaI8InkyeQKbXm8TPsDHFtN7rMR3xvowqAcnr8+yw9CK7PDxhG7D6VPzvJi+TUzDO7ge5lbGsLjJFcl2m+B70CQ5hn5wJS+r7iw8w0AMu8zVB6M9eWabWkgtk7r6tM2YUet1azsthhCVbZbtIYRnV6zrVsaMJOjlsjPR0/0HeBFc6B03bhztt99+xtcPDQ3RjTfe2Pq933770eTJk71s6BZjRo0aRVtuuSVtsMEGNGbMGHr++efp4YcfpoGBASIievrpp+n9738/9ff300knneSVtxfs8XYjCrAOZWHxqPW53jW+rZRo6+hpu/oyBu9XCfG2Kk/b/A3SinbYGJF+cZfZo7UIZ9F1xAiz67gEzm44Bc0ivAXhgOEbiMTqOoqAa5q2Sx2H9Iz1vXYNud68poKk5IsV05ANthh4J1qJETbdTbC+nMVI2+HCEHbAF2vv33YcPIFNxTIvQdjWI7h1n8daxygO9JpsbENTMNrgjGVs4Xa0HZTU6lu6zFqNzSMqcL3ajEnj/ahHHqEIGlYEABCU4ELvRhttRL/+9a+Nr587d26H0Hv00Ud729DX10e77747vfe976VZs2bRTjvtRCNHdlbFwMAAXXnllfS5z32Onn32WSIi+vSnP0177bUX7bTTTt422JMRrRIUhaTE0TasH4whBVsJD+XcfCzaMKRoOyzRwCESUvC47cg7gkAbSHQliuDt6hUmQKhepMRhU6y9NvWKzl79yVmUYq4PjvACknGMAxx6VhiX1yS+ra2np+31NvFYbQTIGCIvkd3n+mXX5tjo7K1o+9jrIzdx0dTj2vSe5iUuvg3NZJk/lW6Q38sJ477na3YzG5swZyaXOnnBDk84ymFUq9b8v1Khq9GnUeFdQ/fHforqMISwrO2lgA+mZbEpMzxXAeBFfeiGyy+/vPX35MmT6cADD/RO853vfCe9853vLL1m7NixdOyxx9Juu+1Gu+66Kw0MDNCqVavo/PPP77ApKNIhENoQE2UFQzK04BRty/57SNFWQjSVCpNga4dvPgawe+0GFF27ERdhQx7g5ZunKaE2E75ia2Rx2btvxdq0FY1HKbG0I2/X8RIgjAZDHqwxeaXbQ9KrlygnbIDBFyNlIRDy4I7l6xpKoXlLloUXY0ye12UvEmzvLbvUdGnrUkUm6x2H8BFFBAkxYJoF05qssH0C9NkslstsVdmUfyjWTkOl23EJ8Zb+MiTUV3qaxIYJ0INqoXfJkiV0zTXXtH6///3vpzFjxgS14fWvfz0dfvjh9MMf/pCIiG699dag+bfIiBpSHr0hxFepg7Kq0jbOl1+0XX2pwbVFZbYVSkMffsaRfnd2Cb/tFhVgWQ4ZElihxjywq5WO4MqbWTiKHhe4m1D2SL0YiXEAoKRXLse9XCEZJPJi8qQdBmeMXs57yEEolTwAzfGgrCpRsbB83OJ2R6YWHs+c9xbd4hMiogzbtVTJWPD1JjTqxxqWcA3iDXVXGKubLwsrjF4wBbCjrpjEWncl1B4nrwwJ768AAG6oFnrnzJlDy5cvb/3mCNvgws4779wSehctWhTFBiJij3nLdr+mg9lMBNuygzS4xVKOEAiueZvawZlPXtY1WVywi3ShPGdN8+EsX8rxXd0zkc+jnYhe5blIl985hAOzXbG8nkMKuIw2BPGqtiVQ7FhnT1ifA9DyEPZ+zRMQrcVtU2w9novwEYIr0pQSAI3EO6Gvooj8w010I+Yp7lMFeSaFWMP6jNG85Jq3a3u5nAJ12LOYlKEO5ewRENICuKJa6G0PkbDtttvSjBkzotixcuXK1t9rr712FBuIyD4uqOuCL4SHbxMhT9rVl3t403YkVGGjlLeyqzjrklde9jVfBIiIf1befiU7VhYP3ipvR0ah0NJeceHVJH1tQmke2jdpIe2TDGshUY6Agm47TuIud/kjld0KW+9WB29Y77AHXFNU97PcxibbdUBL7LRcr5na5LIsyqtHLesbgTASHBSKyoxCcoiw8tbkFC9I6JLSONtC6Tql53Gv8uWMKJJj17ZelUx9AAB51Aq9//jHP+jOO+9s/Y7lzUtEdPvtt7f+3mOPPaLZYf65vVB83Q5bPERICcG2iW9IAm/huirmr2G9WS6mg4qyJm0fWESL6m1btVuR+tQ7oNesc/1ax9gU6DfaxFJt9tiQ2gF1odMTyoPdM7ed0KKsmi8jHO+z9BS1iuPbTdmj1meqFIpn25E2c3iIYdlIeQwTudetSzxkibUbwxjzFZVjex+z0deIf4CWje8NS9tLuaTLJNvzxKxX7jGMPpIP6gU4olbo/dGPftQSr/r6+ujII4+MYsf1119PV199dev3ySefHMUOosxdXHW5j/FBHzQcQmfGvPna5m+bLjEItj4CPCftdjCKd06Co3X8Rgd7uWNEmtpgmK9TvXl5ojnUoSavSldSOFQub47RKj5rtCugTcFi54ZIV1tbOgqSaih61HO/K3MNc+FyoJoFXgJ6FT7LqGb9xxQHA4UmKSPmIWmsrMqSineb3OFmQASVn/obzYkaDQcgXVQKvVmW0Y9+9KPW73333ZemTp0aJO+hoSFavHgxPfjgg3TFFVfQD37wg5b49uUvf5n23nvvIHYMI6NyEc/lBF4nT17XcBABxGYpsbYibeN6DCXCSnrruDA0ZCX8WYmSEiJuWZrGBwkJxdFtmcHkJWwjyGrzptQQfzTFfFPbaCu0V9TDtgrWWNppeDQPI0RoG4H0gnwCbhvf1wSf0A826ZpimL+PZ2JUkZgDX6FZLH5uekJOrmim8LkEEiWlMZGSrQAAnULvHXfcQY899ljrt3TYhkMOOYT+53/+p/C/b7/99nTWWWfRQQcdxJLfueeeS+eee67Rtf39/a2/Mxuh0PdzChMhlHvCL7KZUZQdlrS1mMwg1tb5Qckl6BofIMbr+Vq6eOfOiyrqQCLsQaiDkUJ7Ikpuulw/XfZNxySrtnpuMH1CF1XEjI2U56Jk/iHSCpFuGRJ9ksvr0TJmry9OAqTpsoXroLN2JPpLaTxTnvxCfK4/rC2lfAFcBOQ6rFMl56o61A94lV5vTyUfg4J8VHpogyRQKfS2H8I2ceJEeu973xvNlk033ZQ++tGP0r777suW5pIlS2jhwoX2N65axWYDEZU/2FxEA5/DwwzyZotDWyXWsgvYDPXSLeK5pJknBNpsdqW8conyN/ICwqpRWfPyNciDRbSV8rAV9ZL23ExFul9O0IwjlBaWB55HcjQa6ddvXYTbWO1gGbPXOZtY3sBEvPGAGw3ZlyMxBBvHtjEVk73bvldFnD6oI0Hh7mfcYWhqPA6ix5G2JTV7AUgUdULvyy+/THPmzGn9PvTQQ2ncuHGiee644460dOlSIiJauXIlPf/88/Twww/TwMAAPf744/Sxj32MvvrVr9KPf/xjFsF34sSJxqEo+vv7aagp6hmHCLA9jE3OY7aVhVW83Bx7FIivQQ8866ZL5Gc9fIw5nluhbZxeut1puQq4Jmm3sjDJw0ZQzbHHODQEg5DOmUfoGMAFBPdGTV3k4wL1kBax26tqDeEyjl2ezyHqweWArhw4N/LWwiFnOALudZRw+AdRGwzp5dnVS+ROQNgrGtdBXux4Ii4utm17qupDpdDJYVNVP4hd7tj59wqoZuCIOqH3mmuuoZdeeqn1WzpsAxHRqaeeOuzfVqxYQddccw19/vOfpwULFtCiRYvogAMOoLlz59LMmTO98jv55JOND3WbNm3aau/fjPI3R1UirfRhYNIHvVmkzyLESp4CzCBCVYqNph6hJotIywO1cm2rKrOj9+yaDKvTski7sm59vXK5hVNuT+aq6xk3HuoOmYqdV+pwhbcAr9LL/a/9OSz58ia0SGian3Dbu4giUWLWFsHh6Rd6DvJp0x6eL3P7qk1d2tadadpcbVKQX+kY7cH+EEzI1Va3nPYk8OIDgiYAvKgTetvDNmy11Va05557RrFj9OjRdNhhh9E73vEOmjlzJj344IO0YsUKOuaYY+ivf/0rjRwZuuoyq9AN3h605YnbXe+Rn105POziCjvRTbv4aGqfq0epsMhr5NFa+Pm45yFoqw1gTdvLQ5czHq63B7RQqAWLdKMKt6EEsaJ8tHoSaqbXyw/M0BovWlqwDSE2WNqmymNuVRoej5041h/35+t1QLIvhu7nmsaVCyZbpxB9OAUR05Doc23s/EEnWcAYvWj62qFK6F24cCHddNNNrd8hvHmrmDx5Ml188cW0++67ExHRY489Rr/61a/owAMPDG6Lkegp6WFbkra4N62FEJtJeuW20TDZhDbtlhQjidxFXoP7htlgUu6i8rqKuVVp+8TQdRXQuTyAifjj4jLECzYWb3023CE265J59DWotz+sFSbQXA4io1XQ9cUnbIP0gWMKxWSrpBMXI4yF6hoJWKA+WI0/puNlisaMurkgtj2x8+8G6zgAoqFK6P3xj3/cikfbaDToqKOOimzRambMmEHTp0+nJ554goiIfve730URetkPEXM8JIzFy9ZQuDUWbaU8cokKRctsKDMTe6tEXleB11WAdPHWdT0szVbYdairV5OyzKtK5ObwfOUUS12F6jasPW+lvIW57muiRShKzsssEqbPEC3tKkXR882n3NKbqrq3iRTaNt8hCBWqQqpuAwvVScYhXUMoD2vNddCzBGqTRtF5Ir3qhZ2CgGpdVwmUKQZa+hxIDlVCb3vYhlmzZtFmm20W0ZpOpk2b1hJ6n3vuuThGSIZj6MjGxHO46xoHoVVMxDWx3yMMgJG4m5dWxX3Fh5g5hgkouY/NS1fCQ9fVO1fKM9f0GqICQZwp/YJrxDxvY4m23fgKSxBf9SHRJikuhCVEU440857NtummsAmVRGNs4ZBIxlENRWC7CoWsJlrridoE2NAxbsGr9EKdVpWR67nTPn9zPss0tZEmWwAAIqgReu+55x6aP39+67eGsA3tLF68uPX3OuusE8+QKmLFvy0SSEtE2iLBdJgA3J62iejr+vl/6eWWnqdEvJ66jiJmsXjs4KFrJAbzeP46x8+Vjp3rWgem+XKHVahKU0r8dRE5NAqyGm2qAxIbjFQ+Z0+BqvHrGW/eiNSF4m77TerCp8w2dW2Sj69QnfJYijnvZ5nOuuuukyIbtXlXa6zLOqB5fuZo81UC5dPWF7XZAwBgR43Q2+7NO378eDr44IMjWtPJM8880yFCb7XVVhGtKcHSi7cpphkJvq5vN7tFPgOhttxjdoR53q5wCJ7kGV835IFptuWtDJHAHEO3Kl8Xcdf1YDnOg8wqrqkUcwUOVXO6vh3p0BBc90YivQOEivH6hNalHhKPKdqiDpsrFxFQszAQAunyc6fPkV6qYT7qMEa5iV0n7flrnkuK+rxmm12J3Sfa0WRLFcpsbWjqm4pM0UKDKNhhbIk+sUEJKoTeFStW0E9/+tPW70MOOYQmTJgQ0aJOTj/99FbsYCKid77znRGtKcH0QK4uQbhbYKsUfr28dJQeIWxRJvYD0l5N2CkdljAMUt66LgKvlNdz3jXcYQ88vHmNvHS5xF0uQYvTZqn7HamTKCtBs36CxUyER5cZ3AKdpk0gFyH7RC/PI3XsOwAUkWUynqCh0PKs1GKHAapEUlNC1W9C7QhAXVEh9F577bX0/PPPt367hG1YsGABbbHFFq3fp59+On3pS18adt2SJUvoQx/6EJ1yyim04447lqY5MDBAX/ziF+nCCy9s/dsHP/hBmj59urV9LHCd2lwmHA4NmQuZlmRZpsLDw6l8tqLt6oxY8nDy0i3CVuC1PSytJE2xcAzO6bqK24FCMHDGDra9lkjeOzeQAALBVhbX+lUlEGsU/lxt4n7G9jX0C3aaN5SabZOmDnF6JfF5NvVifdWN1NtQof1BhFeF5bYmRhnqUG+xQNUBR1QIve1hGzbbbDOaNWuWWF5DQ0N01VVX0VVXXUXbbrstvf3tb6c3velNtMkmm9Daa69Ny5cvp/7+frrnnnvoqquuokWLFrXu3Wqrreib3/ymmG2VVAle3aEbXBaRIypCI3hM1M5L2qKQFC7iqwsBhOFSEdRVxLVJjyOucVWcXVdP3VxbHNImiiLuihyWxuHZK33IGbPAyibYVnUx23MlY3+k0G6vqy3uZ3c6oe7k9Pa+FcK2kOV3iRXbTfOekIKvtj4C7KlbG/oc9OuaXsh0fOFyRuklerAOIMZWoMV2LXYAAJyJLvQ+/fTTdMMNN7R+H3XUUWIepd3Mnz+/I/ZuGXvssQf97Gc/o4022kjYqmIq66VKpG3D6iA2VyxjBhdiUS5RHIRl8zAPDEKraTpE7sIud3zijrSZwjEMS9fTa9aUnBOzWcIx+Hr9uHozc+TvgJgHrunwjS3c2pJnb2Dh1pbgYR9MkLQlZjm1xIzU1NYupG4/CEMv9xPTsvdyHQmSTBgBLe2vxQ4XOG1Ppd/0MA3la3qgl+hC75VXXkkrV65s/T7qqKNE8xs/fjydeeaZ9Ktf/YruvfdeWrFiRen1b3nLW+j444+nI444gvpCeZAWwfhJZqPdv1ZqkvcQaCWF6FAvEpzay9QrtygPY09PD+9f8gjD0JF2gQ2ch4wVtYGPuCodb1cyBIKUwOs5psTDK6Qm3vriUt4IC0mTdlclBtsS5IUqPsEkIp02aYfbQ9U2bd+8ejX8C7CmSgQ1/vLKIk1VlPXPWCFWUhszmu3FOgAAkEN0ofekk06ik046yTudzTff3EgcHDVqFJ1yyil0yimn0CuvvEIPPvgg/e1vf6NFixbRsmXLaMyYMTRp0iTacsstaaeddqJ1113X2zYeGvYiYBlZ265eQdzcbhp1OPvRtb3EY6MG8NR1ibVbdJ/JddKesxwxd03SkxJXpb2aizyHDSkS89gEYBsRs06isHIvADERN7V0feAIq6ChXDFt0FB+rYSsG63toMiupMRFS7rXTyHL2pGXovYOgkt5tdeRBvvw1QzgAk0LHIku9MZkzJgxtMsuu9Auu+wS25RqGkQ0wkGBKJz4mcMh1Hjx6QSHeM4h4OZhYVupsGslNDKqZy7iH1c5fOAM0cAdciKSLSxCbp2EWS4iCbwqvG9TEnljPjd9yqOhnV1I1W4h6iwaghJ8n/lClPbHXh27dSu35vJonA8111ce3vYmVl4AlNPTQm9yOHmIBtrxjyhYJGp4SDUf3rE9l7kX0i79wScUQ+cNhvk5Hq5mg2u7coaIoBJv3tgbKElvXsf7WUM1VE1xdRSClXrqZo1GfLG30dDtoSS9mUxJ6NaUnxAQUy1xbXfu52yI0BM+cI+PFMcbp81cXzb1GjHK3Ctzqsb+pNEmAEAhEHpTwkbQaj4IOcM9uNA0OYuoTBSJ0NrwaSuX+GLcnrHt+Mbftb1WigobXOK6sWATKmEoK+4f3Ys2jhiJjnFXxeL0hpp6TIavUoG2neBCbeyNQyxP3VDlrls+JdRCVFVQj0miod402BCTlOMya2877fYR9Y4AW4WmtvJd4xelA4LSQPUDRyD0JoSVMLdG3JQ81MyYoSy+4FyGrQgdoyxMgiJbKIY8uMIzGIvJATxoKwTVprDAIvg2x6qE4Fkm9ubZkIepXY4LyzKBUfywNg4SEHGLSFbcDW03x0ZW2ubU03dAtcArJX4pbIdaod2j1wbNfUWzbRLUubwuzkgpEqMNXdfmRf+tzv0QANACQm8qNIhohEFc3aFOtcHJa7Mb3weChUdtHGF6Tb1KLDwieXw6tTvXm19fTL1Vi8RL7vhzBvZ0Cw0dwm9Vn+5Ou/36snyL0i26p91Gl37p2j9My1OCpBAZS0SOHuKAg5RERe75XXPZA/YtdSJrHcbVUMJvjeqKyfO2Dn2PCw1xgNEesmib+11JoZ+wh/5zdNwAcUA7AEcg9CZDI39i7h78nIdeFeG7CSmZsDiEaWexWFiUtS1bWTlYBPzVCdldb9O/pOL+tmzxjLFrE/7AECvPXluvLduFWdn1ph6+VfkECvUgSZXgWiUEQ7ANnJfE5lKzaBsyzRzYhdzUxktq9gJeqto/Zv/Q6kFMBK/0upOCyGvqZGEzjnqtz7qeeQAAUAGE3pTIexiVeQNKwSEmS3msZBmfCBoZr3KIxTsdMm9/W5HSNnSBbTiCqhclpt6zXKEcbMtr61Fbdb3JQr2qHByft/rOWcLj3VjIrctiWFM5XDeTkmVIRBTuSYFWs42abesFXJ8TdRAstdsHinGdx6UcV1IQeJsgHi0PQffU9di/s5IFjNGLrl87IPSmhMmD20c/NX24cYQFkPA8HhoK80DSuAgIUW7XNtNyAFuVsGrqbWpwnZPgW2Zb2X2cnr7dNBf1nJsGjjjApun1OqHqJsTmLxGBVSJdZ9FW49jQYJMGG0BY6tjmdSxT3QkllKYkyGojtrMQxjUAgAkIvXXDS5BpuC0OTB5KEg9OE/FYwnOYqywMsUvFCREKpB0pr14fezxCLJgINF7xfF3uccmDe9NQ1GbaFrh1jmMWayOYYhiDivSjxqnlLLumfqzJFu1oE3Vcn8ku5Yh0DgIATuT1V23jF8RF637QBTzHeUA1Akcg9KaEiejGfXCaSXqS54a4ev8175MSKjkE5FgP81Dire8BaKZp2h4yZhsnVthz1tn7tyMRxkPbQoRS4NjYSJ3wbCO8+6BtcxdzQc6Vt3AZKgXd2JualGIt28IRasYmLeBOyPrV1pZ1FJ6L6jhmWavaPYV20PRiQ9s4AvWgTqI1AAkCoTclTIQZ7qDyJuk1xWHujd+QoTdlN66HbNnYH9rT1Zd2YdpHpLYpt4THslTcWRMvV5v+4XCAQcOyWaw8gZs2dSQQ4FN1yUWe1Asm3w2Pbb1KCe4+pBzj1gInT1ztISTqFK7DFk6bFPVTlWADn4/GcWFKkYioUTTVaJMtmsoQygtf6gU98O9PqG8AageE3jpgu+B2idWZd7+pHa4bpj7HUBINR+HZtR4lqRJlTYVXn7AWHKK2S7gA03Rt73WJOyt9cFrePRX324tUFp7DBvmbZWkpjkulrYluuzWWQ9Cm4GEOUgsVodFTlgON/Ry4I/lMNwFCMz8SMflBOZxfKsRA0jbN5a4jqG+1BDuMDdQOCL2p0KgWaIw30C5xPsvu51rw56Xj8/aX27u5mxCeeJKew820iwTfVLyWXV9cuHz659rXfQVPxgPMrOMGV+UP3Em4XoMIttq9W3vBezbVPqrBbo2HS4YSSDXUfy8D0SYsri/z0U4gNfCSDYBkgNCbEhWTa+agy7U26z4bEq4Di3w3Re0LJtuNMeehSyG8HznRIOjaxs01TcOWhmMfavY9Dg9W23K4hiopobGKP812cl9aQRgoJKgXrIZ2SC3WbAqiNzeh7PH9GiImpl92aOvv2jfy2u2TBMJgeEycW7TOQURuZzhw5uGTTy+PdQCaaJ5fgGog9NYJB2GsyEvYSFiw3YBJnGBfFKbBZDHsExbCBNfQEVondJvYviHCPXTDsSB0FZxdDivhFKa19pkmXXXIKlxyvCTQhvb2bBLKziBhcjzyCGFfKn3CBI7DTH2J8YIzlTbUPqcivAOQpKrfaPeKt7GP69DkqpB+GIud+H5Z65oHAKCngNCbEtxCaYlIJBImwmVxLnlPdxm1HOQSQ7zj3nhzpGe7EZdaFPu0g7ieUWKbq+ePRP/j9rYOkZ8EWu0yJZVQBbHqWSpfDcJo3dBapz4CtGQYJsl466AaePKWU8c+p71M2u1zIWaZ6lifgAXE6AWuQOitI7ZCjYOw0y4EO8cGtv2EkNOLoyxWVuyTR7nFAq0b2ipC2N3XJyPOhBbr8/q8cz9WeiCLywF3WtEk+Mae7/Lgqh8N9Rxj/tVQ7pTQNJf4fDmjIQwTkR6P4FQ/F9f27DUldr0BPiTa0ve5hP4lh6TDC9oNgGhA6E2Ghn2cJdfQChpi75mWR8LWskW2jajts8kIcaBbCAFCQ18qw6UOTDbTUuUOGeNROE6vP8rErKrNuUZBtZ26HFxGpOvlVg1F18yxTA2t80mqm1jTfq5BAJY4o4GbGDakKurm4RKqrW6EWKOlWrep2u1LHcpdhzKkTP2WkSAQEHrrgou3bPd9joerVYV5aGVlI3SYip0c5ba5z+bANxdvZA560YMsZP6rVg3/t1iLIJ8NvMmLFklv57qxyrGuOL3OYo/DPLjmI41lq8BVEK0rWuqDVXBOIa5i1RjUJASHej5oeQ7ZvgDUKAxrqUvNoI7CgHoOhune3wo0HwCsQOitKy6fYdjGALYUS63DPfiEerDZfLlu1PIeckXlCuEtLSXwKtmc5xFCODASBWLUUaOhy3MxBKEFiV6rX1sC93stQmEL7V7adcdxo+nSj9jEYS1CRGxxV0s9aESjmJtHam2Ymr2pgHqVET0BAMATCL0pYfMgkfI2Zdpo5z0US8VfyQ2+bQiJJqab/FDiRHPj5iNOMduqTphxQG0Z2uxS+0k0N3ne1Ka4jvMeRG2fT1FYzZheFjQUeF1qo6g/OGy6e2YObTI0JC/2aqpTTbY0CS0OaawDKepUVq6y9FBoDQivjrh+7QpEwWFswBUIvXXEdjNsExO2zDPVU0gp9fgNFZs0ZHqcebmIu5b5i4o/KQo4ishsAjj16gK41xasMcYUl6iZAi79yWVz3QqjYviSI/UNPIeg7SAAcz3fKgVjrnmIo52lY/zalFW632aRYs5ret7W6UDTMupWLq6wJonVC8RaSyTaN7E+AwDoBEJvHelryIq9Ia7Pw2XTYJun6X0cIopNjE2OT8gL0rbe4EoKSL0kFMWiWy+Cp6AeUu3/vSaid1M2J7ZvVkPUU+i24N4INseAxLzU3k5CIkL381TMUziUcFkXr1/pfLSLUloFG612tZOCjTVCtcCLvgCCkwV03OjxtXwNgdBbV1zEXkk4Np8uXr02n6GY2ihVrxwir0U9BxN4fcSrXheQQhDbU1DjgW/od6vR9AzxpXseCvWCQ2Mdcm6kpTxUXZ4bNm0aQPQlWv2cVSv2xozTy1knocQXzQJUEaGFKW1CmDZ7GFEtiKZAjfuGMT51gHUyAOqB0AtexTVubxWGaRkd0GabLtc1RHYbdt+4x6birkM7WQm8pXGTmb0PXfqcRhGltqRU1ynZKkSq3sFFuIz1so1w3erHVeS0QbOw4CPcm9SHY9m9RV5JMaJsncEtAnOXI4ZII9hPStEgSGmwwYRU7GwDgm0F2ttU6vwHbhG2DnNwL5JRuC0Ntk61A0JvnWkuHlw2dRECsmd9jWqx19SOkG8auQ+z6958hSiLtchuKZKwxiNmSKtuIg/QR14/9d3Q4cVGJR0vslbx11eQQ7tc+ontnOYijDb7n08/xuawnJD14yvmpuotGkpY09bXQ9ojEataW30akoyQm2j9RsOkvmLVKdoSAEAQetPCJXSB8+f2cQSF5oKoUPCt8jqWEoKL7OEWefOw9LQ29ti18lAWFnYlbXEFohrgpq1PiR5uGBKO+OFFxPy0vADXdrMSiG3mHlcRoX0etRV9fTwbi+rPO9zKkJ9XL7MYE+SFgA0SY4nrkCgTTPLQJqhJ9XUbtIrxEcdHMsIrJ9rmI22gfnjwrke0Qx6NmmwXinjxxRfplltuoVtuuYUeeOAB+utf/0ovvPACjRo1itZdd13aYYcdaJ999qGjjz6aJk+ebJ3+gw8+SJdeein99re/pSeffJJWrFhBU6dOpV122YWOPPJI2n///QVKpQMIvSkhFUPWZePqKVJYhWkIJYhI2FR5qFuJSMIl6nKFX+AK+eBrh08+FFBg8xHAijblkqJaVd4mhLAvFFX1IFHWFE5FDylQrzKMH21L6Hrt65MTiDlCWpjOvy7hIUxFFY5PTV3FXgHhp9ne1oJvez249lOpFyRS44YrXY6Xsxx9QeO8HYpQhwR6YLL/qJ0YbPr80dh2PnMokEVjfwHJMH/+fPrMZz5Dc+fOpRUrVgz77ytWrKBly5bRE088Qddddx2deuqpdOaZZ9InPvEJo7XVypUr6Ytf/CKdffbZNNS1Z/vrX/9Kf/3rX+nKK6+kAw44gC699FLaYIMN2MqmBQi9vY6kR2rO9cYCr4RdHJ7PrjF/BWLu5goHxuXwsEdaxLVI31u0lRImfe2SEri0560J6XqQeBHgm26dKKsj3/Fpu7mxaZMuu03mOGsx0TUEg4uQ6nPoWQKiURneXr2u5S/qbz4CcJ3iO7qIeNr6oTZ7iHTaVELtxNwmibVDLj5l0F5+E/tCiNXa6wnUnj//+c903XXXdfzbiBEj6DWveQ1ttNFGtGrVKnr44Yfp+eefJyKil19+mT75yU/SQw89RBdddFHlGuuEE06gH/7wh63fo0aNou23354mTJhA8+fPp+eee46IiK6//nrad9996a677qIJEyYwlzIuEHrrgIR4aorUQWu+cHs/l6Xn47VbkQZ7GIYi0TV2uIeCdM3LzyhuaRWPQRRUhliAGF9JQ1NICauwDUPWeTh7j4bCVlCxLYeikA1NrNtCm/duO5pFl1BiXdVzQLu3c+o2CNC+L1En+ta0zofRC+V0KaNNyL5eqMNeR+M+hJGRI0fSu971Lpo9ezbtvffeNHHixNZ/y7KMfvnLX9KJJ55ICxcuJCKiSy65hHbaaSf6yEc+UpjmRRdd1CHyHnjggXTBBRfQ1KlTiYhocHCQvv/979MnP/lJWrlyJc2bN49OOOEEuuKKK4RKGQcIvSnhIpYyeOFyYHTQWpkNrp60RZSGM2DMy0NsEA/LEEpEtkiDTdjl6scO7adSGGyH41NvTkLEI3XJqy5IxrT2Edh8MOgHVePQSxQdGrIT2FximToIvrnEFDCK8tayMQ0dl9e23JIiburCZJ0OVNMyHpoos0edCMuBsjoOSt3KLlUeiTNn6lb3IGlGjRpFxx13HJ122mm06aab5l7TaDTooIMOop122ol22203WrRoERERffGLX6TjjjuORo0aNeyel19+mU4//fTW71mzZtHVV19NI0aM6Mj7Yx/7GI0bN46OO+44IiL67//+b/rUpz5FO+20E2cxowKht25Ix9u1TN87VAOH6Mot7rh48PrE2zWx30TM8UnfJp+SNM3Ea88wF0yefF6CrfMhiEq9gYeYBQfXctbMkTVLWHhu9DXi9NdGH8vLirLxbSQCt88zpoKcYCiCQpvz6spVOHEN2zAsJnBJ+7nUT7MfutjnWBdWLwpc29zkWSbpXc55LyepiLxa6qsbrXblkIzIm1CdRqHO9SPxXLfdf9S5fkFtD2M76KCD6KCDDjK6dvr06fTlL3+ZTjjhBCIievbZZ+n222+nffbZZ9i1l112WUsQbjQadMEFF3SIvO186EMfoosvvph+//vfU5ZldPbZZ9PPfvYzxxLpA0JvMmR8XopV6VjkYx2SQTImcDca4u523RckFEMoT12TAy04PHQZxWFr8VaZYBtPGORTWBuBNm4pi6gttAr/RJStIhaPXuv+0F0npjYU9Yec/PPmiUJhz0Zo0+LRaRujlzs0AtemNODBa84e4FIb8Kq+IZFv7JjJIUU/be1tixY7SkhGxJWC+4UXkCVkm6D9ARjGu9/97pbQS7T6MLc8offqq69u/T1z5kzabrvtStM94YQT6Pe//z0REf3qV7+iV155hcaMGcNkdVwg9NYZgc8+nGLtuoZicLneVdz1ERMNbfMSeX08djnSD+GhuzpBs+s4hF2peMPkKDIqFvWcKBBhaiHAFhGyDTUc1mFTXsP+YCT8Cgp8VoJeiPioBhSL0MKeu67pd/ddkzoPKOqy4LNR5+hX7XXMJqwnLvJy2q9diJFof2aa+4laCL4u4XlCtYtNPlLrirx0lfbLQlzqhqOMJvmmVpfAjhpvm2xYd911O34vWbJk2DVLly6l22+/vfV7//33r0z33/7t3zruv/XWW2m//fbzsFQPEHrrAKOHLhFDuAUGG7yFXa44u6HEXSIZgdcnhm/B/ewHpHmEYPCOY5ybaHm+1kKlhPgnKfJxLRhNyh0rxqsUpuXh6BPSC3vBtrHy4pUWHSmAuMvsyVtpr69w4uo1XYbTgTQ6xN0gYRqI/AReifmAI83QIl4owaPoGRxacDHNT6FYVAuBt4lmoY17vai5rNpweQHQTdm9zfRd2xhtCRLin//8Z8fvDTfccNg1f/nLX2hwcLD1e4899qhMd+ONN6bNN9+cFixYQERE8+bNg9ALIhDLQ9d3keAruvrayOV16hNnd1h+FgJsiFAMXGEYfEMwVKVhag/HAXIdlwXy0PUZawkLf7aECP9g2ua2tmRDmaq65Ia1bWzqyTHeKrvIa5KepaDndXBcZeKWfVGzOCPd/lJoE3hTQUvZY9hhm2cgG13F26K9iEoxWEu/46SOZdJEqEOa0Y61o0FEjUD9R3vvaQ/JQJQv4j788MMdv7faaiujtLfaaquW0NudRspA6K0LZQfLSHjoutwTwkvXKh9/cZfVo9Q13m5hfbl57OYm1W1Hj4VjaIpXVoKvi5in/SnbRah4u9b4CKlr+oVU2dTWmS+S4rVlnXmLd6bCm5AnsJP90v3KJX1rMSqMB6+YuGuaLlfID00Hq6UQPzeF/BIWanxEWZWCbpOE26SSOpfNhaL6CCXU+oC2BEz09/fTtGnTjK49+eST6eSTTxa2iGjx4sV0/vnnt36/8Y1vpO23337YdU2xloho5MiRNGXKFKP0N91009w0UgdCb0pIhWDgskPqoKtQAm8Ij11TcVFa4C27v8OMnGt8QzJ40tyk59qWt1koKqfDZ/a1FenK0OZ9Kt4G+SezVlLn2MM+MLSXmDDHdZCapIjrU382Y9c2H8WhGIzrWEO/ykOTgBuKOou3GvLtQrWw6oOS+k0Gl3jpdaSq3JXnFaAeQX0YGhqihQsXGl2bFydXgk996lO0aNGi1u+vfvWrude99NJLrb/XXntt6jNcj02cODE3jdSB0JsKGeNBaKGv5RJ0OQ/+yknLO55ubj4Moii3l2qIuLu2WKRr1U5Eq4UCHxGuW5zgirkbS0BVusFT8Qm1DyP02l81ZtTVPfchZ1zlK7CLtf6cPGcDxbL1EiBlbVTRh2MKvE2qno8S9cTxTMmysOJIrBi7SmL7SjiCqBCPOR0MNMwpoYnpuRqzvrnLnYIHsC+9OD5iEfis7r6+PmNP2HaBVIpLLrmEfvCDH7R+H3bYYfTud78799qlS5e2/h47dqxxHuPGjctNI3Ug9NaNWAekafPOtUyXJbbusEQdQzFw5MMVc7eVnvBTprlBNsine0NvVA6uDQhHfNVAm6Eowge3QAdYUL0cj7lZcOyvQb1yicIIuaFE3NiHpa2+gd0GInr1+aVxHnQtcyjxLoU4t3XLnwkVAq8EOMQsLEpeggBDRMTsHhDIE2DKlCn05JNPxjaDiIhuv/12OvHEE1u/t9hiC/r+979feP3KlStbf48caS5ztl/bfphb6kDoTQmBw9ic7+EQdqWERsuyBAnHoEjgXW2OrVdsH38bMInHpaEcuBHc0Kj4PD0kWLwDoqj9k2XMcc0JPi+QymzgHGeuNnrUkVMbiXitCvRTDjtTnUc12F22ZghhX+z8mWgMZfUVe01JqL2SAvUKQLDD2LTxwAMP0IEHHkgrVqwgIqINN9yQfv3rX9OkSZMK71lrrbVafw8MDBjn1X7t+PHjHazVCYTelNAk4NqmzS3qGqbHelgakf1n+775M8bbbSXp+sCwEXsbjeqy226cK/JW8eluN9IiVqgyaxWLA2DSr4K8ZPAgRBmq8pBO34kYnrZVeMXkDVgeJmGHtV2dvVYF57fYB7/lUWcvXRO02tUOYn4CwOM12j524KUNQBI88sgjtN9++9HixYuJiGjy5Mk0d+5c2nrrrUvvmzBhQuvv5cuXG+f38ssv56aROhB664Drg6tKHDRJ11pIdvDiDO2h25EYo722MdG4YsFyYiv2tuO7wEpFbKyR+OolwvSQh0/DNjiCwoPbpNvauo5iEMFj1ZhQh58RBR27zee1Vf/znWNd584Qc7t0Hj00LwcVYXzzUv6yEBQAoU8fZW3CvS/RNm7RH+tHRuEiWijpzo899hjtu+++9PTTTxPR6kPVbrjhBtphhx0q711//fVbfy9dupSWLl1qJNy2H/S23nrrOVitEwi9KSHx+b9Nuq7XM8XRHX5ZJEFX4tC2YXlY1pnFgWPeoQ7KNsllbV33BUggITqY57KXZ2EionwsRqz5f5tx7lOnsV8a1aE/cAhk0mM3srDLNjd5vXRQ3teixE7vUQ/ekAe82a6nuu3SVneW2BzuVqswDyGFvsT7SDC0ia8h6eWyg1rw5JNP0j777NOKEbzWWmvRddddRzNmzDC6f5tttun4/fjjj9P2229fed8TTzzR+nvbbbe1sFg3EHpTwkiwDBDeoYmQgLv6UgebJD1mOQ6is83TBG2ege2bbOkD3LrzC4i42CqxEZIW2+q0eQvGiOpLuPNpnzPQZvawiZkR5i5tgm5nonbXh577U46n2xzzdR7vqYlgNmvc1MpWgY0oXEStxGJT6ijiae7bzfrWbCPoDeo49nN46qmnaN9996XHHnuMiIjGjBlD//u//0t77bWXcRrbbbddx+8HHnigUugdHBykhx56qDCNlIHQmxIhRVwic6FOm4BLJBNLt5vQoRiYRd1GoyEbWzSCCBslTi/3hiOEACS5ScKiWDcj0D5exPJOFhqzQeZMzR66kuUPPRdy9pHY83js/DWRt05zqZ+aHP5G1MMexHUjBQGryEaTMZOCx3cKNoLa89xzz9G+++5LjzzyCBERjRo1iq666ip6+9vfbpXOlltuSdOmTWt5BN955510+OGHl97zhz/8oSNGr42wrB0IvcmQhYmHW2nGcBuCibdFlAmo3A+wEKKzb34W2Iq9rqJAUR5BRIaQi3yNXrNB4xQq/2wagFgEmIeCv+gS/7IhsfkkVWFX4+a7lzxeQ9mfej050hjKKsVeXy9jrWKySbm02q4ebSK1NnvySMFGEJzFixfTfvvtR3/+85+JiGjEiBF05ZVX0rve9S6n9A488EC64IILiIhozpw5dN5559Ho0aMLr7/iiitaf7/uda+jrbbayilfjUDoTYWM+IVbqUPO8gglkHI+RKQOa+PKm4kQ4oBxHtILTo1CbBNRz7KAgknNNg0mfVfUM94X7rkkVvu6liPh/hjlC4W6iLdS5UgxbE+dqYN4qfn5QVSLOuYIFyGVfpnQKm23dB4QkUETo36mfCqMRaPG9bJs2TI64IAD6A9/+AMREfX19dHll19OhxxyiHOas2fPbgm9zz77LH3/+9+nj3/847nXPvnkk3T55Zd33FsnIPTWAYdFopUwEcr7tvCewAJzGaEONWpfHGmLw0sU6JR5RkFAJOYtR8xGpjKm/Fm3ZgzL32g09G7WA4RqcBG6bfpWlmWic04S/VybjTG9bevmudzOkGxfz4Vj7orVPxFHU56q/oG69yKEmBuLOpetnSJBu1fKD4ALr7zyCr3nPe+hu+66i4hWr8Uvvvhi+uAHP+iV7q677koHHngg/fKXvyQiolNOOYV22mknestb3tJx3ZIlS+jwww+nl156iYiINt54YzrxxBO98tYGhN6UMFiMO3uWuT6MQgm3IR6WsU+ml0bLm3UbcTOIoMwZ19BT/HAsr6hQpXETp/mT7hCHECrDqv8Ji8LeaOzvEmgeQ0Q62iGkDVqez65kWdw243zRpqHvpYTWl5y2oN076W5Xyfrx6UMK2g2CLhClLnNsF+effz799re/bf1eZ5116Oc//zn9/Oc/N7r/7W9/O33qU58qTPvuu++mZ599lpYuXUr77LMPfehDH6J3vOMdNGHCBJo3bx595zvfaR381tfXRxdddBGNGzfOv2CKgNCbEOJeuJJCpyav3NRIYQPoKnDW0dM2llhbl0+utZJXv3nzWi/Wk424rWBTph7OPtTdNrH6p7Z212ZP+zontRecdSPEphr1r48U2j2m4BMrb+6DB7moyxiuqYgI9NN+ABoR0QsvvEC/+c1vjO/feOONC//b5ptvTr/4xS/o3e9+Nz3//PP0yiuv0AUXXNAK6dDOiBEj6LzzzqN3v/vd5sYnAoTelLAVPCWEW44HQmzhtu6eu1WkGq/VZFGlINyDl2Cr7XT6uixkpTYKdakfV5p11IvidipItU1qfV+DvZpe2kps7jXUsQ1F9oYQPvLySK3+gD0Q1ewpGiux6zJ2/qBnaPS4bOHKm9/8Zpo3bx594hOfoF/84he0cuXKYdfsuuuudN5559Gb3/zmCBbKA6E3JbgEytTE2l4XZk2REnBdNqdcGxaOMlWdtmxrK+dmjEuE6YXQDcFFEgXljv1SDICQaJlrpOgez5qEXxvq0k5la+G6lDE23GJYr7eLaX2G8oLl9FAWDgEFIoB2AiV86Utfoi996UuieUydOpWuuuoqeuaZZ+j222+nJ598klasWEGbbLIJ7bLLLrTNNtuI5h8bCL0pEWLClBQWINjao0m8bcK90LYtY9kJxSxhGxzTiC3acrdLDBFCyyZOy+JUoxA05Lgx00Rob0Yt/YmDVNucG41jMxS98LWDjQhcl/HN3WYS9VKXupYmVD1x5tMrbWsyznqlLoAZ6A/ebLDBBnTwwQfHNiM4EHpTISMcSNYrhI4PK7EhYxRvc5MPJYa6iLexQjfkoUnQj0lMr+1utC3YbO2phcBVUAapZ2ydxhJYTVVfqcU4Ablom8O5qGu5ANBEr4+zXi8/AAGB0FsX6i7Q1mVT5SLi+pYtlMhQVDZD+53EW63CrVesXSUevVrz1JS/Dc3FbQyb6/KJsvQGwWXsaQ6vkVceTnvL6ktzvXSTyvohNrZzRYh5J8bn5oCXqjZEexWTZeFfTKM9dAMhFQCwBgi9yZDZi7kpbbR8aZZVy4bNN+SCFk9MT/GWSDgGbgoet6EPpOO+t4cX9VlVfGebObaH69Gaoo2KxjocoTRkw1BmtwbgfnZ2pye5HtHy3K873J9pxwi1lGWycU6BPVrqOVWBLLTdKdVTzAMXAeAC3RU4AqE3JXpJuHWlvY5Cbf644uiGFneFwitUCru+i3pbcZfTnqgH0ykR/yuoEkfrhMay5orPodqfa/Pkaq+2zRunB6Qt2vqmNntAfLSN12587JOec7XXHQQ2oIE69Ddte3/XZ7m2cgDQA0DoTYhM+QOL5SCsVBCOQftqPhb32dgkGRO37FqOA8u4PYS5BIi8fBSJs96iZC+N75BwfnqcZSzis5WncseNPdJHpJ7FHPWnfJ0AQE/Q6+Ow18vfywxl/C/2JERCXxt7Vbjs1XJHpIH5FDgCoTclhoTj8HqKcDZCdBBROO9hZPNgjxF+QdIzt8QeNiF3WJ7CoRVMrw0V91aqHtvwEvK4x12vCHuSVNWhzQKP6TTnEJ7KzmJyaaIOdrsK7VoX3j624TRwPnz7Nzyg60F3P0i1XX3X0ybpgXxs61lD3WqwoYoUbEwVrBMAUAGEXvAqnEJyhcCXJwp7ib8ci2dXYddKPPYR6Qzs4z74jPOwM0nRtIyyOpGoB4trnQU1KYFVs3AbwjaNi1Puz/+l6tGy7mz7vogwTJRfHyZl0fxptq9tGsdBajT7t0u/DSkGmthnak+og/60YlJ+bsHU1Y6U8ul16lrPnM8Zzuexr12abDEFoVb0klG4dkBz1w4IvcAejs/v12AsOEos+rUJuwwHnw3PnlnING17KSGZqLg+JL1pLa93EnA1HpamVfiV8iTSWl4iHQcIldkgHFeX28u4VDjWsFGLGaeYuw/18mZRYu0SWvgx/RS7r8FnW13FrTx6qax1R/NBou22SdjDPc9rjdVvY1cqz75U7AQAWAGhNyW0HAwxNOQt9lp773J6QVQJvJIeujG8coffWJI3s5Brcy2HgOtyr2H6wcTbFIXblLyvNNjqsrHX4EEq4Ska6cCyvPEcPJxEL2yutJcRIpsZqKfehHvfoUmE05B/rPlR+7xMpNdG2CVIHcoggHDkTlBfIPTWjRCLHEeR11mYlIqrG0PQlTwEbfUN5tf6irpWeTF7+AaIhUvkIO6GEnZDjCUpYm/EOHFdWIdqBxtxJkR8W47PAwUPLHN5meMlDkuIwNq8jZr2aN2Ech7Ig9PIgVZijD/tX6EAIA36HwAgIhB6waswhmRoYiVUVm2SpEMtSHjnWtrAJuzGCrHAEQ/X9lrL643FnNDeuJxhLCTzTA3TMroeysWFxIbApX9IisNcn7VKxdBzSDfra4TzBJb+NFZyU9qeV102v93tDsE2LTi+MOCkLuPCFq75vFfrD4QPWYS+BgBIAAi9dcB3YWoh8HodmEZkLzyYiqkhPqv3DLug2jvX9tqQHrqaQiuE8CTm9vYMtXFNXTTmsj90TNYqbO2RPBiIy2vYJB0J4Z7RE7iVJaewLnmIjLaDc7g32hBpy0ntE35XtNrli+QLz9ikaHMvkUr7pGJnqpgc+I4myCGjRrC+iQaoGxB6U4L7wKzCbAJ4FkqFWCBS4Zn7qikSsWwr7A3ppWuSjqA4Ki7u5l3LIVL4iGm++SsQZDMFNrTDuojSItg2ib3Bb+/rrrGJQ3hbu8Qo9qiz4PGA25GuT46+FHrTbTIn11EMjiVuwAszLKhnUAT6Rj0wEVI5MNUYQtkDACgEQm9KOAi43h64uXa4iGuKhF0idnuM61lCrA/ppSsRQ9fynkJxty7eudxjlik9beIsJyHL5iwqSwu2jOEMCmmOB1vBLIQo5CIoe9hV9pJKRARuZZxIXN4YoR7qIORqFG002gRASCTGQB3D4YBX0SqUarWrzmB8A0cg9NYEL0HX1bvQJWZuqM/SmUM+ONWvyT1c3rm21/oeRCcg+IodfBbo4Da2+MS2MKetXtD1DSUeeY2aNRqyn2Fxb/44wwX4CmlSXqXKQh6Ixf0lcveQzsMnHemNS2qiLTZycWiv9xBfY2h/vgI3NB92RxQ2HjswZ2ioeB9YtT+E4AoAqCC40LtgwQLaYostvNJ47LHHaPPNN+cxKIeVK1fSzjvvTPPmzWv928yZM+nWW28Vy9ME1oPNSjNiOpStzp65r95gmHfgGLq5NniGIfAUVn3iWFamX3fv3BS9cvnPduSB2y6HtbZLOziJwyE8fmyEQ5sx5CLUadm8MthhOl8aCcIa2j7v3iJ87U1N5CVCKANTuF9ehXgmahB3NfWr2PHwexHUlR66BVpOwdYldCMEY/1g/AJHkvPoHTlyJE2aNEk0j3POOadD5FVDKM/b2IdBuQrNDnazC7tlD1nJsAeSImSh17SQt25A8bkS5zGnIGTDGqJ56OatHbWKvzakuiaWOsCMAx9hjlv4kfKCjhHPV6I9pfsIR/quIUIkwUaNB40H+YFOql7ioP6BKRpFSJ9zcKTLo7G+AADRCC70jhs3jvbbbz/j64eGhujGG29s/d5vv/1o8uTJEqYREdGjjz5KZ5xxhlj67jTihUqwEt4YlByfE8wl47QS+cfODXGQl5KQBsbiLoOQrNpLV0BoZRNv6yC8hsa1zjzX37ZtHtQD2FdE9jkIy2cs5NkUIlaxJc6hHDR+rturIRs4Q2ZoRjIMClceMZF48cpdH75zasrtUwR3u9WxjiTwPFxcHSmXx1VEtto3u2VRe6DfA0eCC70bbbQR/frXvza+fu7cuR1C79FHHy1hVovjjz+eBgYGaOzYsbTzzjvTXXfdJZqfMQ0K6104LA2Ph1NI4Xb1TebX2j50Q3lqSoa9YBKCRbx0pcXdIOFE3Pp7EM9bLWtM37KmuEnKq3vBxVtefxKNDVyF76fpRWPXR9grE98U9TG2eL0cnsa+4jFnzOc8TOf4GIIws6d3VLrtbpYt1Gf5tvloqeciO6QP27Qhy9zaUUsdawiVwY1UfHqXvLS0MwhLyiI1AD2K+tANl19+eevvyZMn04EHHiiW1w9+8AO65ZZbiIjolFNOob///e96hF4iv8ULV9zddhwFXFVetz75cC4mi+pS2ibD68VCL7jeIy3yVqXP2PaiAm+MdZnGmIcaNyYB3tAHF3Yl8wsd3oHLs9ezTsQOZfNFw4FuroSuU43zjyu2L0akYjGH8BzmzjNW2k1CioUx4Tj8zrT8oQR6Te2BeOIAAJAEqoXeJUuW0DXXXNP6/f73v5/GjBkjktdTTz1Fn/nMZ4iIaNttt6XPfe5zdPzxx4vk5Qy3WMsUi9fJ67Y6UbvrQ3jm+txHxCs02l7nej2ViLwx6tDlfu6Y00TF3kwONLJMTuztFhNDCL+hF/+ShzpJISjysoi72uotxsFsXHUQ65N9zjxjCnK2aBHGQ5Vb4jA8jvTz7tckEIW0IcShmCZwvvDySTMkKcQUdyHm1wHa2xyAmhH1azyQNKqF3jlz5tDy5ctbvyXDNnz84x+nF154gYiIvve979Ho0aPF8nImdAiE4sT87uf+/MPL0zmCaKhExLXy0nXMQ/z6mOFM2mk+hF2E9F6Ot1vHTyyrEPbkbe9PzotDLcKED74CK6dA6yFwOcXndbVboq1DiXshBV5NY0J6DuWMgc3hJSl1WCIXefaV2Zz6M9A2lAtneX3DYLikzZUH94vIRiNcX/IJ35N6f+fE5oyAkDZUIWoj+gcAnKgWetvDNmy77bY0Y8YMkXyuvfZamjNnDhERzZ49m2bOnCmSjy/i8WptCBGrR0N5Q8R09b1fIvyCQ/ps90nCZRNDOt4Cr3ZhV7r9TTbP2j7PJVrdbpo9en3FUe40Y8IlUjrc7xWywVWkTjmGbF8jnNjbK5/A+1I2frjWFS716/FithKN656QhCy/ZF6aX6Bog2vf02tzpYY+oMEGUE2vjQ3Ahlqh9x//+Afdeeedrd9S3rwvvfQSffSjHyUiovXWW4/OOecckXxY8AobIKQKhfSmtUHic/0YwrPF/c5Crkv+IeMZc5VLWV/1DtmQJxZqEn9T+0SVg4gn41oLvBJ1JJFmqLANSmLOesfkTSnMAhehBN5U66cMSa9IibRCpg2ANjSHqorh6FJG7PrwBXMbAMATtULvj370I8rWTNJ9fX105JFHiuTzhS98gZ588kkiIjrnnHNo/fXXF8nHmwbJiLUavGbbkRAr29GweWGuvyAhGLjudaFoE29bbp8DOoQ8gJriHFv4hjKhUZMIXBcYhV2xGFyhTmnnokq0iyniMqQreriappANITfZobx563AIZDcQEwBIg6qxirHcCeoD1IGMAsbfDpMNCIdKoTfLMvrRj37U+r3vvvvS1KlT2fP53e9+RxdeeCEREe211140e/Zs9jz4cIy/pOVBp1XAVf55FruXbmiBV0v/a6JU4BY9kK2JjSgJUbgcD4E32KEKWg9li3FYloQXlDYRT1vIjdBxnqueldr6HQfa+iCwJ9TLg1BrMY44zBrJsvRsBuVw7a84ny0cNlXZI70fBwCoQaXQe8cdd9Bjjz3W+i0RtmFwcJA+/OEP09DQEI0ePZq+973v8R5YVsK5555L5557rtG1/f39r/5wtS+VSb0usb080w8eS1cSycXxUObn1UskE2vRsbymAqC4IEwUNQSBVsQF2tQ8b5twbXJCilYRxF0vT15FHsmi6bgSQ8Q1QVqUSym2JUdsXUlCiPIcedi8QImxJtS4DuVAQ7lM17sh5sMQ+0qXcsTY72rbY4c+WwYAoBaVQm/7IWwTJ06k9773vex5fO1rX6OHHnqIiIg++9nP0nbbbceeRxFLliyhhQsX2t8o+TDRMtHXdGEqEj+XC63x87R6gVdhsoH1yJtDcAwiFidEMC/bmGgqY2hbTPMTsIslVIPr4Wom6RL5px37EDebZ0VIUTjUPBvi0EnusvTaMwgHv/GheT2tgRj1U9UXXZ4HsQRLyWdtaqRqd93QtH4HSaFO6H355Zdpzpw5rd+HHnoojRs3jjWP+fPn01lnnUVERFtttRWdeuqprOlXMXHiRONQFP39/TQ0tMa9rk4erzZosiWH4CIut1eIFL551OHzI6V911bYTF0YViPkpuC12kRSEOMQBrnrUlu83SYh+ozPJl3L2DKl+dzQ6gUcisTndLCGFNZBwI0Qh5NpfhkVCxtbNZZLo6c/ACAK6oTea665hl566aXWb+6wDVmW0fHHH0+vvPIKERFdcMEFNHbsWNY8qjj55JPp5JNPNrp22rRpr3r/2n7O18Oo9qAlSkNo1ZwnR/sKe94GyyMAaoTSUKQUR5cLF+FL0v4EQllkfQ0ZsTelkAqm85emvk4UTiCrm6BcVm/cZdW0jssrmyb7TDEZr9rGKhcJrLVE0XAYNQc+IdhSQel5IiASCKcHHFEn9LaHbdhqq61ozz33ZE3/+9//Pt1xxx1ERPSBD3yA3vGOd7CmL0qMuK+G5G121YuteWh+QGq0TaNNnGgRg12pY/to2YRqsCOWiKRdiBRuG29xNwERm52U4slykuI6yJU6lzXE2QlFYyP0c7yO64aUQP2X02igjgxJbh+emLkAaEeV0Ltw4UK66aabWr+5vXkHBgbo85//PBERrbPOOvStb32LNX1RGronbM22FZLaQkGjIBjrEyFJgcv3gLcYFNVzTNti9VcN7dFEqp9qKmPqXr0UIARDL3iIt+ddNj5TGbugN7A53MwkDS5bepXQZyegzmtdB0nuTUNT4/avCz33VSVgQ5XQ++Mf/7gVj7bRaNBRRx3Fmv7AwAAtXryYiIhefPFF2njjjY3vve2226jRNhleeumlNHv2bFb7KqnbZNwLn9+44lMXZQ+E0HVs+nDS1Pa+ok+MhWWvLQKkyqvtM2vtnrOh0rcgSNzcJinFXJZG6mUhkENb2I3Y64DY+UsiXTabPlKnsGI17jNlIinXc7Znhdga95talw2AxFAl9LaHbZg1axZtttlmEa1RiOTJyjEW/HgYFFN2aIJPG2gV16U3kiHLqU0sBGZoFpy02MZoR1BBthst9WlKaHtNvXM5kDitnSvvbrT2m9htVCd6qayc9Oph0QEJJYo2n81Fz+isryFrS93bt+7lA/xoXXsA9agReu+55x6aP39+6zd32Aai1V7CkyZNMr7+5ZdfpsHBQSIiGjFiBE2YMKH130aPHs1unwjcDxQ8oOQpmtClPj3rhRNa6/qQlBYqOOILaiQlW10IWD5Rwbbu7VRGL5e9HU3PH022xEQqVBDq1w3UWwsuAbLquVZbT9RGg7IRNS1bHqmPnVTsNx4viZQHgERQI/S2e/OOHz+eDj74YPY8Jk2aRC+++KLx9bNnz27Zteeee9Ktt97KbpM5NQk+j81rNZoP3tD+eV6voemTfIzt5OogmldtCvXEdRBjCmXtBnM3H1oO6wzRpr3eb2pY/toJmoZztlG5Jb+y5MpPG7HKEDo2fOhyah+ndei7ACSICqF3xYoV9NOf/rT1+5BDDunwngVrkJ7IQ2z6tU72Gg7LkYrLGwuNNoF0UdyffATTEHHwnNFY51wCLFcaGuuoLmhdL4REQx1osKFm9KSA2k3sw+9ipB9qLGkcsxptIrKzS9O41VqfgJksYEhArGfrhgqh99prr6Xnn3++9dslbMOCBQtoiy22aP0+/fTT6Utf+hKHeb2DzwMstiDhC8fJy7b5gDSAkCNGdCGTG4e+0lhVgzrQNka02QPs6PXnJPdL38j1GfNlVu0E1TxSHS/wMndDwjsVHqj21Klv1aksAIAWKoTe9rANm222Gc2aNSueMZoJNRG7LBRMHtqpiDrd9RxDNNAuVGi3TwDXDWnRRrN2IqcrPdiXerLMHISsN86wDNhEmaHts94UP7N2zDeWIJqMEIsxbA/qLJ+YHsOpjDdOUu6HSmzPlNjRs2DPAByJLvQ+/fTTdMMNN7R+H3XUUdTAhBIXqVhGkgsMKdGs7pOrwvLVTQA1Kg93HEaJduUMO6Kw37FQ13L1IpxtGSrcRChSsrUbG9uZymkqZro8+5IRSrlJuQ/mUbfyxKSudVmnsR6yjfLySuQlXrICa5/jfYkWFwCtRBd6r7zySlq5cmXr91FHHRXRGuVIHZolZUMTSfFDWhSUFM7aCS0QMeVXtjE12YBGEXW1inEcdsUqm22+WtvAFy0vKVw2hKFsr9Nm1ZdUN3FZFt/22PkbYiPERhNtE6lLbyTOQahr3dW1XEVjzPb5p/05Vtf2K8KgvFmjQY3mOGaon2AirKtoKk2v9bFYZBTwHKEw2YBwRBd6TzrpJDrppJO809l8880pYx4Il112GV122WWsaQZDgyjcRFIsbV9safXq5Sq3MnGsSqR1FnGVlZOI9NmkzZ48tIieqcDZpt1xfzUtyLX0Cw0b9VQ9faVsilhWlZ6xGtvelJRtz6OXD9GSQtOYC2lL3dpYaXnyhNhoHrJaxVoite0HAPAnutALzLF5QDVsRAMJLwfXfLhCQ3CKCb5CNZfQLRFHsDsLznpL2UM1D022aBHLirCtK00vpuqIljrTtKHoHkN19KaMXd+R8hcTbWPXZxlabTO1S/KZERuptakmoVQTKfWNIhIpQxDh1EYkHQqQhw2JtGMydvYqWtbwIDkg9NaUjk9UNCJhm1YPUu70PdPzEnMl6yo1YViD2Kp5jNtQVo66lFED3C/btMVm9tmsmI5nDnGlDqfNK9oYBvHG9Smvorpyog79lQOOfhZbnE2hniWQKncNDuSMGgeWW2DlSk9LewWyI9lYwACAUiD01hiTiTuaGBzAO9WYRkOXGBtTyM3DxZ7Qh4FxIyniauprsRhydbvwoK9iBxDDphC0lztm3wuxkei2xzVPaTGm3U6JeklM5A0eNiHFOjdF2g7Ttgr54iRFtPSXlNBYZw42qRbVNIcYKENbncYMQ6StLpqk2rcASBgIvT2EiKhb9UAJ6ann83mci9irRMy0FnZjxQzW5MVdBqed0iJsiuJkLGF61ao4+ZoguTD37SNVArkpIQ/w8s3HZF7R6tmrME31Hrcx0g2VvincbaRJwNVSx3UnwXpWK4r5EkpU015/HGHBApZRXSgMoAMNX5CCJIHQ20PkPUC8xV+f+6W8ekMInwG9XNk9dG1xbQ/X+1zKq0R0tyKGSBtJZOU6KLMReFNRZTebPTFewpnS3U99hN8QHsCh+shQ5iZmaRFiA9STl8CrOVyAVnGjDp7RKRLiOYC6bpGEOKtJTNNWX8rsafUnZXZVoqmPEaVXfwD0ABB6ewCx8AwxReI8Qnm22twXwkPXJ89QXrmh4gJLCpmSwmxAAZZLdI2BNttVC9hS3ill44DL+5foVftd4gAH8YoJKK5yl8cxPRbPXG2Hz8XenNbxEEBbpA8E1lTWMhoNdSJm0f5Bm52saBLQQtZzTdpUTd/U1I/aiVE/xnkqaTttZAl+vQlUAKEXuKHZyzN0fFzLe5yE3Biiusk9nN7GoQVdH+FWSHDUJmQSET4Z4qJE0HFpd2dxWNKr1mRM2YrBLsItxwE5nAJcTAHSJYaklPhoa4s2UbgdTaEJqtAifBTh83ImhGd6wPMumuloEaus7NAqbGlCtcimDy3jgIjC929NZTchNXsB6AEg9PYARQ9K54VpqLAFoQRRbeKuadqxDnyTFN29w3ZYircCwiqbWJuawBrjjXMjoZ1lXnt6iEWm/Sx0uItSXD1+Xb17y6hKyzY0Q0jbGO+L5qWbUkzduoZE0DQ3BEJKOFIlSDVJ6PGoAo1t2I1CG9H3K+ghcT9T2BVqgUYnIJAEEHpriLWAG0sw7MZV2CqyIfChWl4hF7jLYHufNo9p3zAJWgRcbWKths9/XOqxMJ53wPJIiMrCJ9OrEnmJVo9rW7E3hohoW9+hRF7GfKIIvBq9NrlFXG1jrggldqoUiTjRJDhppg79wKUMCg4A86FWh4clUue5pGw7AEAMCL1g9QMixtuiUDF1fSnIVzQEg7RYr/XAusDhFNhDJYQQdjWItT64zDeh4q6W2tBV75zCr4DHoJfIy1nXJsIuR36uaYSKtRtI5GQNu2CSd8y2cyGvfmLPLUUotKv2wmwTCLSr6ZX2zkPzgZAGqB6rvTC+NNe/BfDSVYA2pyGQDBB6E4L9ULWQoRu0HjAW4rA0SdHW2EM2YFxg5zjMFgKmx1hQLe6mLuK2w1nPNVkwD4NZ4PX24HUWSx13bTFjtpbVfax6dLjXW9jV6GVri08daCgHkw2qhZ0yekH04UZ7W5fZx7U20F4HBiQ5ZjWP1x4Oe6NekDV5TmsvAwCJAaG37gQOX2AsenHZpS2+rm36JtdqE3OtvYcZxcuKvFWLuFVoE3l96rLR0LUQ1hbL10OYchZzQ4m43O1um55r3Yao15Airpa4uSE9rTny1ZZHG0mKQk2UTcFspNwmHGiZZwKT3FjUOP401aEmW7pQL+AWkdKBpQDUFAi9dSO4F6YeT1uf+0QOS7O91uV6TrvZRVIH4VJbwPm+hqzYq03cbUfxwreSGMJuDPE2PzG/+128c0OJuxo8NwOEMwgu8PrCnWcq5e9+XhnknScQmX6t1X2dGrFJo4jEhZY61kiCdaNmzLhSp7FWNe+lFhaIgaiiLoRZpWQB98bK9uDAGwi9vUzVxMEhcHF7jDJPdmLeupyeunlIiI+csZo5vXgNYPfklUSzuMuNNk/aKoQXupWCrtSmxDW0QhmhPLm0e7WG8NLV8iKAw44Yh8DFSpPMhCVx8SmxaZiVxIQeVmpc9uQFWyK3cVm2fIw5zrW2R9neIIDN0b1x8563RfvHvH+H+AtA0kDoTYkQYRg0xZ/luI8sxNwQZZE+NM0lj3a6Fz6F/SRcOAYTGo2GnNhru9CparPUxE8NRFhsqjnMrBsf0VbKrtACrk2eAURS1gPQSvJxuifIqejCeYT0xmYiihCl7dGiVfwJSeJ1UAtBNTac4zLEGI/Z5or7W3TRtgybZzDE27TIKJxHb0I+U8AMCL11JcSBadLCqKZYuq73hD64LpTwzkn7wk6DPb60L6JM2rKmiy4TodRFnGcNczA8cf80JLxni4h5aFgT7R63QulZi7shP0P1ySsV0TZUumvoaa9bxeKLOD1W9toLu5rHmQ+a202zbaRcwCUKvkdozgHsB8BTD8wvACgDQm9K2Ey6GkVPjzTFYuj63icdfiFgHZfi48EbSLyVEha9CLRAKys7V5klBNbcNE3zKSpXamJtGdxCaIyXCrHEaIf71MTGjeVNLtU/pDd3vjGRg3g9y2fhRC9tvHuprDn0pMhS13GXklOE5FqNgUaXeeqE37K9osAzW0LgDZF2rUG9AUcg9KZE6M/4fdMJcTCaa16aD63zyUcqzb4+d7HXZTEn9FAT9QZVSpAy10lcJQq3AYEoJ3dPG2yhFUIKsOIepDo8o8XTKqEnha9uHA6US5Ysq3f5uuj5/q1oSTEM7jWucjFVjR0GdAu/7QQXgWv2xV/Pz0kABAZCb50I6ZGqMawCVzoh4uiGgHMhGfhwtaiLQs4Yx1rRJKo2qVtcOMkFukYxNmR6pES85bifI61Uwy0w56NmExljetVSdl/qUg5B1PRzjShc2nhRt3WRB+o8baWpkciLOcuT0HtwUBsg9CZDpjem7BqcPHEZ8mVNg8j8QDLtFD1YXcqTJwzW9cHjuyDRKKI20bTYCmVLiuEKYuTLZDP7wWTdpBjjVYOAq0GQtiT65jDWVB673BJwlslmDROxLvP6r+uny9HHgg2m48Z0Cal4SUVEcl+txW7zKhsD21fmaRuCoEJzjUReIoRsACAWEHrrQODYss6CrpY4wBzpc3vxhnwINhdnvnlqFjS5ib3grkKjfVo8LutiA5GYHaLibIICI2teKYXYkOpfocdPqo8mLfOML6l9cWCIST9uFzRMxI2kBFwTbMZequO0iXTbaQ01otGmNmrh+dvDBzkDAPiA0JsSwh6UQWLjSqWnRbB1pdEI7znskyent7AvWhedIe2KteDjKKO29lNij7hnbDtKykxEfLZIlilE26QSW7uE2olY0rQ/P1OuO9N1QMpl7MLVY62RZWmMkzJRdqjgb5e0UqLZ5hHizrOjzR4PbDx/kxaF8/avEH/rCTyigSMQemtKUK9b6bQlbepryIu9mifoBGJxOpPaYUY21EVgDWCDt2CqoZ4kSTHMhA2peCvHzCN1YgpHaJ9ialY3RWJu1mikIda6YDK2uMdfXesyJjWq025x1iekQ/PepAVfIgi8AIBcIPSmQuYZAzc3zcgxdWOmH9KT13aBpVkYNiX4p7pKFjmavRGlvfe0tEETTYdscaDNHldS9j5PIH+VgpOrEFTlHcghMGmsr1CYlr0Oa5ICVI6XmGjwtpXqbzZtrSw+rTdSZ5hEgCNWb/LCbjfte1pta3HgT42fwUAWCL0pwTnQQ08aHPlpCbNgQwqTs4KFGxGl7Xlnkn77NT79wqEswUTYuomrpqRqd5MUNgaa6lhxjFCVuH7OHUJ0KpuLU61vUyTXJwnXnfTBQarHsW2EOA3CsCkprMc54ehnPuEpFNEUdpsisatYnIRAnOJeuUnCpgOgEQi9dSTGYiZEnnlihPYHWiqLI01Cj0+dSdY3Y9rZCKbdUSLlVTEONPXxMjTUVQhqXk7VYk4RKYk2PqTYNjFIpJ64x1qSY9cFTeOds85d9iOptHloOxXWi4/YmoRQywnnulf7frvOoO6BIxB6U4JDTI31RhuTFB+xBSsN3rE+11vC5g2rKawDZ5316qFhvtSpLMz0jNBShiYRRgPoE8X0eN2UeeBGn0uKxrHs2crpzR/ce5PY7V6FdvvayTJV9hZ54/aciGsC9t4A9DQQelNCQqTFQyAcsQXadrSJtZ73soYmcLVdmydyWZ3AK2QY0QUBbrRv9E2FDu3lSJm69fleowfaT8u8XHYQWyk2gm7VXGeTVvPa1OdPJe3PTkoxgCPaYivecsTvrUKroGxbdq3lAMPJMuk3g6CuQOhNCUlR1kZEDvnQ17wY0iTcNtF0iJeHLc7CbaxwA675+vYhzYL9GrRs1I1IfVOcEqhrc0KMIc4XySmNeUlQD95IxMt1eSYFeY65xrIGdqQQdzav32u2V4gQwq0tGm1yoS7lAAAUA6E3JbQcJKDFDqLgh1p1YCO8S4jCsQ6+YlpsWou5pvnGCpGgwauYMR22ja3WjWkPbpqAEHU40MvHzu7y2z6XU6kjWzStlfKoa71XoDrUQxXdYrDW5ysXkmNIsyNJHtrnkzK01WUedY7tHAvrOk24jwOgEAi9oB6EethKefHGEG0t7/EOjyAVe9el7CE8hgX7pPNmNNSmMJVD4gBwpX0D0+t9stfK32vlTRT1oq0LdRR2bdoJXx7ooI51V8cyhSTlFxGaybJwYTbRhrUDQm8yZHIDUNvDLXXRlijsgWIhY9pKliuUYBugbZw2mBIbuAAvACSp5UYdqMb4U3H0TZA4LvOrllAKyVFHgbadUG1Yp77iWpaiMVinugH+hH4p0n0NREMAogOht84MVQT86luz8gw9GVc9MEJ9PjOU6YmR6nKqrcX1KoRd6VAIIbyaY3nSKgjl0ASbciHyputQduCchxbN/i0haPU0EucA1L2NhOdaLX1cyg5Vz6qqOTZ1Idi1DSXbSPun+Nz9PoSwp2TOAIFBu8cF9Q8cgdCbChlVC7e2tKfXJ7jK5Fg4aVmwS3uDOpbTWMiV9LA1vV7CVod0rDeBLkMkchzkbkQ2vlxTh5YxHosRQumaLBBTERkCCtKqRCJLbIUz07J6CXKaDgoFakl53DmTyvxripY21GKHBhQ5CyQDxLVOOOqjl/oPAAqA0NvrSAq8vnA/ECQFRsF4slbeuJqEaG67Da4RFW9DhuOoQE2MXi2LNi12NIl5OFd33trqph3bjUOIx1UPejdr8ewEOaQ0ngvoSfE2D8XLbTZCt7V0rPSyNKWFL855GXO8Dri/bEmtXVOzVwvcjn6gZ4DQmxIS4kEor94iuBZmmj7zl/bKNclHylYJ0ZnL47aJlHjr2VfZNruuwzQhj7qsLdlGwHVhxlCcRkZxxRifvLWF8YlBn4KNiOeaXoOwBvGYCQVtaYKGPhcF7eJt3dslxIvTvDy45jfMk72BSzujbwAAPIHQmxKpLNiq7NQWq1X4ehGPXE6buT2dpQ8vK9pYaYzRy7kJ9B3/wvMHh0gaMl0pvOxtjsVQJ+x2Y9BHRIT3vA2NS3+N8WmhxGaMY96I7ACSgvAHMTqfFNouCJpFXLSROQm95K5E8rBQiTjqPtRpftYWixqkBdoYOAKht1fx8d7lPHCrDEmBl7EMRkIup0jqUsdlNgp6tbIIuD52lFynzmOY894cxAVT34MNldPeX8QFIs66ZBaN2byeO/5BSfxALYf3cPQvLSKV4i8OfQTNsjkAQmkEtPR3XzT2HVObbOctjWXVjmSdhQ4rZYNUH9RKXcrRRPE6AIA6A6G3zkiJuT7XtuMrZhTlK+B5WirmSoZ0iBUT1zecQggxlDvkQzsJxO51EtwCibF1Fzo4y5eUaNzEUzyWfOng5XEcy4uYw45u6rZRVEbd57jgQKiNj/KvhkDiNBruz6Vee5751FVoIOICoBYIvXXFVuSVFqukDhTzFXuJOTauzXXcnrm2aRpem7uhNe1ewuK2iPDsYIfT9cT0mb8wKgQNBSYY4RKGLVD9sgrKvn1PMDSFzZhiCUOhIdRDjDxMac69ITaDVfM8NqS6gLgbh9TsBfWhu+9VPatM+qqm5x0HscuD56Q6MhzGBhyB0JsKDZIXb13uCy3gel5bKOpKe9uaXMthm4RISuTv4SodC5dIzSF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",
      "text/plain": [
       "<Figure size 1750x750 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extent = (freqs_MHz[freq_select][0], freqs_MHz[freq_select][-1], lstbin_lsts[lstbin_lst_select][-1], lstbin_lsts[lstbin_lst_select[0]])\n",
    "plt.figure(figsize=(7,3), dpi=250)\n",
    "plt.imshow(np.abs(all_data['data_lstbin'].get_data(0,2,'nn')[lstbin_lst_select][:,freq_select]), aspect='auto', extent=extent, vmin=0, vmax=100)\n",
    "plt.ylim(7.475, 6.8725)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "42c2bb15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2000.0, 2000.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s0 = np.argmin(np.abs(lstbin_frates))\n",
    "fr1 = -1\n",
    "s1 = np.argmin(np.abs(lstbin_frates-fr1))\n",
    "fr2 = -2\n",
    "s2 = np.argmin(np.abs(lstbin_frates-fr2))\n",
    "data_fft = np.abs(frdly(all_data['data_lstbin'].get_data(0,2,'ee')[lstbin_lst_select][:,freq_select]))\n",
    "sim_fft = np.abs(frdly(all_data['sim_lstbin'].get_data(0,2,'ee')[lstbin_lst_select][:,freq_select]))\n",
    "data_v0 = data_fft[s0]\n",
    "data_v1 = data_fft[s1]\n",
    "data_v2 = data_fft[s2]\n",
    "sim_v0 = sim_fft[s0]\n",
    "sim_v1 = sim_fft[s1]\n",
    "sim_v2 = sim_fft[s2]\n",
    "plt.figure(figsize=(7,3), dpi=200)\n",
    "plt.plot(delays, data_v1, color='k', label='Data, Main-Lobe FR')\n",
    "plt.plot(delays, data_v2, color='k', ls=':', label='Data, MC FR')\n",
    "plt.plot(delays, data_v0, color='k', ls='--', label='Data, FR 0')\n",
    "plt.plot(delays, sim_v1, color='r', label='Sim, Main-Lobe FR')\n",
    "plt.plot(delays, sim_v2, color='r', ls=':', label='Sim, MC FR')\n",
    "plt.plot(delays, sim_v0, color='r', ls='--', label='Sim, FR 0')\n",
    "plt.yscale('log')\n",
    "plt.legend(ncol=2)\n",
    "plt.xlim(-2000, 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3736279d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.2577514666743832, 1.2719086961473942)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.normal(size=(100,100)) + 1j*np.random.normal(size=(100,100))\n",
    "t = uvtools.dspec.gen_window('bh', 100)[:,None] * uvtools.dspec.gen_window('bh', 100)[None,:]\n",
    "np.abs(x).mean(), np.abs(np.fft.fft2(t*x)).mean() / np.sqrt(np.sum(t**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "124210f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.0318989337647895, 1.9997930838606903)"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(x), np.var(np.fft.fft2(t*x)) / np.sum(t**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "87f9f579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.4254469242187833, 1.4141404045782335)"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(x), np.std(np.fft.fft2(t*x)) / np.sqrt(np.sum(t**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "44dc4f27",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a35cc81d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.5, 7.75, 159.3, 169.9)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst_start, lst_end, freq_start, freq_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ebf9b949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2605656101627316"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6.875 % (2*np.pi) * 12 / np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9fbc2f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.552396790686023"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7.475 % (2*np.pi) * 12 / np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e85820ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "array_layout = dict(zip(*all_data[\"data_lstbin\"].get_ENU_antpos()[::-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9451a7aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c1deb0a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.isinf(all_data[\"data_lstbin\"].get_nsamples(ai, aj, pol)[lstbin_lst_select][:,freq_select]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fc205762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.any(np.isinf(all_data[\"data_lstbin\"].get_nsamples(ai, aj, pol)[lstbin_lst_select][:,freq_select]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ccc5f860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0315259347538904, 0.023131415547881923)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_auto = auto_uvdata.get_data(0,0,pol)[lstbin_select].real.mean()\n",
    "n_samples = all_data[\"data_lstbin\"].get_nsamples(ai, aj, pol)[lstbin_select]\n",
    "wgts = np.ones_like(n_samples)\n",
    "avg_nsample = np.average(n_samples, weights=wgts)\n",
    "\n",
    "avg_auto**2 / (avg_nsample * dt * dnu), noise_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2c728bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "from scipy import interpolate\n",
    "\n",
    "m_mode_spec_file = \"/lustre/aoc/projects/hera/zmartino/hera_frf/spectra_cache/spectra_cache_hera_core.h5\"\n",
    "fr_quantile_low = 0.05\n",
    "fr_quantile_high = 0.95\n",
    "fr_eigenval_cutoff = 1e-12\n",
    "with h5py.File(m_mode_spec_file, \"r\") as h5f:\n",
    "    metadata = h5f[\"metadata\"]\n",
    "    bl_to_index_map = {tuple(ap): int(index) for index, antpairs in metadata[\"baseline_groups\"].items() for ap in antpairs}\n",
    "    spectrum_freqs = metadata[\"frequencies_MHz\"][()] * 1e6\n",
    "    m_modes = metadata[\"erh_mode_integer_index\"][()]\n",
    "    if (ai,aj) in bl_to_index_map:\n",
    "        mmode_spectrum = h5f[\"erh_mode_power_spectrum\"][:, :, bl_to_index_map[(ai,aj)]]\n",
    "    else:\n",
    "        # If ANTPAIR is not in the FR_SPECTRA_FILE, but the reverse is, also reverse the spectrum\n",
    "        mmode_spectrum = h5f[\"erh_mode_power_spectrum\"][:, :, bl_to_index_map[(aj,ai)]]\n",
    "        m_modes *= -1\n",
    "        \n",
    "def m2f(m_modes):\n",
    "    # Convert m-modes to fringe-rates in mHz.\n",
    "    return m_modes / units.sday.to(units.ks)\n",
    "\n",
    "times = np.unique(all_data[\"data_lstbin\"].time_array)[lstbin_lst_select]\n",
    "times_ks = (times - times.mean()) * units.day.to(units.ks)\n",
    "m2f_phasors = np.exp(2j * np.pi * m2f(m_modes)[None, :] * times_ks[:, None])\n",
    "m2f_mixer = np.fft.fftshift(np.fft.fft(np.fft.ifftshift(m2f_phasors, axes=0), axis=0), axes=0)\n",
    "# f is fringe rate, m is m-mode, n is nu (i.e. freqeuency)\n",
    "fr_spectrum = np.abs(np.einsum('fm,mn,mf->fn', m2f_mixer, mmode_spectrum, m2f_mixer.T.conj()))\n",
    "interp_fr_spectrum = interpolate.interp1d(spectrum_freqs, fr_spectrum, kind='cubic', fill_value='extrapolate')(freqs_MHz[freq_select]*1e6)    \n",
    "frates = np.fft.fftshift(np.fft.fftfreq(len(times_ks), d=np.median(np.diff(times_ks))))\n",
    "\n",
    "taper = uvtools.dspec.gen_window(\"bh\", freq_select.size)\n",
    "band_avg_fr_spectrum = np.average(interp_fr_spectrum, weights=taper**2, axis=1)\n",
    "band_avg_fr_spectrum /= np.sum(band_avg_fr_spectrum)\n",
    "cumsum_interpolator = interpolate.interp1d(np.cumsum(band_avg_fr_spectrum), frates)\n",
    "fr_min, fr_max = cumsum_interpolator(fr_quantile_low), cumsum_interpolator(fr_quantile_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "acf9ac68",
   "metadata": {},
   "outputs": [],
   "source": [
    "fc = 0.5 * (fr_min + fr_max)\n",
    "fhw = 0.5 * np.abs(fr_max - fr_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "8e54e4c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x900 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lst_start = 6.55\n",
    "# lst_end = 7.55\n",
    "lst_start = 6.875  # Need to avoid fully flagged spws above and below (it's like one lstbin)\n",
    "lst_end = 7.475\n",
    "freq_start = 140\n",
    "freq_end = 180\n",
    "# spw = 8\n",
    "# freq_start, freq_end = spws[spw]\n",
    "\n",
    "lstbin_lst_select = np.argwhere((lstbin_lsts>=lst_start) & (lstbin_lsts<=lst_end)).flatten()\n",
    "freq_select = np.argwhere((freqs_MHz>=freq_start) & (freqs_MHz<=freq_end)).flatten()\n",
    "lstbin_select = (slice(lstbin_lst_select[0], lstbin_lst_select[-1]), slice(freq_select[0], freq_select[-1]))\n",
    "\n",
    "lstbin_times = all_data['data_lstbin'].time_array\n",
    "lstbin_dt = (lstbin_times[1] - lstbin_times[0]) * 24 * 3600\n",
    "lstbin_frates = np.fft.fftshift(\n",
    "    np.fft.fftfreq(lstbin_lst_select.size, lstbin_dt)\n",
    ") * 1e3  # mHz\n",
    "delays = np.fft.fftshift(\n",
    "    np.fft.fftfreq(freq_select.size, freqs_MHz[1]-freqs_MHz[0])\n",
    ") * 1e3  # ns\n",
    "\n",
    "n_times = lstbin_lst_select.size\n",
    "n_freqs = freq_select.size\n",
    "taper = uvtools.dspec.gen_window(\n",
    "    \"bh\", n_times, sym=False\n",
    ")[:,None] * uvtools.dspec.gen_window(\"bh\", n_freqs, sym=False)[None,:]\n",
    "taper_norm = np.sqrt(np.sum(taper**2))\n",
    "\n",
    "pol = \"ee\"\n",
    "ai, aj = 0, 2\n",
    "plot_bl = (ai, aj, pol)\n",
    "autos = auto_uvdata.get_data(0,0,pol)[lstbin_lst_select][:,freq_select].real\n",
    "n_samples = all_data[\"data_lstbin\"].get_nsamples(ai, aj, pol)[lstbin_lst_select][:,freq_select]\n",
    "n_samples = np.where(n_samples, n_samples, np.inf)\n",
    "dt = all_data[\"data_lstbin\"].integration_time[0]\n",
    "dnu = all_data[\"data_lstbin\"].channel_width[0]\n",
    "noise_var = autos**2 / (n_samples * dt * dnu)\n",
    "wgts = taper / taper_norm\n",
    "noise_var = np.sum(wgts**2 * noise_var)\n",
    "\n",
    "# avg_auto = auto_uvdata.get_data(0,0,pol)[lstbin_select].real.mean()\n",
    "# n_samples = all_data[\"data_lstbin\"].get_nsamples(ai, aj, pol)[lstbin_select]\n",
    "# wgts = np.ones_like(n_samples)\n",
    "# avg_nsample = np.average(n_samples, weights=wgts)\n",
    "\n",
    "# noise_var = avg_auto**2 / (avg_nsample * dt * dnu)\n",
    "# exp_amp = np.sqrt(np.pi * noise_var / 2)\n",
    "# exp_std = np.sqrt(noise_var * (4 - np.pi) / 2)\n",
    "n_sigma = 1\n",
    "cdf_cutoff = stats.norm.cdf(n_sigma)\n",
    "cutoff = stats.rayleigh.isf(1 - cdf_cutoff, scale=np.sqrt(noise_var))\n",
    "\n",
    "measure = (freqs_MHz[1]-freqs_MHz[0]) * lstbin_dt * 1e6\n",
    "data_fft = np.abs(\n",
    "    frdly(all_data['data_lstbin'].get_data(plot_bl)[lstbin_lst_select][:,freq_select])\n",
    ") / taper_norm\n",
    "sim_fft = np.abs(\n",
    "    frdly(all_data['sim_lstbin'].get_data(plot_bl)[lstbin_lst_select][:,freq_select])\n",
    ") / taper_norm\n",
    "\n",
    "vmax = max(data_fft.max(), sim_fft.max())\n",
    "vmin = vmax * 1e-5\n",
    "cmap = plt.cm.turbo\n",
    "norm = plt.cm.colors.LogNorm(vmin=vmin, vmax=vmax)\n",
    "smap = plt.cm.ScalarMappable(norm=norm, cmap=cmap)\n",
    "\n",
    "plot_kwds = dict(\n",
    "    norm=norm,\n",
    "    cmap=cmap,\n",
    "    aspect=\"auto\",\n",
    "    extent=(delays[0], delays[-1], lstbin_frates[-1], lstbin_frates[0]),\n",
    ")\n",
    "\n",
    "DARK_MODE = False\n",
    "\n",
    "x_bounds = (-1000, 1000)\n",
    "y_bounds = (-3.5, 3)\n",
    "ncol = 13\n",
    "\n",
    "fig = plt.figure(figsize=(7,6), dpi=150, facecolor=\"wk\"[DARK_MODE])\n",
    "gs = fig.add_gridspec(nrows=2, ncols=ncol, wspace=0.35, hspace=0.265)\n",
    "data_ax = fig.add_subplot(gs[0,:ncol//2])\n",
    "sim_ax = fig.add_subplot(gs[0,ncol//2:-1])\n",
    "vis_cbar_ax = fig.add_subplot(gs[0,-1])\n",
    "dspec_ax = fig.add_subplot(gs[1,:-1])\n",
    "fr_cbar_ax = fig.add_subplot(gs[1,-1])\n",
    "\n",
    "bl = array_layout[aj] - array_layout[ai]\n",
    "lat = all_data[\"data_lstbin\"].telescope_location_lat_lon_alt[0]\n",
    "omega_e = 2 * np.pi / units.sday.to(units.s)\n",
    "avg_freq = 0.5 * (freq_start + freq_end) * units.MHz.to(units.Hz)\n",
    "fr0 = -1e3 * bl[0] * np.cos(lat) * omega_e * np.cos(lat) * avg_freq / constants.c.si.value\n",
    "test_frates = (0, fc, 2*fc)    \n",
    "colors = (\"kw\"[DARK_MODE], \"#FE6100\", \"#FFB000\")\n",
    "fr_cmap = plt.cm.colors.ListedColormap(colors[::-1])\n",
    "fr_norm = plt.cm.colors.Normalize(vmin=0, vmax=3)\n",
    "fr_smap = plt.cm.ScalarMappable(norm=fr_norm, cmap=fr_cmap)\n",
    "fr_cbar = fig.colorbar(fr_smap, cax=fr_cbar_ax)\n",
    "fr_cbar.set_label(r\"Fringe-Rate, $f_{r,0}$ [mHz]\", color=\"kw\"[DARK_MODE])\n",
    "fr_cbar.set_ticks([0.5, 1.5, 2.5], labels=[f\"{fr:.2f}\" for fr in test_frates[::-1]])\n",
    "\n",
    "data_ax.imshow(data_fft, **plot_kwds)\n",
    "sim_ax.imshow(sim_fft, **plot_kwds)\n",
    "vis_cbar = fig.colorbar(smap, cax=vis_cbar_ax, extend=\"min\")\n",
    "vis_cbar.set_label(\"Visibility Amplitude,\\n\" + r\"|$\\tilde{V}(\\tau,f_r)$| [Jy]\", color=\"kw\"[DARK_MODE])\n",
    "\n",
    "data_ax.set_ylabel(r\"Fringe-Rate, $f_r$ [mHz]\", color=\"kw\"[DARK_MODE])\n",
    "dspec_ax.set_xlabel(r\"Delay , $\\tau$ [ns]\")\n",
    "dspec_ax.set_ylabel(\"Visibility Amplitude,\\n\" + r\"|$\\tilde{V}(\\tau,f_r=f_{r,0})$| [Jy]\", color=\"kw\"[DARK_MODE])\n",
    "\n",
    "for ax in (data_ax, sim_ax):\n",
    "    ax.set_xlim(*x_bounds)\n",
    "    ax.set_ylim(*y_bounds)\n",
    "    ax.set_xticks([-800, -400, 0, 400, 800])\n",
    "    ax.fill_betweenx(\n",
    "        frates, delays[0], delays[-1], where=np.abs(frates-fc)>=fhw, ec=None,\n",
    "        fc='gainsboro', alpha=0.5, label=\"Main-Lobe Filter\\nReject Region\",\n",
    "    )\n",
    "    \n",
    "for fr, color in zip(test_frates, colors):\n",
    "    idx = np.argmin(np.abs(lstbin_frates-fr))\n",
    "    labels = (\n",
    "        None if color not in \"kw\" else \"Data\",\n",
    "        None if color not in \"kw\" else \"Simulation\",\n",
    "    )\n",
    "    dspec_ax.semilogy(delays, data_fft[idx], color=color, ls='-', label=labels[0])\n",
    "    dspec_ax.semilogy(delays, sim_fft[idx], color=color, ls=':', label=labels[1])\n",
    "    for ax in (data_ax, sim_ax):\n",
    "        ax.axhline(fr, color=color, ls='--', lw=1)\n",
    "        \n",
    "sim_ax.yaxis.set_visible(False)\n",
    "dly_max = 2000\n",
    "dspec_ax.set_xlim(-dly_max, dly_max)\n",
    "# dspec_ax.set_xlim(-4000, 4000)\n",
    "# dspec_ax.set_ylim(1e-2, 1)\n",
    "\n",
    "# Invert black -> white for dark mode\n",
    "if DARK_MODE:\n",
    "    for ax in (data_ax, sim_ax, dspec_ax):\n",
    "        ax.xaxis.set_tick_params(color=\"w\")\n",
    "        ax.yaxis.set_tick_params(color=\"w\")\n",
    "        plt.setp(plt.getp(ax.axes, \"xticklabels\"), color=\"w\")\n",
    "        plt.setp(plt.getp(ax.axes, \"yticklabels\"), color=\"w\")\n",
    "        for spine in ax.spines.values():\n",
    "            spine.set_color(\"w\")\n",
    "\n",
    "    dspec_ax.set_facecolor(\"k\")\n",
    "    for cbar in (vis_cbar, fr_cbar):\n",
    "        cbar.outline.set_color(\"w\")\n",
    "        cbar.ax.yaxis.set_tick_params(color=\"w\", which=\"both\")\n",
    "        plt.setp(plt.getp(cbar.ax.axes, \"yticklabels\"), color=\"w\")\n",
    "\n",
    "bbox = dict(boxstyle=\"round\", ec=\"k\", fc=\"w\", alpha=1)\n",
    "dspec_ax.legend(edgecolor=\"kw\"[DARK_MODE], facecolor=\"wk\"[DARK_MODE], labelcolor=\"kw\"[DARK_MODE])\n",
    "data_ax.text(-950, 2.8, \"Data\", bbox=bbox, ha=\"left\", va=\"top\")\n",
    "sim_ax.text(-950, 2.8, \"Simulation\", bbox=bbox, ha=\"left\", va=\"top\")\n",
    "dspec_ax.text(0.01, 0.98, f\"29.2-meter\\nEast-West Baseline\\n{int(freq_start)}-{int(freq_end)} MHz\", transform=dspec_ax.transAxes, ha=\"left\", va=\"top\", color=\"kw\"[DARK_MODE])\n",
    "fig.text(0.475, 0.48, r'Delay, $\\tau$ [ns]', ha='center', va='center', color=\"kw\"[DARK_MODE])\n",
    "\n",
    "# dspec_ax.fill_between(delays, 0, cutoff, color=('0.915', '0.35')[DARK_MODE])\n",
    "# dspec_ax.arrow(\n",
    "#     0, cutoff, 0, -0.175, color=\"kw\"[DARK_MODE], width=20,\n",
    "#     length_includes_head=True, head_width=70, head_length=exp_std,\n",
    "# )\n",
    "# dspec_ax.fill_between(delays, exp_amp-n_sig*exp_std, exp_amp+n_sig*exp_std, color=('0.915','0.35')[DARK_MODE])\n",
    "# dspec_ax.arrow(\n",
    "#     0, exp_amp+n_sig*exp_std, 0, -(n_sig+1)*exp_std, color=\"kw\"[DARK_MODE], width=20,\n",
    "#     length_includes_head=True, head_width=70, head_length=exp_std\n",
    "# )\n",
    "noise_bbox = dict(boxstyle=\"round\", ec=\"kw\"[DARK_MODE], fc=\"wk\"[DARK_MODE], alpha=1)\n",
    "# dspec_ax.text(0, dspec_ax.get_ylim()[0]*2, r\"$\" + f\"{n_sigma}\" + r\"\\sigma$ Expected Noise\", bbox=noise_bbox, ha=\"center\", va=\"bottom\", color=\"kw\"[DARK_MODE])\n",
    "\n",
    "ext = (\"\", \"_dark_mode\")[DARK_MODE]\n",
    "if True:\n",
    "    fig.savefig(f\"data_to_sim_comparison{ext}.pdf\", dpi=150, bbox_inches=\"tight\")\n",
    "    fig.savefig(f\"data_to_sim_comparison{ext}.png\", dpi=150, bbox_inches=\"tight\")\n",
    "    fig.savefig(f\"data_to_sim_comparison{ext}.jpg\", dpi=150, bbox_inches=\"tight\")\n",
    "#     fig.savefig(\n",
    "#         f\"data_to_sim_comparison_{pol}_pol_spw_{spw+1}{ext}.pdf\",\n",
    "#         dpi=150, bbox_inches=\"tight\",\n",
    "#     )\n",
    "#     fig.savefig(\n",
    "#         f\"data_to_sim_comparison_{pol}_pol_spw_{spw+1}{ext}.png\",\n",
    "#         dpi=150, bbox_inches=\"tight\"\n",
    "#     )\n",
    "#     fig.savefig(\n",
    "#         f\"data_to_sim_comparison_{pol}_pol_spw_{spw+1}{ext}.jpg\",\n",
    "#         dpi=150, bbox_inches=\"tight\"\n",
    "#     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "24bef5e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pol = \"ee\"\n",
    "ai, aj = 0, 2\n",
    "plot_bl = (ai, aj, pol)\n",
    "autos = auto_uvdata.get_data(0,0,pol)[lstbin_lst_select][:,freq_select].real\n",
    "n_samples = all_data[\"data_lstbin\"].get_nsamples(ai, aj, pol)[lstbin_lst_select][:,freq_select]\n",
    "n_samples = np.where(n_samples, n_samples, np.inf)\n",
    "dt = all_data[\"data_lstbin\"].integration_time[0]\n",
    "dnu = all_data[\"data_lstbin\"].channel_width[0]\n",
    "noise_var = autos**2 / (n_samples * dt * dnu)\n",
    "wgts = taper / taper_norm\n",
    "\n",
    "dly_diff_mat = delays[:,None] - delays[None,:]\n",
    "freqs_GHz = freqs_MHz / 1e3\n",
    "sigs = np.sum(wgts**2 * noise_var, axis=0)\n",
    "dly_dly_cov = np.sum(sigs[:,None,None] * np.cos(2*np.pi*freqs_GHz[freq_select,None,None]*dly_diff_mat[None,:,:]), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "0b1c0613",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_trial = 100\n",
    "shape = (n_trial,) + noise_var.shape\n",
    "scale = np.sqrt(noise_var)[None]\n",
    "x = np.random.normal(loc=autos[None], size=shape, scale=scale) + 1j*np.random.normal(loc=0, size=shape, scale=scale)\n",
    "x = np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(taper[None]*x, axes=(1,2)), axes=(1,2)), axes=(1,2)) / taper_norm\n",
    "x = np.abs(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "939e530d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2274533150227231, 0.15865525393145707)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_dly = np.abs(delays > 2000)\n",
    "np.sum(x[:,:,high_dly] > cutoff) / x[:,:,high_dly].size, 1 - cdf_cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0202254c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "041d5a4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15865525393145707"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - cdf_cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfc397fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "36553cc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1000.0, 1000.0)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t2LGj3fXm8iNHjrS5ngAAtMWNo7I0LDNFk/+0SpL04/F9ddt5vb0K/m4claXRfTrrqudXqbquUdPPydTPJ/UnoAQAhDSvWyrHjRunlJQU5eXlafPmza3WL1q0SJJ09dVXuyyrW7dukqQNGzbYXf/1119Lkvr06eNhbQEAcF9ibNO719hoqx666gyfBH990xPVJ62pnKuGdSegBACEPK+DytjYWM2aNUuSNGvWrBZjK+fOnautW7dq/PjxGj16dPPyF198UYMHD9bs2bNblDV16lRJ0ptvvqlly5a1WPef//xHCxYskNVq1XXXXedttQEAcKmkslaS1CUpzmFCOk+kJ8c1lV9R67MyAQAIFK+7v0rSQw89pOXLl2vNmjUaMGCAJkyYoP3792vdunVKS0vT/PnzW2xfUlKi3NxcHTx4sMXyqVOn6oYbbtDbb7+ta6+9VqNGjVLfvn21d+/e5tbLJ554QoMGDfJFtQEAcKr4RNCXnhTr03LN8oorCSoBAKHP65ZKSYqPj9eKFSv08MMPKyEhQUuXLtW+ffs0Y8YMbd68Wf3793erHIvFooULF+qVV17RxIkTtXv3bi1ZskT79u3TlVdeqQ8++EAPPvigL6oMAIBLJZV1kqT0pDifltuluaWyzqflAgAQCBajrRNJhglzShFHU44AAPD8p7s095Odunl0lp6+3nXCOXf9fdUe/e697bp2eA89f8sIn5ULAAgvoRKz+KSlEgCAcGSOqfR1S6VZXgndXwEAYYCgEgAAB04Glb4eU0lQCQAIHwSVAAA4YI55NLO1+kp6clOQao7ZBAAglBFUAgDgQHt3fy2rrlNDo82nZQMA4G8ElQAAOFDcTkFlp4RYWS2SYUhHqmitBACENoJKAADsqKlvVEVNgySpi4+DyiirRZ0Tm8pkrkoAQKgjqAQAwA6z62tslFUdO0T7vHxzrsriCoJKAEBoI6gEAMAOM4lOWlKsLBaLz8s3M8qSrAcAEOoIKgEAsKPkRAtiFx9nfjV1YVoRAECYIKgEAMCO9sr8ajKnKSmh+ysAIMQRVAIAYMfJoDK2Xco/2f2VoBIAENoIKgEAsMMc69huLZXN3V8ZUwkACG0ElQAA2NFec1Sa0hlTCQAIEwSVAADYYY51TG+nRD0ElQCAcEFQCQCAHcXtPaYyuanc0qo6NTTa2uUzAADwB4JKAADsaJ5SpJ26v6YlxslqkQxDOlLNuEoAQOgiqAQA4DS1DY06VtMgqf3GVEZZLeqceCIDbAVBJQAgdBFUAgBwmtITGVljoixK6RDTbp/DuEoAQDggqAQA4DRmkJeWGCer1dJun0NQCQAIBwSVAACcxgzyzGQ67cVMAkRQCQAIZQSVAACcxhzj2F7jKU0nWyoZUwkACF0ElQAAnObkdCLtHFSemAPTzDQLAEAoIqgEAOA0xRV+CipPlF9M91cAQAgjqAQA4DTNYyqT2ndMZZcTLZXFtFQCAEIYQSUAAKcxg0oz6GsvJxP1MKYSABC6CCoBADiNGeR1aefur2b5R6pq1Wgz2vWzAABoLwSVAACc5uSUIu0bVHZOjJXFItkMqaya1koAQGgiqAQA4BT1jTaVV9dLav9EPdFRVnVKYK5KAEBoI6gEAOAUpSe6vkZZLUrtENPun9c8rrKClkoAQGgiqAQA4BRmi2FaYqysVku7f57ZGkpLJQAgVBFUAgBwCn/NUWlqnquSaUUAACGKoBIAgFMU+ylJj8mctoSWSgBAqCKoBADgFM2ZX0+MdWxvzS2VBJUAgBBFUAkAwCnMhDntPUelqTlRTyWJegAAoYmgEgCAU5gtlV381P3V7GZbwphKAECIIqgEAOAUJ7u/+mlMJdlfAQAhjqASAIBT+DuoND+ntKpONpvhl88EAMCXCCoBADiFObYxPdk/iXrSToypbLQZKj9e75fPBADAlwgqAQA4ob7RpiNVJ4JKP7VUxkRZlZoQI4m5KgEAoYmgEgCAE8yA0mqROiX4p6VSYlwlACC0EVQCAHCC2VLYOTFOUVaL3z43naASABDCCCoBADjhZJIe/7VSSienFaH7KwAgFBFUAgBwgpmkx19zVJrMINb8fAAAQonPgsqamhrNmTNHAwcOVHx8vHr06KGZM2eqsLDQo/J2796tO++8U3369FF8fLy6dOmi888/X3/84x99VWUAAFowWyq7+ClJj4nurwCAUOaToLKmpkYXX3yxHnvsMVVWVmrKlCnKysrS/PnzNXLkSOXl5bWpvCVLlmjo0KF65ZVXlJaWpuuuu04jRozQ3r179de//tUXVQYAoJWSE91P0/3cUkmiHgBAKIv2RSFPPvmk1qxZo7Fjx+rjjz9WUlKSJGnu3Ln65S9/qZkzZ2rlypVulbVlyxbdfPPNSk5O1ieffKLx48c3r7PZbNq0aZMvqgwAQCuBG1Npdn8lqAQAhB6vWyrr6+v1wgsvSJJeeuml5oBSku677z4NGzZMX3zxhTZu3OhWeXfffbfq6ur0j3/8o0VAKUlWq1WjRo3ytsoAANhV3BxUBqb7K4l6AAChyOugcvXq1SovL1d2drZGjBjRav306dMlScuWLXNZ1vbt27Vq1SoNHDhQV199tbdVAwCgTUoqmhLl+DuoNBMDlVbWyWYz/PrZAAB4y+vur1u2bJEkjRw50u56c7m5nTOffvqpJOnSSy9VTU2NFi5cqA0bNshisWjYsGG68cYb1bFjR2+rDACAXSUBaqlMS2z6vAaboaPH69Up0b/dbwEA8IbXQWV+fr4kKTMz0+56c7m5nTM5OTmSpA4dOujss89Wbm5ui/WzZ8/W4sWLNXHiRG+qDABAKw2NNh2pPtFSmezfoC422qqUDjE6erxeJZW1BJUAgJDidffXyspKSVJCQoLd9YmJiS22c6asrEyS9Kc//UlHjhzRO++8o/LycuXm5urWW29VSUmJpk6dqoMHD7pdvyFDhtj919aMtACA8Hakuk6GIVksUucE/wd1ZnKgYpL1AABCjNdBpWE0jf2wWCxO17ujsbFRktTQ0KA33nhD1113nVJSUjRw4EC9+eabGj16tMrKyvTSSy95W20AAFowx1N2TohVdJTPpnF228m5Kuv8/tkAAHjD6+6vycnJkqSqqiq766urqyWpRVZYV2X17NlTl112Wav1P/rRj/T111/r888/d7t+Zpfa0w0ZMsTtMgAA4c8cT9nFz3NUmsy5MUvIAAsACDFev4rt1auXJKmwsNDuenO5uZ0zffr0kST17t3b6frDhw+3sZYAADgXqCQ9pi7NLZUElQCA0OJ1UDl8+HBJ0qZNm+yuN5cPGzbMZVnmlCRHjhyxu760tFSSe62eAAC0xcmgMjBJcszPJagEAIQar4PKcePGKSUlRXl5edq8eXOr9YsWLZIkt+advPjii5WYmKi8vDwVFBS0Wm92e3U0fQkAAJ4qrghwS+WJ7q/FdH8FAIQYr4PK2NhYzZo1S5I0a9asFmMr586dq61bt2r8+PEaPXp08/IXX3xRgwcP1uzZs1uUlZCQoLvvvlv19fW66667WpT14Ycf6rXXXpPFYtFPfvITb6sNAEALZoKc9ECNqSRRDwAgRHmdqEeSHnroIS1fvlxr1qzRgAEDNGHCBO3fv1/r1q1TWlqa5s+f32L7kpIS5ebm2p0aZM6cOVq1apXee+89DRgwQGPGjNHhw4e1du1a2Ww2PfHEEzr33HN9UW0AAJoFekxlOmMqAQAhyic50+Pj47VixQo9/PDDSkhI0NKlS7Vv3z7NmDFDmzdvVv/+/dtU1meffaYnnnhCqamp+uCDD5STk6NJkybp3Xff1YMPPuiLKgMA0MLJ7q8BGlN5ooW0tLKuTdNxAQAQaBYjQu9c5pQijqYcAQBEllG/W66Sylq9e/d4ndUzxe+fX1PfqMEPfyhJ2vLIZUpJiPF7HQAAwSVUYhb/z+4MAECQabQZOlIV2Hkq42OilBzfNCqlmC6wAIAQQlAJAIh4ZdV1shmSxSJ1TgxM91eJuSoBAKGJoBIAEPHMIK5TQqxiogJ3ayRZDwAgFBFUAgAiXqCT9JjSk5s+n7kqAQChhKASABDxAj2diInurwCAUERQCQCIeCUVdZICH1Q2d389UR8AAEIBQSUAIOIFS0ulOVclLZUAgFBCUAkAiHjmFB7mmMZAIVEPACAUEVQCACJeSWWwdH9tCmrN+gAAEAoIKgEAEa/kRLbVLsmBDiqbPr+4slaGYQS0LgAAuIugEgAQ8czupl0Cnf31RFBb12BTRW1DQOsCAIC7CCoBABHNZjNUWhUc3V/jY6KUFBctibkqAQChg6ASABDRyqrr1Ghr6mqalhTYRD3SydbKEoJKAECIIKgEAEQ0MylOakKMYqICf1skWQ8AINQE/u4JAEAABcsclSamFQEAhBqCSgBARDsZVAa+66tEUAkACD0ElQCAiGYmxKGlEgAAzxBUAgAimjl2MWiCyuSmFtPiCsZUAgBCA0ElACCiNc9RmRwkQSUtlQCAEENQCQCIaGb31y7B0lJ5oh7MUwkACBUElQCAiNacqCc5OBL1ZCSfbKk0DCPAtQEAwDWCSgBARAvWKUVqG2yqrG0IcG0AAHCNoBIAELFsNkOlQZaop0NslBJjoySdTCIEAEAwI6gEAESso8fr1WBr6mKaFiTzVEpSejLJegAAoYOgEgAQscygrWN8tOKiowJcm5OaM8CSrAcAEAIIKgEAEau4OUlPcHR9NaWfaDWlpRIAEAoIKgEAEaskyMZTmpqnFWFMJQAgBBBUAgAiVvMclUHXUslclQCA0EFQCQCIWGb30i5B1lLZhUQ9AIAQQlAJAIhYZiKc9CDK/CqdkqiHoBIAEAIIKgEAEcsM2oJtTGWXZBL1AABCB0ElACBiBXuinpIKEvUAAIIfQSUAIGKVBO2UIk31OV7fqKrahgDXBgAA5wgqAQARyTAMlTa3VAbXmMrEuGh1iImSRBdYAEDwI6gEAESkY8cbVNdokxR83V8lKZ1xlQCAEEFQCQCISMWVNZKk5LhoxZ9oFQwmzFUJAAgVBJUAgIhUfCIJTpcgG09pMufOLK4kWQ8AILgRVAIAIlKwTidiMpMHldBSCQAIcgSVAICIdDLza3Al6TE1TyvCmEoAQJAjqAQARJy9JVVatuV7SdL+0mrtLakKcI1as8qQJH26/ZCe/mBHUNYRAACJoBIAEGHe2lCgS+au1Kb8cklSzvfHdMnclXprQ0FgK3aKtzYU6PnPdkuSio7Vat7KvKCrIwAAJoJKAEDE2FtSpdnvfKtGm9FieaPN0Ox3vg2K1kCzjqdVMajqCADAqQgqAQARY+HXBa0CSlOjzQiKlsBQqCMAAKfyWVBZU1OjOXPmaODAgYqPj1ePHj00c+ZMFRYWelXurl271KFDB1ksFk2ePNlHtQUARKLCsmoX64/7qSbO6hD8dQQA4FQ+CSpramp08cUX67HHHlNlZaWmTJmirKwszZ8/XyNHjlReXp7HZf/0pz9VbS2Z7wAA3svslOBifQc/1cRZHYK/jgAAnMonQeWTTz6pNWvWaOzYsdq5c6cWLlyodevW6dlnn1VxcbFmzpzpUbmvvPKKVqxYoTvvvNMX1QQARLibRmcpymqxuy7KatGNo7L8XKPWQqGOAACcyuugsr6+Xi+88IIk6aWXXlJSUlLzuvvuu0/Dhg3TF198oY0bN7ap3MOHD+tXv/qVLrnkEt1yyy3eVhMAAPVNT9RT04bq9JgtymrR09OGqm96YmAqdgqzjqcHlsFURwAATuV1ULl69WqVl5crOztbI0aMaLV++vTpkqRly5a1qdx77rlHx48f1//+7/96W0UAAJrdOCpLd12QLUnqnhKvuy7M1vL7LtANQdQCeOOoLC2/7wKd0ztVknRm945BV0cAAExeB5VbtmyRJI0cOdLuenO5uZ073n//fS1cuFAPPvig+vfv720VAQBoobq+UZJ09bDuun/y4KBs/eubnqibRvWSJKUlxQZlHQEAkHwQVObn50uSMjMz7a43l5vbuVJVVaWf/exnGjRokO6//35vqwcAQCtmBtWszs6T4gRaZuempDwHyPgKAAhi0d4WUFlZKUlKSLB/Y05MTGyxnSsPPfSQ9u/fr88++0yxsbHeVk9DhgyxuzwvL0/Z2dlelw8ACD0FR5qm7chykWk10Mz6FZYdl81myOoggQ8AAIHkdUulYTRN0Gyx2L/RmevdsWHDBr3wwgu6/fbbNWnSJG+rBgBAK4ZhnNJSGdzTc3RPiVeU1aK6RpsOVzC9FgAgOHndUpmcnCypqduqPdXVTW+DT80Ka09DQ4PuvPNOpaSk6JlnnvG2Ws1ycnLsLnfUggkACG9Hj9ersrZBktQzNbhbKqOjrOqeEq/CsuMqLKtWt5T4QFcJAIBWvA4qe/VqSiJQWFhod7253NzOkcLCQn3zzTfq1q2bbrjhhhbrysvLJUnr16/XhRdeqKSkJL377rte1hwAEIkKjjS1UqYnxalDbFSAa+NaVqcEFZYdV0FZtUb16Rzo6gAA0IrXQeXw4cMlSZs2bbK73lw+bNgwt8orKipSUVGR3XVlZWVauXKlUlJSPKgpAABSQdmJ8ZRB3vXVlNW5g77aczIYBgAg2Hg9pnLcuHFKSUlRXl6eNm/e3Gr9okWLJElXX32103L69OkjwzDs/luxYoUk6fLLL5dhGM0tlwAAtFXhiaAyM8iT9JjMeprJhQAACDZeB5WxsbGaNWuWJGnWrFktxlbOnTtXW7du1fjx4zV69Ojm5S+++KIGDx6s2bNne/vxAAC0idnil9UpdFoqpZPToAAAEGy87v4qNU0Dsnz5cq1Zs0YDBgzQhAkTtH//fq1bt05paWmaP39+i+1LSkqUm5urgwcP+uLjAQBw28nur6HRUmlOK2LWGwCAYON1S6UkxcfHa8WKFXr44YeVkJCgpUuXat++fZoxY4Y2b96s/v37++JjAADwWqjMUWkyu78ePFqjhkZbgGsDAEBrFqMtE0mGEXNKEUdTjgAAwo9hGBr88IeqbbDp8/+5UH3SEwNdJZdsNkODH/lQdQ02rfr1pJBpYQUAeC9UYhaftFQCABAKiitrVdtgk8Ui9UgNjTGVVqtFmSfqShdYAEAwIqgEAEQMM0lP947xio0OnVtg5onWyUKmFQEABKHQuaMCAOCl5ulEQqwLaWYnWioBAMGLoBIAEDHMaTkyQ2Q6EZOZVIhpRQAAwYigEgAQMUIt86vJnKvSrD8AAMGEoBIAEDHMlr5Qy6CayVyVAIAgRlAJAIgYZlAWet1fm+p76FitahsaA1wbAABaIqgEAESERpuh78tDs6Wyc2KsEmKjJEkHGFcJAAgyBJUAgIhQdKxG9Y2GYqIs6tYxPtDVaROLxXJKBliCSgBAcCGoBABEhMITSW56pHZQlNUS4Nq0nZlciGQ9AIBgQ1AJAIgIBSE6nYjJ7LLLtCIAgGBDUAkAiAihOp2I6WT3V1oqAQDBhaASABARQnU6EZM5rUgh3V8BAEGGoBIAEBFCdToRU1bnpnrT/RUAEGwIKgEAEcFs4csM2e6vTfUurapTVW1DgGsDAMBJBJUAgLBX12BT0bEaSSdb/EJNSocYdYyPlkRrJQAguBBUAgDC3sGjx2UzpLhoq7okxQW6Oh4zx4MyrQgAIJgQVAIAwl7BkZPTiVgsoTdHpcnMXFtIBlgAQBAhqAQAhD0zSU+oZn41mV13C+j+CgAIIgSVAICwZ7bsheoclSYzWQ/dXwEAwYSgEgAQ9k7t/hrKmFYEABCMCCoBAGEvbLq/mi2VjKkEAAQRgkoAQNgzW/ZCvftrzxMtrRU1DTpaXR/g2gAA0ISgEgAQ1mrqG1VcUSspdOeoNCXERis9KVYSrZUAgOBBUAkACGtmkp6kuGildIgJcG28l8m0IgCAIENQCQAIa+b0G6E+R6XJTDZkJh8CACDQCCoBAGGt8Eh4JOkxmd+D7q8AgGBBUAkACGuntlSGg6zm7q+0VAIAggNBJQAgrBWYLZUhnvnVZCYbMr8XAACBRlAJAAhrzdOJhEn318xTWioNwwhwbQAAIKgEAIQ5c+xhqE8nYuqRGi+LRTpe36iSyrpAVwcAAIJKAED4qqipV3l1vaSTLXyhLi46St06xktiWhEAQHAgqAQAhC2z62unhBglxUUHuDa+0zytCMl6AABBgKASABC2CsJsOhGTmXSIZD0AgGBAUAkACFvhNp2IKbMz04oAAIIHQSUAIGyZYw7DZToRkxkkM6YSABAMCCoBAGGr4MiJlkq6vwIA0G4IKgEAYetkS2V4dX81p0c5UH5cjTbmqgQABBZBJQAgLBmG0dySFy7TiZi6dYxXtNWi+kZDhytqAl0dAECEI6gEAISl8up6VdU1Sgq/RD3RUVZ1T22aq9Ls4gsAQKAQVAIAwlLBia6vGclxio+JCnBtfI9xlQCAYEFQCQAIS81JesKsldJkBpVMKwIACDSfBZU1NTWaM2eOBg4cqPj4ePXo0UMzZ85UYWGh22WUl5drwYIFuvXWW3XmmWcqMTFRycnJGjNmjP785z+rvr7eV9UFAIS55iQ9YZb51WQGywVMKwIACDCfBJU1NTW6+OKL9dhjj6myslJTpkxRVlaW5s+fr5EjRyovL8+tcp555hn94Ac/0MKFC5WQkKBrrrlG5557rrZs2aJf/OIXuuiii1Rdzc0TAOBaQZjOUWkyg2W6vwIAAs0nQeWTTz6pNWvWaOzYsdq5c6cWLlyodevW6dlnn1VxcbFmzpzpVjlJSUl68MEHlZ+frw0bNujf//63Pv30U3377bfq1auXVq9erd/97ne+qDIAIMyZ3V/N6TfCjfm96P4KAAg0r4PK+vp6vfDCC5Kkl156SUlJSc3r7rvvPg0bNkxffPGFNm7c6LKsBx54QE888YR69uzZYvmAAQP09NNPS5L+9a9/eVtlAEAEMFsqw206EZPZAnvw6HHVN9oCXBsAQCTzOqhcvXq1ysvLlZ2drREjRrRaP336dEnSsmXLvPqc4cOHS5K+//57r8oBAIQ/wzB04EQLXrh2f01PilNstFU2QzpYzlyVAIDA8Tqo3LJliyRp5MiRdteby83tPLVnzx5JUrdu3bwqBwAQ/ooralXbYJPVoub5HMON1WohWQ8AICh4HVTm5+dLkjIzM+2uN5eb23nqz3/+syRpypQpXpUDAAh/ZpDVPaWDYqLCd/ask9OKEFQCAAIn2tsCKisrJUkJCfa7FyUmJrbYzhPz5s3T8uXLlZqaqgceeKBN+w4ZMsTu8ry8PGVnZ3tcJwBA8DKT14TrHJWm5pbKIyTrAQAEjtevbw3DkCRZLBan6z21cuVK3XvvvbJYLHr11VfVo0cPr8oDAIQ/c5qNcJ2j0tQ8rQgtlQCAAPK6pTI5OVmSVFVVZXe9Oa/kqVlh3bV161ZNnTpVdXV1ev7553Xddde1uYycnBy7yx21YAIAQl/zdCJhmqTHZH4/5qoEAASS1y2VvXr1kiQVFhbaXW8uN7dzV15eni6//HKVl5fr0Ucf1d133+1dRQEAEaOw3JxOJDK6vzJXJQAgkLwOKs2pPjZt2mR3vbl82LBhbpf5/fff69JLL1VRUZHuvfdezZkzx9tqAgAiSHNLZYR0fz1cUaua+sYA1wYAEKm8DirHjRunlJQU5eXlafPmza3WL1q0SJJ09dVXu1VeWVmZLr/8cu3du1c/+tGP9Nxzz3lbRQBABGm0Gfq+3Awqw7ulslNCjBJjoyTRWgkACByvg8rY2FjNmjVLkjRr1qwWYyvnzp2rrVu3avz48Ro9enTz8hdffFGDBw/W7NmzW5RVXV2tK6+8Utu2bdONN96ol19+2WECIAAA7Dl49LgabIZioizKSA7POSpNFoulubWSaUUAAIHidaIeSXrooYe0fPlyrVmzRgMGDNCECRO0f/9+rVu3TmlpaZo/f36L7UtKSpSbm6uDBw+2WP6b3/xGa9euVVRUlKKjo/XjH//Y7uf94x//8EW1AQBhyGyx65naQVHW8H8xmdmpg3YUVaiAlkoAQID4JKiMj4/XihUr9NRTT2nBggVaunSpOnXqpBkzZujxxx9XVlaWW+WUlZVJkhobG7VgwQKH2xFUAgAciZTpREyZJzLAFpIBFgAQIBbD24kkQ5Q5pYijKUcAAKFnb0mV/uftLdq4v0xndO+ov/xgpPqmJwa6Wu3qDx/u0F8+z1P3lHhNObunbhqdFfbfGQAiRajELF6PqQQAIBi8taFAl8xdqY37m3q9bD94TJfMXam3NhQEuGbt560NBZq3Mk+SdPBojeatzAv77wwACD4ElQCAkLe3pEqz3/lWjbaWnW8abYZmv/Ot9pZUOdgzdJnf+bSvHNbfGQAQnAgqAQAhb+HXBa0CSlOjzQjLlrtI/M4AgOBEUAkACHmuptMIxzkcI/E7AwCCE0ElACDkmRlQHa/v4Kea+E8kfmcAQHAiqAQAhLybRmc5nJMyymrRjaPcm9oqlETidwYABCeCSgBAyOubnqjHpgxptTzKatHT04aG5RQbfdMT9dS0oa0CS6tFYfudAQDBKTrQFQAAwBd6pjZ190yMjdJFgzOU2TlBN44K7zkbbxyVpdF9OuutDQVatKFAxZV1+u8LsnUDrZQAAD8iqAQAhIXPc4slSdee3UNPTRsW4Nr4T9/0RN0/ebDSk+L0+LvfaUtheaCrBACIMHR/BQCEPMMw9NmOw5KkCwdlBLg2gTFpUBdJ0vq9R1RZ2xDg2gAAIglBJQAg5O0tqVL+kWrFRFk0vn96oKsTEP26JKlPWoLqGw19ubsk0NUBAEQQgkoAQMhbcaLr65i+aUqMi9yRHWYr7ee5hwNcEwBAJCGoBACEvBXNXV+7BLgmgTVpcFNQuWJHsQzDCHBtAACRgqASABDSqmobtG5vqaSTQVWkGtO3szrERKnoWI22H6wIdHUAABGCoBIAENK+3F2i+kZDvdMS1C+Mpw9xR3xMlMb1T5MkraALLADATwgqAQAhzRxPOWlQhiwWS4BrE3iMqwQA+BtBJQAgZBmG0Rw8Rfp4SpP5O2zcX6by6roA1wYAEAkIKgEAIWtHUYUOHq1RfIxV5/VLC3R1gkJmpwQN7JokmyF9sYupRQAA7Y+gEgAQssxxg+dnpys+JirAtQkek8wusDvoAgsAaH8ElQCAkPX5jhPjKSM86+vpzN/j853FstmYWgQA0L4IKgEAIelodb025pdJki4cyHjKU53Tu5OS46J1pKpOWw8cDXR1AABhjqASABCSvthVrEaboQEZScrqnBDo6gSVmCirJgxMlyR9RhdYAEA7I6gEAIQkczwlXV/tY2oRAIC/EFQCAEKOzWZo5SnzU6I1c2qRrYVHVVxRG+DaAADCGUElACDkfHvgqEqr6pQUF61RfToFujpBKSM5XkN7pkiSVu4sDnBtAADhjKASABByzHGCEwakKyaKW5kjk060Vq5gXCUAoB1FB7oCQCTbW1KlhV8XqLCsWpmdEnTT6Cz1TU/0eDsgUpjjBOn66tyFgzP0/Ge79cWuYtU32gjAEdG45wLth6ASCJC3NhRo9jvfqvGUOeReXrVHT00bqhtHZbV5OyBSFFfUakth0zQZ5rhB2Dc8M1WdE2N1pKpOm/aXaUy/tEBXCQgI7rlA++KVJRAAe0uqWt20JKnRZmj24m+Vd7jS9XbvfKu9JVV+qzMQLL44MT7wrJ4dldExPsC1CW5RVosuODGH54pcxlUiMrlzLzUMQ9sPHtPsxVu55wIeoKUSCIAF6/Jb3bRMjYahi+euVEJslAzDcLydzdBbGwp0/+TB7VlVIOisoOtrm1w4qIuWbD6gz3MP64EruF4g8iz8usDpvfSy51aqwWbIsL9J83bccwHHCCqBduBoPEZJZa0WrMvXP7/a57KM6rpGl9sUlh33QW2B0NHQaGtuqbyQoNItEwd0kdUi7Siq0IHy4+qZ2iHQVQL8Kr/UeQtjfaOTaPIUH24r0i2je6lXWkLzMsZfAk0IKgEfszce429f5Glkr07aeuCo6hpsLsu44/w+mjmur174bJfe3ljocLtDR4+rsrZBSXGcyogMm/LLdaymQZ0SYnR2VmqgqxMSOiXGakSvTtq4v0yf5x7WD8b0DnSVAL+ob7Tp7Q2FWrnLedfvW8/tpV9cOkB/XblHr6ze63C7vSVVuuCZFbp4cFfNHN9HhUeqNXvJNsZfAiKoBHzK0bgNmyFt2F8mSRqemaKrh/fQ0+/vUKOdvjZRVotmnN9HvdIS9LNJ/fXO5gMOu+2s31emCb//THddmK0fntdHRcdqeGOKsGZ2fb1gYBdFWS0Brk3ouGhwhjbuL9OKHcUElQg7p7cWTj8nU1sKyvXnT3cp/0i1032jrBbdObGfMpLjddt5vfWPNfvs3nOtFumc3p309b4yLd9+SMu3H7Jbnjn+cnSfztx/EVEIKgEfcjZuQ5KuG9FTc28cLovFopQOMa0C0CirRU9PG9p8I+qbnqinpg1tvZ3FopvPzdJXeaXaU1KlJ9/foRc+263K2oYWY0J4Y4pwY863OGkwXV/b4sJBXfTHj3L15e4S1TY0Ki46KtBVAnzCXu+geSvzmv9/elKcZk3KVky0VY/8J8eze+6J7W4YlaXdhyv1jzV79e/1BWog5wHQjKAS8JFjNfXNc+c50mAzZLE0ta7cOCpLo/t01lsbClRYdlyZnTroxlGtWxadbdfQaNOSzQf0zMe5OnSsttXn8cYU4WJvSZVeWb1HO4oqJEm9Oye42AOnOrN7R3VOjNGRqnrd/sp6jejViZ4MCHmOegeZfjKxn35xyQAlxDY97p6fne7VPVeS+mck6XdTh6q4ok4f5RQ5rBs5DxBpCCoBNzlLvjP/y73655r9qqhtcFpGZqeWCTL6pie69SbT0XbRUVbdMCpLOw9V6uVVe+zuyxtThDp7LRHXz/uKVvg2eHtjocqq6iVJ6/Ye0bq9R+jJgJA3/8u9TnsHRVktzQGl5P099/RtnNl9uFL7S6vUO61pOxL6INwRVAJucJR8Z2y/NG3ML1NNfVPynV6dE1RYVi1797goq6XdHt4OHnX+RvTtDQUa1jNFl57ZVdFRVm5uCBmu5pejFd418zc8/bLEb4hgZ+9e1SctQV/tKdUba/frg28dtxRK7dtaeNPoLL28ao/DoHb7wWOa9MznumZ4D2V3SdSfP91NQh+ENYJKwAVnyXe+zCuV1JR852eT+uvSM7pq0aZCl2MlfS2zk/OugCWVdbrrzU3q2jFOwzJT9On2wy0CX0c3N4JPBJqr+eVohXeN3xDBxp17i72XuX/9Ik/pSbEqrqhz63NO7x3kS87GX/50Yj/lfH9MK3cW6z/ffG93f17qINwQVCLiubq5/Wt9vtPuNdcM667nbxnR5rGSvuTsjanVIt1ybpY+yjmkQ8dq9cl3rcd92ru52buh82YV/lZY5jxzI+OWXOM3RDBx596yt6RKsxd/2ypDumFIxRV16hBj1fXnZGrSoAz95PWNdu997dk7yOTqfr/twFHd++/Nyiu2P0+mvZc6vMxFqCKoRMhx94Lr6ZvQl1ft0a8uH6SO8TFatatYn3xnP214M4ulOaA0uTtuw1fcyVj3yDWN+tmbm/TpdvvJhBpthm6c95VG9k5VYly0lmw+oNNnPHH2ZpUbIdpDz1TnLQ3t2RIRLlz1ZOA3hK+4ug84685+/6Ktev/bgzp6vF65RRV2p9wy3Tqmtx6++kxJcnrv88c9yNn9/qyeKTqje0eHQaUkLdpYqLTEWE0c2EXf5Je5Pe+lL5+FAF8gqERIcbf1zO03oQ5ubk9/sMPtOgXLA5mrN6Zx0VHqEON8GoHiylp9lOM8iG60GXr83e/0y8sGqn9GkuKio2jVRLuprK13uM4fLRHhwFlPBouk6SMz/V8phB1H94Enpp6lUX06a0fRMb262nFiHUPS57nFbn3W4YqT2c4D0TuoLVy91CmuqNXv3tsuvbfd7npvehJxb4Y/WQzDyaugMDZkyBBJUk5OToBrApM7bzgvmbvSfjcXi0Vv3zVWXZLilFdcqZn/+NpushyLpPOz03S8vlG7D1fqWI3jbK3dUuJ167m91D8jSXcv2Gz3rWmU1aLl910QNDcvV57+YEeL+btOd8VZ3TQ2O02vrdnn9M2qKdpqUVanBO0rrWqVBESy//vw1hTu+vf6fD3wzreSmrpxn3pOn9oKD9fsPVya7pzQV7+56swA1AqhwJ1rtrP7c1sM6dFRd1/UX598d1iLNxU63O6uC7NDZhyws9/GapH++4Js5Xx/TKt3F6vR5ricjOQ4De2Zothoiz7cdsjuPddqkd748Rj175qkQ8dqNPXFNW4/u3BvDl6hErMQVAb5AYoU9h54oqwWPTVtqKaPzFRh2XE98f53LlvRfOma4T30wi0jnNYv1B5qnQbmp9xkXAWf3VPiVV3XqKPHHbcimX48vm9zNyVnx5m3pjjVql3FumP+12q0Gbrnov66bmRm0LZEhIq9JVUtfsPOibF64kTryONThuiHY/sEtoIIOq6u2TX1jdqcX65nPs7Vxv1lDsuJtlo0NDNFNfU2bT94zOF2ZrDo7r0qVLjzDPGzNzbq/W3Os9n60s2js/S7qWcpOsrKvTnIhUrM4rOgsqamRk899ZT+9a9/KT8/X507d9bkyZP12GOPKTOzbV1rysvL9eijj2rJkiUqKipSt27dNHXqVP32t79VamqqL6obMgcoErh6w5kQE6Xq+ka3yoqPsarRZqi+0fGf9dCeKfr5pGx9uK1ISx1kZZNavwk9/YEsVB9q3bm5uXND75OWoKJjNbr3X99o/b4jTj9zeGbTuJK3NhQ4nG6Ft6Yw5RZVaPr/rlFFbYOmnt1Dz910dqtxy/CNFz/bpWc+3imrRfr7jFG6aHDXQFcJfuJN7yCLpGFZKdp+sEJ1DU6a1064elh3vXjryDYFi+HyMtfk6hnCnZ5EFwzsovlr9im3qMLhdlEWi2wyWuVEsCcu2qreaQnadajS7d5G8L9QiVl8ElTW1NTo4osv1po1a9S9e3dNmDBB+/bt0/r169WlSxd99dVXys7Odqus0tJSjR07Vrt27VK/fv00atQo5eTkKCcnR/3799fatWuVlpbmbZVD5gBFAlcXUkmKjbKqY4dolVQ6TiN+1wX9dP8VZ7gsL1zfhLaFOwGyuzd0d46fO04N4nlrGrkOH6vR1Je+1PdHa3Ru3856/cfnKi7a+VhgeM4wDN2/eKve2lCohNgovfXTsTqrZ0qgq4V25s419qn3t+uvX+xxWVZGcpw6dojR7sOVDrdx5/puL1gMl5e57vBVT6K7LszWry8fpCff366XV+11uF201aIGN7orh1JX43AVKjGLT4LKRx55RI8//rjGjh2rjz/+WElJSZKkuXPn6pe//KUmTpyolStXulXW7bffrtdff13Tpk3TwoULFR3dlEvonnvu0QsvvKDbb79dr732mrdVDpkDFA4cvQ1ttBn6bMdhPbB4q0qrHAeLFw7sopdnjFJh2XG3LriR/CbU19y5obv6vRf81xgdKD+u5z7ZqQInUxckx0fr0jO6qltKvOatzHO7RRPho6q2QTf97SttO3BM/bok6p27zldqQmygqxX26httumP+en25u1RdO8Zp6c/HqXtKcCQgg+85bYG0SFcN7aaCI8f17YGjdq/DpmGZKXruprPVLz1R+0qr2/SSNpKCxbbwVU8id56FPv7FREVZLfrl21ucdl0enpWihT8Zq/hTEv3Rk8i/QiVm8TqorK+vV0ZGhsrLy7Vp0yaNGDGixfrhw4dr69at2rBhg8455xynZRUVFalnz56KiopSQUGBunY92Q2ntrZWWVlZOnLkiA4cONBinSdC5QCFOrsXSItFVw3rri2F5dpf6nz+NMmzN5y8CfUvd37v9mjRROgzH04KjlRr2/dHtb+0Wp0TY7XkZ+erdxrnob8cPV6vG+at0c5DleqXnqgLB3XR4YpaHhjDUHtdi3lJ6xu+7Enkq3tzWmKsfnBeb912Xi99nltMTyI/C5WYxespRVavXq3y8nJlZ2e3Ciglafr06dq6dauWLVvmMqj84IMPZLPZNGnSpFZBY1xcnK655hq9+uqr+uCDD3THHXd4W3WXmAPIOw6n7DAM/d+WprGMKR1idMVZ3ZyOtTv1IuVu6vC2pBj395yS4cid39vZtAZWi/T4lLNUWlWnBevyVXSsxuFnMVF7+HCUkfSWc7MIKP0spUOMXr1jtK748yrtKanSnpKT2Z+ZgiC87C91ntl7QEaSfnHJQHXsEK07Xv3aYfbQ0/8egn1qj1DhzjOJL5+FnE45ZJHSE2NVXFmn5z/dpb+s2KVGm1qNv3Q2h3WkieTYweugcsuWLZKkkSNH2l1vLje387asV1991a2yvMUcQN5b+HWB0/TiEweka94Pz1FCbLRG9u7k9uTF7gaBBIv+5er37pue6HSSavOtaXVdo9O3pgfKqnW0ul4pCTGSwvPCHAkcvXSSpHkr92j6ORxHf6tvNFRV23qaJR4YQ4uja2JNfaMWrMvX57mHne5/yZldddWw7pKkp653fM3mJW1g+epZyNW9eeqInvoop0ivrt6rTfnlDstptBl6a0NBRB//SI8dvA4q8/PzJclhhldzubmdv8ryhsMWNpuh2Yu3qntKvFI6xGjXoUo9sHhrqxY2ZzfgSHoALjzivGtrSkKsEmKb/gR5wxkZvH1rKkmb8ss1/g+f6ScT+ik1MVaP/l9O2F2Yw4Gza92hYzV6/N0ch8eYh5PAWPi1/R4jUtMxeWX1Hv1u6tAWyyPpnhYK7D6sfrFHU0b00JrdpU57gUie9w5CaHN1nK8e1kNXD+uh2/6+Vqt3lzosJ5x7ErmTLXn24m9btew32gw9sHirEmOjNLh7R5VV1zncLtRf3nkdVFZWNmX7SkhIsLs+MTGxxXb+Kstk9kM+XV5ensOMtM5a2BoN6YevrHf5uY02Qw8v3abHpgxR3/REWSyWsH0zYc/35cf1TUG5020yO7VMBMEbzsjg8VtTi0U/HNtLa/cc0Y6iCj37yU67+4fDhTnU2bvW/e2LPJ3XL02HK2qdZok0hfPDSbAqLHP+IvCNtflavatE4/qna8KAdB06VqvH3v0uIu5pocDZkJN3Nh2Q1DTH8D0XD5AkPbR0m097ByG0uXOcz+qZ6jSoPP25Llw4e36fMCBdq3aV6G8r99jtKi5JNkP6+YLNLj8n1F+oeh1Umnl+HM0h1pY8QL4syx0Hj9bo6Q92tHjbcKD8uD7bccjpftFWi7okx6mypkEVdroKmVbvLtFFz65Uz9QOOjsrRR9sK2pTq2YoMk7cvB5dlqOKGse/jb3xGIDJ2VtTm83Qu98e1MNLt+no8Xq7+4f6hTlYudMq5ehtrc2Q1uQ1PYw0jdOJU3FlrcPPCteHk2CW2cn+C12TRdK+0mrtK83Xm+vs9xhydE+jRbP9uRpyMr5/mv4+Y3RzFs/z+qXRAok2cdWTqL7BpoZGm6KjrH6uWftx1nvx14u2ul1Oh5goRUdZnD4bS9L274+1+vySylqn868HC6+DyuTkZElSVZX9gd/V1U1vPs1pRvxVlslRpqQhQ4Zo16FKzVuZp5e/2KNbx/TS/iPVWrWr2OWEsXdO7Kf7Jw92mTGrZ2q8iivqdKD8uA6UO37rHooPwPYeEJLjo/XgO9/q4++agvIRvVJ1yRldNfeTnW6PxwBMjt6aWq0WXTu8hz7aVqT3vj3ocH9aunzL2Zva60dm6tsDR/Xl7hItWLff4dtaSbp8SFf9/vphKquud5runpdO/ufsgTHKatF/fn6+Dh6t1Ze7S7R08wGVO3mp85cVu/X764fJao2sXjqB5KqluVNiXItpIWiBRFs56klkUVPynr+v3quN+WV69obh6tclKSxeJrl6WSNJZ2elKsoibXQy5vSOcX10/+TBLud//Xxnsaa+9KVuHp2lepuhR/8vR2VV9q+1wcbroLJXr16SpMLCQrvrzeXmdv4qqy0aDUOvr93f/N9nZ6Vqa2G5y2ykrm7Ab/zXeerWMV7r9pbqt8u+094SxxnXQukB2FHXtg4xUaqqa1RMlEW/uGSgfjqxn6KjrLpyaHfehsLnsjo7b1Whpct3nL2pvX/RVv32/3JUVdfoVlmx0VFKTYhVakKs0+QQXCP8z1XCjrN6puqsntKlZ3ZVSWWt3t3q+KXO2xsLtSL3sIZlpurz3MNh30snGPRMdX7N45oIX7DXk+iGczL1TUG55vwnR5vzy3Xl86s0eUg3LdtysMVLxlB7mVRaWasVO5wntpo8pKvm/XCUy3lBze9887m99PfVe+1n21VTNvxvCspdDiELRl4HlcOHD5ckbdq0ye56c/mwYcP8WpYnRvZK1XM3na3eaYlO5/Yxb4CubsDmdhcOytDaPUectmpuKyzX2j2lGtO3sywWS9C+3XH0cGkzpKq6RvVLT9SLt47UmT06Nq/jbSjag6tuOJef6d1ctpHE1fXG2ZtaQ03nfnJ8tMb2S1N1XaNW7y5x+FmnPtiSBCT4uHtMXHWVjbZaVFJZp8+cPJDZ66UTrPe+YFfb0KjdxY7HKtP6D1+y91zXr0uSzuuXpl8v2qrVu0u09JvvW+0XbC+THF1vth88pvlf7tXSb75XXYPNaRl9uzT1nnQ3JnC13YWDMvTOpkL95fM8h0N8gpXF8HKgYl1dnTIyMnT06FFt2rSp1VyVw4cP19atW7V+/XqNHj3aaVkHDx5UZmamoqOjVVBQoIyMjOZ1tbW1ysrKUmlpqQ4cOKBu3bp5U+3m7q89/usvzcuuGd5DL9xysv7uTEDr7nbO3mCc6ozuHXVWz456Z+OBFm93gmViWVddfn8ysa8evPJMP9YIkczRPIeS1K9Lol770bkuWzTDmTsP6I5eoP1u6lnql56oL3YV6421+U5vbhMGpGv+HaMVHWV1+bZ2+X0XBMXDBLzj6jh/cM8ElVXX6aGl27TLSWKmARlJenzqWRrZq5OWfnPA7UnVCT5POnq8Xv/9+kZ9tadU1hMpKU49LKdP2wS0J5vN0O2vrnf6cvGuC7MD3thg795ntTQFfXnFJ3sWDuqapF2HKx32Xjz9nuar2GHWgk3NvUG+//vPJEl1JftblRNMvG6pjI2N1axZs/TEE09o1qxZ+vjjj5uztM6dO1dbt27V+PHjWwSUL774ol588UVdd911euqpp5qXd+/eXbfccovefPNN/exnP9O///1vRUc3VfHXv/61iouLddttt3kdUDriaTZSd7Zz9mbivksH6PvyGr2z6YC2Hzym7QePtdo/WN7uuBqzcfCo48QbgK/Za1U5t09n/WbJt9pTXKVp/7tG8+8YrbN6pgS6qn7nzjg2p9MnvfOt2591Vs+U5sQM7r6tRWhzdZwHdmvKkXDxGV2dBpW7Dlfq5r+tVVy0VbV2WgTs3fsYo3nS9+XH9aP5Xyv3UIWS4qL1v7eNVGanBFr/ETBWq0WpJ+aRdiTQQ76c9brLK66S1SJdMbS7Zo7ro5G9OuntjYV+n0vdVW+QYOR1UClJDz30kJYvX641a9ZowIABmjBhgvbv369169YpLS1N8+fPb7F9SUmJcnNzdfBg6/EYf/rTn7R27VotXrxYgwcP1qhRo5STk6Nt27YpOztbzz33nC+q3Io/uoa46lb0q8sH6b/f2Ki1e47Y3T8YEvqkJcU6Xc+YDfibvQvzOz8bpzvmr9eOogrd+Nev9JcfjFTvtMSwadlwa74sR2MgF2/VN/llarAZWrvniNPeE/ExVl0+pJsGd+uoP360w+U4cxPdWiODt/POWi1Nw0O2FJSrtKrO4ec02gz99PUNmjQoQ1aLRX/9Is/tMZrh1KJ5+ncZ3aeTfrNkm4qO1SgjOU7zfzRaQ3o0vUALdCsQIpurgCjQz4quku/8YExvPT71rOb/DsQ9zdUQn2Dkk6AyPj5eK1as0FNPPaUFCxZo6dKl6tSpk2bMmKHHH39cWVnuB2vp6en6+uuvNWfOHC1dulRLlixR165dNWvWLP32t79V586dfVHlFvz5Bt3Zm4nUhFilJ8U53T+Qb3e+3ndE721xnJiBMRsIFt1S4vXWf4/VXW9s1Je7S/Wjf3wti1p2CQvVlg1nrTRXDe2u7QeP6bnlOx3eiAxDWrC+wK3PuuSMrvrzzU1DAtKSYtvU+shY6sjg8byzp3TJtNkM3TF/vb7Y5bi73M5Dldp5yPn8po02Q3M/ydXjU85SakJsWLVo2vsu81Y2/W//jCT940ejQ7JlA+HJVUCU7qKBor3tKGrdI/BU9jJb+/ueduq1M1R4PaYyVA0ZMkRFR2v067+/F1Rv0F2NWRzYNUlv/HiMMjrG+61ONpuhv63aoz9+lKtGm6EuybEqraxjzAaCXl2DTT9/c5M+2W5/7tlQG+Pn7thsV/qkJej6kZnasL9MK3cWO9zu9HEv7o4VAU7n6m/H1b1vTN/OGtIjRR/mHNT35TUuP69bSpwOHa2VvTMl3M77//v5OA3LSvVvpQAXnOU8kKTbzuulh646s8U0N+2tsrZBL3y6S3/7Yo/da4MpGMZ8mvaWVOm8c4arodFQaaHja2Qw8ElLZajqlhIfNH80Jldvd3YeqtRFz67ULy4ZoBnn91Fh2XGfdu05vXvN5CFd9fxnu5sz+E05u4eevG6oDlfU8nCJoBcbbVV2RqI+2W5/fTB0KW+Lf63PdxlQZiTHqUNMlPYfcTz++Yqh3XX3xQPcToFuovURnnL1t+Nqiq6nrx+mvumJio22Og0+O8ZH61hNg4qcjO8PtfPeVVe9D3KKCCoRdOx1Gb1+RE8t+eaAXlqRpzfW5uubgnK9dOtI2Qy167PsjaMyte37Y3rive906Jjz3B/B1uuub3qiy16MwSKiWyolKScnJ8A1ac1RNsafX5itlTuLtaXwqCSpW8d4Ha6oadVi6GnXHmdvlWKjrXr0miG65dwsWSyWtn8pIEBOzaBmz+lZnwPJ3vivPmkJyvn+mN7ZdEBvrNvvNL35ZWd21d9udz1f1qmtNM6mT6LnAfzJnb9Fd/62OyfE6qdvbHCYn0CSsjp10O+nD9N5fdNktQbvNF5SaF3DAHd8nntY/2/hNyqrrld8tFV1jTa/PMtKUq/OCXr02jNVUlkXMve+YI5ZThXRLZXBytmA4F9cMlBvbSjQk+9vV9Gx1l2APM0S6yixh+nFW0fosjPbJ+su0J5cjTNqaLTJMIyAvyyxdyP86xd56pIUp8MV7mVVzs5o23xZEkl1EDzc+Vt092/77KxOToPKgrLjuvXldeqREq9B3ZK1cmdxUI653pRfpq/3Of4eUuCTngBtdeGgDL13zwTd+c8NyvnedzMeuHqW/dH5fXT/FYObu9xy7/MtWiqDPOp35Lf/l6P5a/Y5XG9vLJSzt7CuxrMEU/9yoC3cGYc4pm9n/eaqMzQsM7VdWizcydZ6ybMrW8xNe6qYKIsuG9JN47LT9PDSHLvbeTNfFhBqXP1tOzvvrRbpirO66YtdJaqoaXD4GY7GXvr6GmGvvCiLRb//aIfec9JC6ayOQCh48v3t+tsXexyub+uz7BPvbdfLq9wvL1SESsxCUBnkB8gRV91heqR20O+mDtGEAV20ZLPjCaXH9kvT+98e1P9+nmc325WJ7jUIZXa71VksumBgur7MK22eH29EVqq2FJa71Q3H3QdLR136Hrn6TPVJT9Tm/DIt3lioAieZnWeO66tHrjnTaXnB2GUHCCRX50pNfaPu/tdmffKd/UReknR2VqruujBbI3qlKiM53mGZnl4j7JVnUVPg22hIFot0wzmZGtg1WU99sIPzHmHF1bPsgIwkPX/LCA3uluxwrsgnrztL/bokacnmA3p7Q4HqGx2HNaH6LBsqMQtBZZAfIEdctSyaUjrE6NjxeqdZrtwRqm93AJOjlo0D5cf17Ee5emfzAYf7ujsO8fQHS1ctkO46/UZICyTgHlfniquH2lN1TW7qiu5ORll3rhGuelGM6t1Jj005S2f26OjWdwFCjbvPslmdOqiw7HjEPsuGSsxCUBnkB8gRV117po7oqS92lqik0vVYrLH90jSmX2e98Oku2XvBQ/caRIJfvrVFizcVOlzfu3OChmWlyjAMvbf1oN2bm0XSmH6dVVNv05GqOhUdPa46J29NU+KjddEZXVVcUavVux3P0ReqN0Ig2Ll6qD2je7IMQ8o9VCFXT0tdkuJ0Zo+Oio2yavn2Qw6vERefkSFJyvn+mA4edTw9yl0X9NP9V5zhxrcAQpOrZ9nz+qVpw/4ypwnqpKYhIlPO7qnz+qbp/sVb3R4iEipCJWYhUU+IcmdC6YZGm37w93Vat9fxIH8zW6TU1GW2LZObA+GktqHR6fr9R6qdTtMhSYbkNDnI6SYOytBzN53d5qk9APiGq6lM/vKDc9Q3PVGVtQ36r9e+dnp+F1fWOp33VWq6RizfftituhW6MR8nEMrceZatqKnX7a+u1+b8coflXHpmVz1zw3BJkk0Gz7IBQlAZwlxlyouOsmpEr05Og0ozW6Q75QHhzFWW2EmDumj8gC5a+HWBdh6qcLjd8MwU/XxSf3VOjNWijYX699cFTj6zKWtjW7K1AvAdd8+9pLholxllrzyrmyYNztCrX+7V9oOOrxFndu+oH47trU+3H3IaYJLVFZHA1bNncnyMxvRNcxpU9k4jo3kwIKgMcd5OKM3k5kATV+fKI9cMUd/0RBVX1DoNKs/vn67LhjRNv5OWFKe3Nxa6df5xIwQCw91zz9U14leTB6tveqLyiqucBpUXDOqiW87tpfP6pWlFbjE9FBDxeJYND9ZAVwDty3wLG2VtOQcfLSBAS+6eKzeNzmq1zanbnnpza+v5Z94IX7hlhO4/8YAKoP25c+75+hrB/RlwD+dKaCBRT5APevUVssYB7nHnXGnrtB6cf0D48PU1gusD4J5IPVdCJWYhqAzyAwQgOEXqzQ2Ae7hGAPCFUIlZCCqD/AABAAAAiEyhErMwphIAAAAA4DGCSgAAAACAxwgqAQAAAAAeI6gEAAAAAHiMoBIAAAAA4DGCSgAAAACAxwgqAQAAAAAeI6gEAAAAAHjMYhiGEehKBEJycrLq6+uVnZ0d6KoAAAAAQCt5eXmKiYlRRUVFoKviVHSgKxAox48fV4TG00ErLy9Pkgj0gwzHJThxXIITxyX4cEyCE8clOHFcgk99fb0aGhoCXQ2XIjaoHDRokCQpJycnwDWBaciQIZI4JsGG4xKcOC7BieMSfDgmwYnjEpw4LsHHPCbBjjGVAAAAAACPEVQCAAAAADxGUAkAAAAA8BhBJQAAAADAYwSVAAAAAACPRew8lQAAAAAA79FSCQAAAADwGEElAAAAAMBjBJUAAAAAAI8RVAIAAAAAPEZQCQAAAADwGEElAAAAAMBjBJUAAAAAAI+FbFBZVVWl119/XXfffbfOPfdcxcXFyWKx6Omnn3a5b2FhoWbOnKkePXooPj5eAwcO1COPPKKamhqH+9TU1GjOnDkaOHCg4uPj1aNHD82cOVOFhYVOP+uf//ynzj33XCUlJalz58668sortWbNmjZ/31B34YUXymKxOP1ntbb+c3S1j6Nj5unxijSff/6509/3vPPOc7ivP8+jSLJjxw79/ve/18UXX6xevXopLi5O3bp107Rp07Rq1SqH+3GuBA6/Yfuprq7W0qVL9eMf/1jDhg1Tx44dlZiYqOHDh+uxxx5TZWVlq30effRRp+fCAw884PDz1qxZoyuvvFKdO3dWUlKSzj33XL322mvt+RVDlqv7+ocffmh3P0+eizgu7nF1Tzf/PfbYY837cL74xsaNG/X0009r2rRp6tmzpywWi+Lj413u56/zwZNntraK9llJfrZr1y7dfvvtbd4vLy9PY8eOVXFxsc466yxNmDBBGzZs0OOPP67ly5drxYoViouLa7FPTU2NLr74Yq1Zs0bdu3fXlClTtG/fPs2fP1/vvvuuvvrqK2VnZ7f6rPvuu0/PPfecOnTooMsuu0w1NTX65JNP9PHHH+vtt9/Wdddd5/H3DzWTJ09Wnz597K7buHGjtm3bpgkTJthdn5iYqOnTp9tdFxUV1WqZp8crkmVnZ2v8+PF2l9vjz/Mo0lxyySU6cOCAOnbsqDFjxmjs2LH67rvvtGTJEi1dulRz587VL37xC7v7cq74H79h+1qwYIHuvPNOSdKQIUM0efJkHTt2TGvWrNGcOXP0r3/9SytXrlRGRkarfceNG6f+/fu3Wn7OOefY/awlS5bohhtukM1m08SJE5Wenq5PP/1Ud9xxh7Zs2aK5c+f69suFieuvv15JSUmtlvfs2bPVMk+eizgu7uvWrZtmzJhhd11jY6PeeOMNSbL7vMX54p3HH39c//nPf9q0j7/OB0+e2TxihKjdu3cbP/7xj42//vWvxqZNm4zf/OY3hiTjqaeecrrfxIkTDUnGPffc07ysvr7euO666wxJxiOPPNJqn4cfftiQZIwdO9aoqKhoXv7ss88akoyJEye22ufTTz81JBlpaWnGzp07m5evWbPGiI2NNVJSUowjR4548tXDzrnnnmtIMl5++eVW6yQZvXv3blN5nhyvSLVixQpDkjFjxow27eev8ygSXXrppcaCBQuM2traFsvnzZtnSDKioqKMnJycVvtxrgQGv2H7eu2114y77rqrxX3UMAzj+++/N0aMGGFIMm655ZYW6+bMmWNIMubPn+/25xw5csRISUkxJBmLFy9uXl5UVGT079/fkGR89tlnXn2XcHPBBRcYkoy9e/e6tb0nz0UcF995//33DUlGVlaW0djY2Lyc88U3nn76aeORRx4xli1bZhQVFRmSjLi4OIfb+/N88OSZzRMhG1SezjwpnAWV69evNyQZGRkZRk1NTYt1RUVFRkxMjNGpUyejrq6ueXldXZ2RmppqSDI2bdrUqsxhw4YZkowNGza0WH7llVcakoznnnuu1T733HOPIcl45pln2vgtw8/OnTubT7zy8vJW69v6oOzp8YpUngSV/jyP0NJll11mSDIeffTRVus4V/yP3zCw1qxZ03z/OPUljCcPyX/4wx8MScaUKVNarXvnnXcMScbVV1/tg1qHj7YGlZ48F3FcfOfWW281JBkPPPBAi+WcL+3DVVDpr/PBk2c2T4XsmEpPvPvuu5Kka665plUzb9euXTVhwgSVlZXpyy+/bF6+evVqlZeXKzs7WyNGjGhVptnVbNmyZc3Lampq9Omnn7ZY72qfSGV2xbj22muVkpLidXmeHC+0jb/OI7Q2fPhwSdL333/vdVkcE+/xGwaWeT7U1taqtLTUq7LM65q9e/ZVV12l+Ph4LV++3KfjjyKJp89FHBffqKqqau6aedttt3ldHsfFO/48Hzx5ZvNUyI6p9MSWLVskSSNHjrS7fuTIkfrss8+0ZcsWXXjhhW7vc+p2UlOijdraWnXp0kWZmZkO99m6datnXySMvPnmm5KcX+Sqqqr0xBNPKD8/XwkJCRoxYoSmTZtmdwyHJ8cLTWOUZ8+erdLSUqWnp2v8+PGaPHmy3eRJ/jqP0NqePXskNY2bsYdzxb/4DQPLPB9iYmLUuXPnVus/++wzffPNN6qpqVFmZqauuOIKh+PDzPuxvWMZGxurs846Sxs2bFBubm5zMIsmr7zyikpLS2W1WjVw4EBNnTpVvXr1arGNp89FHBffeOedd1RVVaURI0ZoyJAhdrfhfPEff54PnjyzeSqigsr8/HxJsnsAT11ubtde+yQmJio1NVVlZWWqqKhQcnJyW75G2Pjqq6+Ul5entLQ0XXHFFQ63Kykp0UMPPdRi2X333afXXntNV111VYvlnhwvNGUSOz3b2NChQ7V48WINGDCgxXJ/nUdoKS8vr/mN47XXXmt3G84V/+I3DKw///nPkpoSwdlLMvH666+3+O+HH35Y119/vf7xj3+0eNFy7NgxlZeXS3J+LDds2KD8/Hwekk/zu9/9rsV//8///I8efvhhPfzww83LPHku4rj4jtkr7Ic//KHDbThf/Mef54M/71MR1f3VTD2ekJBgd31iYmKL7dprH0f7RRrzAnbzzTcrJibG7ja33367PvzwQx04cECVlZXavHmzfvjDH6q0tFTTpk3T+vXrW2zvyfGKZCkpKfrVr36ltWvXqrS0VKWlpfr000913nnn6dtvv9Wll16qo0ePttjHX+cRTmpoaNAdd9yh2tpa3XTTTXbfHnOu+B+/YeC8//77euWVVxQTE6PHH3+8xbr+/fvrmWeeUU5OjiorK1VQUKA333xTPXv21OLFi1s9WJ96fDiW7ps4caJef/115eXlqbq6Wrm5uXriiScUHR2tRx55pDnolzx7LuK4+EZRUZE+/fRTRUVF6ZZbbmm1nvPF//x5PvjzPhWwlsrp06dr27ZtbdrHnMvFU4ZhSGqaz83Z+vbex9F+wczXx6u+vl5vvfWWJOdvzk6fd+fss8/WP//5T2VmZuqpp57SQw89pI8//rh5vSfHK5R5e1xGjBjRaizYRRddpNWrV2vSpElatWqVXnrpJT344IPN6/11HoWq9ri23X333Vq9erX69eunv/zlL3a34VzxP37DwNi+fbtuu+02GYahP/7xj61aQk4fTpGYmKhbb71VkyZN0tChQ7V06VKtWbNG559/viT3jhPHsrVT5zqUpIEDB+rBBx/UqFGjdPnll2vOnDn6yU9+og4dOnj0XMRx8Y0FCxaosbFRkydPtjt0gvPF//x5PvjzPhWwoHLfvn3Kzc1t0z7V1dVefabZzbSqqspp+ac287fHPo72C2a+Pl4ffPCBSktLNWDAAI0ZM6bN9bn//vv1hz/8QZ9//rnq6uoUGxsrybPjFcra6zyKiorS/fffr1WrVumjjz5qEVT66zwKVb4+Jo899pjmzZunrl276qOPPrI7dswZzpX2w2/of4WFhZo8ebLKysp033336d5773V73+7du+tHP/qRnnnmGX300UfND8mnDkGprq5Wx44dW+3LsXTfZZddplGjRmnDhg1au3atJk2a5NFzEcfFN9zp+moP50v78ef54M/7VMC6v27YsEFG05Qmbv/zdgCpOXC8sLDQ7npz+akDzNtjn6qqKpWXlys1NTVkxlP6+niZFzlPs5ClpKQoIyND9fX1KikpaV7uyfEKZe15HpljKQ8ePNhiub/Oo1Dly2Py0ksvac6cOUpJSdGHH35od2JqVzhX2g+/oX+VlJTo0ksvVX5+fvPDblvZu6517NixOfs4x9I3Tv+dPXku4rh4b/v27dq8ebOSkpI0derUNu/P+dI+/Hk++PM+FVFjKs0uMps2bbK73lw+bNgwr/YZNGiQ4uLiVFxcbPcg2tsnkhw7dqw5TbKnQaXNZtOxY8cktXy74snxgn1lZWWSWr+98td5FOnefPNN3X333UpISNB7772ns88+26NyOFfaD7+h/1RUVOiKK67Qjh07NG3aNL388stOu4454sl1rb6+Xtu2bVNcXJwGDRrkQe0jz+m/s6fPRRwX75i5K6ZNm+Z0/J4jnC/tw5/ngz/vUxEVVJrZD5ctW6ba2toW6w4dOqRVq1YpJSVF48ePb14+btw4paSkKC8vT5s3b25V5qJFiyRJV199dfOyDh066KKLLmqx3tU+kWTRokWqqanRuHHj1K9fP4/K+PDDD1VVVaX+/fu36ALgyfGCfYsXL5akVklh/HUeRbL3339fd9xxh2JiYrRkyRKNGzfO47I4V9oPv6F/1NbWasqUKdqwYYMuv/xy/etf/1JUVFSbyzEMQ0uWLJHk+Lpm75797rvvqqamRhdffLHi4+M9+AaRpbi4WKtWrZJ0choDT5+LOC6eMwxDCxYskNT2rq/m/pwv7cOf54Mnz2weM8LEnDlzDEnGU0895XS7cePGGZKMe++9t3lZfX29MW3aNEOS8dBDD7Xa5ze/+Y0hyTj//PONysrK5uXPPvusIckYP358q30++eQTQ5KRlpZm7Ny5s3n5mjVrjLi4OKNjx45GaWmpB9809E2aNMmQZMybN8/pdm+//baRm5vbavnnn39u9OjRw5BkzJ07t9V6T45XpJo3b55RUlLSYpnNZjPmzZtnREdHGxaLxdiwYUOr/fx1HkWi1atXGx06dDCio6ONJUuWuLUP50rg8Bu2r4aGBuO6664zJBkTJkwwqqqqnG5fXFxsvPbaa0ZNTU2L5RUVFcZPf/pTQ5LRrVu3VuWUlpYaHTt2NCQZixcvbl5+6NAho3///oYkY/ny5b77YiHuq6++Mj777DPDZrO1WL53797m+8O1117bYp0nz0UcF8+tXLnSkGT06NHDaGxstLsN50v7kWTExcU5XO/P88GTZzZPhHRQOXXqVGPMmDHGmDFjjJ49exqSjKysrOZlU6dObbXPzp07jbS0NEOSMXToUOOmm24y+vXrZ0gyxowZYxw/frzVPsePHzfGjBljSDK6d+9u3Hjjjc3/nZaWZuzatctu/e69915DkpGQkGBMmTLFuOKKK4zo6GjDarUaixYt8vnvEQoKCwsNq9VqxMbGGkeOHHG67YwZMwxJxoABA4zLLrvMuOGGG4yzzz7bkGRIMm6++Wa7F0pPj1ck6t27txETE2MMHz7cuPbaa41rr73W6Nu3ryHJsFqtxvPPP293P3+eR5EmNTXVkGT07dvXmDFjht1/L7/8cot9OFcCh9+wff3pT39q/ju+7rrrHJ4TxcXFhmE0BTWSjI4dOxpjxowxbrjhBuPSSy9tvl6lpqYaq1evtvtZixYtMqxWq2GxWIwLL7zQmD59evP5eM899/jzawe9+fPnN//NX3DBBcZNN91kjBs3zoiPjzckGUOGDDEOHTrUaj9Pnos4Lp658847DUnGr371K4fbcL74zrvvvtscf5j3AIvF0mLZu+++22Iff50PnjyzeSKkg8revXs332zs/evdu7fd/fLz84077rjD6NatmxEbG2tkZ2cbDz30kFFdXe3ws6qrq42HH37YyM7ONmJjY42uXbsaM2bMMPLz853Wcf78+cY555xjJCQkGCkpKcbll19urFq1ypuvHdJ+//vfNz8cuPL+++8bP/jBD4zBgwcbqampRnR0tJGRkWFcccUVxttvv+10X0+PV6R5/vnnjauvvtro27evkZiYaMTGxhq9e/c2brvtNmP9+vVO9/XneRRJnF3TzH8zZsxosQ/nSmDxG7YfsxeSq3979+41DMMwjh07Ztx///3GBRdcYPTs2dOIi4szEhISjCFDhhi//OUvjcLCQqeft3r1amPy5MlGamqqkZCQYJxzzjnGq6++6odvGlq+++4746677jJGjhxpdOnSxYiOjjZSUlKM8847z3j22Wed3gc8eS7iuLRNTU2N0alTJ0OSsWXLFofbcb74jvmixdm/+fPn293PH+eDJ89sbWUxDCaTAQAAAAB4JqIS9QAAAAAAfIugEgAAAADgMYJKAAAAAIDHCCoBAAAAAB4jqAQAAAAAeIygEgAAAADgMYJKAAAAAIDHCCoBAAAAAB4jqAQAAAAAeIygEgAAAADgMYJKAAAAAIDHCCoBAAAAAB4jqAQAAAAAeIygEgAAAADgMYJKAAAAAIDHCCoBAAAAAB4jqAQAAAAAeIygEgAAAADgsf8P76BXNdm0Tq8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1050x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,3), dpi=150)\n",
    "plt.plot(delays, dly_dly_cov[delays.size//2]/np.sum(wgts**2 * noise_var), marker='.', lw=1)\n",
    "plt.xlim(-1000, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f00f0bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f0205452250>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,6), dpi=150)\n",
    "plt.imshow(dly_dly_cov/np.sum(wgts**2 * noise_var), vmax=1, vmin=-1, cmap='RdBu_r', aspect='auto')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "14e62803",
   "metadata": {},
   "outputs": [],
   "source": [
    "x0 = np.sqrt(2 * noise_var * np.log(1/(1-cdf_cutoff)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d27aa6e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2918404930894766, 0.2918404930894766)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x0, cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "56593b3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(high_dly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "4f7437db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.16790547, -0.3086041 , -0.58049469, -0.82934197, -0.74123534,\n",
       "       -0.51145221, -0.57075353, -0.67812863, -0.7478507 , -0.85116193,\n",
       "       -0.37652697, -0.49808752, -0.71666197, -0.74810617, -0.78830527,\n",
       "       -0.89109032, -0.73034174, -0.83683941, -0.73652652, -0.8210336 ,\n",
       "       -0.75309509, -0.71237416, -0.5938939 , -0.58128488, -0.52827519,\n",
       "       -0.62929365, -0.6962505 , -0.149826  ,  0.25340703, -0.19683871,\n",
       "       -0.4368511 , -0.55821497, -0.70663543, -0.26033105, -0.74299691,\n",
       "       -0.39099907, -0.26302785, -0.50878339, -0.4644975 , -0.26637901,\n",
       "       -0.22779745, -0.45517985, -0.66229725, -0.66662503, -0.52754409,\n",
       "       -0.12187167, -0.65090122, -0.90302216, -0.82219507, -0.79534806,\n",
       "       -0.80954221, -0.64671633, -0.59375805, -0.48231561, -0.52973364,\n",
       "       -0.50942736, -0.33696738, -0.37983805, -0.55772536, -0.65130087,\n",
       "       -0.70720451, -0.92177358, -0.3945561 , -0.30566494, -0.57276764,\n",
       "       -0.57552548, -0.10436657, -0.42985596, -0.74683868, -0.56851864,\n",
       "       -0.3933163 , -0.9265168 , -0.35781651, -0.39469857, -0.60248809,\n",
       "       -0.59346899, -0.84054967, -0.68950674, -0.51046659, -0.57413355])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_fft[idx,high_dly]/cutoff - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "9a82d4b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0125\n",
      "0.1375\n",
      "\n",
      "0.0125\n",
      "0.1125\n",
      "\n",
      "0.0125\n",
      "0.175\n",
      "\n",
      "0.15865525393145707\n"
     ]
    }
   ],
   "source": [
    "high_dly = (np.abs(delays) > 1000) & (np.abs(delays) < 2000)\n",
    "for fr in test_frates:\n",
    "    idx = np.argmin(np.abs(lstbin_frates-fr))\n",
    "    print(np.sum(data_fft[idx,high_dly] > cutoff) / np.sum(high_dly))\n",
    "    print(np.sum(sim_fft[idx,high_dly] > cutoff) / np.sum(high_dly))\n",
    "    print()\n",
    "print(1-cdf_cutoff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4fcd8fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15865525393145707"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - cdf_cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "767dd504",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "246b465d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_vals = np.linspace(0, 100, 10000)\n",
    "rayleigh_cdf = stats.rayleigh.cdf(test_vals, scale=exp_amp)\n",
    "test_val = interp1d(rayleigh_cdf, test_vals)(stats.norm.cdf(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "0deb21aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.067131248047424"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sig = 2\n",
    "F = stats.norm.cdf(1, scale=sig)\n",
    "sig * np.sqrt(2 * np.log(1/(1-F)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "ffc21a05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.067131248047424"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.rayleigh.isf(1-F, scale=sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c47d9cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.rayleigh.isf(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "d2ca90e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.12385225749067134, 0.18904805431742955)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.rayleigh.mean(scale=exp_std), exp_amp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3b89323",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(test_vals, rayleigh_cdf)\n",
    "plt.plot(test_vals, 1 - np.exp(-test_vals**2/(2*)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "deedf80d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8414641691361872, 0.8413447460685429)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.rayleigh.cdf(test_val, scale=exp_amp), stats.norm.cdf(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "19bc2344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37003700370037007"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "b6963f16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6902627063338314"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.rayleigh.cdf(2 * np.sqrt(np.log(1/(1-stats.norm.cdf(1))) / np.pi) * exp_amp, scale=exp_amp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "906278a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8413447460685429"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.cdf(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "9d0e7031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6902627063338316, 0.8413447460685429)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.rayleigh.cdf(cutoff / exp_amp), stats.norm.cdf(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "4cb958ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.average?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b56481e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x750 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# freq_start = 135\n",
    "# freq_end = 185\n",
    "# lst_start = 0.8 * np.pi / 12 + 2*np.pi\n",
    "# lst_end = 5.2 * np.pi / 12 + 2*np.pi\n",
    "\n",
    "lst_bounds = np.array([lst_end, lst_start]) % (2*np.pi) * 12 / np.pi\n",
    "freq_select = np.argwhere((freqs_MHz>=freq_start) & (freqs_MHz<=freq_end)).flatten()\n",
    "lstbin_lst_select = np.argwhere((lstbin_lsts>=lst_start) & (lstbin_lsts<=lst_end)).flatten()\n",
    "wf_extent = (freq_start, freq_end) + tuple(lst_bounds)\n",
    "\n",
    "plot_bl = (0, 2, \"ee\")\n",
    "data_vis = all_data['data_lstbin'].get_data(plot_bl)[lstbin_lst_select][:,freq_select]\n",
    "sim_vis = all_data['sim_lstbin'].get_data(plot_bl)[lstbin_lst_select][:,freq_select]\n",
    "\n",
    "vmax = max(np.abs(data_vis).max(), np.abs(sim_vis).max())\n",
    "vmin = vmax * 1e-3\n",
    "cmap = plt.cm.inferno\n",
    "norm = plt.cm.colors.LogNorm(vmin=vmin, vmax=vmax)\n",
    "smap = plt.cm.ScalarMappable(norm=norm, cmap=cmap)\n",
    "phs_cmap = plt.cm.twilight\n",
    "phs_norm = plt.cm.colors.Normalize(vmin=-np.pi, vmax=np.pi)\n",
    "phs_smap = plt.cm.ScalarMappable(norm=phs_norm, cmap=phs_cmap)\n",
    "\n",
    "amp_plot_kwds = dict(\n",
    "    norm=norm,\n",
    "    cmap=cmap,\n",
    "    aspect=\"auto\",\n",
    "    extent=wf_extent,\n",
    ")\n",
    "phs_plot_kwds = dict(\n",
    "    norm=phs_norm,\n",
    "    cmap=phs_cmap,\n",
    "    aspect=\"auto\",\n",
    "    extent=wf_extent,\n",
    ")\n",
    "\n",
    "DARK_MODE = False\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(7,5), dpi=150, facecolor=\"wk\"[DARK_MODE])\n",
    "\n",
    "axes[0,0].imshow(np.abs(data_vis), **amp_plot_kwds)\n",
    "axes[0,1].imshow(np.abs(sim_vis), **amp_plot_kwds)\n",
    "axes[1,0].imshow(np.angle(data_vis), **phs_plot_kwds)\n",
    "axes[1,1].imshow(np.angle(sim_vis), **phs_plot_kwds)\n",
    "\n",
    "\n",
    "for ax in axes[:,1]:\n",
    "    ax.yaxis.set_visible(False)\n",
    "for ax in axes[0]:\n",
    "    ax.xaxis.set_visible(False)\n",
    "    \n",
    "fig.tight_layout()\n",
    "axes[0,0].set_title(\"Data\", color=\"kw\"[DARK_MODE])\n",
    "axes[0,1].set_title(\"Simulation\", color=\"kw\"[DARK_MODE])\n",
    "    \n",
    "amp_cbar = fig.colorbar(smap, ax=axes[0], extend=\"min\", pad=0.015, aspect=10)\n",
    "amp_cbar.set_label(\"Visibility Amplitude,\\n\" + r\"|$V(\\nu,t)$| [Jy]\", color=\"kw\"[DARK_MODE])\n",
    "phs_cbar = fig.colorbar(phs_smap, ax=axes[1], pad=0.015, aspect=10)\n",
    "phs_cbar.set_label(\"Visibility Phase [rad]\", color=\"kw\"[DARK_MODE])\n",
    "    \n",
    "data_ax.set_ylabel(r\"Fringe-Rate, $f_r$ [mHz]\", color=\"kw\"[DARK_MODE])\n",
    "dspec_ax.set_xlabel(r\"Delay , $\\tau$ [ns]\")\n",
    "dspec_ax.set_ylabel(\"Visibility Amplitude,\\n\" + r\"|$\\tilde{V}(\\tau,f_r=f_{r,0})$| [Jy]\", color=\"kw\"[DARK_MODE])\n",
    "\n",
    "\n",
    "# Invert black -> white for dark mode\n",
    "if DARK_MODE:\n",
    "    for ax in axes.ravel():\n",
    "        ax.xaxis.set_tick_params(color=\"w\")\n",
    "        ax.yaxis.set_tick_params(color=\"w\")\n",
    "        plt.setp(plt.getp(ax.axes, \"xticklabels\"), color=\"w\")\n",
    "        plt.setp(plt.getp(ax.axes, \"yticklabels\"), color=\"w\")\n",
    "        for spine in ax.spines.values():\n",
    "            spine.set_color(\"w\")\n",
    "\n",
    "    for cbar in (amp_cbar, phs_cbar):\n",
    "        cbar.outline.set_color(\"w\")\n",
    "        cbar.ax.yaxis.set_tick_params(color=\"w\", which=\"both\")\n",
    "        plt.setp(plt.getp(cbar.ax.axes, \"yticklabels\"), color=\"w\")\n",
    "\n",
    "fig.text(0.455, 0.02, r'Frequency, $\\nu$ [MHz]', ha='center', va='top', color=\"kw\"[DARK_MODE])\n",
    "fig.text(0.02, 0.525, r'LST [hour]', ha='right', va='center', rotation='vertical', color='kw'[DARK_MODE])\n",
    "\n",
    "ext = (\"\", \"_dark_mode\")[DARK_MODE]\n",
    "if True:\n",
    "    fig.savefig(\n",
    "        f\"example_amp_and_phs_waterfalls{ext}.pdf\",\n",
    "        dpi=150, bbox_inches=\"tight\",\n",
    "    )\n",
    "    fig.savefig(\n",
    "        f\"example_amp_and_phs_waterfalls{ext}.png\",\n",
    "        dpi=150, bbox_inches=\"tight\"\n",
    "    )\n",
    "    fig.savefig(\n",
    "        f\"example_amp_and_phs_waterfalls{ext}.jpg\",\n",
    "        dpi=150, bbox_inches=\"tight\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "194e3be9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.83887569, 1.01915705])"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst_bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "757d6d14",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyuvsim import AnalyticBeam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "ab62355d",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = AnalyticBeam(\"airy\", diameter=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "817d9f56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55.520272822678734"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.efield_to_power()\n",
    "750 * b.interp(az_array=np.array([0]), za_array=np.array([7*np.pi/180]), freq_array=np.array([160e6]))[0][0,0,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "af67f140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44765786.73458443, 19.835861241005766)"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fornax_flux = 750  # Jy \n",
    "fornax_idx = -0.77\n",
    "fornax_ref_freq = 154  # MHz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "f1d60d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[29.21568414,  0.11157583,  0.05041572]])"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(all_data['data_lstbin'].get_ENU_antpos()[0][::2][:2], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "13685d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.matplotlib.patches.FancyBboxPatch?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "430950c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 2), (0, 2)]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data[f'{dset}_{name}'].get_antpairs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "25a6dd66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sim', 'lstbin')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset, name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3a97443c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f667cf25a10>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extent = (delays[0], delays[-1], daily_frates[-1], daily_frates[0])\n",
    "vmax = 3e5\n",
    "vmin = vmax * 1e-4\n",
    "plt.imshow(\n",
    "    np.abs(frdly(all_data['data_daily'].get_data(3,5,'ee')[daily_select])),\n",
    "    aspect='auto', extent=extent, norm=plt.cm.colors.LogNorm(vmax=vmax, vmin=vmin), cmap='turbo'\n",
    ")\n",
    "plt.xlim(-1000, 1000)\n",
    "plt.ylim(-5, 5)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a349cd0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f667de7a590>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extent = (delays[0], delays[-1], daily_frates[-1], daily_frates[0])\n",
    "vmax = 1e6\n",
    "vmin = vmax * 1e-4\n",
    "# vmin, vmax = None, None\n",
    "plt.imshow(\n",
    "    np.abs(frdly(all_data['sim_daily'].get_data(3,5,'ee')[daily_select])),\n",
    "    aspect='auto', extent=extent, norm=plt.cm.colors.LogNorm(vmax=vmax, vmin=vmin), cmap='turbo'\n",
    ")\n",
    "plt.xlim(-1000, 1000)\n",
    "plt.ylim(-5, 5)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ca7be78f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f667d0836d0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extent = (delays[0], delays[-1], lstbin_frates[-1], lstbin_frates[0])\n",
    "vmax = 3e5\n",
    "vmin = vmax * 1e-4\n",
    "plt.imshow(\n",
    "    np.abs(frdly(all_data['data_lstbin'].get_data(0,2,'ee')[daily_select])),\n",
    "    aspect='auto', extent=extent, norm=plt.cm.colors.LogNorm(vmax=vmax, vmin=vmin), cmap='turbo'\n",
    ")\n",
    "plt.xlim(-1000, 1000)\n",
    "plt.ylim(-5, 5)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ac663888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f6688bf36d0>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extent = (delays[0], delays[-1], lstbin_frates[-1], lstbin_frates[0])\n",
    "vmax = 3e5\n",
    "vmin = vmax * 1e-4\n",
    "plt.imshow(\n",
    "    np.abs(frdly(all_data['sim_lstbin'].get_data(0,2,'ee')[daily_select])),\n",
    "    aspect='auto', extent=extent, norm=plt.cm.colors.LogNorm(vmax=vmax, vmin=vmin), cmap='turbo'\n",
    ")\n",
    "plt.xlim(-1000, 1000)\n",
    "plt.ylim(-5, 5)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "82a471f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f660091b6d0>"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmax = 500\n",
    "plt.figure(figsize=(7,3), dpi=150)\n",
    "extent = (freqs_MHz[0], freqs_MHz[-1], daily_lsts[-1], daily_lsts[0])\n",
    "plt.imshow(np.abs(all_data['data_lstbin'].get_data(0,2,'nn')), aspect='auto', norm=plt.cm.colors.LogNorm(vmin=1e-3*vmax, vmax=vmax), extent=extent, cmap='inferno')\n",
    "plt.xlim(140, 180)\n",
    "plt.ylim(8, 6.5)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "d397e1e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(3, 5), (3, 32)]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['data_daily'].get_antpairs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b48497cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f66868d5a10>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmax = 50\n",
    "extent = (freqs_MHz[0], freqs_MHz[-1], daily_lsts[-1], daily_lsts[0])\n",
    "plt.imshow(all_data['data_daily'].get_data(3,32,'ee').real, aspect='auto', vmin=-vmax, vmax=vmax, extent=extent, cmap='RdBu_r')\n",
    "plt.xlim(155, 180)\n",
    "plt.ylim(7.6, 6.6)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "9398f940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.592182246175334"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*np.pi + 5 * np.pi / 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "42282365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20.84277371, 20.84546542, 20.84815712, ...,  6.85860427,\n",
       "        6.86129597,  6.86398768])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_lsts % (2*np.pi) * 12 / np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "dd35a224",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f668695fad0>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmax = 50\n",
    "extent = (freqs_MHz[0], freqs_MHz[-1], daily_lsts[-1], daily_lsts[0])\n",
    "plt.imshow(all_data['sim_daily'].get_data(3,32,'ee').real, aspect='auto', vmin=-vmax, vmax=vmax, extent=extent, cmap='RdBu_r')\n",
    "plt.xlim(155, 180)\n",
    "plt.ylim(7.6, 6.6)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5e9d1a0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  2, 29, ...,  2, 29, 29])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['data_lstbin'].ant_2_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "382594d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.4082891 , 5.40899381, 5.4082891 , 5.40899381, 5.40969852,\n",
       "       5.41040323, 5.40969852, 5.41040323, 5.41110794, 5.41181265,\n",
       "       5.41110794, 5.41181265, 5.41251736, 5.41322207, 5.41251736,\n",
       "       5.41322207, 5.41392677, 5.41463148, 5.41392677, 5.41463148])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['data_lstbin'].lst_array[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a9ae6f52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sim_daily': <pyuvdata.uvdata.uvdata.UVData at 0x7f668ab7fb90>,\n",
       " 'sim_lstbin': <pyuvdata.uvdata.uvdata.UVData at 0x7f668c9de610>,\n",
       " 'data_daily': <pyuvdata.uvdata.uvdata.UVData at 0x7f668b2acd10>,\n",
       " 'data_lstbin': <pyuvdata.uvdata.uvdata.UVData at 0x7f668c969ad0>}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "da2befea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2459861.25303763, 2459861.25314948, 2459861.25303763,\n",
       "       2459861.25314948, 2459861.25326134, 2459861.25337319,\n",
       "       2459861.25326134, 2459861.25337319, 2459861.25348504,\n",
       "       2459861.25359689, 2459861.25348504, 2459861.25359689,\n",
       "       2459861.25370874, 2459861.25382059, 2459861.25370874,\n",
       "       2459861.25382059, 2459861.25393245, 2459861.2540443 ,\n",
       "       2459861.25393245, 2459861.2540443 ])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['sim_lstbin'].time_array[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fa62ae56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4088)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['sim_lstbin'].Nbls, all_data['sim_lstbin'].Ntimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "62bd592a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2459861.25303763, 2459861.25303763, 2459861.25326134, ...,\n",
       "       2459861.70983994, 2459861.71006364, 2459861.71006364])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['sim_lstbin'].time_array[::all_data['sim_lstbin'].Nbls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c327bf2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3724, 4088)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['sim_daily'].Ntimes, all_data['sim_lstbin'].Ntimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "83d57ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3724, 4088)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['data_daily'].Ntimes, all_data['data_lstbin'].Ntimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "970365e3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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