{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dadea85a-296e-4abc-9e39-b67ca4459a2a",
   "metadata": {},
   "source": [
    "# Astrometric field photometry\n",
    "\n",
    "Try to calibrate NIRCam on NIRISS images of the astrometric field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f9fec90e-59bb-422e-98bf-34c9fa647628",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from grizli import utils\n",
    "from grizli.aws import visit_processor, db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 943,
   "id": "37f2270b-f4f9-492d-8d28-fcb2b173e8dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   N  value     \n",
      "====  ==========\n",
      "  19  NIRISS-F115W\n",
      "  20  NIRCAM-F410M\n",
      "  23  NIRISS-F090W\n",
      "  28  NIRISS-F200W\n",
      "  39  NIRISS-F150W\n",
      "  90  NIRCAM-F356W\n",
      " 102  NIRCAM-F444W\n",
      " 106  NIRCAM-F277W\n",
      " 120  NIRCAM-F090W\n",
      " 216  NIRCAM-F115W\n",
      " 488  NIRCAM-F150W\n",
      " 497  NIRCAM-F200W\n",
      "   N  value     \n",
      "====  ==========\n",
      " 109  NIS       \n",
      " 159  NRCALONG  \n",
      " 159  NRCBLONG  \n",
      " 165  NRCA1     \n",
      " 165  NRCA2     \n",
      " 165  NRCA3     \n",
      " 165  NRCA4     \n",
      " 165  NRCB2     \n",
      " 165  NRCB3     \n",
      " 165  NRCB4     \n",
      " 166  NRCB1     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<grizli.utils.Unique at 0x7fe136c2b9d0>"
      ]
     },
     "execution_count": 943,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mastquery import jwst\n",
    "\n",
    "ra, dec = 80.51521, -69.49386\n",
    "ra, dec = 80.4853643, -69.5005817\n",
    "\n",
    "filters = []\n",
    "filters += jwst.make_query_filter('targ_ra', range=[ra-0.1,ra+0.1])\n",
    "filters += jwst.make_query_filter('targ_dec', range=[dec-0.1,dec+0.1])\n",
    "filters += jwst.make_query_filter('expstart', range=[59680., 61000.])\n",
    "# filters += jwst.make_program_filter([1074])\n",
    "filters += jwst.make_query_filter('subarray', values=['FULL'])\n",
    "if 1:\n",
    "    filters += jwst.make_query_filter('filter', values=['F090W','F115W','F150W','F200W','CLEAR',\n",
    "                                                        'F444W','F356W','F410M','f277W'])\n",
    "    filters += jwst.make_query_filter('pupil', values=['F090W','F115W','F150W','F200W','CLEAR'])\n",
    "else:\n",
    "    filters += jwst.make_query_filter('filter', values=['F090W','CLEAR'])\n",
    "    filters += jwst.make_query_filter('pupil', values=['F090W','CLEAR'])\n",
    "    \n",
    "# filters += jwst.make_query_filter('apername', values=['NIS_CEN'] + \n",
    "#                                                       [f'NRCA{i+1}_FULL' for i in range(4)] + \n",
    "#                                                       [f'NRCB{i+1}_FULL' for i in range(4)])\n",
    "\n",
    "exp = jwst.query_all_jwst(instruments=['NIS','NRC'], recent_days=None, filters=filters, columns='*')\n",
    "\n",
    "# keep = np.in1d(exp['pupil'], ['F115W','F090W']) & (exp['orig-filter'] == 'CLEAR') & (exp['apername'] == 'NIS_SUB64')\n",
    "# exp = exp[keep]\n",
    "\n",
    "utils.Unique(exp['inst-mode'])\n",
    "utils.Unique(exp['detector'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 986,
   "id": "dcd34ec5-a1fa-49b1-9b7f-55462143a0fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from shapely.geometry import Point\n",
    "\n",
    "pt = Point(80.5, -69.5).buffer(0.04)\n",
    "pt = Point(80.4853643, -69.5005817).buffer(0.06)\n",
    "\n",
    "nis = (exp['instrument_name'] == 'NIRISS') & (exp['pupil'] == 'F200W')\n",
    "nrc = (exp['instrument_name'] == 'NIRCAM') & (exp['orig-filter'] == 'F444W')\n",
    "\n",
    "#nrc &= exp['detector'] == 'NRCALONG'\n",
    "\n",
    "# nis &= (exp['effexptm'] > 50)\n",
    "nrc &= (exp['effexptm'] > 30)\n",
    "\n",
    "keep = exp['ra'] < 0\n",
    "\n",
    "plt.scatter(exp['ra'][nis], exp['dec'][nis])\n",
    "ax = plt.gca()\n",
    "for i in np.where(nis)[0]:\n",
    "    sr = utils.SRegion(exp['footprint'][i])\n",
    "    if sr.shapely[0].overlaps(pt):\n",
    "        keep[i] = True\n",
    "        for p in sr.patch(ec='r', fc='None'):\n",
    "            ax.add_patch(p)\n",
    "\n",
    "for i in np.where(nrc)[0]:\n",
    "    sr = utils.SRegion(exp['footprint'][i])\n",
    "    if sr.shapely[0].overlaps(pt):\n",
    "        keep[i] = True\n",
    "        for p in sr.patch(ec='g', fc='None', alpha=0.2):\n",
    "            ax.add_patch(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 987,
   "id": "64fb56cf-dc0f-49fa-99a8-370b55b9eea7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   N  value     \n",
      "====  ==========\n",
      "  23  NIS       \n",
      "  32  NRCBLONG  \n",
      "  40  NRCALONG  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 987,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates = np.array([f.replace('_cal.fits','_rate.fits') for f in exp['dataURL']])\n",
    "un = utils.Unique(exp['detector'][keep])\n",
    "keep.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 988,
   "id": "6d2f4748-fa9b-46e9-9dde-1dd80d257339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 988,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keep.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e967cc-ab4c-4aa6-9f9d-0ca780db4c48",
   "metadata": {},
   "source": [
    "# Download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 989,
   "id": "2460c88b-f6be-4c04-862b-4bbe1b8fdbcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001001_02101_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001001_02101_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001001_02101_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001001_02101_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001002_02101_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001002_02101_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001002_02101_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001002_02101_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001004_02101_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001004_02101_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001004_02101_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001004_02101_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069002002_02101_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069002002_02101_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069002002_02101_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069002002_02101_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069002003_02101_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069002003_02101_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069002003_02101_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069002003_02101_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069002004_02101_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069002004_02101_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069002004_02101_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069002004_02101_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069021001_02101_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069021001_02101_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069021001_02101_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069021001_02101_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01072001001_02105_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01072001001_02105_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01072001001_02105_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01072001001_02105_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074001001_08101_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074001001_08101_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074001001_08101_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074001001_08101_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074001001_08101_00003_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074001001_08101_00003_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074001002_08101_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074001002_08101_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074001002_08101_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074001002_08101_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074001002_08101_00003_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074001002_08101_00003_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002004_02105_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002004_02105_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002004_02105_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002004_02105_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002004_02105_00003_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002004_02105_00003_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002003_02105_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002003_02105_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002003_02105_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002003_02105_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002003_02105_00003_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002003_02105_00003_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002002_02105_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002002_02105_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002002_02105_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002002_02105_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002002_02105_00003_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002002_02105_00003_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002001_02105_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002001_02105_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002001_02105_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002001_02105_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002001_02105_00003_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002001_02105_00003_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074003001_08101_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074003001_08101_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074003001_08101_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074003001_08101_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074003001_08101_00003_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074003001_08101_00003_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074003002_08101_00001_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074003002_08101_00001_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074003002_08101_00002_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074003002_08101_00002_nrcalong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074003002_08101_00003_nrcalong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074003002_08101_00003_nrcalong_rate.fits ... [Done]\n",
      "32\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001001_02101_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001001_02101_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001001_02101_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001001_02101_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001002_02101_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001002_02101_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001002_02101_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001002_02101_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001003_02101_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001003_02101_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001003_02101_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001003_02101_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001004_02101_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001004_02101_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069001004_02101_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069001004_02101_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069002003_02101_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069002003_02101_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069002003_02101_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069002003_02101_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069002004_02101_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069002004_02101_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01069002004_02101_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01069002004_02101_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01072001001_02105_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01072001001_02105_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01072001001_02105_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01072001001_02105_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074001002_08101_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074001002_08101_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074001002_08101_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074001002_08101_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074001002_08101_00003_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074001002_08101_00003_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002004_02105_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002004_02105_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002004_02105_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002004_02105_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002004_02105_00003_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002004_02105_00003_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002003_02105_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002003_02105_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002003_02105_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002003_02105_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002003_02105_00003_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002003_02105_00003_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002002_02105_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002002_02105_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002002_02105_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002002_02105_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002002_02105_00003_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002002_02105_00003_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002001_02105_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002001_02105_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002001_02105_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002001_02105_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074002001_02105_00003_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074002001_02105_00003_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074003002_08101_00001_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074003002_08101_00001_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074003002_08101_00002_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074003002_08101_00002_nrcblong_rate.fits ... [Done]\n",
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01074003002_08101_00003_nrcblong_rate.fits to /GrizliImaging/AstrometricField/RAW/jw01074003002_08101_00003_nrcblong_rate.fits ... [Done]\n"
     ]
    }
   ],
   "source": [
    "import mastquery.utils\n",
    "\n",
    "path = '/GrizliImaging/AstrometricField/RAW'\n",
    "if not os.path.exists(path):\n",
    "    os.makedirs(path)\n",
    "    \n",
    "os.chdir(path)\n",
    "\n",
    "mastquery.utils.download_from_mast(rates[keep & (exp['detector'] == f'NIS')].tolist())\n",
    "\n",
    "for m in 'AB':\n",
    "    for d in [1,2,3,4,'LONG']:\n",
    "        _det = keep & (exp['detector'] == f'NRC{m}{d}')\n",
    "        if (keep & _det).sum() > 0:\n",
    "            print(_det.sum())\n",
    "            mastquery.utils.download_from_mast(rates[keep & _det].tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 990,
   "id": "839356c3-7224-4175-a8e9-68604b930019",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/GrizliImaging/AstrometricField/Prep'\n",
    "if not os.path.exists(path):\n",
    "    os.makedirs(path)\n",
    "    \n",
    "os.chdir(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd28568c-4e76-48ae-9cb9-8bd4493cc444",
   "metadata": {},
   "source": [
    "# Copy files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 991,
   "id": "d3ba560a-72ba-4753-973e-b14e4f6b1a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "889\n",
      "../RAW/jw01018003001_02101_00001_nrca1_rate.fits\n",
      "../RAW/jw01018003001_02101_00001_nrca2_rate.fits\n",
      "../RAW/jw01018003001_02101_00001_nrca3_rate.fits\n",
      "../RAW/jw01018003001_02101_00001_nrcalong_rate.fits\n",
      "../RAW/jw01018004001_02101_00001_nrca1_rate.fits\n",
      "../RAW/jw01018004001_02101_00001_nrca2_rate.fits\n",
      "../RAW/jw01018004001_02101_00001_nrca3_rate.fits\n",
      "../RAW/jw01018004001_02101_00001_nrcalong_rate.fits\n",
      "../RAW/jw01069001001_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001001_02101_00001_nrcalong_rate.fits -> jw01069001001_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:51:01.348)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001001_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:51:06,022 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:06,023 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:06,023 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:06,052 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:51:06.516)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001001_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.57e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001001_02101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001001_02101_00001_nrcblong_rate.fits -> jw01069001001_02101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:51:10.966)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001001_02101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:51:14,821 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:14,822 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:14,822 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:14,851 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:51:15.308)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001001_02101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001001_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001001_02101_00002_nrcalong_rate.fits -> jw01069001001_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:51:20.309)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001001_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:51:23,825 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:23,826 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:23,827 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:23,855 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:51:24.261)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001001_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.57e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001001_02101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001001_02101_00002_nrcblong_rate.fits -> jw01069001001_02101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:51:28.668)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001001_02101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:51:32,248 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:32,249 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:32,250 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:32,279 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:51:32.715)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001001_02101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001001_04101_00001_nrca4_rate.fits\n",
      "../RAW/jw01069001001_04101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001001_04101_00001_nrcalong_rate.fits -> jw01069001001_04101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:51:37.449)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001001_04101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:51:41,325 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:41,326 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:41,327 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:41,356 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:51:41.764)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001001_04101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001001_04101_00001_nrcb1_rate.fits\n",
      "../RAW/jw01069001001_04101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01069001001_04101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01069001001_04101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01069001001_04101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001001_04101_00001_nrcblong_rate.fits -> jw01069001001_04101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:51:46.174)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001001_04101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:51:49,918 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:49,919 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:49,919 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:49,947 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:51:50.364)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001001_04101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001001_04101_00002_nrca4_rate.fits\n",
      "../RAW/jw01069001001_04101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001001_04101_00002_nrcalong_rate.fits -> jw01069001001_04101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:51:54.771)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001001_04101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:51:58,237 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:58,238 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:58,238 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:51:58,266 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:51:58.666)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001001_04101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001001_04101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01069001001_04101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01069001001_04101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01069001001_04101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01069001001_04101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001001_04101_00002_nrcblong_rate.fits -> jw01069001001_04101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:52:03.683)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001001_04101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:52:07,186 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:07,187 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:07,188 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:07,216 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:52:07.615)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001001_04101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001001_06101_00001_nrca4_rate.fits\n",
      "../RAW/jw01069001001_06101_00001_nrcalong_rate.fits\n",
      "../RAW/jw01069001001_06101_00001_nrcb1_rate.fits\n",
      "../RAW/jw01069001001_06101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01069001001_06101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01069001001_06101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01069001001_06101_00001_nrcblong_rate.fits\n",
      "../RAW/jw01069001001_06101_00002_nrca4_rate.fits\n",
      "../RAW/jw01069001001_06101_00002_nrcalong_rate.fits\n",
      "../RAW/jw01069001001_06101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01069001001_06101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01069001001_06101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01069001001_06101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01069001001_06101_00002_nrcblong_rate.fits\n",
      "../RAW/jw01069001002_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001002_02101_00001_nrcalong_rate.fits -> jw01069001002_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:52:12.451)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001002_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:52:15,904 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:15,905 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:15,905 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:15,934 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:52:16.333)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001002_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.57e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001002_02101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001002_02101_00001_nrcblong_rate.fits -> jw01069001002_02101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:52:20.534)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001002_02101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:52:23,991 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:23,992 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:23,993 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:24,020 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:52:24.420)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001002_02101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001002_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001002_02101_00002_nrcalong_rate.fits -> jw01069001002_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:52:29.393)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001002_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:52:32,883 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:32,884 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:32,885 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:32,914 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:52:33.323)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001002_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.57e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001002_02101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001002_02101_00002_nrcblong_rate.fits -> jw01069001002_02101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:52:37.472)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001002_02101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:52:40,939 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:40,940 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:40,941 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:40,968 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:52:41.367)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001002_02101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001002_04101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001002_04101_00001_nrcalong_rate.fits -> jw01069001002_04101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:52:46.116)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001002_04101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:52:49,571 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:49,572 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:49,573 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:49,601 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:52:49.999)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001002_04101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001002_04101_00001_nrcb1_rate.fits\n",
      "../RAW/jw01069001002_04101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01069001002_04101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01069001002_04101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01069001002_04101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001002_04101_00001_nrcblong_rate.fits -> jw01069001002_04101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:52:54.238)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001002_04101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:52:57,684 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:57,685 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:57,686 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:52:57,713 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:52:58.109)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001002_04101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001002_04101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001002_04101_00002_nrcalong_rate.fits -> jw01069001002_04101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:53:02.516)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001002_04101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:53:05,987 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:05,988 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:05,988 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:06,016 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:53:06.412)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001002_04101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001002_04101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01069001002_04101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01069001002_04101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01069001002_04101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01069001002_04101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001002_04101_00002_nrcblong_rate.fits -> jw01069001002_04101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:53:11.705)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001002_04101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:53:15,149 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:15,150 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:15,151 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:15,179 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:53:15.579)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001002_04101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001002_06101_00001_nrcalong_rate.fits\n",
      "../RAW/jw01069001002_06101_00001_nrcb1_rate.fits\n",
      "../RAW/jw01069001002_06101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01069001002_06101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01069001002_06101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01069001002_06101_00001_nrcblong_rate.fits\n",
      "../RAW/jw01069001002_06101_00002_nrcalong_rate.fits\n",
      "../RAW/jw01069001002_06101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01069001002_06101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01069001002_06101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01069001002_06101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01069001002_06101_00002_nrcblong_rate.fits\n",
      "../RAW/jw01069001003_02101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001003_02101_00001_nrcblong_rate.fits -> jw01069001003_02101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:53:19.843)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001003_02101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:53:23,332 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:23,333 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:23,334 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:23,361 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:53:23.785)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001003_02101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001003_02101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001003_02101_00002_nrcblong_rate.fits -> jw01069001003_02101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:53:28.198)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001003_02101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:53:31,671 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:31,672 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:31,673 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:31,701 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:53:32.100)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001003_02101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001003_04101_00001_nrcb1_rate.fits\n",
      "../RAW/jw01069001003_04101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01069001003_04101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01069001003_04101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001003_04101_00001_nrcblong_rate.fits -> jw01069001003_04101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:53:36.686)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001003_04101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:53:40,164 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:40,165 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:40,166 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:40,194 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:53:40.618)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001003_04101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001003_04101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01069001003_04101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01069001003_04101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01069001003_04101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001003_04101_00002_nrcblong_rate.fits -> jw01069001003_04101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:53:45.266)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001003_04101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:53:48,741 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:48,742 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:48,743 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:48,771 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:53:49.170)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001003_04101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001003_06101_00001_nrcb1_rate.fits\n",
      "../RAW/jw01069001003_06101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01069001003_06101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01069001003_06101_00001_nrcblong_rate.fits\n",
      "../RAW/jw01069001003_06101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01069001003_06101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01069001003_06101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01069001003_06101_00002_nrcblong_rate.fits\n",
      "../RAW/jw01069001004_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001004_02101_00001_nrcalong_rate.fits -> jw01069001004_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:53:53.614)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001004_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:53:57,082 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:57,083 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:57,083 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:53:57,111 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:53:57.511)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001004_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.57e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001004_02101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001004_02101_00001_nrcblong_rate.fits -> jw01069001004_02101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:54:02.512)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001004_02101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:54:06,011 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:06,012 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:06,012 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:06,040 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:54:06.440)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001004_02101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001004_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001004_02101_00002_nrcalong_rate.fits -> jw01069001004_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:54:11.051)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001004_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:54:14,538 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:14,538 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:14,539 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:14,567 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:54:14.968)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001004_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.57e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001004_02101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001004_02101_00002_nrcblong_rate.fits -> jw01069001004_02101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:54:19.217)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001004_02101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:54:22,696 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:22,697 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:22,698 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:22,726 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:54:23.125)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001004_02101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001004_04101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001004_04101_00001_nrcalong_rate.fits -> jw01069001004_04101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:54:27.575)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001004_04101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:54:31,045 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:31,046 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:31,046 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:31,074 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:54:31.474)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001004_04101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001004_04101_00001_nrcb1_rate.fits\n",
      "../RAW/jw01069001004_04101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01069001004_04101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01069001004_04101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01069001004_04101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001004_04101_00001_nrcblong_rate.fits -> jw01069001004_04101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:54:35.929)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001004_04101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:54:39,385 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:39,386 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:39,387 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:39,415 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:54:39.814)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001004_04101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001004_04101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069001004_04101_00002_nrcalong_rate.fits -> jw01069001004_04101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:54:44.534)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001004_04101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:54:48,093 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:48,094 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:48,095 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:48,123 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:54:48.524)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001004_04101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001004_04101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01069001004_04101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01069001004_04101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01069001004_04101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01069001004_04101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069001004_04101_00002_nrcblong_rate.fits -> jw01069001004_04101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:54:52.858)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069001004_04101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:54:56,353 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:56,353 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:56,354 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:54:56,382 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:54:56.779)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069001004_04101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069001004_06101_00001_nrcb1_rate.fits\n",
      "../RAW/jw01069001004_06101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01069001004_06101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01069001004_06101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01069001004_06101_00001_nrcblong_rate.fits\n",
      "../RAW/jw01069001004_06101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01069001004_06101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01069001004_06101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01069001004_06101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01069001004_06101_00002_nrcblong_rate.fits\n",
      "../RAW/jw01069002002_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002002_02101_00001_nrcalong_rate.fits -> jw01069002002_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:55:01.485)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002002_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:55:04,970 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:04,971 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:04,972 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:05,000 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:55:05.425)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002002_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002002_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002002_02101_00002_nrcalong_rate.fits -> jw01069002002_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:55:10.048)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002002_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:55:13,541 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:13,542 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:13,543 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:13,572 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:55:13.976)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002002_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002002_04101_00001_nrca1_rate.fits\n",
      "../RAW/jw01069002002_04101_00001_nrca2_rate.fits\n",
      "../RAW/jw01069002002_04101_00001_nrca3_rate.fits\n",
      "../RAW/jw01069002002_04101_00001_nrca4_rate.fits\n",
      "../RAW/jw01069002002_04101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002002_04101_00001_nrcalong_rate.fits -> jw01069002002_04101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:55:18.662)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002002_04101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:55:22,177 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:22,178 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:22,178 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:22,207 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:55:22.609)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002002_04101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002002_04101_00002_nrca1_rate.fits\n",
      "../RAW/jw01069002002_04101_00002_nrca2_rate.fits\n",
      "../RAW/jw01069002002_04101_00002_nrca3_rate.fits\n",
      "../RAW/jw01069002002_04101_00002_nrca4_rate.fits\n",
      "../RAW/jw01069002002_04101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002002_04101_00002_nrcalong_rate.fits -> jw01069002002_04101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:55:27.450)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002002_04101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:55:31,010 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:31,011 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:31,012 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:31,041 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:55:31.451)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002002_04101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002002_06101_00001_nrca1_rate.fits\n",
      "../RAW/jw01069002002_06101_00001_nrca2_rate.fits\n",
      "../RAW/jw01069002002_06101_00001_nrca3_rate.fits\n",
      "../RAW/jw01069002002_06101_00001_nrca4_rate.fits\n",
      "../RAW/jw01069002002_06101_00001_nrcalong_rate.fits\n",
      "../RAW/jw01069002002_06101_00002_nrca1_rate.fits\n",
      "../RAW/jw01069002002_06101_00002_nrca2_rate.fits\n",
      "../RAW/jw01069002002_06101_00002_nrca3_rate.fits\n",
      "../RAW/jw01069002002_06101_00002_nrca4_rate.fits\n",
      "../RAW/jw01069002002_06101_00002_nrcalong_rate.fits\n",
      "../RAW/jw01069002003_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002003_02101_00001_nrcalong_rate.fits -> jw01069002003_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:55:36.119)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002003_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:55:39,739 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:39,740 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:39,741 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:39,771 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:55:40.180)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002003_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002003_02101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069002003_02101_00001_nrcblong_rate.fits -> jw01069002003_02101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:55:44.787)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002003_02101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:55:48,369 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:48,370 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:48,371 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:48,400 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:55:48.811)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002003_02101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002003_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002003_02101_00002_nrcalong_rate.fits -> jw01069002003_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:55:53.281)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002003_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:55:56,857 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:56,858 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:56,859 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:55:56,888 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:55:57.301)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002003_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002003_02101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069002003_02101_00002_nrcblong_rate.fits -> jw01069002003_02101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:56:01.909)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002003_02101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:56:05,468 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:05,469 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:05,470 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:05,499 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:56:05.909)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002003_02101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002003_04101_00001_nrca2_rate.fits\n",
      "../RAW/jw01069002003_04101_00001_nrca3_rate.fits\n",
      "../RAW/jw01069002003_04101_00001_nrca4_rate.fits\n",
      "../RAW/jw01069002003_04101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002003_04101_00001_nrcalong_rate.fits -> jw01069002003_04101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:56:10.435)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002003_04101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:56:13,997 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:13,998 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:13,998 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:14,028 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:56:14.437)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002003_04101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002003_04101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01069002003_04101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01069002003_04101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069002003_04101_00001_nrcblong_rate.fits -> jw01069002003_04101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:56:19.149)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002003_04101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:56:22,700 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:22,701 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:22,701 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:22,729 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:56:23.134)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002003_04101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002003_04101_00002_nrca2_rate.fits\n",
      "../RAW/jw01069002003_04101_00002_nrca3_rate.fits\n",
      "../RAW/jw01069002003_04101_00002_nrca4_rate.fits\n",
      "../RAW/jw01069002003_04101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002003_04101_00002_nrcalong_rate.fits -> jw01069002003_04101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:56:27.550)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002003_04101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:56:31,088 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:31,089 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:31,089 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:31,118 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:56:31.522)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002003_04101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002003_04101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01069002003_04101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01069002003_04101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069002003_04101_00002_nrcblong_rate.fits -> jw01069002003_04101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:56:35.998)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002003_04101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:56:39,605 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:39,606 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:39,607 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:39,636 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:56:40.039)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002003_04101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002003_06101_00001_nrca2_rate.fits\n",
      "../RAW/jw01069002003_06101_00001_nrca3_rate.fits\n",
      "../RAW/jw01069002003_06101_00001_nrca4_rate.fits\n",
      "../RAW/jw01069002003_06101_00001_nrcalong_rate.fits\n",
      "../RAW/jw01069002003_06101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01069002003_06101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01069002003_06101_00001_nrcblong_rate.fits\n",
      "../RAW/jw01069002003_06101_00002_nrca2_rate.fits\n",
      "../RAW/jw01069002003_06101_00002_nrca3_rate.fits\n",
      "../RAW/jw01069002003_06101_00002_nrca4_rate.fits\n",
      "../RAW/jw01069002003_06101_00002_nrcalong_rate.fits\n",
      "../RAW/jw01069002003_06101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01069002003_06101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01069002003_06101_00002_nrcblong_rate.fits\n",
      "../RAW/jw01069002004_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002004_02101_00001_nrcalong_rate.fits -> jw01069002004_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:56:44.484)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002004_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:56:48,026 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:48,026 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:48,027 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:48,055 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:56:48.480)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002004_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002004_02101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069002004_02101_00001_nrcblong_rate.fits -> jw01069002004_02101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:56:52.889)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002004_02101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:56:56,417 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:56,418 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:56,418 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:56:56,448 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:56:56.867)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002004_02101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002004_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002004_02101_00002_nrcalong_rate.fits -> jw01069002004_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:57:01.284)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002004_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:57:04,918 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:04,919 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:04,919 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:04,950 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:57:05.367)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002004_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.57e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002004_02101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069002004_02101_00002_nrcblong_rate.fits -> jw01069002004_02101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:57:10.691)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002004_02101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:57:14,382 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:14,383 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:14,383 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:14,414 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:57:14.864)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002004_02101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002004_04101_00001_nrca1_rate.fits\n",
      "../RAW/jw01069002004_04101_00001_nrca2_rate.fits\n",
      "../RAW/jw01069002004_04101_00001_nrca3_rate.fits\n",
      "../RAW/jw01069002004_04101_00001_nrca4_rate.fits\n",
      "../RAW/jw01069002004_04101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002004_04101_00001_nrcalong_rate.fits -> jw01069002004_04101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:57:19.740)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002004_04101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:57:23,408 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:23,409 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:23,409 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:23,439 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:57:23.851)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002004_04101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002004_04101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01069002004_04101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01069002004_04101_00001_nrcblong_rate.fits -> jw01069002004_04101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:57:28.532)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002004_04101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:57:32,140 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:32,141 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:32,141 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:32,171 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:57:32.587)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002004_04101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002004_04101_00002_nrca1_rate.fits\n",
      "../RAW/jw01069002004_04101_00002_nrca2_rate.fits\n",
      "../RAW/jw01069002004_04101_00002_nrca3_rate.fits\n",
      "../RAW/jw01069002004_04101_00002_nrca4_rate.fits\n",
      "../RAW/jw01069002004_04101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069002004_04101_00002_nrcalong_rate.fits -> jw01069002004_04101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:57:37.418)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002004_04101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:57:41,024 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:41,025 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:41,026 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:41,055 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:57:41.467)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002004_04101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002004_04101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01069002004_04101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01069002004_04101_00002_nrcblong_rate.fits -> jw01069002004_04101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:57:46.002)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069002004_04101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:57:49,555 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:49,556 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:49,557 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:49,585 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:57:49.990)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069002004_04101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069002004_06101_00001_nrca1_rate.fits\n",
      "../RAW/jw01069002004_06101_00001_nrca2_rate.fits\n",
      "../RAW/jw01069002004_06101_00001_nrca3_rate.fits\n",
      "../RAW/jw01069002004_06101_00001_nrca4_rate.fits\n",
      "../RAW/jw01069002004_06101_00001_nrcalong_rate.fits\n",
      "../RAW/jw01069002004_06101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01069002004_06101_00001_nrcblong_rate.fits\n",
      "../RAW/jw01069002004_06101_00002_nrca1_rate.fits\n",
      "../RAW/jw01069002004_06101_00002_nrca2_rate.fits\n",
      "../RAW/jw01069002004_06101_00002_nrca3_rate.fits\n",
      "../RAW/jw01069002004_06101_00002_nrca4_rate.fits\n",
      "../RAW/jw01069002004_06101_00002_nrcalong_rate.fits\n",
      "../RAW/jw01069002004_06101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01069002004_06101_00002_nrcblong_rate.fits\n",
      "../RAW/jw01069021001_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069021001_02101_00001_nrcalong_rate.fits -> jw01069021001_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:57:54.580)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069021001_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:57:58,197 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:58,198 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:58,199 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:57:58,228 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:57:58.639)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069021001_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.57e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069021001_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069021001_02101_00002_nrcalong_rate.fits -> jw01069021001_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:58:04.210)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069021001_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:58:07,834 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:07,835 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:07,836 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:07,864 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:58:08.274)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069021001_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.57e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.98e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.37e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069021001_02103_00001_nrca1_rate.fits\n",
      "../RAW/jw01069021001_02103_00001_nrca2_rate.fits\n",
      "../RAW/jw01069021001_02103_00001_nrca3_rate.fits\n",
      "../RAW/jw01069021001_02103_00001_nrca4_rate.fits\n",
      "../RAW/jw01069021001_02103_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01069021001_02103_00001_nrcalong_rate.fits -> jw01069021001_02103_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:58:12.703)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069021001_02103_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:58:16,262 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:16,263 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:16,264 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:16,293 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:58:16.704)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069021001_02103_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069021001_02103_00002_nrca1_rate.fits\n",
      "../RAW/jw01069021001_02103_00002_nrca2_rate.fits\n",
      "../RAW/jw01069021001_02103_00002_nrca3_rate.fits\n",
      "../RAW/jw01069021001_02103_00002_nrca4_rate.fits\n",
      "../RAW/jw01069021001_02103_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01069021001_02103_00002_nrcalong_rate.fits -> jw01069021001_02103_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:58:21.130)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01069021001_02103_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:58:24,732 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:24,733 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:24,734 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:24,763 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:58:25.171)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01069021001_02103_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "Fit SIP degree=5 rms= 4.38e-04, 4.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01069021001_02105_00001_nrca1_rate.fits\n",
      "../RAW/jw01069021001_02105_00001_nrca2_rate.fits\n",
      "../RAW/jw01069021001_02105_00001_nrca3_rate.fits\n",
      "../RAW/jw01069021001_02105_00001_nrca4_rate.fits\n",
      "../RAW/jw01069021001_02105_00001_nrcalong_rate.fits\n",
      "../RAW/jw01069021001_02105_00002_nrca1_rate.fits\n",
      "../RAW/jw01069021001_02105_00002_nrca2_rate.fits\n",
      "../RAW/jw01069021001_02105_00002_nrca3_rate.fits\n",
      "../RAW/jw01069021001_02105_00002_nrca4_rate.fits\n",
      "../RAW/jw01069021001_02105_00002_nrcalong_rate.fits\n",
      "../RAW/jw01072001001_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01072001001_02101_00001_nrcalong_rate.fits -> jw01072001001_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:58:30.044)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01072001001_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:58:33,507 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:33,508 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:33,509 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:33,539 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:58:33.978)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01072001001_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.36e-04, 4.35e-04 pix\n",
      "Fit SIP degree=5 rms= 4.36e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01072001001_02101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01072001001_02101_00001_nrcblong_rate.fits -> jw01072001001_02101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:58:38.511)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01072001001_02101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:58:41,951 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:41,952 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:41,953 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:41,983 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:58:42.395)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01072001001_02101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.49e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01072001001_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01072001001_02101_00002_nrcalong_rate.fits -> jw01072001001_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:58:47.501)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01072001001_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:58:50,937 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:50,938 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:50,939 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:50,968 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:58:51.377)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01072001001_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.86e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.44e-03, 5.72e-03 pix\n",
      "Fit SIP degree=5 rms= 4.36e-04, 4.35e-04 pix\n",
      "Fit SIP degree=5 rms= 4.36e-04, 4.35e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01072001001_02101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01072001001_02101_00002_nrcblong_rate.fits -> jw01072001001_02101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:58:56.068)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01072001001_02101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:58:59,485 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:59,486 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:59,487 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:58:59,516 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:58:59.925)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01072001001_02101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 3.99e-03 pix\n",
      "Fit SIP degree=4 rms= 4.38e-03, 3.56e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.49e-04 pix\n",
      "Fit SIP degree=5 rms= 3.49e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01072001001_02103_00001_nrca1_rate.fits\n",
      "../RAW/jw01072001001_02103_00001_nrca2_rate.fits\n",
      "../RAW/jw01072001001_02103_00001_nrca3_rate.fits\n",
      "../RAW/jw01072001001_02103_00001_nrcalong_rate.fits\n",
      "../RAW/jw01072001001_02103_00001_nrcb1_rate.fits\n",
      "../RAW/jw01072001001_02103_00001_nrcb2_rate.fits\n",
      "../RAW/jw01072001001_02103_00001_nrcb3_rate.fits\n",
      "../RAW/jw01072001001_02103_00001_nrcblong_rate.fits\n",
      "../RAW/jw01072001001_02103_00002_nrca1_rate.fits\n",
      "../RAW/jw01072001001_02103_00002_nrca2_rate.fits\n",
      "../RAW/jw01072001001_02103_00002_nrca3_rate.fits\n",
      "../RAW/jw01072001001_02103_00002_nrcalong_rate.fits\n",
      "../RAW/jw01072001001_02103_00002_nrcb1_rate.fits\n",
      "../RAW/jw01072001001_02103_00002_nrcb2_rate.fits\n",
      "../RAW/jw01072001001_02103_00002_nrcb3_rate.fits\n",
      "../RAW/jw01072001001_02103_00002_nrcblong_rate.fits\n",
      "../RAW/jw01072001001_02105_00001_nrca1_rate.fits\n",
      "../RAW/jw01072001001_02105_00001_nrca2_rate.fits\n",
      "../RAW/jw01072001001_02105_00001_nrca3_rate.fits\n",
      "../RAW/jw01072001001_02105_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01072001001_02105_00001_nrcalong_rate.fits -> jw01072001001_02105_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:59:04.525)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01072001001_02105_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:59:07,957 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:07,958 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:07,959 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:07,989 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:59:08.400)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01072001001_02105_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.38e-03 pix\n",
      "Fit SIP degree=4 rms= 6.99e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.35e-04, 4.36e-04 pix\n",
      "Fit SIP degree=5 rms= 4.35e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01072001001_02105_00001_nrcb1_rate.fits\n",
      "../RAW/jw01072001001_02105_00001_nrcb2_rate.fits\n",
      "../RAW/jw01072001001_02105_00001_nrcb3_rate.fits\n",
      "../RAW/jw01072001001_02105_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01072001001_02105_00001_nrcblong_rate.fits -> jw01072001001_02105_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 10:59:13.225)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01072001001_02105_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:59:16,713 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:16,714 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:16,715 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:16,744 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:59:17.152)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01072001001_02105_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01072001001_02105_00002_nrca1_rate.fits\n",
      "../RAW/jw01072001001_02105_00002_nrca3_rate.fits\n",
      "../RAW/jw01072001001_02105_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01072001001_02105_00002_nrcalong_rate.fits -> jw01072001001_02105_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:59:21.525)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01072001001_02105_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:59:24,957 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:24,958 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:24,959 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:24,988 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:59:25.398)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01072001001_02105_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.37e-03 pix\n",
      "Fit SIP degree=4 rms= 6.99e-03, 6.59e-03 pix\n",
      "Fit SIP degree=5 rms= 4.35e-04, 4.36e-04 pix\n",
      "Fit SIP degree=5 rms= 4.35e-04, 4.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01072001001_02105_00002_nrcb1_rate.fits\n",
      "../RAW/jw01072001001_02105_00002_nrcb2_rate.fits\n",
      "../RAW/jw01072001001_02105_00002_nrcb3_rate.fits\n",
      "../RAW/jw01072001001_02105_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01072001001_02105_00002_nrcblong_rate.fits -> jw01072001001_02105_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 10:59:29.976)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01072001001_02105_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:59:33,416 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:33,417 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:33,418 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:33,447 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:59:33.877)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01072001001_02105_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.31e-03, 4.79e-03 pix\n",
      "Fit SIP degree=4 rms= 4.51e-03, 4.24e-03 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "Fit SIP degree=5 rms= 3.48e-04, 3.48e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01073001001_02101_00001_nrca1_rate.fits\n",
      "../RAW/jw01073001001_02101_00001_nrca2_rate.fits\n",
      "../RAW/jw01073001001_02101_00001_nrca4_rate.fits\n",
      "../RAW/jw01073001001_02101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01073001001_02101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01073001001_02101_00002_nrca1_rate.fits\n",
      "../RAW/jw01073001001_02101_00002_nrca3_rate.fits\n",
      "../RAW/jw01073001001_02101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01073001002_02101_00001_nrca1_rate.fits\n",
      "../RAW/jw01073001002_02101_00001_nrca3_rate.fits\n",
      "../RAW/jw01073001002_02101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01073001002_02101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01073001002_02101_00002_nrca1_rate.fits\n",
      "../RAW/jw01073001002_02101_00002_nrca2_rate.fits\n",
      "../RAW/jw01073001002_02101_00002_nrca3_rate.fits\n",
      "../RAW/jw01073001002_02101_00002_nrca4_rate.fits\n",
      "../RAW/jw01073001002_02101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01073001002_02101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01073001002_02101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01073001003_02101_00001_nrca4_rate.fits\n",
      "../RAW/jw01073001003_02101_00001_nrcb1_rate.fits\n",
      "../RAW/jw01073001003_02101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01073001003_02101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01073001003_02101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01073002001_02101_00001_nrca1_rate.fits\n",
      "../RAW/jw01073002001_02101_00001_nrca2_rate.fits\n",
      "../RAW/jw01073002001_02101_00001_nrca3_rate.fits\n",
      "../RAW/jw01073002001_02101_00001_nrca4_rate.fits\n",
      "../RAW/jw01073002001_02101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01073002001_02101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01073002001_02101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01073002001_02101_00002_nrca2_rate.fits\n",
      "../RAW/jw01073002001_02101_00002_nrca3_rate.fits\n",
      "../RAW/jw01073002001_02101_00002_nrca4_rate.fits\n",
      "../RAW/jw01073002001_02101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01073002001_02101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01073002001_02101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01073002001_02101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01073002001_02101_00003_nrca2_rate.fits\n",
      "../RAW/jw01073002001_02101_00003_nrca3_rate.fits\n",
      "../RAW/jw01073002001_02101_00003_nrca4_rate.fits\n",
      "../RAW/jw01073002001_02101_00003_nrcb1_rate.fits\n",
      "../RAW/jw01073002001_02101_00003_nrcb2_rate.fits\n",
      "../RAW/jw01073002001_02101_00003_nrcb3_rate.fits\n",
      "../RAW/jw01073002001_02101_00003_nrcb4_rate.fits\n",
      "../RAW/jw01073002001_02101_00004_nrca2_rate.fits\n",
      "../RAW/jw01073002001_02101_00004_nrca3_rate.fits\n",
      "../RAW/jw01073002001_02101_00004_nrca4_rate.fits\n",
      "../RAW/jw01073002001_02101_00004_nrcb1_rate.fits\n",
      "../RAW/jw01073002001_02101_00004_nrcb2_rate.fits\n",
      "../RAW/jw01073002001_02101_00004_nrcb3_rate.fits\n",
      "../RAW/jw01073002001_02101_00004_nrcb4_rate.fits\n",
      "../RAW/jw01073003001_02101_00001_nrca2_rate.fits\n",
      "../RAW/jw01073003001_02101_00001_nrca3_rate.fits\n",
      "../RAW/jw01073003001_02101_00001_nrcb1_rate.fits\n",
      "../RAW/jw01073003001_02101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01073003001_02101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01073003001_02101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01073003001_02101_00002_nrca2_rate.fits\n",
      "../RAW/jw01073003001_02101_00002_nrca3_rate.fits\n",
      "../RAW/jw01073003001_02101_00002_nrca4_rate.fits\n",
      "../RAW/jw01073003001_02101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01073003001_02101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01073003001_02101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01073003001_02101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01073003001_02101_00003_nrca2_rate.fits\n",
      "../RAW/jw01073003001_02101_00003_nrca3_rate.fits\n",
      "../RAW/jw01073003001_02101_00003_nrca4_rate.fits\n",
      "../RAW/jw01073003001_02101_00003_nrcb1_rate.fits\n",
      "../RAW/jw01073003001_02101_00003_nrcb2_rate.fits\n",
      "../RAW/jw01073003001_02101_00003_nrcb3_rate.fits\n",
      "../RAW/jw01073003001_02101_00003_nrcb4_rate.fits\n",
      "../RAW/jw01073003001_02101_00004_nrca2_rate.fits\n",
      "../RAW/jw01073003001_02101_00004_nrca3_rate.fits\n",
      "../RAW/jw01073003001_02101_00004_nrca4_rate.fits\n",
      "../RAW/jw01073003001_02101_00004_nrcb1_rate.fits\n",
      "../RAW/jw01073003001_02101_00004_nrcb2_rate.fits\n",
      "../RAW/jw01073003001_02101_00004_nrcb3_rate.fits\n",
      "../RAW/jw01073003001_02101_00004_nrcb4_rate.fits\n",
      "../RAW/jw01074001001_04101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001001_04101_00001_nrcalong_rate.fits -> jw01074001001_04101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 10:59:38.287)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001001_04101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:59:42,036 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:42,037 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:42,038 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:42,067 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:59:42.475)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001001_04101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.43e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001001_04101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001001_04101_00002_nrcalong_rate.fits -> jw01074001001_04101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 10:59:47.009)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001001_04101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:59:50,654 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:50,655 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:50,655 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:50,683 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:59:51.081)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001001_04101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.43e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001001_04101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001001_04101_00003_nrcalong_rate.fits -> jw01074001001_04101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 10:59:55.418)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001001_04101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 10:59:59,102 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:59,102 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:59,103 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 10:59:59,132 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 10:59:59.568)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001001_04101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.43e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001001_06101_00001_nrca1_rate.fits\n",
      "../RAW/jw01074001001_06101_00001_nrca2_rate.fits\n",
      "../RAW/jw01074001001_06101_00002_nrca1_rate.fits\n",
      "../RAW/jw01074001001_06101_00002_nrca2_rate.fits\n",
      "../RAW/jw01074001001_06101_00003_nrca2_rate.fits\n",
      "../RAW/jw01074001001_06101_00003_nrca3_rate.fits\n",
      "../RAW/jw01074001001_08101_00001_nrca1_rate.fits\n",
      "../RAW/jw01074001001_08101_00001_nrca2_rate.fits\n",
      "../RAW/jw01074001001_08101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001001_08101_00001_nrcalong_rate.fits -> jw01074001001_08101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:00:04.105)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001001_08101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:00:07,906 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:07,907 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:07,907 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:07,937 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:00:08.378)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001001_08101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001001_08101_00002_nrca1_rate.fits\n",
      "../RAW/jw01074001001_08101_00002_nrca2_rate.fits\n",
      "../RAW/jw01074001001_08101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001001_08101_00002_nrcalong_rate.fits -> jw01074001001_08101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:00:13.119)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001001_08101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:00:16,878 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:16,878 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:16,879 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:16,909 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:00:17.318)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001001_08101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001001_08101_00003_nrca1_rate.fits\n",
      "../RAW/jw01074001001_08101_00003_nrca2_rate.fits\n",
      "../RAW/jw01074001001_08101_00003_nrca3_rate.fits\n",
      "../RAW/jw01074001001_08101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001001_08101_00003_nrcalong_rate.fits -> jw01074001001_08101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:00:21.998)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001001_08101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:00:25,723 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:25,724 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:25,725 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:25,754 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:00:26.166)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001001_08101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_04101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001002_04101_00001_nrcalong_rate.fits -> jw01074001002_04101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:00:30.797)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_04101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:00:34,499 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:34,500 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:34,500 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:34,529 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:00:34.943)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_04101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_04101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074001002_04101_00001_nrcblong_rate.fits -> jw01074001002_04101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:00:39.386)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_04101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:00:43,126 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:43,128 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:43,128 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:43,158 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:00:43.566)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_04101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_04101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001002_04101_00002_nrcalong_rate.fits -> jw01074001002_04101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:00:47.826)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_04101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:00:51,505 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:51,506 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:51,507 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:00:51,535 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:00:51.936)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_04101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_04101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074001002_04101_00002_nrcblong_rate.fits -> jw01074001002_04101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:00:56.530)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_04101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:01:00,189 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:00,190 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:00,190 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:00,218 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:01:00.619)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_04101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_04101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001002_04101_00003_nrcalong_rate.fits -> jw01074001002_04101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:01:05.182)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_04101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:01:08,930 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:08,931 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:08,932 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:08,961 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:01:09.372)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_04101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_04101_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074001002_04101_00003_nrcblong_rate.fits -> jw01074001002_04101_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:01:14.090)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_04101_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:01:17,818 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:17,819 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:17,820 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:17,849 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:01:18.255)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_04101_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_06101_00001_nrca3_rate.fits\n",
      "../RAW/jw01074001002_06101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01074001002_06101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01074001002_06101_00002_nrca3_rate.fits\n",
      "../RAW/jw01074001002_06101_00002_nrca4_rate.fits\n",
      "../RAW/jw01074001002_06101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01074001002_06101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01074001002_06101_00003_nrca3_rate.fits\n",
      "../RAW/jw01074001002_06101_00003_nrca4_rate.fits\n",
      "../RAW/jw01074001002_06101_00003_nrcb2_rate.fits\n",
      "../RAW/jw01074001002_06101_00003_nrcb3_rate.fits\n",
      "../RAW/jw01074001002_06101_00003_nrcb4_rate.fits\n",
      "../RAW/jw01074001002_08101_00001_nrca3_rate.fits\n",
      "../RAW/jw01074001002_08101_00001_nrca4_rate.fits\n",
      "../RAW/jw01074001002_08101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001002_08101_00001_nrcalong_rate.fits -> jw01074001002_08101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:01:23.273)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_08101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:01:27,047 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:27,048 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:27,048 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:27,078 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:01:27.487)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_08101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_08101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01074001002_08101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01074001002_08101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074001002_08101_00001_nrcblong_rate.fits -> jw01074001002_08101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:01:32.221)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_08101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:01:35,992 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:35,993 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:35,994 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:36,023 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:01:36.437)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_08101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_08101_00002_nrca3_rate.fits\n",
      "../RAW/jw01074001002_08101_00002_nrca4_rate.fits\n",
      "../RAW/jw01074001002_08101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001002_08101_00002_nrcalong_rate.fits -> jw01074001002_08101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:01:41.164)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_08101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:01:44,976 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:44,977 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:44,977 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:45,008 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:01:45.420)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_08101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_08101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01074001002_08101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01074001002_08101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074001002_08101_00002_nrcblong_rate.fits -> jw01074001002_08101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:01:50.392)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_08101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:01:54,120 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:54,121 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:54,121 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:01:54,151 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:01:54.562)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_08101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_08101_00003_nrca3_rate.fits\n",
      "../RAW/jw01074001002_08101_00003_nrca4_rate.fits\n",
      "../RAW/jw01074001002_08101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074001002_08101_00003_nrcalong_rate.fits -> jw01074001002_08101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:01:59.733)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_08101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:02:03,493 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:03,494 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:03,495 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:03,525 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:02:03.936)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_08101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074001002_08101_00003_nrcb2_rate.fits\n",
      "../RAW/jw01074001002_08101_00003_nrcb3_rate.fits\n",
      "../RAW/jw01074001002_08101_00003_nrcb4_rate.fits\n",
      "../RAW/jw01074001002_08101_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074001002_08101_00003_nrcblong_rate.fits -> jw01074001002_08101_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:02:08.969)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074001002_08101_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:02:12,693 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:12,694 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:12,694 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:12,724 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:02:13.136)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074001002_08101_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002001_02101_00001_nrcalong_rate.fits -> jw01074002001_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:02:17.954)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:02:21,696 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:21,697 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:21,698 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:21,728 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:02:22.141)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002001_02101_00001_nrcblong_rate.fits -> jw01074002001_02101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:02:27.547)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:02:31,339 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:31,340 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:31,341 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:31,371 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:02:31.783)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002001_02101_00002_nrcalong_rate.fits -> jw01074002001_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:02:36.634)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:02:40,401 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:40,402 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:40,403 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:40,432 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:02:40.868)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002001_02101_00002_nrcblong_rate.fits -> jw01074002001_02101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:02:45.999)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:02:49,767 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:49,768 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:49,769 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:49,799 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:02:50.237)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002001_02101_00003_nrcalong_rate.fits -> jw01074002001_02101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:02:55.436)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:02:59,224 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:59,225 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:59,226 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:02:59,255 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:02:59.669)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02101_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002001_02101_00003_nrcblong_rate.fits -> jw01074002001_02101_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:03:04.320)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02101_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:03:08,110 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:08,111 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:08,112 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:08,142 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:03:08.557)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02101_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.46e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02103_00001_nrca1_rate.fits\n",
      "../RAW/jw01074002001_02103_00001_nrca4_rate.fits\n",
      "../RAW/jw01074002001_02103_00001_nrcalong_rate.fits\n",
      "../RAW/jw01074002001_02103_00002_nrcalong_rate.fits\n",
      "../RAW/jw01074002001_02103_00003_nrca3_rate.fits\n",
      "../RAW/jw01074002001_02103_00003_nrcalong_rate.fits\n",
      "../RAW/jw01074002001_02105_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002001_02105_00001_nrcalong_rate.fits -> jw01074002001_02105_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:03:13.261)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02105_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:03:17,022 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:17,023 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:17,023 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:17,053 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:03:17.462)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02105_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02105_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002001_02105_00001_nrcblong_rate.fits -> jw01074002001_02105_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:03:21.974)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02105_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:03:25,713 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:25,714 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:25,714 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:25,744 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:03:26.154)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02105_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02105_00002_nrca3_rate.fits\n",
      "../RAW/jw01074002001_02105_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002001_02105_00002_nrcalong_rate.fits -> jw01074002001_02105_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:03:31.009)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02105_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:03:34,745 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:34,746 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:34,747 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:34,776 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:03:35.184)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02105_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02105_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002001_02105_00002_nrcblong_rate.fits -> jw01074002001_02105_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:03:39.983)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02105_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:03:43,727 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:43,728 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:43,729 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:43,758 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:03:44.191)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02105_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02105_00003_nrca3_rate.fits\n",
      "../RAW/jw01074002001_02105_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002001_02105_00003_nrcalong_rate.fits -> jw01074002001_02105_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:03:48.897)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02105_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:03:52,616 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:52,617 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:52,618 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:03:52,647 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:03:53.053)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02105_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02105_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002001_02105_00003_nrcblong_rate.fits -> jw01074002001_02105_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:03:57.718)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002001_02105_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:04:01,399 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:01,400 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:01,401 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:01,430 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:04:01.842)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002001_02105_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002001_02107_00002_nrca3_rate.fits\n",
      "../RAW/jw01074002001_02107_00003_nrca3_rate.fits\n",
      "../RAW/jw01074002001_02107_00003_nrca4_rate.fits\n",
      "../RAW/jw01074002002_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002002_02101_00001_nrcalong_rate.fits -> jw01074002002_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:04:06.882)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:04:10,549 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:10,550 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:10,551 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:10,579 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:04:10.981)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.41e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002002_02101_00001_nrcblong_rate.fits -> jw01074002002_02101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:04:15.582)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:04:19,264 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:19,265 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:19,265 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:19,294 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:04:19.701)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002002_02101_00002_nrcalong_rate.fits -> jw01074002002_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:04:24.405)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:04:28,110 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:28,111 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:28,112 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:28,141 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:04:28.546)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.41e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002002_02101_00002_nrcblong_rate.fits -> jw01074002002_02101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:04:33.014)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:04:36,709 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:36,710 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:36,711 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:36,740 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:04:37.146)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.46e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002002_02101_00003_nrcalong_rate.fits -> jw01074002002_02101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:04:42.209)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:04:45,841 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:45,842 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:45,842 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:45,871 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:04:46.274)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.41e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02101_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002002_02101_00003_nrcblong_rate.fits -> jw01074002002_02101_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:04:51.550)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02101_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:04:55,293 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:55,294 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:55,295 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:04:55,325 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:04:55.734)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02101_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.46e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.46e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02103_00001_nrca1_rate.fits\n",
      "../RAW/jw01074002002_02103_00001_nrca2_rate.fits\n",
      "../RAW/jw01074002002_02103_00001_nrca3_rate.fits\n",
      "../RAW/jw01074002002_02103_00001_nrca4_rate.fits\n",
      "../RAW/jw01074002002_02103_00001_nrcalong_rate.fits\n",
      "../RAW/jw01074002002_02103_00001_nrcb3_rate.fits\n",
      "../RAW/jw01074002002_02103_00001_nrcb4_rate.fits\n",
      "../RAW/jw01074002002_02103_00001_nrcblong_rate.fits\n",
      "../RAW/jw01074002002_02103_00002_nrca1_rate.fits\n",
      "../RAW/jw01074002002_02103_00002_nrca2_rate.fits\n",
      "../RAW/jw01074002002_02103_00002_nrca3_rate.fits\n",
      "../RAW/jw01074002002_02103_00002_nrca4_rate.fits\n",
      "../RAW/jw01074002002_02103_00002_nrcalong_rate.fits\n",
      "../RAW/jw01074002002_02103_00002_nrcb3_rate.fits\n",
      "../RAW/jw01074002002_02103_00002_nrcb4_rate.fits\n",
      "../RAW/jw01074002002_02103_00002_nrcblong_rate.fits\n",
      "../RAW/jw01074002002_02103_00003_nrca1_rate.fits\n",
      "../RAW/jw01074002002_02103_00003_nrca2_rate.fits\n",
      "../RAW/jw01074002002_02103_00003_nrca3_rate.fits\n",
      "../RAW/jw01074002002_02103_00003_nrca4_rate.fits\n",
      "../RAW/jw01074002002_02103_00003_nrcalong_rate.fits\n",
      "../RAW/jw01074002002_02103_00003_nrcb3_rate.fits\n",
      "../RAW/jw01074002002_02103_00003_nrcb4_rate.fits\n",
      "../RAW/jw01074002002_02103_00003_nrcblong_rate.fits\n",
      "../RAW/jw01074002002_02105_00001_nrca3_rate.fits\n",
      "../RAW/jw01074002002_02105_00001_nrca4_rate.fits\n",
      "../RAW/jw01074002002_02105_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002002_02105_00001_nrcalong_rate.fits -> jw01074002002_02105_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:05:00.732)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02105_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:05:04,525 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:04,526 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:04,526 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:04,556 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:05:04.989)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02105_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02105_00001_nrcb3_rate.fits\n",
      "../RAW/jw01074002002_02105_00001_nrcb4_rate.fits\n",
      "../RAW/jw01074002002_02105_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002002_02105_00001_nrcblong_rate.fits -> jw01074002002_02105_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:05:09.518)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02105_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:05:13,281 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:13,282 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:13,283 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:13,313 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:05:13.725)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02105_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02105_00002_nrca3_rate.fits\n",
      "../RAW/jw01074002002_02105_00002_nrca4_rate.fits\n",
      "../RAW/jw01074002002_02105_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002002_02105_00002_nrcalong_rate.fits -> jw01074002002_02105_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:05:18.108)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02105_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:05:21,878 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:21,879 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:21,879 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:21,908 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:05:22.339)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02105_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02105_00002_nrcb3_rate.fits\n",
      "../RAW/jw01074002002_02105_00002_nrcb4_rate.fits\n",
      "../RAW/jw01074002002_02105_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002002_02105_00002_nrcblong_rate.fits -> jw01074002002_02105_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:05:26.778)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02105_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:05:30,562 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:30,563 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:30,564 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:30,594 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:05:31.011)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02105_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02105_00003_nrca3_rate.fits\n",
      "../RAW/jw01074002002_02105_00003_nrca4_rate.fits\n",
      "../RAW/jw01074002002_02105_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002002_02105_00003_nrcalong_rate.fits -> jw01074002002_02105_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:05:35.682)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02105_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:05:39,523 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:39,524 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:39,525 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:39,555 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:05:39.970)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02105_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02105_00003_nrcb3_rate.fits\n",
      "../RAW/jw01074002002_02105_00003_nrcb4_rate.fits\n",
      "../RAW/jw01074002002_02105_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002002_02105_00003_nrcblong_rate.fits -> jw01074002002_02105_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:05:44.700)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002002_02105_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:05:48,418 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:48,419 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:48,419 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:48,448 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:05:48.873)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002002_02105_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002002_02107_00001_nrca3_rate.fits\n",
      "../RAW/jw01074002002_02107_00001_nrca4_rate.fits\n",
      "../RAW/jw01074002002_02107_00001_nrcb3_rate.fits\n",
      "../RAW/jw01074002002_02107_00001_nrcb4_rate.fits\n",
      "../RAW/jw01074002002_02107_00002_nrca3_rate.fits\n",
      "../RAW/jw01074002002_02107_00002_nrca4_rate.fits\n",
      "../RAW/jw01074002002_02107_00002_nrcb3_rate.fits\n",
      "../RAW/jw01074002002_02107_00002_nrcb4_rate.fits\n",
      "../RAW/jw01074002002_02107_00003_nrca3_rate.fits\n",
      "../RAW/jw01074002002_02107_00003_nrca4_rate.fits\n",
      "../RAW/jw01074002002_02107_00003_nrcb3_rate.fits\n",
      "../RAW/jw01074002002_02107_00003_nrcb4_rate.fits\n",
      "../RAW/jw01074002003_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002003_02101_00001_nrcalong_rate.fits -> jw01074002003_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:05:53.961)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:05:57,740 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:57,741 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:57,742 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:05:57,772 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:05:58.186)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002003_02101_00001_nrcblong_rate.fits -> jw01074002003_02101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:06:03.007)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:06:06,827 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:06,828 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:06,829 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:06,859 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:06:07.275)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.46e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002003_02101_00002_nrcalong_rate.fits -> jw01074002003_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:06:12.418)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:06:16,217 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:16,218 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:16,219 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:16,248 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:06:16.686)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002003_02101_00002_nrcblong_rate.fits -> jw01074002003_02101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:06:21.374)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:06:25,147 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:25,148 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:25,148 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:25,177 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:06:25.588)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002003_02101_00003_nrcalong_rate.fits -> jw01074002003_02101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:06:30.514)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:06:34,294 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:34,295 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:34,296 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:34,325 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:06:34.737)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02101_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002003_02101_00003_nrcblong_rate.fits -> jw01074002003_02101_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:06:39.717)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02101_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:06:43,469 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:43,470 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:43,471 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:43,500 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:06:43.912)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02101_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02103_00001_nrca3_rate.fits\n",
      "../RAW/jw01074002003_02103_00001_nrca4_rate.fits\n",
      "../RAW/jw01074002003_02103_00001_nrcalong_rate.fits\n",
      "../RAW/jw01074002003_02103_00001_nrcb1_rate.fits\n",
      "../RAW/jw01074002003_02103_00001_nrcb2_rate.fits\n",
      "../RAW/jw01074002003_02103_00001_nrcb4_rate.fits\n",
      "../RAW/jw01074002003_02103_00001_nrcblong_rate.fits\n",
      "../RAW/jw01074002003_02103_00002_nrca3_rate.fits\n",
      "../RAW/jw01074002003_02103_00002_nrca4_rate.fits\n",
      "../RAW/jw01074002003_02103_00002_nrcalong_rate.fits\n",
      "../RAW/jw01074002003_02103_00002_nrcb1_rate.fits\n",
      "../RAW/jw01074002003_02103_00002_nrcb2_rate.fits\n",
      "../RAW/jw01074002003_02103_00002_nrcb4_rate.fits\n",
      "../RAW/jw01074002003_02103_00002_nrcblong_rate.fits\n",
      "../RAW/jw01074002003_02103_00003_nrca3_rate.fits\n",
      "../RAW/jw01074002003_02103_00003_nrca4_rate.fits\n",
      "../RAW/jw01074002003_02103_00003_nrcalong_rate.fits\n",
      "../RAW/jw01074002003_02103_00003_nrcb1_rate.fits\n",
      "../RAW/jw01074002003_02103_00003_nrcb2_rate.fits\n",
      "../RAW/jw01074002003_02103_00003_nrcb4_rate.fits\n",
      "../RAW/jw01074002003_02103_00003_nrcblong_rate.fits\n",
      "../RAW/jw01074002003_02105_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002003_02105_00001_nrcalong_rate.fits -> jw01074002003_02105_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:06:48.375)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02105_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:06:52,130 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:52,131 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:52,132 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:06:52,161 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:06:52.572)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02105_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02105_00001_nrcb1_rate.fits\n",
      "../RAW/jw01074002003_02105_00001_nrcb2_rate.fits\n",
      "../RAW/jw01074002003_02105_00001_nrcb4_rate.fits\n",
      "../RAW/jw01074002003_02105_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002003_02105_00001_nrcblong_rate.fits -> jw01074002003_02105_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:06:57.793)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02105_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:07:01,575 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:01,576 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:01,576 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:01,605 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:07:02.017)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02105_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02105_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002003_02105_00002_nrcalong_rate.fits -> jw01074002003_02105_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:07:06.850)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02105_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:07:10,633 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:10,635 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:10,635 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:10,665 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:07:11.106)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02105_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02105_00002_nrcb1_rate.fits\n",
      "../RAW/jw01074002003_02105_00002_nrcb2_rate.fits\n",
      "../RAW/jw01074002003_02105_00002_nrcb4_rate.fits\n",
      "../RAW/jw01074002003_02105_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002003_02105_00002_nrcblong_rate.fits -> jw01074002003_02105_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:07:15.629)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02105_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:07:19,438 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:19,439 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:19,440 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:19,470 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:07:19.880)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02105_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02105_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002003_02105_00003_nrcalong_rate.fits -> jw01074002003_02105_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:07:25.325)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02105_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:07:29,127 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:29,128 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:29,129 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:29,158 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:07:29.591)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02105_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02105_00003_nrcb1_rate.fits\n",
      "../RAW/jw01074002003_02105_00003_nrcb2_rate.fits\n",
      "../RAW/jw01074002003_02105_00003_nrcb4_rate.fits\n",
      "../RAW/jw01074002003_02105_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002003_02105_00003_nrcblong_rate.fits -> jw01074002003_02105_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:07:34.648)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002003_02105_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:07:38,417 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:38,419 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:38,420 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:38,450 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:07:38.862)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002003_02105_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002003_02107_00001_nrcb1_rate.fits\n",
      "../RAW/jw01074002003_02107_00001_nrcb2_rate.fits\n",
      "../RAW/jw01074002003_02107_00001_nrcb4_rate.fits\n",
      "../RAW/jw01074002003_02107_00002_nrcb1_rate.fits\n",
      "../RAW/jw01074002003_02107_00002_nrcb2_rate.fits\n",
      "../RAW/jw01074002003_02107_00002_nrcb4_rate.fits\n",
      "../RAW/jw01074002004_02101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002004_02101_00001_nrcalong_rate.fits -> jw01074002004_02101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:07:45.071)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:07:48,822 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:48,823 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:48,824 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:48,855 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:07:49.277)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002004_02101_00001_nrcblong_rate.fits -> jw01074002004_02101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:07:54.075)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:07:57,780 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:57,781 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:57,782 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:07:57,812 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:07:58.226)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002004_02101_00002_nrcalong_rate.fits -> jw01074002004_02101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:08:03.173)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:08:06,964 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:06,965 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:06,966 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:06,995 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:08:07.408)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002004_02101_00002_nrcblong_rate.fits -> jw01074002004_02101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:08:12.109)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:08:15,836 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:15,837 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:15,838 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:15,866 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:08:16.276)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.46e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002004_02101_00003_nrcalong_rate.fits -> jw01074002004_02101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:08:20.995)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:08:24,749 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:24,750 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:24,751 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:24,781 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:08:25.197)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.87e-03, 8.16e-03 pix\n",
      "Fit SIP degree=4 rms= 6.42e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02101_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002004_02101_00003_nrcblong_rate.fits -> jw01074002004_02101_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:08:29.875)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02101_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:08:33,650 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:33,651 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:33,651 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:33,681 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:08:34.095)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02101_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.34e-03, 4.01e-03 pix\n",
      "Fit SIP degree=4 rms= 4.40e-03, 3.57e-03 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.46e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02103_00001_nrca3_rate.fits\n",
      "../RAW/jw01074002004_02103_00001_nrca4_rate.fits\n",
      "../RAW/jw01074002004_02103_00001_nrcalong_rate.fits\n",
      "../RAW/jw01074002004_02103_00001_nrcb1_rate.fits\n",
      "../RAW/jw01074002004_02103_00001_nrcb2_rate.fits\n",
      "../RAW/jw01074002004_02103_00001_nrcb3_rate.fits\n",
      "../RAW/jw01074002004_02103_00001_nrcblong_rate.fits\n",
      "../RAW/jw01074002004_02103_00002_nrca3_rate.fits\n",
      "../RAW/jw01074002004_02103_00002_nrca4_rate.fits\n",
      "../RAW/jw01074002004_02103_00002_nrcalong_rate.fits\n",
      "../RAW/jw01074002004_02103_00002_nrcb1_rate.fits\n",
      "../RAW/jw01074002004_02103_00002_nrcb2_rate.fits\n",
      "../RAW/jw01074002004_02103_00002_nrcb3_rate.fits\n",
      "../RAW/jw01074002004_02103_00002_nrcblong_rate.fits\n",
      "../RAW/jw01074002004_02103_00003_nrca3_rate.fits\n",
      "../RAW/jw01074002004_02103_00003_nrca4_rate.fits\n",
      "../RAW/jw01074002004_02103_00003_nrcalong_rate.fits\n",
      "../RAW/jw01074002004_02103_00003_nrcb1_rate.fits\n",
      "../RAW/jw01074002004_02103_00003_nrcb2_rate.fits\n",
      "../RAW/jw01074002004_02103_00003_nrcb3_rate.fits\n",
      "../RAW/jw01074002004_02103_00003_nrcblong_rate.fits\n",
      "../RAW/jw01074002004_02105_00001_nrca4_rate.fits\n",
      "../RAW/jw01074002004_02105_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002004_02105_00001_nrcalong_rate.fits -> jw01074002004_02105_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:08:39.124)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02105_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:08:42,945 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:42,946 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:42,947 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:42,976 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:08:43.390)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02105_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02105_00001_nrcb2_rate.fits\n",
      "../RAW/jw01074002004_02105_00001_nrcb3_rate.fits\n",
      "../RAW/jw01074002004_02105_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002004_02105_00001_nrcblong_rate.fits -> jw01074002004_02105_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:08:48.276)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02105_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:08:52,017 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:52,018 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:52,018 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:08:52,048 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:08:52.456)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02105_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02105_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002004_02105_00002_nrcalong_rate.fits -> jw01074002004_02105_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:08:56.908)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02105_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:09:00,777 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:00,778 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:00,779 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:00,810 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:09:01.231)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02105_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.41e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02105_00002_nrcb1_rate.fits\n",
      "../RAW/jw01074002004_02105_00002_nrcb2_rate.fits\n",
      "../RAW/jw01074002004_02105_00002_nrcb3_rate.fits\n",
      "../RAW/jw01074002004_02105_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002004_02105_00002_nrcblong_rate.fits -> jw01074002004_02105_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:09:06.077)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02105_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:09:09,807 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:09,808 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:09,809 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:09,837 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:09:10.246)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02105_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.46e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02105_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074002004_02105_00003_nrcalong_rate.fits -> jw01074002004_02105_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:09:15.366)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02105_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:09:19,113 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:19,114 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:19,114 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:19,143 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:09:19.551)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02105_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.59e-03, 8.33e-03 pix\n",
      "Fit SIP degree=4 rms= 6.97e-03, 6.60e-03 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "Fit SIP degree=5 rms= 4.42e-04, 4.42e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02105_00003_nrcb1_rate.fits\n",
      "../RAW/jw01074002004_02105_00003_nrcb2_rate.fits\n",
      "../RAW/jw01074002004_02105_00003_nrcb3_rate.fits\n",
      "../RAW/jw01074002004_02105_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074002004_02105_00003_nrcblong_rate.fits -> jw01074002004_02105_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:09:24.603)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074002004_02105_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:09:28,338 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:28,339 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:28,340 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:28,369 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:09:28.777)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074002004_02105_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.30e-03, 4.81e-03 pix\n",
      "Fit SIP degree=4 rms= 4.54e-03, 4.26e-03 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "Fit SIP degree=5 rms= 3.45e-04, 3.47e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074002004_02107_00001_nrca4_rate.fits\n",
      "../RAW/jw01074002004_02107_00001_nrcb2_rate.fits\n",
      "../RAW/jw01074002004_02107_00001_nrcb3_rate.fits\n",
      "../RAW/jw01074002004_02107_00002_nrcb1_rate.fits\n",
      "../RAW/jw01074002004_02107_00002_nrcb2_rate.fits\n",
      "../RAW/jw01074002004_02107_00002_nrcb3_rate.fits\n",
      "../RAW/jw01074002004_02107_00003_nrcb1_rate.fits\n",
      "../RAW/jw01074002004_02107_00003_nrcb2_rate.fits\n",
      "../RAW/jw01074002004_02107_00003_nrcb3_rate.fits\n",
      "../RAW/jw01074003001_04101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003001_04101_00001_nrcalong_rate.fits -> jw01074003001_04101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:09:33.196)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003001_04101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:09:36,938 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:36,939 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:36,940 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:36,969 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:09:37.401)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003001_04101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.89e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.38e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003001_04101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003001_04101_00002_nrcalong_rate.fits -> jw01074003001_04101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:09:42.150)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003001_04101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:09:45,895 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:45,896 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:45,897 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:45,926 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:09:46.335)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003001_04101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.89e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.38e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003001_04101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003001_04101_00003_nrcalong_rate.fits -> jw01074003001_04101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:09:51.476)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003001_04101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:09:55,147 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:55,148 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:55,149 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:09:55,176 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:09:55.578)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003001_04101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.89e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.38e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003001_06101_00001_nrca1_rate.fits\n",
      "../RAW/jw01074003001_06101_00001_nrca2_rate.fits\n",
      "../RAW/jw01074003001_06101_00002_nrca1_rate.fits\n",
      "../RAW/jw01074003001_06101_00002_nrca2_rate.fits\n",
      "../RAW/jw01074003001_06101_00003_nrca1_rate.fits\n",
      "../RAW/jw01074003001_06101_00003_nrca2_rate.fits\n",
      "../RAW/jw01074003001_08101_00001_nrca1_rate.fits\n",
      "../RAW/jw01074003001_08101_00001_nrca2_rate.fits\n",
      "../RAW/jw01074003001_08101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003001_08101_00001_nrcalong_rate.fits -> jw01074003001_08101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:09:59.739)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003001_08101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:10:03,398 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:03,399 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:03,400 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:03,429 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:10:03.834)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003001_08101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.31e-03 pix\n",
      "Fit SIP degree=4 rms= 6.94e-03, 6.58e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003001_08101_00002_nrca1_rate.fits\n",
      "../RAW/jw01074003001_08101_00002_nrca2_rate.fits\n",
      "../RAW/jw01074003001_08101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003001_08101_00002_nrcalong_rate.fits -> jw01074003001_08101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:10:08.304)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003001_08101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:10:12,076 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:12,077 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:12,078 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:12,107 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:10:12.512)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003001_08101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.31e-03 pix\n",
      "Fit SIP degree=4 rms= 6.94e-03, 6.58e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003001_08101_00003_nrca1_rate.fits\n",
      "../RAW/jw01074003001_08101_00003_nrca2_rate.fits\n",
      "../RAW/jw01074003001_08101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003001_08101_00003_nrcalong_rate.fits -> jw01074003001_08101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:10:17.088)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003001_08101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:10:20,834 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:20,835 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:20,836 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:20,865 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:10:21.295)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003001_08101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.31e-03 pix\n",
      "Fit SIP degree=4 rms= 6.94e-03, 6.58e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_04101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003002_04101_00001_nrcalong_rate.fits -> jw01074003002_04101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:10:26.290)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_04101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:10:30,084 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:30,085 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:30,085 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:30,114 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:10:30.522)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_04101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.89e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.38e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_04101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074003002_04101_00001_nrcblong_rate.fits -> jw01074003002_04101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:10:35.532)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_04101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:10:39,317 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:39,318 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:39,319 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:39,349 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:10:39.786)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_04101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.32e-03, 4.03e-03 pix\n",
      "Fit SIP degree=4 rms= 4.42e-03, 3.60e-03 pix\n",
      "Fit SIP degree=5 rms= 3.37e-04, 3.36e-04 pix\n",
      "Fit SIP degree=5 rms= 3.37e-04, 3.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_04101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003002_04101_00002_nrcalong_rate.fits -> jw01074003002_04101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:10:44.318)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_04101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:10:48,077 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:48,078 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:48,079 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:48,109 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:10:48.521)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_04101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.89e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.38e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_04101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074003002_04101_00002_nrcblong_rate.fits -> jw01074003002_04101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:10:53.919)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_04101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:10:57,699 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:57,699 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:57,700 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:10:57,730 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:10:58.141)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_04101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.32e-03, 4.03e-03 pix\n",
      "Fit SIP degree=4 rms= 4.42e-03, 3.60e-03 pix\n",
      "Fit SIP degree=5 rms= 3.37e-04, 3.36e-04 pix\n",
      "Fit SIP degree=5 rms= 3.37e-04, 3.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_04101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003002_04101_00003_nrcalong_rate.fits -> jw01074003002_04101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:11:02.808)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_04101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:11:06,604 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:06,605 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:06,606 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:06,636 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:11:07.048)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_04101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.89e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.38e-03, 5.73e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.56e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_04101_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074003002_04101_00003_nrcblong_rate.fits -> jw01074003002_04101_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:11:11.611)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_04101_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:11:15,349 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:15,350 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:15,350 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:15,379 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:11:15.790)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_04101_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 4.03e-03 pix\n",
      "Fit SIP degree=4 rms= 4.42e-03, 3.60e-03 pix\n",
      "Fit SIP degree=5 rms= 3.37e-04, 3.36e-04 pix\n",
      "Fit SIP degree=5 rms= 3.37e-04, 3.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_06101_00001_nrca3_rate.fits\n",
      "../RAW/jw01074003002_06101_00001_nrca4_rate.fits\n",
      "../RAW/jw01074003002_06101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01074003002_06101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01074003002_06101_00002_nrca3_rate.fits\n",
      "../RAW/jw01074003002_06101_00002_nrca4_rate.fits\n",
      "../RAW/jw01074003002_06101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01074003002_06101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01074003002_06101_00003_nrca3_rate.fits\n",
      "../RAW/jw01074003002_06101_00003_nrca4_rate.fits\n",
      "../RAW/jw01074003002_06101_00003_nrcb3_rate.fits\n",
      "../RAW/jw01074003002_06101_00003_nrcb4_rate.fits\n",
      "../RAW/jw01074003002_08101_00001_nrca3_rate.fits\n",
      "../RAW/jw01074003002_08101_00001_nrca4_rate.fits\n",
      "../RAW/jw01074003002_08101_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003002_08101_00001_nrcalong_rate.fits -> jw01074003002_08101_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:11:20.266)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_08101_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:11:24,045 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:24,046 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:24,047 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:24,077 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:11:24.485)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_08101_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.31e-03 pix\n",
      "Fit SIP degree=4 rms= 6.94e-03, 6.57e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_08101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01074003002_08101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01074003002_08101_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01074003002_08101_00001_nrcblong_rate.fits -> jw01074003002_08101_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:11:29.040)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_08101_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:11:32,761 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:32,762 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:32,763 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:32,792 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:11:33.227)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_08101_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.27e-03, 4.87e-03 pix\n",
      "Fit SIP degree=4 rms= 4.55e-03, 4.29e-03 pix\n",
      "Fit SIP degree=5 rms= 3.36e-04, 3.37e-04 pix\n",
      "Fit SIP degree=5 rms= 3.36e-04, 3.37e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_08101_00002_nrca3_rate.fits\n",
      "../RAW/jw01074003002_08101_00002_nrca4_rate.fits\n",
      "../RAW/jw01074003002_08101_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003002_08101_00002_nrcalong_rate.fits -> jw01074003002_08101_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:11:38.064)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_08101_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:11:41,844 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:41,845 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:41,846 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:41,876 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:11:42.284)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_08101_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.31e-03 pix\n",
      "Fit SIP degree=4 rms= 6.94e-03, 6.57e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_08101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01074003002_08101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01074003002_08101_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01074003002_08101_00002_nrcblong_rate.fits -> jw01074003002_08101_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:11:46.767)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_08101_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:11:50,502 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:50,503 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:50,504 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:50,533 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:11:50.941)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_08101_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.27e-03, 4.87e-03 pix\n",
      "Fit SIP degree=4 rms= 4.55e-03, 4.29e-03 pix\n",
      "Fit SIP degree=5 rms= 3.36e-04, 3.36e-04 pix\n",
      "Fit SIP degree=5 rms= 3.37e-04, 3.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_08101_00003_nrca3_rate.fits\n",
      "../RAW/jw01074003002_08101_00003_nrca4_rate.fits\n",
      "../RAW/jw01074003002_08101_00003_nrcalong_rate.fits\n",
      "# ../RAW/jw01074003002_08101_00003_nrcalong_rate.fits -> jw01074003002_08101_00003_nrcalong_rate.fits \n",
      "# (2022-08-26 11:11:55.779)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_08101_00003_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0313.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=11243\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:11:59,495 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:59,496 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:59,496 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0141.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:11:59,526 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:11:59.935)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_08101_00003_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 8.56e-03, 8.31e-03 pix\n",
      "Fit SIP degree=4 rms= 6.94e-03, 6.57e-03 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "Fit SIP degree=5 rms= 4.57e-04, 4.55e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4351e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01074003002_08101_00003_nrcb3_rate.fits\n",
      "../RAW/jw01074003002_08101_00003_nrcb4_rate.fits\n",
      "../RAW/jw01074003002_08101_00003_nrcblong_rate.fits\n",
      "# ../RAW/jw01074003002_08101_00003_nrcblong_rate.fits -> jw01074003002_08101_00003_nrcblong_rate.fits \n",
      "# (2022-08-26 11:12:05.222)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01074003002_08101_00003_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F444W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME > LMC-ASTROMETRIC-FIELD (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0339.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f444w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2889\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:12:08,957 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:08,957 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:08,958 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0148.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:08,987 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:12:09.395)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01074003002_08101_00003_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 6.27e-03, 4.87e-03 pix\n",
      "Fit SIP degree=4 rms= 4.55e-03, 4.29e-03 pix\n",
      "Fit SIP degree=5 rms= 3.36e-04, 3.36e-04 pix\n",
      "Fit SIP degree=5 rms= 3.37e-04, 3.36e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 44036.7\n",
      "PHOTFLAM = 1.4387e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01086001001_14101_00001_nis_rate.fits\n",
      "../RAW/jw01086001001_14101_00002_nis_rate.fits\n",
      "../RAW/jw01086001001_16101_00001_nis_rate.fits\n",
      "../RAW/jw01086001001_16101_00002_nis_rate.fits\n",
      "../RAW/jw01086001001_22101_00001_nis_rate.fits\n",
      "../RAW/jw01086001001_22101_00002_nis_rate.fits\n",
      "../RAW/jw01086001001_24101_00001_nis_rate.fits\n",
      "../RAW/jw01086001001_24101_00002_nis_rate.fits\n",
      "../RAW/jw01086001001_38101_00001_nis_rate.fits\n",
      "../RAW/jw01086001001_38101_00002_nis_rate.fits\n",
      "../RAW/jw01086001001_40101_00001_nis_rate.fits\n",
      "../RAW/jw01086001001_40101_00002_nis_rate.fits\n",
      "../RAW/jw01086001001_46101_00001_nis_rate.fits\n",
      "../RAW/jw01086001001_46101_00002_nis_rate.fits\n",
      "../RAW/jw01086001001_48101_00001_nis_rate.fits\n",
      "../RAW/jw01086001001_48101_00002_nis_rate.fits\n",
      "../RAW/jw01086001001_62101_00001_nis_rate.fits\n",
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      "../RAW/jw01473011001_02101_00001_nrca2_rate.fits\n",
      "../RAW/jw01473011001_02101_00001_nrca3_rate.fits\n",
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      "../RAW/jw01473011001_02101_00002_nrca3_rate.fits\n",
      "../RAW/jw01473011001_02101_00002_nrca4_rate.fits\n",
      "../RAW/jw01473011001_02101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01473011001_02101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01473011001_02101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01473011001_02101_00003_nrca2_rate.fits\n",
      "../RAW/jw01473011001_02101_00003_nrca3_rate.fits\n",
      "../RAW/jw01473011001_02101_00003_nrca4_rate.fits\n",
      "../RAW/jw01473011001_02101_00003_nrcb2_rate.fits\n",
      "../RAW/jw01473011001_02101_00003_nrcb3_rate.fits\n",
      "../RAW/jw01473011001_02101_00003_nrcb4_rate.fits\n",
      "../RAW/jw01473011001_02101_00004_nrca3_rate.fits\n",
      "../RAW/jw01473011001_02101_00004_nrca4_rate.fits\n",
      "../RAW/jw01473011001_02101_00004_nrcb2_rate.fits\n",
      "../RAW/jw01473011001_02101_00004_nrcb3_rate.fits\n",
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      "../RAW/jw01473012001_02101_00001_nrca2_rate.fits\n",
      "../RAW/jw01473012001_02101_00001_nrca3_rate.fits\n",
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      "../RAW/jw01473012001_02101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01473012001_02101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01473012001_02101_00003_nrca2_rate.fits\n",
      "../RAW/jw01473012001_02101_00003_nrca3_rate.fits\n",
      "../RAW/jw01473012001_02101_00003_nrca4_rate.fits\n",
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      "../RAW/jw01473012001_02101_00003_nrcb3_rate.fits\n",
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      "../RAW/jw01473012001_02101_00004_nrca3_rate.fits\n",
      "../RAW/jw01473012001_02101_00004_nrca4_rate.fits\n",
      "../RAW/jw01473012001_02101_00004_nrcb2_rate.fits\n",
      "../RAW/jw01473012001_02101_00004_nrcb3_rate.fits\n",
      "../RAW/jw01473012001_02101_00004_nrcb4_rate.fits\n",
      "../RAW/jw01473013001_04101_00001_nrca2_rate.fits\n",
      "../RAW/jw01473013001_04101_00001_nrca3_rate.fits\n",
      "../RAW/jw01473013001_04101_00001_nrca4_rate.fits\n",
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      "../RAW/jw01473013001_04101_00003_nrca2_rate.fits\n",
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      "../RAW/jw01473014001_06101_00001_nrca2_rate.fits\n",
      "../RAW/jw01473014001_06101_00001_nrca3_rate.fits\n",
      "../RAW/jw01473014001_06101_00001_nrca4_rate.fits\n",
      "../RAW/jw01473014001_06101_00001_nrcalong_rate.fits\n",
      "../RAW/jw01473014001_06101_00001_nrcb1_rate.fits\n",
      "../RAW/jw01473014001_06101_00001_nrcb2_rate.fits\n",
      "../RAW/jw01473014001_06101_00001_nrcb3_rate.fits\n",
      "../RAW/jw01473014001_06101_00001_nrcb4_rate.fits\n",
      "../RAW/jw01473014001_06101_00001_nrcblong_rate.fits\n",
      "../RAW/jw01473014001_06101_00002_nrca1_rate.fits\n",
      "../RAW/jw01473014001_06101_00002_nrca2_rate.fits\n",
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      "../RAW/jw01473014001_06101_00002_nrcb1_rate.fits\n",
      "../RAW/jw01473014001_06101_00002_nrcb2_rate.fits\n",
      "../RAW/jw01473014001_06101_00002_nrcb3_rate.fits\n",
      "../RAW/jw01473014001_06101_00002_nrcb4_rate.fits\n",
      "../RAW/jw01473014001_06101_00002_nrcblong_rate.fits\n",
      "../RAW/jw01473014001_06101_00003_nrca1_rate.fits\n",
      "../RAW/jw01473014001_06101_00003_nrca2_rate.fits\n",
      "../RAW/jw01473014001_06101_00003_nrca3_rate.fits\n",
      "../RAW/jw01473014001_06101_00003_nrca4_rate.fits\n",
      "../RAW/jw01473014001_06101_00003_nrcalong_rate.fits\n",
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      "../RAW/jw01473014001_06101_00003_nrcb2_rate.fits\n",
      "../RAW/jw01473014001_06101_00003_nrcb3_rate.fits\n",
      "../RAW/jw01473014001_06101_00003_nrcb4_rate.fits\n",
      "../RAW/jw01473014001_06101_00003_nrcblong_rate.fits\n",
      "../RAW/jw01473014001_06101_00004_nrca1_rate.fits\n",
      "../RAW/jw01473014001_06101_00004_nrca2_rate.fits\n",
      "../RAW/jw01473014001_06101_00004_nrca3_rate.fits\n",
      "../RAW/jw01473014001_06101_00004_nrca4_rate.fits\n",
      "../RAW/jw01473014001_06101_00004_nrcalong_rate.fits\n",
      "../RAW/jw01473014001_06101_00004_nrcb1_rate.fits\n",
      "../RAW/jw01473014001_06101_00004_nrcb2_rate.fits\n",
      "../RAW/jw01473014001_06101_00004_nrcb3_rate.fits\n",
      "../RAW/jw01473014001_06101_00004_nrcb4_rate.fits\n",
      "../RAW/jw01473014001_06101_00004_nrcblong_rate.fits\n",
      "../RAW/jw01473015001_04201_00001_nrca1_rate.fits\n",
      "../RAW/jw01473015001_04201_00001_nrca2_rate.fits\n",
      "../RAW/jw01473015001_04201_00001_nrca3_rate.fits\n",
      "../RAW/jw01473015001_04201_00001_nrcb3_rate.fits\n",
      "../RAW/jw01473015001_04201_00001_nrcb4_rate.fits\n",
      "../RAW/jw01473015001_04201_00002_nrca1_rate.fits\n",
      "../RAW/jw01473015001_04201_00002_nrca2_rate.fits\n",
      "../RAW/jw01473015001_04201_00002_nrca3_rate.fits\n",
      "../RAW/jw01473015001_04201_00002_nrcb3_rate.fits\n",
      "../RAW/jw01473015001_04201_00002_nrcb4_rate.fits\n",
      "../RAW/jw01473015001_04201_00003_nrca1_rate.fits\n",
      "../RAW/jw01473015001_04201_00003_nrca2_rate.fits\n",
      "../RAW/jw01473015001_04201_00003_nrca3_rate.fits\n",
      "../RAW/jw01473015001_04201_00003_nrcb3_rate.fits\n",
      "../RAW/jw01473015001_04201_00003_nrcb4_rate.fits\n",
      "../RAW/jw01473015001_04201_00004_nrca1_rate.fits\n",
      "../RAW/jw01473015001_04201_00004_nrca2_rate.fits\n",
      "../RAW/jw01473015001_04201_00004_nrca3_rate.fits\n",
      "../RAW/jw01473015001_04201_00004_nrcb3_rate.fits\n",
      "../RAW/jw01473015001_04201_00004_nrcb4_rate.fits\n",
      "../RAW/jw01477001001_02103_00001_nrca1_rate.fits\n",
      "../RAW/jw01477001001_02103_00001_nrca2_rate.fits\n",
      "../RAW/jw01477001001_02103_00001_nrca3_rate.fits\n",
      "../RAW/jw01477001001_02103_00001_nrca4_rate.fits\n",
      "../RAW/jw01477001001_02103_00001_nrcalong_rate.fits\n",
      "# ../RAW/jw01477001001_02103_00001_nrcalong_rate.fits -> jw01477001001_02103_00001_nrcalong_rate.fits \n",
      "# (2022-08-26 11:12:14.219)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01477001001_02103_00001_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:12:18,241 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:18,241 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:18,242 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:18,272 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:12:18.680)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01477001001_02103_00001_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.91e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.38e-03, 5.68e-03 pix\n",
      "Fit SIP degree=5 rms= 4.60e-04, 4.60e-04 pix\n",
      "Fit SIP degree=5 rms= 4.60e-04, 4.60e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01477001001_02103_00002_nrca1_rate.fits\n",
      "../RAW/jw01477001001_02103_00002_nrca2_rate.fits\n",
      "../RAW/jw01477001001_02103_00002_nrca3_rate.fits\n",
      "../RAW/jw01477001001_02103_00002_nrca4_rate.fits\n",
      "../RAW/jw01477001001_02103_00002_nrcalong_rate.fits\n",
      "# ../RAW/jw01477001001_02103_00002_nrcalong_rate.fits -> jw01477001001_02103_00002_nrcalong_rate.fits \n",
      "# (2022-08-26 11:12:23.687)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01477001001_02103_00002_nrcalong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCALONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0101.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0266.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcalong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=17221\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:12:27,701 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:27,702 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:27,702 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0156.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:27,732 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0007.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:12:28.140)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01477001001_02103_00002_nrcalong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.91e-03, 8.15e-03 pix\n",
      "Fit SIP degree=4 rms= 6.38e-03, 5.68e-03 pix\n",
      "Fit SIP degree=5 rms= 4.60e-04, 4.60e-04 pix\n",
      "Fit SIP degree=5 rms= 4.60e-04, 4.60e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3078e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6592e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01477002001_02103_00001_nrcblong_rate.fits\n",
      "# ../RAW/jw01477002001_02103_00001_nrcblong_rate.fits -> jw01477002001_02103_00001_nrcblong_rate.fits \n",
      "# (2022-08-26 11:12:32.857)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01477002001_02103_00001_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:12:36,810 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:36,811 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:36,812 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:36,840 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:12:37.248)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01477002001_02103_00001_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 4.07e-03 pix\n",
      "Fit SIP degree=4 rms= 4.45e-03, 3.65e-03 pix\n",
      "Fit SIP degree=5 rms= 3.16e-04, 3.16e-04 pix\n",
      "Fit SIP degree=5 rms= 3.16e-04, 3.16e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01477002001_02103_00002_nrcblong_rate.fits\n",
      "# ../RAW/jw01477002001_02103_00002_nrcblong_rate.fits -> jw01477002001_02103_00002_nrcblong_rate.fits \n",
      "# (2022-08-26 11:12:41.971)\n",
      "\n",
      "jwst_utils.initialize_jwst_image(**{'filename': 'jw01477002001_02103_00002_nrcblong_rate.fits', 'verbose': True, 'max_dq_bit': 14, 'orig_keys': ['TELESCOP', 'INSTRUME', 'DETECTOR', 'FILTER', 'PUPIL', 'EXP_TYPE']})\n",
      "\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "OTELESCO = TELESCOP JWST\n",
      "OINSTRUM = INSTRUME NIRCAM\n",
      "ODETECTO = DETECTOR NRCBLONG\n",
      "OFILTER = FILTER F277W\n",
      "OPUPIL = PUPIL CLEAR\n",
      "OEXP_TYP = EXP_TYPE NRC_IMAGE\n",
      "ENGQLPTG = CALCULATED_TR_202105\n",
      "TARGNAME >  (no spaces)\n",
      "PFLTFILE = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "Clip MAXDQBIT = 14\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.flatfield.FlatFieldStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst.flatfield.PhotomStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_photom_0104.fits\n",
      "jwst_utils.get_jwst_skyflat: pipeline flat = /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_flat_0361.fits\n",
      "jwst_utils.get_jwst_skyflat: new sky flat = /home/ec2-user/telescopes/grizli_conf/CONF/NircamSkyFlat/nrcblong-f277w-clear_skyflat_smooth.fits\n",
      "jwst_utils.get_jwst_skyflat: valid_flat=(0.7, 1.4) nmask=2905\n",
      "ENV CRDS_CONTEXT = jwst_0942.pmap\n",
      "jwst.assign_wcs.AssignWcsStep: /home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:12:45,949 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.11.1), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:45,950 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:45,951 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_distortion_0159.asdf' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.2.1), but older package (asdf-astropy==0.1.2) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n",
      "2022-08-26 11:12:45,981 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///home/ec2-user/telescopes/crds_cache/references/jwst/nircam/jwst_nircam_filteroffset_0008.asdf' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.12.0), but older package (asdf==2.9.0) is installed.\n",
      "  warnings.warn(msg, AsdfWarning)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# (2022-08-26 11:12:46.391)\n",
      "\n",
      "jwst_utils.pipeline_model_wcs_header(**{'datamodel': <ImageModel(2048, 2048) from jw01477002001_02103_00002_nrcblong_rate.fits>, 'step': 64, 'degrees': [3, 4, 5, 5], 'lsq_args': {'jac': '2-point', 'bounds': (-inf, inf), 'method': 'trf', 'ftol': 1e-12, 'xtol': 1e-12, 'gtol': 1e-12, 'x_scale': 1.0, 'loss': 'soft_l1', 'f_scale': 1000.0, 'diff_step': 1e-06, 'tr_solver': None, 'tr_options': {}, 'jac_sparsity': None, 'max_nfev': 100, 'verbose': 0, 'kwargs': {}}, 'crpix': None, 'verbose': True, 'initial_header': None, 'max_rms': 0.0001, 'set_diff_step': False, 'get_guess': False, 'fast_coeffs': True})\n",
      "\n",
      "Fit SIP degree=3 rms= 7.33e-03, 4.07e-03 pix\n",
      "Fit SIP degree=4 rms= 4.45e-03, 3.65e-03 pix\n",
      "Fit SIP degree=5 rms= 3.16e-04, 3.17e-04 pix\n",
      "Fit SIP degree=5 rms= 3.16e-04, 3.17e-04 pix\n",
      "# photometry keywords\n",
      "PHOTFNU = 9.3313e-08\n",
      "PHOTPLAM = 27578.0\n",
      "PHOTFLAM = 3.6685e-21\n",
      "ZP = 26.48\n",
      "TO_MJYSR = 1.000\n",
      "\n",
      "../RAW/jw01515001001_03101_00001_nis_rate.fits\n",
      "../RAW/jw01515001001_03101_00002_nis_rate.fits\n",
      "../RAW/jw01515001001_03101_00003_nis_rate.fits\n",
      "../RAW/jw01515001001_03101_00004_nis_rate.fits\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "from grizli import jwst_utils, prep\n",
    "\n",
    "files = glob.glob('../RAW/*nis_*rate.fits')\n",
    "files.sort()\n",
    "files = glob.glob('../RAW/*_nrc*rate.fits')\n",
    "files = glob.glob('../RAW/*rate.fits')\n",
    "files.sort()\n",
    "print(len(files))\n",
    "\n",
    "for file in files:\n",
    "    print(file)\n",
    "    if not os.path.exists(os.path.basename(file)):\n",
    "        prep.fresh_flt_file(os.path.basename(file))\n",
    "        \n",
    "#         os.system(f'cp {file} ./')\n",
    "#         _file = os.path.basename(file)\n",
    "\n",
    "#         jwst_utils.img_with_wcs(_file, fit_sip_header=False)\n",
    "#         jwst_utils.img_with_flat(_file)            \n",
    "#         jwst_utils.get_phot_keywords(_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96212c95-0180-4c63-8a24-6b612d928adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import grizli.catalog\n",
    "import glob\n",
    "import astropy.io.fits as pyfits\n",
    "import grizli.catalog\n",
    "\n",
    "ra, dec = 80.5, -69.5\n",
    "\n",
    "files = glob.glob('*rate.fits')\n",
    "files.sort()\n",
    "im = pyfits.open(files[0])\n",
    "\n",
    "gaia = grizli.catalog.get_gaia_DR2_vizier(ra=ra, dec=dec, radius=3.5, \n",
    "                                          mjd=im[0].header['EXPSTART'])\n",
    "\n",
    "t = utils.GTable()\n",
    "t['ra'] = gaia['ra_time']\n",
    "t['dec'] = gaia['dec_time']\n",
    "grizli.catalog.table_to_regions(t, 'gaia_epoch.reg')\n",
    "grizli.catalog.table_to_radec(t, 'gaia_epoch.radec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 992,
   "id": "3a7ed3e8-6cff-48ec-9949-16a148e974fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "207"
      ]
     },
     "execution_count": 992,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = glob.glob('*_nis*rate.fits')\n",
    "files = glob.glob('*_nrc*rate.fits')\n",
    "files = glob.glob('*rate.fits')\n",
    "files = glob.glob('*nrc*long*rate.fits')\n",
    "\n",
    "files.sort()\n",
    "\n",
    "skip = True\n",
    "\n",
    "len(files)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 993,
   "id": "3bdf0d08-9a5c-48ae-bb4d-0ae21dbeb842",
   "metadata": {},
   "outputs": [],
   "source": [
    "coo = np.array([t['ra'], t['dec']]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db509b43-94e1-43b6-8f59-23f3646ec82e",
   "metadata": {},
   "source": [
    "# Align astrometry to GAIA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 957,
   "id": "edf4f1f0-23ea-4888-8847-52461b95eb02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 957,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if 1:\n",
    "    xx = (res['filter'] == 'F444W-CLEAR') & (res['detector'] == 'NRCALONG')\n",
    "else:\n",
    "    xx = res['exptime'] > 0\n",
    "    \n",
    "files = [f'{d}_rate.fits' for d in res['dataset'][xx]]\n",
    "\n",
    "skip = True\n",
    "\n",
    "len(files)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 994,
   "id": "622e9ae6-4141-4a88-be76-d82c145b0034",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skip  jw01018003001_02101_00001_nrcalong_rate.fits\n",
      "skip  jw01018004001_02101_00001_nrcalong_rate.fits\n",
      "2 jw01069001001_02101_00001_nrcalong_rate.fits  777  0.0052 0.0062\n",
      "3 jw01069001001_02101_00001_nrcblong_rate.fits  806  0.0061 0.0066\n",
      "4 jw01069001001_02101_00002_nrcalong_rate.fits  790  0.0052 0.0063\n",
      "5 jw01069001001_02101_00002_nrcblong_rate.fits  803  0.0058 0.0065\n",
      "6 jw01069001001_04101_00001_nrcalong_rate.fits  936  0.0062 0.0069\n",
      "7 jw01069001001_04101_00001_nrcblong_rate.fits  998  0.0071 0.0073\n",
      "8 jw01069001001_04101_00002_nrcalong_rate.fits  935  0.0062 0.0069\n",
      "9 jw01069001001_04101_00002_nrcblong_rate.fits  998  0.0072 0.0072\n",
      "skip  jw01069001001_06101_00001_nrcalong_rate.fits\n",
      "skip  jw01069001001_06101_00001_nrcblong_rate.fits\n",
      "skip  jw01069001001_06101_00002_nrcalong_rate.fits\n",
      "skip  jw01069001001_06101_00002_nrcblong_rate.fits\n",
      "14 jw01069001002_02101_00001_nrcalong_rate.fits  764  0.0052 0.0062\n",
      "15 jw01069001002_02101_00001_nrcblong_rate.fits  816  0.0061 0.0062\n",
      "16 jw01069001002_02101_00002_nrcalong_rate.fits  748  0.0054 0.0060\n",
      "17 jw01069001002_02101_00002_nrcblong_rate.fits  814  0.0056 0.0065\n",
      "18 jw01069001002_04101_00001_nrcalong_rate.fits  896  0.0061 0.0066\n",
      "19 jw01069001002_04101_00001_nrcblong_rate.fits  979  0.0063 0.0069\n",
      "20 jw01069001002_04101_00002_nrcalong_rate.fits  886  0.0061 0.0064\n",
      "21 jw01069001002_04101_00002_nrcblong_rate.fits  974  0.0062 0.0070\n",
      "skip  jw01069001002_06101_00001_nrcalong_rate.fits\n",
      "skip  jw01069001002_06101_00001_nrcblong_rate.fits\n",
      "skip  jw01069001002_06101_00002_nrcalong_rate.fits\n",
      "skip  jw01069001002_06101_00002_nrcblong_rate.fits\n",
      "26 jw01069001003_02101_00001_nrcblong_rate.fits  808  0.0062 0.0063\n",
      "27 jw01069001003_02101_00002_nrcblong_rate.fits  809  0.0056 0.0065\n",
      "28 jw01069001003_04101_00001_nrcblong_rate.fits  960  0.0068 0.0069\n",
      "29 jw01069001003_04101_00002_nrcblong_rate.fits  965  0.0063 0.0068\n",
      "skip  jw01069001003_06101_00001_nrcblong_rate.fits\n",
      "skip  jw01069001003_06101_00002_nrcblong_rate.fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:13:18,459 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:120: RuntimeWarning: Mean of empty slice.\n",
      "  src_mean = src.mean(axis=0)\n",
      "\n",
      "2022-08-26 11:13:18,460 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:121: RuntimeWarning: Mean of empty slice.\n",
      "  dst_mean = dst.mean(axis=0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed\n",
      "33 jw01069001004_02101_00001_nrcblong_rate.fits  828  0.0064 0.0067\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:13:20,739 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:120: RuntimeWarning: Mean of empty slice.\n",
      "  src_mean = src.mean(axis=0)\n",
      "\n",
      "2022-08-26 11:13:20,740 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:121: RuntimeWarning: Mean of empty slice.\n",
      "  dst_mean = dst.mean(axis=0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed\n",
      "35 jw01069001004_02101_00002_nrcblong_rate.fits  817  0.0064 0.0067\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:13:23,206 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:120: RuntimeWarning: Mean of empty slice.\n",
      "  src_mean = src.mean(axis=0)\n",
      "\n",
      "2022-08-26 11:13:23,207 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:121: RuntimeWarning: Mean of empty slice.\n",
      "  dst_mean = dst.mean(axis=0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed\n",
      "37 jw01069001004_04101_00001_nrcblong_rate.fits 1025  0.0067 0.0071\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:13:25,975 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:120: RuntimeWarning: Mean of empty slice.\n",
      "  src_mean = src.mean(axis=0)\n",
      "\n",
      "2022-08-26 11:13:25,976 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:121: RuntimeWarning: Mean of empty slice.\n",
      "  dst_mean = dst.mean(axis=0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed\n",
      "39 jw01069001004_04101_00002_nrcblong_rate.fits  996  0.0068 0.0070\n",
      "skip  jw01069001004_06101_00001_nrcblong_rate.fits\n",
      "skip  jw01069001004_06101_00002_nrcblong_rate.fits\n",
      "42 jw01069002002_02101_00001_nrcalong_rate.fits  824  0.0057 0.0063\n",
      "43 jw01069002002_02101_00002_nrcalong_rate.fits  822  0.0053 0.0061\n",
      "44 jw01069002002_04101_00001_nrcalong_rate.fits  970  0.0063 0.0068\n",
      "45 jw01069002002_04101_00002_nrcalong_rate.fits  953  0.0064 0.0065\n",
      "skip  jw01069002002_06101_00001_nrcalong_rate.fits\n",
      "skip  jw01069002002_06101_00002_nrcalong_rate.fits\n",
      "48 jw01069002003_02101_00001_nrcalong_rate.fits  821  0.0051 0.0060\n",
      "49 jw01069002003_02101_00001_nrcblong_rate.fits  760  0.0059 0.0065\n",
      "50 jw01069002003_02101_00002_nrcalong_rate.fits  813  0.0050 0.0059\n",
      "51 jw01069002003_02101_00002_nrcblong_rate.fits  764  0.0063 0.0066\n",
      "52 jw01069002003_04101_00001_nrcalong_rate.fits  944  0.0057 0.0067\n",
      "53 jw01069002003_04101_00001_nrcblong_rate.fits  913  0.0066 0.0076\n",
      "54 jw01069002003_04101_00002_nrcalong_rate.fits  960  0.0059 0.0069\n",
      "55 jw01069002003_04101_00002_nrcblong_rate.fits  928  0.0066 0.0077\n",
      "skip  jw01069002003_06101_00001_nrcalong_rate.fits\n",
      "skip  jw01069002003_06101_00001_nrcblong_rate.fits\n",
      "skip  jw01069002003_06101_00002_nrcalong_rate.fits\n",
      "skip  jw01069002003_06101_00002_nrcblong_rate.fits\n",
      "60 jw01069002004_02101_00001_nrcalong_rate.fits  825  0.0054 0.0061\n",
      "61 jw01069002004_02101_00001_nrcblong_rate.fits  783  0.0055 0.0062\n",
      "62 jw01069002004_02101_00002_nrcalong_rate.fits  829  0.0054 0.0062\n",
      "63 jw01069002004_02101_00002_nrcblong_rate.fits  786  0.0056 0.0055\n",
      "64 jw01069002004_04101_00001_nrcalong_rate.fits  992  0.0061 0.0071\n",
      "65 jw01069002004_04101_00001_nrcblong_rate.fits  936  0.0061 0.0069\n",
      "66 jw01069002004_04101_00002_nrcalong_rate.fits  982  0.0062 0.0072\n",
      "67 jw01069002004_04101_00002_nrcblong_rate.fits  951  0.0063 0.0067\n",
      "skip  jw01069002004_06101_00001_nrcalong_rate.fits\n",
      "skip  jw01069002004_06101_00001_nrcblong_rate.fits\n",
      "skip  jw01069002004_06101_00002_nrcalong_rate.fits\n",
      "skip  jw01069002004_06101_00002_nrcblong_rate.fits\n",
      "72 jw01069021001_02101_00001_nrcalong_rate.fits  806  0.0054 0.0063\n",
      "73 jw01069021001_02101_00002_nrcalong_rate.fits  819  0.0057 0.0064\n",
      "74 jw01069021001_02103_00001_nrcalong_rate.fits  973  0.0067 0.0068\n",
      "75 jw01069021001_02103_00002_nrcalong_rate.fits  975  0.0064 0.0068\n",
      "skip  jw01069021001_02105_00001_nrcalong_rate.fits\n",
      "skip  jw01069021001_02105_00002_nrcalong_rate.fits\n",
      "78 jw01072001001_02101_00001_nrcalong_rate.fits  700  0.0056 0.0072\n",
      "79 jw01072001001_02101_00001_nrcblong_rate.fits  654  0.0066 0.0064\n",
      "80 jw01072001001_02101_00002_nrcalong_rate.fits  680  0.0058 0.0066\n",
      "81 jw01072001001_02101_00002_nrcblong_rate.fits  651  0.0062 0.0071\n",
      "skip  jw01072001001_02103_00001_nrcalong_rate.fits\n",
      "skip  jw01072001001_02103_00001_nrcblong_rate.fits\n",
      "skip  jw01072001001_02103_00002_nrcalong_rate.fits\n",
      "skip  jw01072001001_02103_00002_nrcblong_rate.fits\n",
      "86 jw01072001001_02105_00001_nrcalong_rate.fits  253  0.0042 0.0041\n",
      "87 jw01072001001_02105_00001_nrcblong_rate.fits  218  0.0053 0.0040\n",
      "88 jw01072001001_02105_00002_nrcalong_rate.fits  256  0.0043 0.0043\n",
      "89 jw01072001001_02105_00002_nrcblong_rate.fits  211  0.0054 0.0050\n",
      "90 jw01074001001_04101_00001_nrcalong_rate.fits  904  0.0065 0.0072\n",
      "91 jw01074001001_04101_00002_nrcalong_rate.fits  929  0.0067 0.0075\n",
      "92 jw01074001001_04101_00003_nrcalong_rate.fits  922  0.0068 0.0075\n",
      "93 jw01074001001_08101_00001_nrcalong_rate.fits  797  0.0059 0.0062\n",
      "94 jw01074001001_08101_00002_nrcalong_rate.fits  803  0.0059 0.0068\n",
      "95 jw01074001001_08101_00003_nrcalong_rate.fits  791  0.0058 0.0064\n",
      "96 jw01074001002_04101_00001_nrcalong_rate.fits  925  0.0065 0.0068\n",
      "97 jw01074001002_04101_00001_nrcblong_rate.fits  910  0.0072 0.0077\n",
      "98 jw01074001002_04101_00002_nrcalong_rate.fits  921  0.0066 0.0076\n",
      "99 jw01074001002_04101_00002_nrcblong_rate.fits  921  0.0072 0.0075\n",
      "100 jw01074001002_04101_00003_nrcalong_rate.fits  910  0.0064 0.0069\n",
      "101 jw01074001002_04101_00003_nrcblong_rate.fits  925  0.0078 0.0075\n",
      "102 jw01074001002_08101_00001_nrcalong_rate.fits  836  0.0055 0.0063\n",
      "103 jw01074001002_08101_00001_nrcblong_rate.fits  794  0.0063 0.0064\n",
      "104 jw01074001002_08101_00002_nrcalong_rate.fits  831  0.0058 0.0064\n",
      "105 jw01074001002_08101_00002_nrcblong_rate.fits  795  0.0068 0.0066\n",
      "106 jw01074001002_08101_00003_nrcalong_rate.fits  832  0.0055 0.0067\n",
      "107 jw01074001002_08101_00003_nrcblong_rate.fits  783  0.0069 0.0062\n",
      "108 jw01074002001_02101_00001_nrcalong_rate.fits  879  0.0899 0.0715\n",
      "Failed\n",
      "110 jw01074002001_02101_00002_nrcalong_rate.fits  896  0.0100 0.0166\n",
      "Failed\n",
      "112 jw01074002001_02101_00003_nrcalong_rate.fits  895  0.0089 0.0115\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:14:43,981 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:120: RuntimeWarning: Mean of empty slice.\n",
      "  src_mean = src.mean(axis=0)\n",
      "\n",
      "2022-08-26 11:14:43,982 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:121: RuntimeWarning: Mean of empty slice.\n",
      "  dst_mean = dst.mean(axis=0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed\n",
      "skip  jw01074002001_02103_00001_nrcalong_rate.fits\n",
      "skip  jw01074002001_02103_00002_nrcalong_rate.fits\n",
      "skip  jw01074002001_02103_00003_nrcalong_rate.fits\n",
      "117 jw01074002001_02105_00001_nrcalong_rate.fits  806  0.0082 0.0068\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:14:46,222 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:120: RuntimeWarning: Mean of empty slice.\n",
      "  src_mean = src.mean(axis=0)\n",
      "\n",
      "2022-08-26 11:14:46,223 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:121: RuntimeWarning: Mean of empty slice.\n",
      "  dst_mean = dst.mean(axis=0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed\n",
      "119 jw01074002001_02105_00002_nrcalong_rate.fits  831  0.0075 0.0064\n",
      "Failed\n",
      "121 jw01074002001_02105_00003_nrcalong_rate.fits  824  0.0068 0.0063\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:14:50,625 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:120: RuntimeWarning: Mean of empty slice.\n",
      "  src_mean = src.mean(axis=0)\n",
      "\n",
      "2022-08-26 11:14:50,626 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:121: RuntimeWarning: Mean of empty slice.\n",
      "  dst_mean = dst.mean(axis=0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed\n",
      "123 jw01074002002_02101_00001_nrcalong_rate.fits  854  0.0056 0.0067\n",
      "124 jw01074002002_02101_00001_nrcblong_rate.fits  850  0.0065 0.0071\n",
      "125 jw01074002002_02101_00002_nrcalong_rate.fits  871  0.0058 0.0067\n",
      "126 jw01074002002_02101_00002_nrcblong_rate.fits  854  0.0069 0.0077\n",
      "127 jw01074002002_02101_00003_nrcalong_rate.fits  879  0.0060 0.0068\n",
      "128 jw01074002002_02101_00003_nrcblong_rate.fits  869  0.0070 0.0074\n",
      "skip  jw01074002002_02103_00001_nrcalong_rate.fits\n",
      "skip  jw01074002002_02103_00001_nrcblong_rate.fits\n",
      "skip  jw01074002002_02103_00002_nrcalong_rate.fits\n",
      "skip  jw01074002002_02103_00002_nrcblong_rate.fits\n",
      "skip  jw01074002002_02103_00003_nrcalong_rate.fits\n",
      "skip  jw01074002002_02103_00003_nrcblong_rate.fits\n",
      "135 jw01074002002_02105_00001_nrcalong_rate.fits  781  0.0053 0.0064\n",
      "136 jw01074002002_02105_00001_nrcblong_rate.fits  779  0.0058 0.0061\n",
      "137 jw01074002002_02105_00002_nrcalong_rate.fits  796  0.0054 0.0062\n",
      "138 jw01074002002_02105_00002_nrcblong_rate.fits  771  0.0056 0.0058\n",
      "139 jw01074002002_02105_00003_nrcalong_rate.fits  798  0.0054 0.0061\n",
      "140 jw01074002002_02105_00003_nrcblong_rate.fits  775  0.0057 0.0060\n",
      "141 jw01074002003_02101_00001_nrcalong_rate.fits  833  0.0063 0.0066\n",
      "142 jw01074002003_02101_00001_nrcblong_rate.fits  887  0.0063 0.0072\n",
      "143 jw01074002003_02101_00002_nrcalong_rate.fits  844  0.0067 0.0067\n",
      "144 jw01074002003_02101_00002_nrcblong_rate.fits  883  0.0065 0.0078\n",
      "145 jw01074002003_02101_00003_nrcalong_rate.fits  828  0.0066 0.0068\n",
      "146 jw01074002003_02101_00003_nrcblong_rate.fits  885  0.0064 0.0076\n",
      "skip  jw01074002003_02103_00001_nrcalong_rate.fits\n",
      "skip  jw01074002003_02103_00001_nrcblong_rate.fits\n",
      "skip  jw01074002003_02103_00002_nrcalong_rate.fits\n",
      "skip  jw01074002003_02103_00002_nrcblong_rate.fits\n",
      "skip  jw01074002003_02103_00003_nrcalong_rate.fits\n",
      "skip  jw01074002003_02103_00003_nrcblong_rate.fits\n",
      "153 jw01074002003_02105_00001_nrcalong_rate.fits  777  0.0056 0.0061\n",
      "154 jw01074002003_02105_00001_nrcblong_rate.fits  785  0.0057 0.0065\n",
      "155 jw01074002003_02105_00002_nrcalong_rate.fits  784  0.0057 0.0063\n",
      "156 jw01074002003_02105_00002_nrcblong_rate.fits  786  0.0056 0.0066\n",
      "157 jw01074002003_02105_00003_nrcalong_rate.fits  759  0.0056 0.0059\n",
      "158 jw01074002003_02105_00003_nrcblong_rate.fits  810  0.0058 0.0064\n",
      "159 jw01074002004_02101_00001_nrcalong_rate.fits  897  0.0065 0.0070\n",
      "160 jw01074002004_02101_00001_nrcblong_rate.fits  920  0.0074 0.0075\n",
      "161 jw01074002004_02101_00002_nrcalong_rate.fits  895  0.0064 0.0071\n",
      "162 jw01074002004_02101_00002_nrcblong_rate.fits  915  0.0076 0.0077\n",
      "163 jw01074002004_02101_00003_nrcalong_rate.fits  895  0.0064 0.0072\n",
      "164 jw01074002004_02101_00003_nrcblong_rate.fits  936  0.0076 0.0075\n",
      "skip  jw01074002004_02103_00001_nrcalong_rate.fits\n",
      "skip  jw01074002004_02103_00001_nrcblong_rate.fits\n",
      "skip  jw01074002004_02103_00002_nrcalong_rate.fits\n",
      "skip  jw01074002004_02103_00002_nrcblong_rate.fits\n",
      "skip  jw01074002004_02103_00003_nrcalong_rate.fits\n",
      "skip  jw01074002004_02103_00003_nrcblong_rate.fits\n",
      "171 jw01074002004_02105_00001_nrcalong_rate.fits  821  0.0052 0.0062\n",
      "172 jw01074002004_02105_00001_nrcblong_rate.fits  776  0.0062 0.0061\n",
      "173 jw01074002004_02105_00002_nrcalong_rate.fits  827  0.0051 0.0062\n",
      "174 jw01074002004_02105_00002_nrcblong_rate.fits  790  0.0065 0.0069\n",
      "175 jw01074002004_02105_00003_nrcalong_rate.fits  798  0.0055 0.0064\n",
      "176 jw01074002004_02105_00003_nrcblong_rate.fits  789  0.0064 0.0065\n",
      "177 jw01074003001_04101_00001_nrcalong_rate.fits  908  0.0066 0.0075\n",
      "178 jw01074003001_04101_00002_nrcalong_rate.fits  911  0.0066 0.0073\n",
      "179 jw01074003001_04101_00003_nrcalong_rate.fits  913  0.0069 0.0072\n",
      "180 jw01074003001_08101_00001_nrcalong_rate.fits  791  0.0057 0.0067\n",
      "181 jw01074003001_08101_00002_nrcalong_rate.fits  797  0.0056 0.0064\n",
      "182 jw01074003001_08101_00003_nrcalong_rate.fits  794  0.0063 0.0066\n",
      "183 jw01074003002_04101_00001_nrcalong_rate.fits  878  0.0064 0.0069\n",
      "184 jw01074003002_04101_00001_nrcblong_rate.fits  897  0.0082 0.0077\n",
      "185 jw01074003002_04101_00002_nrcalong_rate.fits  869  0.0065 0.0067\n",
      "186 jw01074003002_04101_00002_nrcblong_rate.fits  902  0.0075 0.0070\n",
      "187 jw01074003002_04101_00003_nrcalong_rate.fits  873  0.0062 0.0068\n",
      "188 jw01074003002_04101_00003_nrcblong_rate.fits  911  0.0072 0.0072\n",
      "189 jw01074003002_08101_00001_nrcalong_rate.fits  793  0.0058 0.0065\n",
      "190 jw01074003002_08101_00001_nrcblong_rate.fits  781  0.0066 0.0064\n",
      "191 jw01074003002_08101_00002_nrcalong_rate.fits  794  0.0056 0.0065\n",
      "192 jw01074003002_08101_00002_nrcblong_rate.fits  783  0.0064 0.0065\n",
      "193 jw01074003002_08101_00003_nrcalong_rate.fits  807  0.0058 0.0064\n",
      "194 jw01074003002_08101_00003_nrcblong_rate.fits  787  0.0066 0.0065\n",
      "skip  jw01473014001_06101_00001_nrcalong_rate.fits\n",
      "skip  jw01473014001_06101_00001_nrcblong_rate.fits\n",
      "skip  jw01473014001_06101_00002_nrcalong_rate.fits\n",
      "skip  jw01473014001_06101_00002_nrcblong_rate.fits\n",
      "skip  jw01473014001_06101_00003_nrcalong_rate.fits\n",
      "skip  jw01473014001_06101_00003_nrcblong_rate.fits\n",
      "skip  jw01473014001_06101_00004_nrcalong_rate.fits\n",
      "skip  jw01473014001_06101_00004_nrcblong_rate.fits\n",
      "203 jw01477001001_02103_00001_nrcalong_rate.fits 1087  0.0693 0.0172\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 11:16:08,300 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:120: RuntimeWarning: Mean of empty slice.\n",
      "  src_mean = src.mean(axis=0)\n",
      "\n",
      "2022-08-26 11:16:08,301 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/skimage/transform/_geometric.py:121: RuntimeWarning: Mean of empty slice.\n",
      "  dst_mean = dst.mean(axis=0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed\n",
      "205 jw01477002001_02103_00001_nrcblong_rate.fits   10  0.0818 0.0827\n",
      "206 jw01477002001_02103_00002_nrcblong_rate.fits   11  0.0708 0.0661\n"
     ]
    }
   ],
   "source": [
    "import astropy.units as u\n",
    "import skimage.transform\n",
    "import astropy.wcs as pywcs\n",
    "\n",
    "file = os.path.basename(files[1])\n",
    "\n",
    "coo = np.array([t['ra'], t['dec']]).T\n",
    "pop = []\n",
    "\n",
    "close_plots = True\n",
    "\n",
    "for _i, _file in enumerate(files):\n",
    "    \n",
    "    file = os.path.basename(_file)\n",
    "    \n",
    "    if not os.path.exists(file):\n",
    "        prep.fresh_flt_file(file)\n",
    "\n",
    "    utils.set_warnings()\n",
    "    im = pyfits.open(file)\n",
    "    wcs = pywcs.WCS(im['SCI'].header, relax=True)\n",
    "    \n",
    "    if ('WCSFIX' in im['SCI'].header) & skip:\n",
    "        print('skip ', file)\n",
    "        continue\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "    NITER = 5\n",
    "    fig, axes = plt.subplots(1,NITER, figsize=(3*NITER,3))\n",
    "    \n",
    "    failed = False\n",
    "\n",
    "    _cat, _seg = prep.make_SEP_catalog_from_arrays(im['SCI'].data - np.nanmedian(im['SCI'].data),\n",
    "                                     im['ERR'].data, \n",
    "                                     (~np.isfinite(im['SCI'].data)), \n",
    "                                     wcs=wcs,\n",
    "                                     threshold=30, verbose=False)\n",
    "    \n",
    "    dp = np.sqrt((_cat['x'] - _cat['xpeak'])**2 +  (_cat['y'] -_cat['ypeak'])**2)\n",
    "    _cat = _cat[dp < 1]\n",
    "    \n",
    "    for _iter in range(NITER):\n",
    "        try:\n",
    "            _cat['ra'], _cat['dec'] = wcs.all_pix2world(_cat['x'], _cat['y'], 0)\n",
    "            \n",
    "            idx, dr, dx, dy = t.match_to_catalog_sky(_cat, get_2d_offset=True)\n",
    "\n",
    "            if _iter == 0:\n",
    "                _sep = 0.2*u.arcsec\n",
    "            else:\n",
    "                _sep = 0.05*u.arcsec\n",
    "                if '_nrc' in _file:\n",
    "                    _sep = 0.06*u.arcsec\n",
    "\n",
    "\n",
    "            hasm = np.abs(dx - np.median(dx)) < _sep\n",
    "            hasm &= np.abs(dy - np.median(dy)) < _sep\n",
    "\n",
    "            axes[_iter].scatter(dx[hasm], dy[hasm], alpha=0.1)\n",
    "            axes[_iter].grid()\n",
    "\n",
    "            tf = skimage.transform.SimilarityTransform()\n",
    "            xi, yi = wcs.all_world2pix(t['ra'][idx], t['dec'][idx], 0)\n",
    "\n",
    "            V1 = np.array([_cat['x'], _cat['y']]).T[hasm,:]\n",
    "            V2 = np.array([xi, yi]).T[hasm,:]\n",
    "\n",
    "            tf.estimate(V1, V2)\n",
    "            \n",
    "            wcs = utils.transform_wcs(wcs, translation=np.array(tf.translation)*-1, \n",
    "                            rotation=-tf.rotation, \n",
    "                            scale=1./tf.scale)\n",
    "        except:\n",
    "            if _i not in pop:\n",
    "                pop.append(_i)\n",
    "            failed = True\n",
    "            continue\n",
    "\n",
    "    if close_plots:\n",
    "        plt.close('all')\n",
    "    \n",
    "    if failed:\n",
    "        print('Failed')\n",
    "        continue\n",
    "    \n",
    "    xerr = 1.42*np.nanmedian(np.abs(dx[hasm].value))\n",
    "    yerr = 1.42*np.nanmedian(np.abs(dy[hasm].value))\n",
    "    \n",
    "    h = utils.to_header(wcs)\n",
    "    with pyfits.open(file, mode='update') as _im:\n",
    "        for k in h:\n",
    "            if k.startswith('CRVAL') | k.startswith('CD'):\n",
    "                _im['SCI'].header[k] = h[k]\n",
    "        \n",
    "        _im['SCI'].header['WCSFIX'] = True\n",
    "        _im['SCI'].header['WCSXERR'] = xerr\n",
    "        _im['SCI'].header['WCSYERR'] = yerr\n",
    "        print(f'{_i} {file} {hasm.sum():4}  {xerr:.4f} {yerr:.4f}')\n",
    "\n",
    "        _im.flush()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 995,
   "id": "cce23367-9c49-45d2-b89e-fa3f973bde5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jw01477001001_02103_00002_nrcalong_rate.fits\n",
      "jw01074002001_02105_00003_nrcblong_rate.fits\n",
      "jw01074002001_02105_00002_nrcblong_rate.fits\n",
      "jw01074002001_02105_00001_nrcblong_rate.fits\n",
      "jw01074002001_02101_00003_nrcblong_rate.fits\n",
      "jw01074002001_02101_00002_nrcblong_rate.fits\n",
      "jw01074002001_02101_00001_nrcblong_rate.fits\n",
      "jw01069001004_04101_00002_nrcalong_rate.fits\n",
      "jw01069001004_04101_00001_nrcalong_rate.fits\n",
      "jw01069001004_02101_00002_nrcalong_rate.fits\n",
      "jw01069001004_02101_00001_nrcalong_rate.fits\n"
     ]
    }
   ],
   "source": [
    "for _i in pop[::-1]:\n",
    "    print(files[_i])\n",
    "    if 1:\n",
    "        if os.path.exists(files[_i]):\n",
    "            os.remove(files[_i])\n",
    "\n",
    "        if os.path.exists('../RAW/'+files[_i]):\n",
    "            os.remove('../RAW/'+files[_i])\n",
    "\n",
    "        files.pop(_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 996,
   "id": "6dc61a58-c3d5-4fb7-9e4f-0489cc58a0e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rm: cannot remove ‘jw01145*’: No such file or directory\n",
      "rm: cannot remove ‘../RAW/jw01145*fits’: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!rm jw01145* ../RAW/jw01145*fits\n",
    "files = glob.glob('*rate.fits')\n",
    "files.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "id": "9dca0407-b27b-4383-9225-222efcc74181",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.36099246 0.3598679551104894 0.36324147410786334 0.30245763\n"
     ]
    }
   ],
   "source": [
    "#files = glob.glob('*rate.fits')\n",
    "file = files[0]\n",
    "import astropy.stats\n",
    "\n",
    "with pyfits.open(file) as im:\n",
    "    med = np.nanmedian(im['SCI'].data)\n",
    "    for _iter in range(2):\n",
    "        sn = (im['SCI'].data - med) / im['ERR'].data\n",
    "\n",
    "        msk = (sn < 10) & (dq == 0) & (np.isfinite(sn))\n",
    "        #mean = np.nanmean(im['SCI'].data)\n",
    "        med = np.nanmedian(im['SCI'].data[msk])\n",
    "        mean = astropy.stats.biweight_location(im['SCI'].data[msk])\n",
    "    \n",
    "    mode = utils.mode_statistic(im['SCI'].data[msk].flatten())\n",
    "        \n",
    "    print(med, mean, 3*med-2*mean, mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "id": "ef8a100e-bd52-4e66-b008-298718d5effb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 jw01018003001_02101_00001_nrca1_rate.fits 0.3022826910018921\n",
      "1 jw01018003001_02101_00001_nrca2_rate.fits 0.3547099232673645\n",
      "2 jw01018003001_02101_00001_nrca3_rate.fits 0.3824682235717773\n",
      "3 jw01018003001_02101_00001_nrcalong_rate.fits 0.1549003571271896\n",
      "4 jw01018004001_02101_00001_nrca1_rate.fits 0.3715194165706635\n",
      "5 jw01018004001_02101_00001_nrca2_rate.fits 0.3512464761734009\n",
      "6 jw01018004001_02101_00001_nrca3_rate.fits 0.3916155397891998\n",
      "7 jw01018004001_02101_00001_nrcalong_rate.fits 0.1516810655593872\n",
      "8 jw01069001001_02101_00001_nrcalong_rate.fits F444W CLEAR 0.2817867\n",
      "9 jw01069001001_02101_00001_nrcblong_rate.fits F444W CLEAR 0.29185778\n",
      "10 jw01069001001_02101_00002_nrcalong_rate.fits F444W CLEAR 0.27991906\n",
      "11 jw01069001001_02101_00002_nrcblong_rate.fits F444W CLEAR 0.27105445\n",
      "12 jw01069001001_04101_00001_nrca4_rate.fits 0.3906337022781372\n",
      "13 jw01069001001_04101_00001_nrcalong_rate.fits F277W CLEAR 0.17160663\n",
      "14 jw01069001001_04101_00001_nrcb1_rate.fits 0.4221983551979065\n",
      "15 jw01069001001_04101_00001_nrcb2_rate.fits 0.331054300069809\n",
      "16 jw01069001001_04101_00001_nrcb3_rate.fits 0.51329106092453\n",
      "17 jw01069001001_04101_00001_nrcb4_rate.fits 0.4592458605766296\n",
      "18 jw01069001001_04101_00001_nrcblong_rate.fits F277W CLEAR 0.18032153\n",
      "19 jw01069001001_04101_00002_nrca4_rate.fits 0.412922203540802\n",
      "20 jw01069001001_04101_00002_nrcalong_rate.fits F277W CLEAR 0.17548545\n",
      "21 jw01069001001_04101_00002_nrcb1_rate.fits 0.467865526676178\n",
      "22 jw01069001001_04101_00002_nrcb2_rate.fits 0.3745520710945129\n",
      "23 jw01069001001_04101_00002_nrcb3_rate.fits 0.4318750500679016\n",
      "24 jw01069001001_04101_00002_nrcb4_rate.fits 0.4541402459144592\n",
      "25 jw01069001001_04101_00002_nrcblong_rate.fits F277W CLEAR 0.18694441\n",
      "26 jw01069001001_06101_00001_nrca4_rate.fits 0.3525525331497192\n",
      "27 jw01069001001_06101_00001_nrcalong_rate.fits 0.1608176231384277\n",
      "28 jw01069001001_06101_00001_nrcb1_rate.fits 0.3803767561912537\n",
      "29 jw01069001001_06101_00001_nrcb2_rate.fits 0.3806756138801575\n",
      "30 jw01069001001_06101_00001_nrcb3_rate.fits 0.3945107161998749\n",
      "31 jw01069001001_06101_00001_nrcb4_rate.fits 0.466152548789978\n",
      "32 jw01069001001_06101_00001_nrcblong_rate.fits 0.161611944437027\n",
      "33 jw01069001001_06101_00002_nrca4_rate.fits 0.3057170212268829\n",
      "34 jw01069001001_06101_00002_nrcalong_rate.fits 0.1570062339305878\n",
      "35 jw01069001001_06101_00002_nrcb1_rate.fits 0.3646573126316071\n",
      "36 jw01069001001_06101_00002_nrcb2_rate.fits 0.3499171435832977\n",
      "37 jw01069001001_06101_00002_nrcb3_rate.fits 0.3851317465305328\n",
      "38 jw01069001001_06101_00002_nrcb4_rate.fits 0.3852960765361786\n",
      "39 jw01069001001_06101_00002_nrcblong_rate.fits 0.1657264977693558\n",
      "40 jw01069001002_02101_00001_nrcalong_rate.fits F444W CLEAR 0.2894331\n",
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      "42 jw01069001002_02101_00002_nrcalong_rate.fits F444W CLEAR 0.2853671\n",
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      "862 jw01473015001_04201_00004_nrcb4_rate.fits 0.3660743832588196\n",
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      "864 jw01477001001_02103_00001_nrca2_rate.fits 0.3781556189060211\n",
      "865 jw01477001001_02103_00001_nrca3_rate.fits 0.4030376374721527\n",
      "866 jw01477001001_02103_00001_nrca4_rate.fits 0.4520141780376434\n",
      "867 jw01477001001_02103_00001_nrcalong_rate.fits F277W CLEAR 0.16383846\n",
      "868 jw01477001001_02103_00002_nrca1_rate.fits 0.3711549043655396\n",
      "869 jw01477001001_02103_00002_nrca2_rate.fits 0.3727938830852509\n",
      "870 jw01477001001_02103_00002_nrca3_rate.fits 0.4011643528938293\n",
      "871 jw01477001001_02103_00002_nrca4_rate.fits 0.4196425676345825\n",
      "872 jw01477002001_02103_00001_nrcblong_rate.fits F277W CLEAR 0.18435043\n",
      "873 jw01477002001_02103_00002_nrcblong_rate.fits F277W CLEAR 0.18232049\n",
      "874 jw01515001001_03101_00001_nis_rate.fits 0.3050990104675293\n",
      "875 jw01515001001_03101_00002_nis_rate.fits 0.2991969585418701\n",
      "876 jw01515001001_03101_00003_nis_rate.fits 0.3023011982440948\n",
      "877 jw01515001001_03101_00004_nis_rate.fits 0.3081762492656708\n"
     ]
    }
   ],
   "source": [
    "for _i, file in enumerate(files):\n",
    "    with pyfits.open(file, mode='update') as im:\n",
    "        #med = np.nanmedian(im['SCI'].data)\n",
    "        dq = utils.unset_dq_bits(im['DQ'].data, 4)\n",
    "        \n",
    "        if 'MDRIZSKY' in im['SCI'].header:\n",
    "            print(_i, file, im['SCI'].header['MDRIZSKY'])\n",
    "            im.close()\n",
    "            continue\n",
    "            \n",
    "        med = np.nanmedian(im['SCI'].data)\n",
    "        \n",
    "        for _iter in range(3):\n",
    "            sn = (im['SCI'].data - med) / im['ERR'].data\n",
    "\n",
    "            msk = (sn < 10) & (dq == 0)\n",
    "            med = np.nanmedian(im['SCI'].data[msk].flatten())\n",
    "        \n",
    "        med = utils.mode_statistic(im['SCI'].data[msk].flatten())\n",
    "\n",
    "        im['SCI'].header['MDRIZSKY'] = med\n",
    "        print(_i, file, im[0].header['FILTER'], im[0].header['PUPIL'], med)\n",
    "        im.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "id": "8d418f4d-7d33-4011-ac7f-3cc327736a7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "878\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><i>GTable length=3</i>\n",
       "<table id=\"table140605476396112\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>dataset</th><th>extension</th><th>sciext</th><th>assoc</th><th>filter</th><th>pupil</th><th>exptime</th><th>footprint</th><th>detector</th></tr></thead>\n",
       "<thead><tr><th>str34</th><th>str4</th><th>int64</th><th>str6</th><th>str11</th><th>str5</th><th>float64</th><th>str93</th><th>str8</th></tr></thead>\n",
       "<tr><td>jw01018003001_02101_00001_nrca1</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>CLEAR</td><td>236.209</td><td>((80.466308,-69.519605),(80.496384,-69.533968),(80.537223,-69.523694),(80.507634,-69.509160))</td><td>NRCA1</td></tr>\n",
       "<tr><td>jw01018003001_02101_00001_nrca2</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>CLEAR</td><td>236.209</td><td>((80.498554,-69.534828),(80.527880,-69.549039),(80.568375,-69.538931),(80.539408,-69.524584))</td><td>NRCA2</td></tr>\n",
       "<tr><td>jw01018003001_02101_00001_nrca3</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>CLEAR</td><td>236.209</td><td>((80.510687,-69.508345),(80.540243,-69.522893),(80.581538,-69.512716),(80.552387,-69.497953))</td><td>NRCA3</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<GTable length=3>\n",
       "            dataset             extension ... detector\n",
       "             str34                 str4   ...   str8  \n",
       "------------------------------- --------- ... --------\n",
       "jw01018003001_02101_00001_nrca1      rate ...    NRCA1\n",
       "jw01018003001_02101_00001_nrca2      rate ...    NRCA2\n",
       "jw01018003001_02101_00001_nrca3      rate ...    NRCA3"
      ]
     },
     "execution_count": 998,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from grizli.aws import visit_processor\n",
    "res = visit_processor.res_query_from_local()\n",
    "print(len(res))\n",
    "res[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1069,
   "id": "af81581c-c322-43ae-b8dd-84c9af972f65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fix NIRISS: filters = F090W-CLEAR F090WN-CLEAR F115W-CLEAR F115WN-CLEAR F150W-CLEAR F150WN-CLEAR F200W-CLEAR F200WN-CLEAR F277W-CLEAR F356W-CLEAR F444W-CLEAR\n",
      "============ lmc-astrometric-f090w-clear ============\n",
      "\n",
      "(   1/  44) Add exposure jw01072001001_02103_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39032  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.42e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.606\n",
      "       PHOTFLAM=8.42e-21, scale=1.0\n",
      "# (2022-08-26 13:12:06.237)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   2/  44) Add exposure jw01072001001_02103_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33354  ( 0.8 %)\n",
      "  0    PHOTFLAM=8.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.505\n",
      "       PHOTFLAM=8.18e-21, scale=1.0\n",
      "# (2022-08-26 13:12:08.452)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/  44) Add exposure jw01072001001_02103_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34190  ( 0.8 %)\n",
      "  0    PHOTFLAM=8.49e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.760\n",
      "       PHOTFLAM=8.49e-21, scale=1.0\n",
      "# (2022-08-26 13:12:10.117)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   4/  44) Add exposure jw01072001001_02103_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34163  ( 0.8 %)\n",
      "  0    PHOTFLAM=8.16e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.609\n",
      "       PHOTFLAM=8.16e-21, scale=1.0\n",
      "# (2022-08-26 13:12:11.801)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/  44) Add exposure jw01072001001_02103_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33580  ( 0.8 %)\n",
      "  0    PHOTFLAM=8.40e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.051\n",
      "       PHOTFLAM=8.40e-21, scale=1.0\n",
      "# (2022-08-26 13:12:13.431)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   6/  44) Add exposure jw01072001001_02103_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33201  ( 0.8 %)\n",
      "  0    PHOTFLAM=8.23e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.602\n",
      "       PHOTFLAM=8.23e-21, scale=1.0\n",
      "# (2022-08-26 13:12:15.102)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/  44) Add exposure jw01072001001_02103_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39015  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.42e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.358\n",
      "       PHOTFLAM=8.42e-21, scale=1.0\n",
      "# (2022-08-26 13:12:16.744)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   8/  44) Add exposure jw01072001001_02103_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33352  ( 0.8 %)\n",
      "  0    PHOTFLAM=8.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.953\n",
      "       PHOTFLAM=8.18e-21, scale=1.0\n",
      "# (2022-08-26 13:12:18.378)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/  44) Add exposure jw01072001001_02103_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34193  ( 0.8 %)\n",
      "  0    PHOTFLAM=8.49e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.428\n",
      "       PHOTFLAM=8.49e-21, scale=1.0\n",
      "# (2022-08-26 13:12:20.009)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  10/  44) Add exposure jw01072001001_02103_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34176  ( 0.8 %)\n",
      "  0    PHOTFLAM=8.16e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.308\n",
      "       PHOTFLAM=8.16e-21, scale=1.0\n",
      "# (2022-08-26 13:12:21.659)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/  44) Add exposure jw01072001001_02103_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33556  ( 0.8 %)\n",
      "  0    PHOTFLAM=8.40e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.242\n",
      "       PHOTFLAM=8.40e-21, scale=1.0\n",
      "# (2022-08-26 13:12:23.249)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/  44) Add exposure jw01072001001_02103_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33196  ( 0.8 %)\n",
      "  0    PHOTFLAM=8.23e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.448\n",
      "       PHOTFLAM=8.23e-21, scale=1.0\n",
      "# (2022-08-26 13:12:24.886)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/  44) Add exposure jw01473014001_06101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46847  ( 1e+00 %)\n",
      "  0    PHOTFLAM=8.42e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.458\n",
      "       PHOTFLAM=8.42e-21, scale=1.0\n",
      "# (2022-08-26 13:12:26.482)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/  44) Add exposure jw01473014001_06101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36748  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.499\n",
      "       PHOTFLAM=8.18e-21, scale=1.0\n",
      "# (2022-08-26 13:12:28.112)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/  44) Add exposure jw01473014001_06101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39256  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.49e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.571\n",
      "       PHOTFLAM=8.49e-21, scale=1.0\n",
      "# (2022-08-26 13:12:29.733)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/  44) Add exposure jw01473014001_06101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40490  ( 1e+00 %)\n",
      "  0    PHOTFLAM=8.25e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.452\n",
      "       PHOTFLAM=8.25e-21, scale=1.0\n",
      "# (2022-08-26 13:12:31.365)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  17/  44) Add exposure jw01473014001_06101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39402  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.16e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.470\n",
      "       PHOTFLAM=8.16e-21, scale=1.0\n",
      "# (2022-08-26 13:12:32.988)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/  44) Add exposure jw01473014001_06101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37243  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.40e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.369\n",
      "       PHOTFLAM=8.40e-21, scale=1.0\n",
      "# (2022-08-26 13:12:34.611)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  19/  44) Add exposure jw01473014001_06101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35700  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.23e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.452\n",
      "       PHOTFLAM=8.23e-21, scale=1.0\n",
      "# (2022-08-26 13:12:36.236)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  20/  44) Add exposure jw01473014001_06101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37709  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.47e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.449\n",
      "       PHOTFLAM=8.47e-21, scale=1.0\n",
      "# (2022-08-26 13:12:37.852)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  21/  44) Add exposure jw01473014001_06101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46770  ( 1e+00 %)\n",
      "  0    PHOTFLAM=8.42e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.462\n",
      "       PHOTFLAM=8.42e-21, scale=1.0\n",
      "# (2022-08-26 13:12:39.488)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  22/  44) Add exposure jw01473014001_06101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36734  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.417\n",
      "       PHOTFLAM=8.18e-21, scale=1.0\n",
      "# (2022-08-26 13:12:41.112)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  23/  44) Add exposure jw01473014001_06101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39028  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.49e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.596\n",
      "       PHOTFLAM=8.49e-21, scale=1.0\n",
      "# (2022-08-26 13:12:42.720)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  24/  44) Add exposure jw01473014001_06101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40927  ( 1e+00 %)\n",
      "  0    PHOTFLAM=8.25e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.622\n",
      "       PHOTFLAM=8.25e-21, scale=1.0\n",
      "# (2022-08-26 13:12:44.340)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  25/  44) Add exposure jw01473014001_06101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39375  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.16e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.438\n",
      "       PHOTFLAM=8.16e-21, scale=1.0\n",
      "# (2022-08-26 13:12:45.955)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  26/  44) Add exposure jw01473014001_06101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36957  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.40e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.469\n",
      "       PHOTFLAM=8.40e-21, scale=1.0\n",
      "# (2022-08-26 13:12:47.568)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  27/  44) Add exposure jw01473014001_06101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35653  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.23e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.485\n",
      "       PHOTFLAM=8.23e-21, scale=1.0\n",
      "# (2022-08-26 13:12:49.183)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  28/  44) Add exposure jw01473014001_06101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37726  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.47e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.499\n",
      "       PHOTFLAM=8.47e-21, scale=1.0\n",
      "# (2022-08-26 13:12:50.794)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  29/  44) Add exposure jw01473014001_06101_00003_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 47055  ( 1e+00 %)\n",
      "  0    PHOTFLAM=8.42e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.569\n",
      "       PHOTFLAM=8.42e-21, scale=1.0\n",
      "# (2022-08-26 13:12:52.414)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  30/  44) Add exposure jw01473014001_06101_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36673  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.460\n",
      "       PHOTFLAM=8.18e-21, scale=1.0\n",
      "# (2022-08-26 13:12:54.043)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  31/  44) Add exposure jw01473014001_06101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39372  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.49e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.569\n",
      "       PHOTFLAM=8.49e-21, scale=1.0\n",
      "# (2022-08-26 13:12:55.647)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  32/  44) Add exposure jw01473014001_06101_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41047  ( 1e+00 %)\n",
      "  0    PHOTFLAM=8.25e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.586\n",
      "       PHOTFLAM=8.25e-21, scale=1.0\n",
      "# (2022-08-26 13:12:57.266)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  33/  44) Add exposure jw01473014001_06101_00003_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39362  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.16e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.472\n",
      "       PHOTFLAM=8.16e-21, scale=1.0\n",
      "# (2022-08-26 13:12:58.871)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  34/  44) Add exposure jw01473014001_06101_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37366  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.40e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.432\n",
      "       PHOTFLAM=8.40e-21, scale=1.0\n",
      "# (2022-08-26 13:13:00.471)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  35/  44) Add exposure jw01473014001_06101_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35623  ( 0.8 %)\n",
      "  0    PHOTFLAM=8.23e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.463\n",
      "       PHOTFLAM=8.23e-21, scale=1.0\n",
      "# (2022-08-26 13:13:02.082)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  36/  44) Add exposure jw01473014001_06101_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38253  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.47e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.562\n",
      "       PHOTFLAM=8.47e-21, scale=1.0\n",
      "# (2022-08-26 13:13:03.694)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  37/  44) Add exposure jw01473014001_06101_00004_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46654  ( 1e+00 %)\n",
      "  0    PHOTFLAM=8.42e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.490\n",
      "       PHOTFLAM=8.42e-21, scale=1.0\n",
      "# (2022-08-26 13:13:05.315)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  38/  44) Add exposure jw01473014001_06101_00004_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36746  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.482\n",
      "       PHOTFLAM=8.18e-21, scale=1.0\n",
      "# (2022-08-26 13:13:06.943)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  39/  44) Add exposure jw01473014001_06101_00004_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39413  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.49e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.552\n",
      "       PHOTFLAM=8.49e-21, scale=1.0\n",
      "# (2022-08-26 13:13:08.553)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  40/  44) Add exposure jw01473014001_06101_00004_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40879  ( 1e+00 %)\n",
      "  0    PHOTFLAM=8.25e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.563\n",
      "       PHOTFLAM=8.25e-21, scale=1.0\n",
      "# (2022-08-26 13:13:10.171)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  41/  44) Add exposure jw01473014001_06101_00004_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39365  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.16e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.464\n",
      "       PHOTFLAM=8.16e-21, scale=1.0\n",
      "# (2022-08-26 13:13:11.774)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  42/  44) Add exposure jw01473014001_06101_00004_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37114  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.40e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.439\n",
      "       PHOTFLAM=8.40e-21, scale=1.0\n",
      "# (2022-08-26 13:13:13.382)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  43/  44) Add exposure jw01473014001_06101_00004_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35659  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.23e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.446\n",
      "       PHOTFLAM=8.23e-21, scale=1.0\n",
      "# (2022-08-26 13:13:15.003)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  44/  44) Add exposure jw01473014001_06101_00004_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37912  ( 0.9 %)\n",
      "  0    PHOTFLAM=8.47e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.441\n",
      "       PHOTFLAM=8.47e-21, scale=1.0\n",
      "# (2022-08-26 13:13:16.623)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.436 to 10.0 nJy\n",
      "============ lmc-astrometric-f090wn-clear ============\n",
      "\n",
      "(   1/  18) Add exposure jw01086001001_14101_00001_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42758  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.449\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:20.124)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   2/  18) Add exposure jw01086001001_14101_00002_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42740  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.450\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:23.376)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/  18) Add exposure jw01086001001_38101_00001_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42437  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.455\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:26.082)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   4/  18) Add exposure jw01086001001_38101_00002_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42384  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.433\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:28.774)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/  18) Add exposure jw01086001001_62101_00001_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42162  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.455\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:31.470)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   6/  18) Add exposure jw01086001001_62101_00002_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 41592  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.416\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:34.144)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/  18) Add exposure jw01086001001_86101_00001_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 41825  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.434\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:36.839)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   8/  18) Add exposure jw01086001001_86101_00002_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 41247  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.406\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:39.500)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/  18) Add exposure jw01086001002_14101_00001_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 41602  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.435\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:42.215)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  10/  18) Add exposure jw01086001002_14101_00002_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 41519  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.421\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:44.860)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/  18) Add exposure jw01086001002_38101_00001_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 41483  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.427\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:47.517)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/  18) Add exposure jw01086001002_38101_00002_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 41254  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.420\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:50.156)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/  18) Add exposure jw01086001003_14101_00001_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 40971  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.396\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:52.853)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/  18) Add exposure jw01086001003_14101_00002_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 41003  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.417\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:55.544)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/  18) Add exposure jw01086001003_38101_00001_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 41167  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.420\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:13:58.209)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/  18) Add exposure jw01086001003_38101_00002_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 41784  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.425\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:14:00.849)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  17/  18) Add exposure jw01086001004_14101_00001_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42277  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.426\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:14:03.526)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/  18) Add exposure jw01086001004_14101_00002_nis_rate.fits\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42204  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.70e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.445\n",
      "       PHOTFLAM=3.70e-20, scale=1.0\n",
      "# (2022-08-26 13:14:06.199)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.099 to 10.0 nJy\n",
      "============ lmc-astrometric-f115w-clear ============\n",
      "\n",
      "(   1/  94) Add exposure jw01072001001_02105_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39058  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.14e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.378\n",
      "       PHOTFLAM=5.14e-21, scale=1.0\n",
      "# (2022-08-26 13:14:11.391)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   2/  94) Add exposure jw01072001001_02105_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33361  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.00e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.360\n",
      "       PHOTFLAM=5.00e-21, scale=1.0\n",
      "# (2022-08-26 13:14:13.592)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/  94) Add exposure jw01072001001_02105_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34197  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.414\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:14:15.273)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   4/  94) Add exposure jw01072001001_02105_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34172  ( 0.8 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.654\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:14:16.963)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/  94) Add exposure jw01072001001_02105_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33595  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.488\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:14:18.616)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   6/  94) Add exposure jw01072001001_02105_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33217  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.172\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:14:20.259)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/  94) Add exposure jw01072001001_02105_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39036  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.14e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.535\n",
      "       PHOTFLAM=5.14e-21, scale=1.0\n",
      "# (2022-08-26 13:14:21.911)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   8/  94) Add exposure jw01072001001_02105_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34220  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.249\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:14:23.570)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/  94) Add exposure jw01072001001_02105_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34184  ( 0.8 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.295\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:14:25.239)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  10/  94) Add exposure jw01072001001_02105_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33586  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.879\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:14:26.867)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/  94) Add exposure jw01072001001_02105_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33207  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.428\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:14:28.499)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/  94) Add exposure jw01074002001_02103_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 45774  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.14e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.396\n",
      "       PHOTFLAM=5.14e-21, scale=1.0\n",
      "# (2022-08-26 13:14:30.128)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/  94) Add exposure jw01074002001_02103_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39918  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.624\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:14:31.689)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/  94) Add exposure jw01074002001_02103_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37980  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.530\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:14:33.243)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/  94) Add exposure jw01074002002_02103_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 45823  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.14e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.493\n",
      "       PHOTFLAM=5.14e-21, scale=1.0\n",
      "# (2022-08-26 13:14:34.807)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/  94) Add exposure jw01074002002_02103_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36084  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.00e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.413\n",
      "       PHOTFLAM=5.00e-21, scale=1.0\n",
      "# (2022-08-26 13:14:36.370)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  17/  94) Add exposure jw01074002002_02103_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37817  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.418\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:14:37.909)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/  94) Add exposure jw01074002002_02103_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39589  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.529\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:14:39.456)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  19/  94) Add exposure jw01074002002_02103_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35119  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.381\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:14:40.979)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  20/  94) Add exposure jw01074002002_02103_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36880  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.406\n",
      "       PHOTFLAM=5.18e-21, scale=1.0\n",
      "# (2022-08-26 13:14:42.513)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  21/  94) Add exposure jw01074002002_02103_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 45799  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.14e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.439\n",
      "       PHOTFLAM=5.14e-21, scale=1.0\n",
      "# (2022-08-26 13:14:44.065)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  22/  94) Add exposure jw01074002002_02103_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36089  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.00e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.437\n",
      "       PHOTFLAM=5.00e-21, scale=1.0\n",
      "# (2022-08-26 13:14:45.625)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  23/  94) Add exposure jw01074002002_02103_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37852  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.509\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:14:47.156)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  24/  94) Add exposure jw01074002002_02103_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39515  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.512\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:14:48.700)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  25/  94) Add exposure jw01074002002_02103_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35177  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.357\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:14:50.230)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  26/  94) Add exposure jw01074002002_02103_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37049  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.513\n",
      "       PHOTFLAM=5.18e-21, scale=1.0\n",
      "# (2022-08-26 13:14:51.777)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  27/  94) Add exposure jw01074002002_02103_00003_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 45849  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.14e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.414\n",
      "       PHOTFLAM=5.14e-21, scale=1.0\n",
      "# (2022-08-26 13:14:53.322)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  28/  94) Add exposure jw01074002002_02103_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36037  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.00e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.511\n",
      "       PHOTFLAM=5.00e-21, scale=1.0\n",
      "# (2022-08-26 13:14:54.864)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  29/  94) Add exposure jw01074002002_02103_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37822  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.442\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:14:56.422)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  30/  94) Add exposure jw01074002002_02103_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39573  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.489\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:14:57.960)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  31/  94) Add exposure jw01074002002_02103_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35203  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.449\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:14:59.491)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  32/  94) Add exposure jw01074002002_02103_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37160  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.456\n",
      "       PHOTFLAM=5.18e-21, scale=1.0\n",
      "# (2022-08-26 13:15:01.028)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  33/  94) Add exposure jw01074002003_02103_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38078  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.484\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:15:02.582)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  34/  94) Add exposure jw01074002003_02103_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39613  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.505\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:15:04.131)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  35/  94) Add exposure jw01074002003_02103_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38775  ( 0.9 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.448\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:15:05.688)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  36/  94) Add exposure jw01074002003_02103_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36470  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.386\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:15:07.233)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  37/  94) Add exposure jw01074002003_02103_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37155  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.493\n",
      "       PHOTFLAM=5.18e-21, scale=1.0\n",
      "# (2022-08-26 13:15:08.783)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  38/  94) Add exposure jw01074002003_02103_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37925  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.421\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:15:10.328)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  39/  94) Add exposure jw01074002003_02103_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39636  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.579\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:15:11.864)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  40/  94) Add exposure jw01074002003_02103_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38973  ( 0.9 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.495\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:15:13.393)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  41/  94) Add exposure jw01074002003_02103_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36430  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.368\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:15:14.914)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  42/  94) Add exposure jw01074002003_02103_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37140  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.321\n",
      "       PHOTFLAM=5.18e-21, scale=1.0\n",
      "# (2022-08-26 13:15:16.450)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  43/  94) Add exposure jw01074002003_02103_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37874  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.453\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:15:17.980)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  44/  94) Add exposure jw01074002003_02103_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39499  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.444\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:15:19.523)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  45/  94) Add exposure jw01074002003_02103_00003_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38883  ( 0.9 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.381\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:15:21.067)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  46/  94) Add exposure jw01074002003_02103_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36567  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.536\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:15:22.594)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  47/  94) Add exposure jw01074002003_02103_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36966  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.362\n",
      "       PHOTFLAM=5.18e-21, scale=1.0\n",
      "# (2022-08-26 13:15:24.138)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  48/  94) Add exposure jw01074002004_02103_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37926  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.451\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:15:25.726)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  49/  94) Add exposure jw01074002004_02103_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39670  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.594\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:15:27.278)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  50/  94) Add exposure jw01074002004_02103_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39017  ( 0.9 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.511\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:15:28.837)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  51/  94) Add exposure jw01074002004_02103_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36617  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.526\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:15:30.416)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  52/  94) Add exposure jw01074002004_02103_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35380  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.513\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:15:31.960)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  53/  94) Add exposure jw01074002004_02103_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37922  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.468\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:15:33.521)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  54/  94) Add exposure jw01074002004_02103_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39660  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.521\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:15:35.073)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  55/  94) Add exposure jw01074002004_02103_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39157  ( 0.9 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.494\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:15:36.616)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  56/  94) Add exposure jw01074002004_02103_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36604  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.537\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:15:38.153)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  57/  94) Add exposure jw01074002004_02103_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35379  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.425\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:15:39.688)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  58/  94) Add exposure jw01074002004_02103_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37847  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.457\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:15:41.240)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  59/  94) Add exposure jw01074002004_02103_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39633  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.418\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:15:42.778)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  60/  94) Add exposure jw01074002004_02103_00003_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39196  ( 0.9 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.528\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:15:44.327)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  61/  94) Add exposure jw01074002004_02103_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36691  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.473\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:15:45.903)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  62/  94) Add exposure jw01074002004_02103_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35362  ( 0.8 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.447\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:15:47.462)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  63/  94) Add exposure jw01473014001_04101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46906  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.14e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.419\n",
      "       PHOTFLAM=5.14e-21, scale=1.0\n",
      "# (2022-08-26 13:15:49.024)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  64/  94) Add exposure jw01473014001_04101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36860  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.00e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.420\n",
      "       PHOTFLAM=5.00e-21, scale=1.0\n",
      "# (2022-08-26 13:15:50.713)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  65/  94) Add exposure jw01473014001_04101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39430  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.517\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:15:52.404)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  66/  94) Add exposure jw01473014001_04101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41438  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.535\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:15:54.098)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  67/  94) Add exposure jw01473014001_04101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39452  ( 0.9 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.408\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:15:55.767)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  68/  94) Add exposure jw01473014001_04101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37348  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.410\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:15:57.422)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  69/  94) Add exposure jw01473014001_04101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35831  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.394\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:15:59.086)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  70/  94) Add exposure jw01473014001_04101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38922  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.518\n",
      "       PHOTFLAM=5.18e-21, scale=1.0\n",
      "# (2022-08-26 13:16:00.755)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  71/  94) Add exposure jw01473014001_04101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 47435  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.14e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.501\n",
      "       PHOTFLAM=5.14e-21, scale=1.0\n",
      "# (2022-08-26 13:16:02.443)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  72/  94) Add exposure jw01473014001_04101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36899  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.00e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.414\n",
      "       PHOTFLAM=5.00e-21, scale=1.0\n",
      "# (2022-08-26 13:16:04.112)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  73/  94) Add exposure jw01473014001_04101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39567  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.519\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:16:05.780)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  74/  94) Add exposure jw01473014001_04101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41369  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.529\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:16:07.463)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  75/  94) Add exposure jw01473014001_04101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39429  ( 0.9 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.418\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:16:09.116)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  76/  94) Add exposure jw01473014001_04101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37248  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.432\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:16:10.765)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  77/  94) Add exposure jw01473014001_04101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35845  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.451\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:16:12.421)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  78/  94) Add exposure jw01473014001_04101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39333  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.531\n",
      "       PHOTFLAM=5.18e-21, scale=1.0\n",
      "# (2022-08-26 13:16:14.070)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  79/  94) Add exposure jw01473014001_04101_00003_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46869  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.14e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.417\n",
      "       PHOTFLAM=5.14e-21, scale=1.0\n",
      "# (2022-08-26 13:16:15.752)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  80/  94) Add exposure jw01473014001_04101_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36934  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.00e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.367\n",
      "       PHOTFLAM=5.00e-21, scale=1.0\n",
      "# (2022-08-26 13:16:17.424)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  81/  94) Add exposure jw01473014001_04101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39486  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.502\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:16:19.097)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  82/  94) Add exposure jw01473014001_04101_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41114  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.547\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:16:20.787)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  83/  94) Add exposure jw01473014001_04101_00003_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39514  ( 0.9 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.414\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:16:22.454)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  84/  94) Add exposure jw01473014001_04101_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37470  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.381\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:16:24.106)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  85/  94) Add exposure jw01473014001_04101_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35896  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.391\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:16:25.771)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  86/  94) Add exposure jw01473014001_04101_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38004  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.579\n",
      "       PHOTFLAM=5.18e-21, scale=1.0\n",
      "# (2022-08-26 13:16:27.426)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  87/  94) Add exposure jw01473014001_04101_00004_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 47208  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.14e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.487\n",
      "       PHOTFLAM=5.14e-21, scale=1.0\n",
      "# (2022-08-26 13:16:29.100)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  88/  94) Add exposure jw01473014001_04101_00004_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36890  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.00e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.399\n",
      "       PHOTFLAM=5.00e-21, scale=1.0\n",
      "# (2022-08-26 13:16:30.776)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  89/  94) Add exposure jw01473014001_04101_00004_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39126  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.531\n",
      "       PHOTFLAM=5.19e-21, scale=1.0\n",
      "# (2022-08-26 13:16:32.429)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  90/  94) Add exposure jw01473014001_04101_00004_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41389  ( 1e+00 %)\n",
      "  0    PHOTFLAM=5.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.512\n",
      "       PHOTFLAM=5.04e-21, scale=1.0\n",
      "# (2022-08-26 13:16:34.119)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  91/  94) Add exposure jw01473014001_04101_00004_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39511  ( 0.9 %)\n",
      "  0    PHOTFLAM=4.99e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.399\n",
      "       PHOTFLAM=4.99e-21, scale=1.0\n",
      "# (2022-08-26 13:16:35.776)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  92/  94) Add exposure jw01473014001_04101_00004_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37297  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.13e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.420\n",
      "       PHOTFLAM=5.13e-21, scale=1.0\n",
      "# (2022-08-26 13:16:37.427)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  93/  94) Add exposure jw01473014001_04101_00004_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35786  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.03e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.419\n",
      "       PHOTFLAM=5.03e-21, scale=1.0\n",
      "# (2022-08-26 13:16:39.084)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  94/  94) Add exposure jw01473014001_04101_00004_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38392  ( 0.9 %)\n",
      "  0    PHOTFLAM=5.18e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.529\n",
      "       PHOTFLAM=5.18e-21, scale=1.0\n",
      "# (2022-08-26 13:16:40.738)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.436 to 10.0 nJy\n",
      "============ lmc-astrometric-f115wn-clear ============\n",
      "\n",
      "(   1/  18) Add exposure jw01086001001_16101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 162481\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45883  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.418\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:16:44.844)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   2/  18) Add exposure jw01086001001_16101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 158706\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45752  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.423\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:16:48.517)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/  18) Add exposure jw01086001001_40101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 165604\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45752  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.411\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:16:53.305)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   4/  18) Add exposure jw01086001001_40101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 160901\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45718  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.431\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:16:56.480)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/  18) Add exposure jw01086001001_64101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 157753\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44990  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.373\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:16:59.548)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   6/  18) Add exposure jw01086001001_64101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 154514\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45133  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.409\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:02.534)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/  18) Add exposure jw01086001001_88101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 155361\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45011  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.416\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:05.578)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   8/  18) Add exposure jw01086001001_88101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 152328\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45325  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.427\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:08.685)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/  18) Add exposure jw01086001002_16101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 154960\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44885  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.400\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:11.786)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  10/  18) Add exposure jw01086001002_16101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 154272\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44564  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.369\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:14.804)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/  18) Add exposure jw01086001002_40101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 153859\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44769  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.400\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:17.798)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/  18) Add exposure jw01086001002_40101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 152215\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44486  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.393\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:20.768)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/  18) Add exposure jw01086001003_16101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 153936\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44158  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.382\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:23.736)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/  18) Add exposure jw01086001003_16101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 155747\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44159  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.369\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:26.811)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/  18) Add exposure jw01086001003_40101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 155837\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45021  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.401\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:29.900)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/  18) Add exposure jw01086001003_40101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 162095\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45106  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.406\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:32.918)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  17/  18) Add exposure jw01086001004_16101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 161884\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45747  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.417\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:35.974)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/  18) Add exposure jw01086001004_16101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F115W Npix: 165814\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45658  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.28e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.403\n",
      "       PHOTFLAM=2.28e-20, scale=1.0\n",
      "# (2022-08-26 13:17:38.992)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.099 to 10.0 nJy\n",
      "============ lmc-astrometric-f150w-clear ============\n",
      "\n",
      "(   1/ 269) Add exposure jw01069001001_04101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34500  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.391\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:17:44.275)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   2/ 269) Add exposure jw01069001001_04101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34613  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.422\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:17:46.477)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/ 269) Add exposure jw01069001001_04101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33799  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.331\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:17:48.132)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   4/ 269) Add exposure jw01069001001_04101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33401  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.513\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:17:49.802)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/ 269) Add exposure jw01069001001_04101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34013  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.459\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:17:51.477)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   6/ 269) Add exposure jw01069001001_04101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34462  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.413\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:17:53.126)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/ 269) Add exposure jw01069001001_04101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34630  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.468\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:17:54.752)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   8/ 269) Add exposure jw01069001001_04101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33824  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.375\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:17:56.364)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/ 269) Add exposure jw01069001001_04101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33413  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.432\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:17:57.990)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  10/ 269) Add exposure jw01069001001_04101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33998  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.454\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:17:59.611)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/ 269) Add exposure jw01069001002_04101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34604  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.400\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:18:01.235)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/ 269) Add exposure jw01069001002_04101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33828  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.369\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:18:02.855)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/ 269) Add exposure jw01069001002_04101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33310  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.334\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:18:04.494)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/ 269) Add exposure jw01069001002_04101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34056  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.541\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:18:06.119)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/ 269) Add exposure jw01069001002_04101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34620  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.376\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:18:07.757)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/ 269) Add exposure jw01069001002_04101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33831  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.450\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:18:09.373)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  17/ 269) Add exposure jw01069001002_04101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33298  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.420\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:18:10.996)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/ 269) Add exposure jw01069001002_04101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34008  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.535\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:18:12.617)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  19/ 269) Add exposure jw01069001003_04101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34548  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.412\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:18:14.242)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  20/ 269) Add exposure jw01069001003_04101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33788  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.429\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:18:15.848)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  21/ 269) Add exposure jw01069001003_04101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33347  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.454\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:18:17.469)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  22/ 269) Add exposure jw01069001003_04101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34562  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.433\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:18:19.109)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  23/ 269) Add exposure jw01069001003_04101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33791  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.419\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:18:20.714)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  24/ 269) Add exposure jw01069001003_04101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33369  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.426\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:18:22.335)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  25/ 269) Add exposure jw01069001004_04101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34629  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.454\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:18:23.947)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  26/ 269) Add exposure jw01069001004_04101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33767  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.509\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:18:25.565)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  27/ 269) Add exposure jw01069001004_04101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33334  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.429\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:18:27.184)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  28/ 269) Add exposure jw01069001004_04101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34128  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.426\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:18:28.865)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  29/ 269) Add exposure jw01069001004_04101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34591  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.458\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:18:30.500)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  30/ 269) Add exposure jw01069001004_04101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33761  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.442\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:18:32.116)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  31/ 269) Add exposure jw01069001004_04101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33308  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.385\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:18:33.740)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  32/ 269) Add exposure jw01069001004_04101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34089  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.481\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:18:35.358)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  33/ 269) Add exposure jw01069002002_04101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39279  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.386\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:18:36.995)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  34/ 269) Add exposure jw01069002002_04101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34223  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.415\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:18:38.630)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  35/ 269) Add exposure jw01069002002_04101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34622  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.446\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:18:40.252)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  36/ 269) Add exposure jw01069002002_04101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34577  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.458\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:18:41.894)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  37/ 269) Add exposure jw01069002002_04101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39294  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.428\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:18:43.523)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  38/ 269) Add exposure jw01069002002_04101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34136  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.482\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:18:45.152)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  39/ 269) Add exposure jw01069002002_04101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34414  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.418\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:18:46.757)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  40/ 269) Add exposure jw01069002002_04101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34548  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.492\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:18:48.392)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  41/ 269) Add exposure jw01069002003_04101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34239  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.459\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:18:50.003)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  42/ 269) Add exposure jw01069002003_04101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34416  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.403\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:18:51.631)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  43/ 269) Add exposure jw01069002003_04101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34525  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.400\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:18:53.246)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  44/ 269) Add exposure jw01069002003_04101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33325  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.418\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:18:54.866)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  45/ 269) Add exposure jw01069002003_04101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34001  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.338\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:18:56.494)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  46/ 269) Add exposure jw01069002003_04101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34149  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.401\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:18:58.149)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  47/ 269) Add exposure jw01069002003_04101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34432  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.419\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:18:59.755)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  48/ 269) Add exposure jw01069002003_04101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34489  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.490\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:19:01.382)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  49/ 269) Add exposure jw01069002003_04101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33302  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.402\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:19:02.990)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  50/ 269) Add exposure jw01069002003_04101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34020  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.423\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:19:04.628)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:19:05,989 - stpipe - WARNING - ! 3547 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  51/ 269) Add exposure jw01069002004_04101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39353  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.440\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:19:06.314)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  52/ 269) Add exposure jw01069002004_04101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34003  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.423\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:19:07.945)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  53/ 269) Add exposure jw01069002004_04101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34409  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.490\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:19:09.567)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  54/ 269) Add exposure jw01069002004_04101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34612  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.457\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:19:11.210)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  55/ 269) Add exposure jw01069002004_04101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34041  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.440\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:19:12.820)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  56/ 269) Add exposure jw01069002004_04101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39294  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.397\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:19:14.440)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  57/ 269) Add exposure jw01069002004_04101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34181  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.492\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:19:16.062)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  58/ 269) Add exposure jw01069002004_04101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34387  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.481\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:19:17.699)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  59/ 269) Add exposure jw01069002004_04101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34618  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.525\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:19:19.321)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  60/ 269) Add exposure jw01069002004_04101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34036  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.473\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:19:20.946)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  61/ 269) Add exposure jw01069021001_02103_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39321  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.453\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:19:22.576)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  62/ 269) Add exposure jw01069021001_02103_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34370  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.407\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:19:24.220)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  63/ 269) Add exposure jw01069021001_02103_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34488  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:19:25.838)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  64/ 269) Add exposure jw01069021001_02103_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34563  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.416\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:19:27.474)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  65/ 269) Add exposure jw01069021001_02103_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39254  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.334\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:19:29.095)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  66/ 269) Add exposure jw01069021001_02103_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34281  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.449\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:19:30.739)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  67/ 269) Add exposure jw01069021001_02103_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34449  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.447\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:19:32.356)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  68/ 269) Add exposure jw01069021001_02103_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34601  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.492\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:19:34.024)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  69/ 269) Add exposure jw01073001001_02101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39396  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.464\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:19:35.641)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  70/ 269) Add exposure jw01073001001_02101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34168  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.385\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:19:37.342)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  71/ 269) Add exposure jw01073001001_02101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34672  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.502\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:19:38.984)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  72/ 269) Add exposure jw01073001001_02101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33870  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.452\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:19:40.652)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  73/ 269) Add exposure jw01073001001_02101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34116  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.430\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:19:42.336)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  74/ 269) Add exposure jw01073001001_02101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39405  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.394\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:19:44.035)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  75/ 269) Add exposure jw01073001001_02101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34742  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.569\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:19:45.716)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  76/ 269) Add exposure jw01073001001_02101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34061  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.426\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:19:47.409)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  77/ 269) Add exposure jw01073001002_02101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39395  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.470\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:19:49.089)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:19:50,442 - stpipe - WARNING - ! 59670 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  78/ 269) Add exposure jw01073001002_02101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34462  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.512\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:19:50.767)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  79/ 269) Add exposure jw01073001002_02101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33790  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.418\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:19:52.453)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  80/ 269) Add exposure jw01073001002_02101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34149  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.474\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:19:54.165)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  81/ 269) Add exposure jw01073001002_02101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39389  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.379\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:19:55.865)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  82/ 269) Add exposure jw01073001002_02101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34110  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.370\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:19:57.543)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:19:58,761 - stpipe - WARNING - ! 586783 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  83/ 269) Add exposure jw01073001002_02101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34529  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.468\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:19:59.071)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  84/ 269) Add exposure jw01073001002_02101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34573  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.425\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:20:00.767)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  85/ 269) Add exposure jw01073001002_02101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33859  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.474\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:20:02.449)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  86/ 269) Add exposure jw01073001002_02101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33379  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.441\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:20:04.148)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  87/ 269) Add exposure jw01073001002_02101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34137  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.488\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:20:05.836)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  88/ 269) Add exposure jw01073001003_02101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34668  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.479\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:20:07.558)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  89/ 269) Add exposure jw01073001003_02101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34620  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.434\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:20:09.237)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  90/ 269) Add exposure jw01073001003_02101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33355  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.334\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:20:10.918)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  91/ 269) Add exposure jw01073001003_02101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34620  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.390\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:20:12.591)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  92/ 269) Add exposure jw01073001003_02101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33330  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.437\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:20:14.270)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  93/ 269) Add exposure jw01073002001_02101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39382  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.416\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:20:15.959)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  94/ 269) Add exposure jw01073002001_02101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34277  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.484\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:20:17.668)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  95/ 269) Add exposure jw01073002001_02101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34494  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.447\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:20:19.347)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  96/ 269) Add exposure jw01073002001_02101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34653  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.427\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:20:21.078)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  97/ 269) Add exposure jw01073002001_02101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33782  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.513\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:20:22.781)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:20:24,083 - stpipe - WARNING - ! 346467 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  98/ 269) Add exposure jw01073002001_02101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33346  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.373\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:20:24.407)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  99/ 269) Add exposure jw01073002001_02101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34142  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.492\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:20:26.119)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 100/ 269) Add exposure jw01073002001_02101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34092  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.436\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:20:27.868)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 101/ 269) Add exposure jw01073002001_02101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34552  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.430\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:20:29.574)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 102/ 269) Add exposure jw01073002001_02101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34613  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.419\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:20:31.283)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 103/ 269) Add exposure jw01073002001_02101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34578  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.391\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:20:32.980)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 104/ 269) Add exposure jw01073002001_02101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33807  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.443\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:20:34.725)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:20:35,856 - stpipe - WARNING - ! 876202 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "( 105/ 269) Add exposure jw01073002001_02101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33385  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.392\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:20:36.168)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 106/ 269) Add exposure jw01073002001_02101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34063  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.534\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:20:37.895)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 107/ 269) Add exposure jw01073002001_02101_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34097  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.482\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:20:39.591)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 108/ 269) Add exposure jw01073002001_02101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34518  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.442\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:20:41.272)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 109/ 269) Add exposure jw01073002001_02101_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34689  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.491\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:20:42.964)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 110/ 269) Add exposure jw01073002001_02101_00003_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34592  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.369\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:20:44.649)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 111/ 269) Add exposure jw01073002001_02101_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33822  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.467\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:20:46.324)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 112/ 269) Add exposure jw01073002001_02101_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33365  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.440\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:20:48.071)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 113/ 269) Add exposure jw01073002001_02101_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34111  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.403\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:20:49.746)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 114/ 269) Add exposure jw01073002001_02101_00004_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34103  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.530\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:20:51.471)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:20:52,844 - stpipe - WARNING - ! 7685 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "( 115/ 269) Add exposure jw01073002001_02101_00004_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34600  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.418\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:20:53.160)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 116/ 269) Add exposure jw01073002001_02101_00004_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34566  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.559\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:20:54.858)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 117/ 269) Add exposure jw01073002001_02101_00004_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34584  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:20:56.549)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 118/ 269) Add exposure jw01073002001_02101_00004_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33926  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.366\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:20:58.274)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 119/ 269) Add exposure jw01073002001_02101_00004_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33383  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.392\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:20:59.985)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 120/ 269) Add exposure jw01073002001_02101_00004_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34145  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.491\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:21:01.726)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 121/ 269) Add exposure jw01073003001_02101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34096  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.360\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:21:03.423)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 122/ 269) Add exposure jw01073003001_02101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34586  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.427\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:21:05.118)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 123/ 269) Add exposure jw01073003001_02101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34609  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.484\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:21:06.819)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 124/ 269) Add exposure jw01073003001_02101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33816  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.463\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:21:08.549)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 125/ 269) Add exposure jw01073003001_02101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33377  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.378\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:21:10.249)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 126/ 269) Add exposure jw01073003001_02101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34111  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.393\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:21:11.963)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 127/ 269) Add exposure jw01073003001_02101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34099  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.488\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:21:13.668)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 128/ 269) Add exposure jw01073003001_02101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34503  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.480\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:21:15.359)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 129/ 269) Add exposure jw01073003001_02101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34670  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.481\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:21:17.056)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 130/ 269) Add exposure jw01073003001_02101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34562  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:21:18.752)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 131/ 269) Add exposure jw01073003001_02101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33805  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.468\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:21:20.442)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:21:21,830 - stpipe - WARNING - ! 2005 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "( 132/ 269) Add exposure jw01073003001_02101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33384  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.397\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:21:22.154)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 133/ 269) Add exposure jw01073003001_02101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34100  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.457\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:21:23.832)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 134/ 269) Add exposure jw01073003001_02101_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33970  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.473\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:21:25.558)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 135/ 269) Add exposure jw01073003001_02101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34498  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.461\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:21:27.243)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 136/ 269) Add exposure jw01073003001_02101_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34681  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.504\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:21:29.020)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 137/ 269) Add exposure jw01073003001_02101_00003_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34556  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.408\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:21:30.706)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 138/ 269) Add exposure jw01073003001_02101_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33871  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.471\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:21:32.414)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 139/ 269) Add exposure jw01073003001_02101_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33394  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.427\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:21:34.110)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 140/ 269) Add exposure jw01073003001_02101_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34027  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.627\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:21:35.802)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 141/ 269) Add exposure jw01073003001_02101_00004_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34099  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.483\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:21:37.499)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 142/ 269) Add exposure jw01073003001_02101_00004_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34564  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.484\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:21:39.208)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 143/ 269) Add exposure jw01073003001_02101_00004_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34681  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.477\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:21:40.908)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 144/ 269) Add exposure jw01073003001_02101_00004_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34597  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.385\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:21:42.638)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 145/ 269) Add exposure jw01073003001_02101_00004_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33816  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.476\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:21:44.330)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 146/ 269) Add exposure jw01073003001_02101_00004_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33379  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.400\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:21:46.095)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 147/ 269) Add exposure jw01073003001_02101_00004_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34070  ( 0.8 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.553\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:21:47.786)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 148/ 269) Add exposure jw01074001001_06101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46032  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.426\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:21:49.517)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 149/ 269) Add exposure jw01074001001_06101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36601  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.488\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:21:51.072)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 150/ 269) Add exposure jw01074001001_06101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46018  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.442\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:21:52.633)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 151/ 269) Add exposure jw01074001001_06101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36559  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.446\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:21:54.169)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 152/ 269) Add exposure jw01074001001_06101_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36545  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.455\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:21:55.742)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 153/ 269) Add exposure jw01074001001_06101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38192  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.469\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:21:57.272)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 154/ 269) Add exposure jw01074001002_06101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38161  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.465\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:21:58.850)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 155/ 269) Add exposure jw01074001002_06101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35712  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.498\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:22:00.388)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 156/ 269) Add exposure jw01074001002_06101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37381  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.429\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:22:01.921)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 157/ 269) Add exposure jw01074001002_06101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38329  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.501\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:22:03.470)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 158/ 269) Add exposure jw01074001002_06101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39908  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.452\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:22:05.050)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 159/ 269) Add exposure jw01074001002_06101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35590  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.477\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:22:06.646)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 160/ 269) Add exposure jw01074001002_06101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37393  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.465\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:22:08.194)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 161/ 269) Add exposure jw01074001002_06101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38239  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.436\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:22:09.745)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 162/ 269) Add exposure jw01074001002_06101_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39797  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.431\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:22:11.350)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 163/ 269) Add exposure jw01074001002_06101_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37057  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.487\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:22:12.894)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 164/ 269) Add exposure jw01074001002_06101_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35525  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.428\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:22:14.490)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 165/ 269) Add exposure jw01074001002_06101_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37347  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.513\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:22:16.029)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 166/ 269) Add exposure jw01074002001_02105_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38041  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.454\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:22:17.589)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 167/ 269) Add exposure jw01074002001_02105_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38187  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.349\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:22:19.126)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 168/ 269) Add exposure jw01074002002_02105_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37932  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.416\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:22:20.684)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 169/ 269) Add exposure jw01074002002_02105_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39783  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.480\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:22:22.236)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 170/ 269) Add exposure jw01074002002_02105_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35272  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.374\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:22:23.797)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 171/ 269) Add exposure jw01074002002_02105_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37151  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.415\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:22:25.334)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 172/ 269) Add exposure jw01074002002_02105_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38108  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.472\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:22:26.881)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 173/ 269) Add exposure jw01074002002_02105_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39673  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.477\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:22:28.429)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 174/ 269) Add exposure jw01074002002_02105_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35401  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.426\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:22:29.988)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 175/ 269) Add exposure jw01074002002_02105_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37095  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.429\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:22:31.528)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 176/ 269) Add exposure jw01074002002_02105_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37999  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.493\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:22:33.068)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 177/ 269) Add exposure jw01074002002_02105_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39715  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.486\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:22:34.603)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 178/ 269) Add exposure jw01074002002_02105_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35349  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.452\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:22:36.144)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 179/ 269) Add exposure jw01074002002_02105_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37454  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.475\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:22:37.681)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 180/ 269) Add exposure jw01074002003_02105_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38969  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.415\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:22:39.215)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 181/ 269) Add exposure jw01074002003_02105_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36654  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.382\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:22:40.742)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 182/ 269) Add exposure jw01074002003_02105_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37403  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.508\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:22:42.311)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 183/ 269) Add exposure jw01074002003_02105_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39127  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.440\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:22:43.869)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 184/ 269) Add exposure jw01074002003_02105_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36672  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.376\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:22:45.417)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 185/ 269) Add exposure jw01074002003_02105_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37426  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.424\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:22:46.952)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 186/ 269) Add exposure jw01074002003_02105_00003_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39153  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.421\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:22:48.490)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 187/ 269) Add exposure jw01074002003_02105_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36675  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.420\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:22:50.000)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 188/ 269) Add exposure jw01074002003_02105_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37137  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.357\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:22:51.560)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 189/ 269) Add exposure jw01074002004_02105_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39823  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.489\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:22:53.099)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 190/ 269) Add exposure jw01074002004_02105_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36773  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.385\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:22:54.645)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 191/ 269) Add exposure jw01074002004_02105_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35625  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.382\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:22:56.165)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 192/ 269) Add exposure jw01074002004_02105_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39411  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.401\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:22:57.696)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 193/ 269) Add exposure jw01074002004_02105_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36881  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.410\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:22:59.217)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 194/ 269) Add exposure jw01074002004_02105_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35662  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.462\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:23:00.788)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 195/ 269) Add exposure jw01074002004_02105_00003_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39399  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.95e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.405\n",
      "       PHOTFLAM=2.95e-21, scale=1.0\n",
      "# (2022-08-26 13:23:02.330)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 196/ 269) Add exposure jw01074002004_02105_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36843  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.429\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:23:03.868)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 197/ 269) Add exposure jw01074002004_02105_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35640  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.437\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:23:05.407)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 198/ 269) Add exposure jw01074003001_06101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46259  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.425\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:23:06.985)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 199/ 269) Add exposure jw01074003001_06101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36602  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.455\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:23:08.422)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 200/ 269) Add exposure jw01074003001_06101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46033  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.393\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:23:09.861)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 201/ 269) Add exposure jw01074003001_06101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36665  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.510\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:23:11.291)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 202/ 269) Add exposure jw01074003001_06101_00003_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 45986  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.409\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:23:12.750)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 203/ 269) Add exposure jw01074003001_06101_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36562  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.557\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:23:14.209)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 204/ 269) Add exposure jw01074003002_06101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37973  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.433\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:23:15.664)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 205/ 269) Add exposure jw01074003002_06101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40035  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.452\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:23:17.093)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 206/ 269) Add exposure jw01074003002_06101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35589  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.465\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:23:18.524)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 207/ 269) Add exposure jw01074003002_06101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37481  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.419\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:23:19.962)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 208/ 269) Add exposure jw01074003002_06101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38471  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.439\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:23:21.439)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 209/ 269) Add exposure jw01074003002_06101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40032  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.506\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:23:22.887)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 210/ 269) Add exposure jw01074003002_06101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35649  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.466\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:23:24.323)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 211/ 269) Add exposure jw01074003002_06101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37446  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.449\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:23:25.752)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 212/ 269) Add exposure jw01074003002_06101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38293  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.490\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:23:27.192)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 213/ 269) Add exposure jw01074003002_06101_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39882  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.458\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:23:28.630)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 214/ 269) Add exposure jw01074003002_06101_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35625  ( 0.8 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.469\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:23:30.059)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 215/ 269) Add exposure jw01074003002_06101_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37290  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.490\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:23:31.481)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 216/ 269) Add exposure jw01473011001_02101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36921  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.420\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:23:32.958)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 217/ 269) Add exposure jw01473011001_02101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39497  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.408\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:23:34.634)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 218/ 269) Add exposure jw01473011001_02101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41120  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.448\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:23:36.332)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 219/ 269) Add exposure jw01473011001_02101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37513  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.376\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:23:37.978)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 220/ 269) Add exposure jw01473011001_02101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36052  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.378\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:23:39.691)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 221/ 269) Add exposure jw01473011001_02101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38642  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.482\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:23:41.353)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 222/ 269) Add exposure jw01473011001_02101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36976  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.436\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:23:43.038)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 223/ 269) Add exposure jw01473011001_02101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39364  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.483\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:23:44.684)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 224/ 269) Add exposure jw01473011001_02101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41056  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.506\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:23:46.351)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 225/ 269) Add exposure jw01473011001_02101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37593  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.419\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:23:47.995)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 226/ 269) Add exposure jw01473011001_02101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36043  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.424\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:23:49.642)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 227/ 269) Add exposure jw01473011001_02101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38515  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.411\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:23:51.285)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 228/ 269) Add exposure jw01473011001_02101_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37006  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.374\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:23:52.987)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 229/ 269) Add exposure jw01473011001_02101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39298  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.432\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:23:54.658)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 230/ 269) Add exposure jw01473011001_02101_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41075  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.474\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:23:56.378)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 231/ 269) Add exposure jw01473011001_02101_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37626  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.392\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:23:58.040)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 232/ 269) Add exposure jw01473011001_02101_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36191  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.412\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:23:59.714)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 233/ 269) Add exposure jw01473011001_02101_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38563  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.449\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:24:01.383)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 234/ 269) Add exposure jw01473011001_02101_00004_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39504  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.461\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:24:03.050)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 235/ 269) Add exposure jw01473011001_02101_00004_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40966  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.451\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:24:04.711)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 236/ 269) Add exposure jw01473011001_02101_00004_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37407  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.399\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:24:06.358)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 237/ 269) Add exposure jw01473011001_02101_00004_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36346  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.416\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:24:08.001)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 238/ 269) Add exposure jw01473011001_02101_00004_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38324  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.448\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:24:09.679)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 239/ 269) Add exposure jw01473012001_02101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36872  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.415\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:24:11.341)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 240/ 269) Add exposure jw01473012001_02101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39346  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.450\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:24:13.027)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 241/ 269) Add exposure jw01473012001_02101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40963  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.532\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:24:14.680)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 242/ 269) Add exposure jw01473012001_02101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37429  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.402\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:24:16.353)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 243/ 269) Add exposure jw01473012001_02101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36010  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.384\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:24:17.999)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 244/ 269) Add exposure jw01473012001_02101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38638  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.376\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:24:19.643)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 245/ 269) Add exposure jw01473012001_02101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37018  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.449\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:24:21.300)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 246/ 269) Add exposure jw01473012001_02101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39265  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.423\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:24:22.951)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 247/ 269) Add exposure jw01473012001_02101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41248  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.463\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:24:24.599)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 248/ 269) Add exposure jw01473012001_02101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37499  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.412\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:24:26.281)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 249/ 269) Add exposure jw01473012001_02101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36141  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.438\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:24:27.933)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 250/ 269) Add exposure jw01473012001_02101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38565  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.367\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:24:29.610)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 251/ 269) Add exposure jw01473012001_02101_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36921  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.384\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:24:31.277)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 252/ 269) Add exposure jw01473012001_02101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39141  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.441\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:24:32.969)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 253/ 269) Add exposure jw01473012001_02101_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41036  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.479\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:24:34.625)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 254/ 269) Add exposure jw01473012001_02101_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37557  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.356\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:24:36.284)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 255/ 269) Add exposure jw01473012001_02101_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36165  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.413\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:24:37.939)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 256/ 269) Add exposure jw01473012001_02101_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38594  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.550\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:24:39.591)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 257/ 269) Add exposure jw01473012001_02101_00004_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39471  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.396\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:24:41.253)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 258/ 269) Add exposure jw01473012001_02101_00004_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40924  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.399\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:24:42.950)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 259/ 269) Add exposure jw01473012001_02101_00004_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37479  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.375\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:24:44.602)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 260/ 269) Add exposure jw01473012001_02101_00004_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36435  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.97e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.420\n",
      "       PHOTFLAM=2.97e-21, scale=1.0\n",
      "# (2022-08-26 13:24:46.306)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 261/ 269) Add exposure jw01473012001_02101_00004_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38289  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.06e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.394\n",
      "       PHOTFLAM=3.06e-21, scale=1.0\n",
      "# (2022-08-26 13:24:48.029)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 262/ 269) Add exposure jw01477001001_02103_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 67466  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.359\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:24:49.733)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 263/ 269) Add exposure jw01477001001_02103_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 87968  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.378\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:24:51.291)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 264/ 269) Add exposure jw01477001001_02103_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 69893  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.403\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:24:52.885)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 265/ 269) Add exposure jw01477001001_02103_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 73094  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.452\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:24:54.445)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 266/ 269) Add exposure jw01477001001_02103_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 67116  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.04e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.371\n",
      "       PHOTFLAM=3.04e-21, scale=1.0\n",
      "# (2022-08-26 13:24:56.048)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 267/ 269) Add exposure jw01477001001_02103_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 74046  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.96e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.373\n",
      "       PHOTFLAM=2.96e-21, scale=1.0\n",
      "# (2022-08-26 13:24:57.603)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 268/ 269) Add exposure jw01477001001_02103_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80484  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.07e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.401\n",
      "       PHOTFLAM=3.07e-21, scale=1.0\n",
      "# (2022-08-26 13:24:59.186)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 269/ 269) Add exposure jw01477001001_02103_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 67704  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.98e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.420\n",
      "       PHOTFLAM=2.98e-21, scale=1.0\n",
      "# (2022-08-26 13:25:00.768)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.445 to 10.0 nJy\n",
      "============ lmc-astrometric-f150wn-clear ============\n",
      "\n",
      "(   1/  18) Add exposure jw01086001001_22101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 122799\n",
      "\n",
      "Extra -5 sigma low pixels: N= 43863  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.375\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:04.585)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   2/  18) Add exposure jw01086001001_22101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 122834\n",
      "\n",
      "Extra -5 sigma low pixels: N= 45085  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.391\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:08.285)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/  18) Add exposure jw01086001001_46101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 128782\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44126  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:11.415)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   4/  18) Add exposure jw01086001001_46101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 125453\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44892  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.394\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:14.475)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/  18) Add exposure jw01086001001_70101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 118169\n",
      "\n",
      "Extra -5 sigma low pixels: N= 43412  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.374\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:17.471)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   6/  18) Add exposure jw01086001001_70101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 114642\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42834  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.370\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:20.406)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/  18) Add exposure jw01086001001_94101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 115259\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42030  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.366\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:23.394)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   8/  18) Add exposure jw01086001001_94101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 113510\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44518  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.386\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:26.369)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/  18) Add exposure jw01086001002_22101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 112848\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42065  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.367\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:29.340)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  10/  18) Add exposure jw01086001002_22101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 113947\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42216  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.369\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:32.292)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/  18) Add exposure jw01086001002_46101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 113270\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42348  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.367\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:35.261)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/  18) Add exposure jw01086001002_46101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 114312\n",
      "\n",
      "Extra -5 sigma low pixels: N= 41930  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.366\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:38.222)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/  18) Add exposure jw01086001003_22101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 115037\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42306  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.371\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:41.197)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/  18) Add exposure jw01086001003_22101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 117056\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42162  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.370\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:44.190)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/  18) Add exposure jw01086001003_46101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 117845\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42647  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.369\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:47.188)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/  18) Add exposure jw01086001003_46101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 115943\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44099  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.383\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:50.140)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  17/  18) Add exposure jw01086001004_22101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 123829\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44303  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:53.127)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/  18) Add exposure jw01086001004_22101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F150W Npix: 122857\n",
      "\n",
      "Extra -5 sigma low pixels: N= 44730  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.35e-20, scale=1.0\n",
      "  ext (SCI,1), sky=0.384\n",
      "       PHOTFLAM=1.35e-20, scale=1.0\n",
      "# (2022-08-26 13:25:56.154)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.099 to 10.0 nJy\n",
      "============ lmc-astrometric-f200w-clear ============\n",
      "\n",
      "(   1/ 198) Add exposure jw01018003001_02101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33119  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.302\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:26:01.299)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   2/ 198) Add exposure jw01018003001_02101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 32942  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.355\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:26:03.459)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/ 198) Add exposure jw01018003001_02101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33022  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.382\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:26:05.085)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   4/ 198) Add exposure jw01018004001_02101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33123  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.372\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:26:06.721)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/ 198) Add exposure jw01018004001_02101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 32958  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.351\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:26:08.307)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   6/ 198) Add exposure jw01018004001_02101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33029  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.392\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:26:10.013)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/ 198) Add exposure jw01069001001_06101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34508  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.353\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:26:11.630)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   8/ 198) Add exposure jw01069001001_06101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34602  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:26:13.288)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/ 198) Add exposure jw01069001001_06101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33833  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.381\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:26:14.938)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  10/ 198) Add exposure jw01069001001_06101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33431  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.395\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:26:16.592)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/ 198) Add exposure jw01069001001_06101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34012  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.466\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:26:18.193)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/ 198) Add exposure jw01069001001_06101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34459  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.306\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:26:19.831)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/ 198) Add exposure jw01069001001_06101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34584  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.365\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:26:21.435)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/ 198) Add exposure jw01069001001_06101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33777  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.350\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:26:23.036)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/ 198) Add exposure jw01069001001_06101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33444  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.385\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:26:24.645)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/ 198) Add exposure jw01069001001_06101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33989  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.385\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:26:26.256)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  17/ 198) Add exposure jw01069001002_06101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34608  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.323\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:26:27.870)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/ 198) Add exposure jw01069001002_06101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33897  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.352\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:26:29.476)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  19/ 198) Add exposure jw01069001002_06101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33350  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.384\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:26:31.121)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  20/ 198) Add exposure jw01069001002_06101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34033  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.416\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:26:32.727)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  21/ 198) Add exposure jw01069001002_06101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34600  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.363\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:26:34.365)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  22/ 198) Add exposure jw01069001002_06101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33840  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.394\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:26:35.970)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  23/ 198) Add exposure jw01069001002_06101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33302  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.362\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:26:37.589)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  24/ 198) Add exposure jw01069001002_06101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34001  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.385\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:26:39.200)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  25/ 198) Add exposure jw01069001003_06101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34543  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.328\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:26:40.814)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  26/ 198) Add exposure jw01069001003_06101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33796  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.333\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:26:42.423)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  27/ 198) Add exposure jw01069001003_06101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33380  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:26:44.036)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  28/ 198) Add exposure jw01069001003_06101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34509  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.299\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:26:45.666)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  29/ 198) Add exposure jw01069001003_06101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33729  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.394\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:26:47.268)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  30/ 198) Add exposure jw01069001003_06101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33380  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.364\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:26:48.887)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  31/ 198) Add exposure jw01069001004_06101_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34621  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.450\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:26:50.494)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  32/ 198) Add exposure jw01069001004_06101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33741  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.341\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:26:52.105)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  33/ 198) Add exposure jw01069001004_06101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33359  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.350\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:26:53.722)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  34/ 198) Add exposure jw01069001004_06101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34103  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.418\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:26:55.340)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  35/ 198) Add exposure jw01069001004_06101_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34615  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.401\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:26:56.966)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  36/ 198) Add exposure jw01069001004_06101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33775  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.438\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:26:58.575)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  37/ 198) Add exposure jw01069001004_06101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33311  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.357\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:27:00.196)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  38/ 198) Add exposure jw01069001004_06101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34070  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.452\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:27:01.814)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  39/ 198) Add exposure jw01069002002_06101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39296  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.337\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:27:03.436)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  40/ 198) Add exposure jw01069002002_06101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34011  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.266\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:27:05.066)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  41/ 198) Add exposure jw01069002002_06101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34485  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.397\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:27:06.680)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  42/ 198) Add exposure jw01069002002_06101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34553  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.318\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:27:08.318)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  43/ 198) Add exposure jw01069002002_06101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39292  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.325\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:27:09.949)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  44/ 198) Add exposure jw01069002002_06101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33951  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.444\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:27:11.575)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  45/ 198) Add exposure jw01069002002_06101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34398  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.426\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:27:13.189)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  46/ 198) Add exposure jw01069002002_06101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34551  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.366\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:27:14.819)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  47/ 198) Add exposure jw01069002003_06101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34176  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.349\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:27:16.430)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  48/ 198) Add exposure jw01069002003_06101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34356  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.407\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:27:18.050)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  49/ 198) Add exposure jw01069002003_06101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34477  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.399\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:27:19.681)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  50/ 198) Add exposure jw01069002003_06101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33302  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.326\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:27:21.298)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  51/ 198) Add exposure jw01069002003_06101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33972  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.390\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:27:22.951)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  52/ 198) Add exposure jw01069002003_06101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34080  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.347\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:27:24.607)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  53/ 198) Add exposure jw01069002003_06101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34381  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.377\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:27:26.217)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  54/ 198) Add exposure jw01069002003_06101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34498  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.341\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:27:27.850)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  55/ 198) Add exposure jw01069002003_06101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33321  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.324\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:27:29.458)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  56/ 198) Add exposure jw01069002003_06101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33994  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.432\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:27:31.069)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:27:32,393 - stpipe - WARNING - ! 3484 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  57/ 198) Add exposure jw01069002004_06101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39381  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.317\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:27:32.702)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  58/ 198) Add exposure jw01069002004_06101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34061  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.281\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:27:34.328)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  59/ 198) Add exposure jw01069002004_06101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34405  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.382\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:27:35.939)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  60/ 198) Add exposure jw01069002004_06101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34578  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.365\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:27:37.561)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  61/ 198) Add exposure jw01069002004_06101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34029  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.291\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:27:39.172)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  62/ 198) Add exposure jw01069002004_06101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39317  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.389\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:27:40.798)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  63/ 198) Add exposure jw01069002004_06101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33974  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.303\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:27:42.415)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  64/ 198) Add exposure jw01069002004_06101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34419  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.313\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:27:44.030)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  65/ 198) Add exposure jw01069002004_06101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34635  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.382\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:27:45.647)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  66/ 198) Add exposure jw01069002004_06101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34013  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.299\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:27:47.263)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  67/ 198) Add exposure jw01069021001_02105_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39276  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.371\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:27:48.883)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  68/ 198) Add exposure jw01069021001_02105_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34287  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.394\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:27:50.514)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  69/ 198) Add exposure jw01069021001_02105_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34523  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.365\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:27:52.121)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  70/ 198) Add exposure jw01069021001_02105_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34565  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.378\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:27:53.748)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  71/ 198) Add exposure jw01069021001_02105_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39264  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.295\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:27:55.366)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  72/ 198) Add exposure jw01069021001_02105_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34170  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.469\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:27:56.982)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  73/ 198) Add exposure jw01069021001_02105_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34536  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.397\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:27:58.591)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  74/ 198) Add exposure jw01069021001_02105_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 34601  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:28:00.225)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  75/ 198) Add exposure jw01074001001_08101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 45995  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.331\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:28:01.845)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  76/ 198) Add exposure jw01074001001_08101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36671  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:28:03.383)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  77/ 198) Add exposure jw01074001001_08101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46072  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.356\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:28:04.915)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  78/ 198) Add exposure jw01074001001_08101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36489  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.372\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:28:06.456)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  79/ 198) Add exposure jw01074001001_08101_00003_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 45907  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:28:07.987)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  80/ 198) Add exposure jw01074001001_08101_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36416  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.360\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:28:09.525)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  81/ 198) Add exposure jw01074001001_08101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38258  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.427\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:28:11.058)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  82/ 198) Add exposure jw01074001002_08101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38137  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.369\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:28:12.625)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  83/ 198) Add exposure jw01074001002_08101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39891  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.378\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:28:14.163)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  84/ 198) Add exposure jw01074001002_08101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35617  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.399\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:28:15.693)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  85/ 198) Add exposure jw01074001002_08101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37373  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.418\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:28:17.227)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  86/ 198) Add exposure jw01074001002_08101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38207  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.377\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:28:18.773)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  87/ 198) Add exposure jw01074001002_08101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39857  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.383\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:28:20.309)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  88/ 198) Add exposure jw01074001002_08101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35597  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.372\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:28:21.840)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  89/ 198) Add exposure jw01074001002_08101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37342  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.382\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:28:23.368)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  90/ 198) Add exposure jw01074001002_08101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38287  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.363\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:28:24.911)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  91/ 198) Add exposure jw01074001002_08101_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39674  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.388\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:28:26.454)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  92/ 198) Add exposure jw01074001002_08101_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37047  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.396\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:28:27.984)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  93/ 198) Add exposure jw01074001002_08101_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35391  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.405\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:28:29.510)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  94/ 198) Add exposure jw01074001002_08101_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37289  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.330\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:28:31.043)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  95/ 198) Add exposure jw01074002001_02107_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38069  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.374\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:28:32.575)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  96/ 198) Add exposure jw01074002001_02107_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38114  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.402\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:28:34.110)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  97/ 198) Add exposure jw01074002001_02107_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39823  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.383\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:28:35.641)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  98/ 198) Add exposure jw01074002002_02107_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37944  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.396\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:28:37.179)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  99/ 198) Add exposure jw01074002002_02107_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39705  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.413\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:28:38.715)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 100/ 198) Add exposure jw01074002002_02107_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35293  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.332\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:28:40.241)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 101/ 198) Add exposure jw01074002002_02107_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37107  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.304\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:28:41.761)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 102/ 198) Add exposure jw01074002002_02107_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38067  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.374\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:28:43.296)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 103/ 198) Add exposure jw01074002002_02107_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39586  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.489\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:28:44.826)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 104/ 198) Add exposure jw01074002002_02107_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35379  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.313\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:28:46.347)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 105/ 198) Add exposure jw01074002002_02107_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37085  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.451\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:28:47.863)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 106/ 198) Add exposure jw01074002002_02107_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38055  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.425\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:28:49.400)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 107/ 198) Add exposure jw01074002002_02107_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39653  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.454\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:28:50.939)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 108/ 198) Add exposure jw01074002002_02107_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35356  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.349\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:28:52.464)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 109/ 198) Add exposure jw01074002002_02107_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37417  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.290\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:28:53.985)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 110/ 198) Add exposure jw01074002003_02107_00001_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38945  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.364\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:28:55.523)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 111/ 198) Add exposure jw01074002003_02107_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36618  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.312\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:28:57.043)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 112/ 198) Add exposure jw01074002003_02107_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37380  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.325\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:28:58.571)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 113/ 198) Add exposure jw01074002003_02107_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39050  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.348\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:29:00.139)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 114/ 198) Add exposure jw01074002003_02107_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36598  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:29:01.669)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 115/ 198) Add exposure jw01074002003_02107_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37353  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.352\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:29:03.199)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 116/ 198) Add exposure jw01074002004_02107_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39700  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.408\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:29:04.736)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 117/ 198) Add exposure jw01074002004_02107_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36751  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.289\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:29:06.277)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 118/ 198) Add exposure jw01074002004_02107_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35613  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.394\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:29:07.812)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 119/ 198) Add exposure jw01074002004_02107_00002_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39466  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.387\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:29:09.340)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 120/ 198) Add exposure jw01074002004_02107_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36937  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.400\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:29:10.885)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 121/ 198) Add exposure jw01074002004_02107_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35610  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.365\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:29:12.422)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 122/ 198) Add exposure jw01074002004_02107_00003_nrcb1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39362  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.444\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:29:13.955)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 123/ 198) Add exposure jw01074002004_02107_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36799  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.328\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:29:15.484)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 124/ 198) Add exposure jw01074002004_02107_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35642  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.401\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:29:17.021)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 125/ 198) Add exposure jw01074003001_08101_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46139  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.348\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:29:18.554)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 126/ 198) Add exposure jw01074003001_08101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36599  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.355\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:29:19.989)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 127/ 198) Add exposure jw01074003001_08101_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46025  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.335\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:29:21.417)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 128/ 198) Add exposure jw01074003001_08101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36566  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.366\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:29:22.850)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 129/ 198) Add exposure jw01074003001_08101_00003_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 45858  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.360\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:29:24.275)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 130/ 198) Add exposure jw01074003001_08101_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36407  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.426\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:29:25.755)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 131/ 198) Add exposure jw01074003002_08101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37958  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.415\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:29:27.178)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 132/ 198) Add exposure jw01074003002_08101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40064  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.371\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:29:28.609)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 133/ 198) Add exposure jw01074003002_08101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35533  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.404\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:29:30.033)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 134/ 198) Add exposure jw01074003002_08101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37515  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.371\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:29:31.468)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 135/ 198) Add exposure jw01074003002_08101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37974  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.409\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:29:32.903)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 136/ 198) Add exposure jw01074003002_08101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39977  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.446\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:29:34.336)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 137/ 198) Add exposure jw01074003002_08101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35599  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.425\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:29:35.765)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 138/ 198) Add exposure jw01074003002_08101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37419  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.476\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:29:37.195)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 139/ 198) Add exposure jw01074003002_08101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38204  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.396\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:29:38.627)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 140/ 198) Add exposure jw01074003002_08101_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39825  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.318\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:29:40.062)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 141/ 198) Add exposure jw01074003002_08101_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35557  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.392\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:29:41.500)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 142/ 198) Add exposure jw01074003002_08101_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37233  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.294\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:29:42.939)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 143/ 198) Add exposure jw01473013001_04101_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36756  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.383\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:29:44.371)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 144/ 198) Add exposure jw01473013001_04101_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39165  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.415\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:29:46.022)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 145/ 198) Add exposure jw01473013001_04101_00001_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40858  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.419\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:29:47.675)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 146/ 198) Add exposure jw01473013001_04101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37216  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.367\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:29:49.313)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 147/ 198) Add exposure jw01473013001_04101_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35909  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.337\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:29:50.990)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 148/ 198) Add exposure jw01473013001_04101_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38496  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.391\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:29:52.625)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 149/ 198) Add exposure jw01473013001_04101_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36806  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.324\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:29:54.288)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 150/ 198) Add exposure jw01473013001_04101_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39235  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.394\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:29:55.939)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 151/ 198) Add exposure jw01473013001_04101_00002_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40839  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.413\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:29:57.597)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 152/ 198) Add exposure jw01473013001_04101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37215  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.348\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:29:59.236)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 153/ 198) Add exposure jw01473013001_04101_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35916  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.373\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:30:00.913)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 154/ 198) Add exposure jw01473013001_04101_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38360  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.356\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:30:02.565)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 155/ 198) Add exposure jw01473013001_04101_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36933  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.297\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:30:04.215)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 156/ 198) Add exposure jw01473013001_04101_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39144  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.401\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:30:05.868)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 157/ 198) Add exposure jw01473013001_04101_00003_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41085  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.413\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:30:07.521)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 158/ 198) Add exposure jw01473013001_04101_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37284  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.378\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:30:09.164)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 159/ 198) Add exposure jw01473013001_04101_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35995  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.383\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:30:10.823)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 160/ 198) Add exposure jw01473013001_04101_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38351  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.351\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:30:12.478)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 161/ 198) Add exposure jw01473013001_04101_00004_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36860  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.415\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:30:14.127)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 162/ 198) Add exposure jw01473013001_04101_00004_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39147  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.376\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:30:15.784)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 163/ 198) Add exposure jw01473013001_04101_00004_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40979  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.347\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:30:17.445)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 164/ 198) Add exposure jw01473013001_04101_00004_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37308  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.284\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:30:19.086)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 165/ 198) Add exposure jw01473013001_04101_00004_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35908  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.318\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:30:20.741)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 166/ 198) Add exposure jw01473013001_04101_00004_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38357  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.344\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:30:22.397)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 167/ 198) Add exposure jw01473013001_04101_00005_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36787  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.368\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:30:24.056)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 168/ 198) Add exposure jw01473013001_04101_00005_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39082  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.404\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:30:25.717)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 169/ 198) Add exposure jw01473013001_04101_00005_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40992  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.419\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:30:27.373)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 170/ 198) Add exposure jw01473013001_04101_00005_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37331  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.374\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:30:29.026)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 171/ 198) Add exposure jw01473013001_04101_00005_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36013  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.357\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:30:30.678)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 172/ 198) Add exposure jw01473013001_04101_00005_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38289  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.337\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:30:32.326)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 173/ 198) Add exposure jw01473013001_04101_00006_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36788  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.342\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:30:33.976)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 174/ 198) Add exposure jw01473013001_04101_00006_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39003  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.386\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:30:35.622)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 175/ 198) Add exposure jw01473013001_04101_00006_nrca4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40934  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.70e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.384\n",
      "       PHOTFLAM=1.70e-21, scale=1.0\n",
      "# (2022-08-26 13:30:37.275)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 176/ 198) Add exposure jw01473013001_04101_00006_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37402  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.345\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:30:38.923)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 177/ 198) Add exposure jw01473013001_04101_00006_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35966  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.352\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:30:40.580)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 178/ 198) Add exposure jw01473013001_04101_00006_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38419  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.338\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:30:42.233)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 179/ 198) Add exposure jw01473015001_04201_00001_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46697  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.349\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:30:43.887)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 180/ 198) Add exposure jw01473015001_04201_00001_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37075  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.335\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:30:45.543)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 181/ 198) Add exposure jw01473015001_04201_00001_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39078  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.369\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:30:47.179)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 182/ 198) Add exposure jw01473015001_04201_00001_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35851  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.365\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:30:48.890)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 183/ 198) Add exposure jw01473015001_04201_00001_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38119  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.371\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:30:50.538)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 184/ 198) Add exposure jw01473015001_04201_00002_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46630  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.397\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:30:52.186)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 185/ 198) Add exposure jw01473015001_04201_00002_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37083  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.399\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:30:53.831)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 186/ 198) Add exposure jw01473015001_04201_00002_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39041  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.367\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:30:55.472)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 187/ 198) Add exposure jw01473015001_04201_00002_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35925  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.297\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:30:57.117)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 188/ 198) Add exposure jw01473015001_04201_00002_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38087  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.368\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:30:58.757)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 189/ 198) Add exposure jw01473015001_04201_00003_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46646  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.316\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:31:00.407)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 190/ 198) Add exposure jw01473015001_04201_00003_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36997  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.360\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:31:02.057)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 191/ 198) Add exposure jw01473015001_04201_00003_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39087  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.366\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:31:03.693)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 192/ 198) Add exposure jw01473015001_04201_00003_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35885  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.336\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:31:05.348)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 193/ 198) Add exposure jw01473015001_04201_00003_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38131  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.312\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:31:06.990)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 194/ 198) Add exposure jw01473015001_04201_00004_nrca1_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA1.fits.gz\n",
      "Extra -5 sigma low pixels: N= 46692  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.350\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 13:31:08.664)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 195/ 198) Add exposure jw01473015001_04201_00004_nrca2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37044  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.68e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.341\n",
      "       PHOTFLAM=1.68e-21, scale=1.0\n",
      "# (2022-08-26 13:31:10.328)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 196/ 198) Add exposure jw01473015001_04201_00004_nrca3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCA3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38997  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.75e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.372\n",
      "       PHOTFLAM=1.75e-21, scale=1.0\n",
      "# (2022-08-26 13:31:11.974)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 197/ 198) Add exposure jw01473015001_04201_00004_nrcb3_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB3.fits.gz\n",
      "Extra -5 sigma low pixels: N= 35935  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.69e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.373\n",
      "       PHOTFLAM=1.69e-21, scale=1.0\n",
      "# (2022-08-26 13:31:13.627)\n",
      "Drizzle array 1/1\n",
      "\n",
      "( 198/ 198) Add exposure jw01473015001_04201_00004_nrcb4_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB4.fits.gz\n",
      "Extra -5 sigma low pixels: N= 38261  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.74e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.366\n",
      "       PHOTFLAM=1.74e-21, scale=1.0\n",
      "# (2022-08-26 13:31:15.363)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.436 to 10.0 nJy\n",
      "============ lmc-astrometric-f200wn-clear ============\n",
      "\n",
      "(   1/  23) Add exposure jw01086001001_24101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 81853\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39491  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.298\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:19.287)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   2/  23) Add exposure jw01086001001_24101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 82325\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39425  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.294\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:22.906)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/  23) Add exposure jw01086001001_48101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 90002\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39619  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.310\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:25.986)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   4/  23) Add exposure jw01086001001_48101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 84591\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39475  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.303\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:29.133)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/  23) Add exposure jw01086001001_72101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 78042\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39189  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.291\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:32.216)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   6/  23) Add exposure jw01086001001_72101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 77278\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39098  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.280\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:35.165)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/  23) Add exposure jw01086001001_96101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 79292\n",
      "\n",
      "Extra -5 sigma low pixels: N= 38852  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.278\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:38.109)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   8/  23) Add exposure jw01086001001_96101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 78620\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39193  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.354\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:41.087)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/  23) Add exposure jw01086001002_24101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 78240\n",
      "\n",
      "Extra -5 sigma low pixels: N= 38797  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.283\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:44.232)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  10/  23) Add exposure jw01086001002_24101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 76018\n",
      "\n",
      "Extra -5 sigma low pixels: N= 38736  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.295\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:47.211)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/  23) Add exposure jw01086001002_48101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 74234\n",
      "\n",
      "Extra -5 sigma low pixels: N= 38649  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.280\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:50.195)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/  23) Add exposure jw01086001002_48101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 76109\n",
      "\n",
      "Extra -5 sigma low pixels: N= 38443  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.290\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:53.141)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/  23) Add exposure jw01086001003_24101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 79006\n",
      "\n",
      "Extra -5 sigma low pixels: N= 38342  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.282\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:56.120)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/  23) Add exposure jw01086001003_24101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 81660\n",
      "\n",
      "Extra -5 sigma low pixels: N= 38368  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.282\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:31:59.127)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/  23) Add exposure jw01086001003_48101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 79727\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39086  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.357\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:32:02.097)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/  23) Add exposure jw01086001003_48101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 78970\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39024  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.285\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:32:05.030)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  17/  23) Add exposure jw01086001004_24101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 82703\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39412  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.305\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:32:07.981)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/  23) Add exposure jw01086001004_24101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 84572\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39510  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.308\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:32:10.955)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  19/  23) Add exposure jw01086002001_04101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 129630\n",
      "\n",
      "Extra -5 sigma low pixels: N= 39222  ( 0.9 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.292\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:32:14.001)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  20/  23) Add exposure jw01515001001_03101_00001_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 200045\n",
      "\n",
      "Extra -5 sigma low pixels: N= 43611  ( 1e+00 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.305\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:32:16.956)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  21/  23) Add exposure jw01515001001_03101_00002_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 199778\n",
      "\n",
      "Extra -5 sigma low pixels: N= 43238  ( 1e+00 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.299\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:32:19.886)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  22/  23) Add exposure jw01515001001_03101_00003_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 195473\n",
      "\n",
      "Extra -5 sigma low pixels: N= 43281  ( 1e+00 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.302\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:32:22.832)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  23/  23) Add exposure jw01515001001_03101_00004_nis_rate.fits\n",
      "\n",
      "NIRISS ghost mask F200W Npix: 199207\n",
      "\n",
      "Extra -5 sigma low pixels: N= 42251  ( 1e+00 %)\n",
      "  0    PHOTFLAM=7.60e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.308\n",
      "       PHOTFLAM=7.60e-21, scale=1.0\n",
      "# (2022-08-26 13:32:25.790)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.099 to 10.0 nJy\n",
      "============ lmc-astrometric-f277w-clear ============\n",
      "\n",
      "(   1/  70) Add exposure jw01069001001_04101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40237  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.172\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:32:30.628)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:32:33,462 - stpipe - WARNING - ! 86747 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(   2/  70) Add exposure jw01069001001_04101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40161  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.180\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:32:33.796)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/  70) Add exposure jw01069001001_04101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39998  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.175\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:32:36.478)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:32:38,855 - stpipe - WARNING - ! 47799 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(   4/  70) Add exposure jw01069001001_04101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39942  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.187\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:32:39.232)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/  70) Add exposure jw01069001002_04101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40398  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.180\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:32:41.977)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:32:44,188 - stpipe - WARNING - ! 335104 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(   6/  70) Add exposure jw01069001002_04101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39780  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.180\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:32:44.511)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/  70) Add exposure jw01069001002_04101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40382  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.179\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:32:47.178)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:32:49,375 - stpipe - WARNING - ! 406612 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(   8/  70) Add exposure jw01069001002_04101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39914  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.180\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:32:49.688)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/  70) Add exposure jw01069001003_04101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39839  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.182\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:32:52.387)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  10/  70) Add exposure jw01069001003_04101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40041  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.180\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:32:55.077)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/  70) Add exposure jw01069001004_04101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40259  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.178\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:32:57.774)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/  70) Add exposure jw01069001004_04101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39833  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.183\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:33:00.461)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/  70) Add exposure jw01069002002_04101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40399  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.166\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:03.171)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/  70) Add exposure jw01069002002_04101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40477  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.161\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:05.885)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/  70) Add exposure jw01069002003_04101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40233  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.171\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:08.588)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/  70) Add exposure jw01069002003_04101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39922  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.178\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:33:11.283)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:33:13,469 - stpipe - WARNING - ! 410200 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  17/  70) Add exposure jw01069002003_04101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40393  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.162\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:13.776)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/  70) Add exposure jw01069002003_04101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40015  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.177\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:33:16.487)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:33:18,638 - stpipe - WARNING - ! 498546 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  19/  70) Add exposure jw01069002004_04101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40130  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.179\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:18.945)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  20/  70) Add exposure jw01069002004_04101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39799  ( 0.9 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.179\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:33:21.650)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:33:23,851 - stpipe - WARNING - ! 629763 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  21/  70) Add exposure jw01069002004_04101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40358  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.173\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:24.175)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  22/  70) Add exposure jw01069002004_04101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39928  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.178\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:33:26.889)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:33:29,033 - stpipe - WARNING - ! 538392 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  23/  70) Add exposure jw01069021001_02103_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40144  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.178\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:29.342)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  24/  70) Add exposure jw01069021001_02103_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40699  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.171\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:32.058)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  25/  70) Add exposure jw01072001001_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51376  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.207\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:34.774)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  26/  70) Add exposure jw01072001001_02101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51580  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.200\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:33:37.507)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  27/  70) Add exposure jw01072001001_02101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51333  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.206\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:40.250)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  28/  70) Add exposure jw01072001001_02101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51684  ( 1e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.255\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:33:43.005)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  29/  70) Add exposure jw01074001001_04101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82697  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.169\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:45.779)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  30/  70) Add exposure jw01074001001_04101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82629  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.176\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:48.192)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  31/  70) Add exposure jw01074001001_04101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82132  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.171\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:50.595)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  32/  70) Add exposure jw01074001002_04101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82084  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.168\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:52.992)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  33/  70) Add exposure jw01074001002_04101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80323  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.183\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:33:55.386)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  34/  70) Add exposure jw01074001002_04101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82000  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.173\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:33:57.773)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  35/  70) Add exposure jw01074001002_04101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 79220  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.190\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:34:00.163)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  36/  70) Add exposure jw01074001002_04101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82251  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.166\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:02.552)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  37/  70) Add exposure jw01074001002_04101_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78914  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.187\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:34:05.002)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  38/  70) Add exposure jw01074002001_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82021  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.175\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:07.384)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  39/  70) Add exposure jw01074002001_02101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81415  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.174\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:09.762)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  40/  70) Add exposure jw01074002001_02101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81892  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.167\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:12.140)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  41/  70) Add exposure jw01074002002_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81456  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.168\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:14.524)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  42/  70) Add exposure jw01074002002_02101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78623  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.180\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:34:16.913)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:34:18,968 - stpipe - WARNING - ! 28443 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  43/  70) Add exposure jw01074002002_02101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81523  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.167\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:19.279)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  44/  70) Add exposure jw01074002002_02101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78400  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.180\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:34:21.664)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:34:23,725 - stpipe - WARNING - ! 7039 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  45/  70) Add exposure jw01074002002_02101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81675  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.170\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:24.035)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  46/  70) Add exposure jw01074002002_02101_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78812  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.184\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:34:26.426)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  47/  70) Add exposure jw01074002003_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82747  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.179\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:28.793)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:34:30,787 - stpipe - WARNING - ! 195884 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  48/  70) Add exposure jw01074002003_02101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78381  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.180\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:34:31.098)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  49/  70) Add exposure jw01074002003_02101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82649  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.172\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:33.470)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:34:35,431 - stpipe - WARNING - ! 275177 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  50/  70) Add exposure jw01074002003_02101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78734  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.185\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:34:35.749)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  51/  70) Add exposure jw01074002003_02101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82908  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.170\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:38.116)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:34:40,027 - stpipe - WARNING - ! 368080 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  52/  70) Add exposure jw01074002003_02101_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78408  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.189\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:34:40.341)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  53/  70) Add exposure jw01074002004_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81902  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.180\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:42.724)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:34:44,797 - stpipe - WARNING - ! 187 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  54/  70) Add exposure jw01074002004_02101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80347  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.185\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:34:45.116)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  55/  70) Add exposure jw01074002004_02101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81221  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.179\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:47.488)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:34:49,553 - stpipe - WARNING - ! 8895 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  56/  70) Add exposure jw01074002004_02101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78833  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.190\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:34:49.863)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  57/  70) Add exposure jw01074002004_02101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81839  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.176\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:52.233)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:34:54,297 - stpipe - WARNING - ! 30692 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  58/  70) Add exposure jw01074002004_02101_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80485  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.183\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:34:54.605)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  59/  70) Add exposure jw01074003001_04101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82106  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.176\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:56.984)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  60/  70) Add exposure jw01074003001_04101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82268  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.184\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:34:58.986)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  61/  70) Add exposure jw01074003001_04101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82077  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.179\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:35:00.973)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  62/  70) Add exposure jw01074003002_04101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82007  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.178\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:35:02.966)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  63/  70) Add exposure jw01074003002_04101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78973  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.187\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:35:04.963)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  64/  70) Add exposure jw01074003002_04101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82839  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.171\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:35:06.946)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  65/  70) Add exposure jw01074003002_04101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 79737  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.185\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:35:08.927)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  66/  70) Add exposure jw01074003002_04101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 82014  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.178\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:35:10.908)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  67/  70) Add exposure jw01074003002_04101_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 79914  ( 2e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.185\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:35:12.890)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  68/  70) Add exposure jw01477001001_02103_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 186650  ( 4e+00 %)\n",
      "  0    PHOTFLAM=3.66e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.164\n",
      "       PHOTFLAM=3.66e-21, scale=1.0\n",
      "# (2022-08-26 13:35:14.878)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  69/  70) Add exposure jw01477002001_02103_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 214516  ( 5e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.184\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:35:17.244)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  70/  70) Add exposure jw01477002001_02103_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 203743  ( 5e+00 %)\n",
      "  0    PHOTFLAM=3.67e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.182\n",
      "       PHOTFLAM=3.67e-21, scale=1.0\n",
      "# (2022-08-26 13:35:20.007)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.107 to 10.0 nJy\n",
      "============ lmc-astrometric-f356w-clear ============\n",
      "\n",
      "(   1/  59) Add exposure jw01018003001_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40603  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.155\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:24.950)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   2/  59) Add exposure jw01018004001_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 41489  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.152\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:28.176)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/  59) Add exposure jw01069001001_06101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40210  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.161\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:30.877)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:35:33,175 - stpipe - WARNING - ! 87128 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(   4/  59) Add exposure jw01069001001_06101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39898  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.162\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:33.620)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/  59) Add exposure jw01069001001_06101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40035  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.157\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:36.308)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:35:38,696 - stpipe - WARNING - ! 48085 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(   6/  59) Add exposure jw01069001001_06101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39761  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.166\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:39.005)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/  59) Add exposure jw01069001002_06101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40271  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.167\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:41.709)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:35:43,920 - stpipe - WARNING - ! 335591 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(   8/  59) Add exposure jw01069001002_06101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39686  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.164\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:44.233)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/  59) Add exposure jw01069001002_06101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40229  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.165\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:46.915)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:35:49,099 - stpipe - WARNING - ! 407127 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  10/  59) Add exposure jw01069001002_06101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39701  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.166\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:49.408)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/  59) Add exposure jw01069001003_06101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39760  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.161\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:52.086)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/  59) Add exposure jw01069001003_06101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39690  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.163\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:54.767)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/  59) Add exposure jw01069001004_06101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39704  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.171\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:35:57.472)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/  59) Add exposure jw01069001004_06101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40407  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.196\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:00.176)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/  59) Add exposure jw01069002002_06101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39969  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.157\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:02.858)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/  59) Add exposure jw01069002002_06101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39993  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.157\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:05.546)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  17/  59) Add exposure jw01069002003_06101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39893  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.160\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:08.222)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/  59) Add exposure jw01069002003_06101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39850  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.158\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:10.903)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:36:13,037 - stpipe - WARNING - ! 410145 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  19/  59) Add exposure jw01069002003_06101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40002  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.156\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:13.338)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  20/  59) Add exposure jw01069002003_06101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39831  ( 0.9 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.159\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:16.041)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:36:18,174 - stpipe - WARNING - ! 498506 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  21/  59) Add exposure jw01069002004_06101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40050  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.159\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:18.481)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  22/  59) Add exposure jw01069002004_06101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39885  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.161\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:21.173)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:36:23,234 - stpipe - WARNING - ! 629645 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  23/  59) Add exposure jw01069002004_06101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39958  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.167\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:23.536)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  24/  59) Add exposure jw01069002004_06101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39849  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.165\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:26.213)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:36:28,338 - stpipe - WARNING - ! 538288 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  25/  59) Add exposure jw01069021001_02105_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40196  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.159\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:28.638)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  26/  59) Add exposure jw01069021001_02105_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40241  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.160\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:31.331)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  27/  59) Add exposure jw01072001001_02103_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51294  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.217\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:34.009)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  28/  59) Add exposure jw01072001001_02103_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51641  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.197\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:36.724)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  29/  59) Add exposure jw01072001001_02103_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51408  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.162\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:39.433)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  30/  59) Add exposure jw01072001001_02103_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51676  ( 1e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.186\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:42.159)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  31/  59) Add exposure jw01074002001_02103_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80974  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.154\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:44.891)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  32/  59) Add exposure jw01074002001_02103_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80552  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.156\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:47.280)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  33/  59) Add exposure jw01074002001_02103_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80946  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.152\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:49.654)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  34/  59) Add exposure jw01074002002_02103_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80351  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.154\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:52.031)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  35/  59) Add exposure jw01074002002_02103_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77272  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.165\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:54.403)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:36:56,452 - stpipe - WARNING - ! 28408 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  36/  59) Add exposure jw01074002002_02103_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80338  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.158\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:56.785)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  37/  59) Add exposure jw01074002002_02103_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77593  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.161\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:36:59.161)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:37:01,226 - stpipe - WARNING - ! 7021 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  38/  59) Add exposure jw01074002002_02103_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80288  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.160\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:01.541)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  39/  59) Add exposure jw01074002002_02103_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77716  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.168\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:03.923)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  40/  59) Add exposure jw01074002003_02103_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81221  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.160\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:06.301)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:37:08,305 - stpipe - WARNING - ! 196572 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  41/  59) Add exposure jw01074002003_02103_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77479  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.161\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:08.608)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  42/  59) Add exposure jw01074002003_02103_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81422  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.160\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:10.990)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:37:12,949 - stpipe - WARNING - ! 275908 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  43/  59) Add exposure jw01074002003_02103_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78055  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.169\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:13.252)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  44/  59) Add exposure jw01074002003_02103_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81303  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.163\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:15.637)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:37:17,552 - stpipe - WARNING - ! 368944 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  45/  59) Add exposure jw01074002003_02103_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77713  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.167\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:17.856)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  46/  59) Add exposure jw01074002004_02103_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 81035  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.157\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:20.238)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:37:22,311 - stpipe - WARNING - ! 207 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  47/  59) Add exposure jw01074002004_02103_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78685  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.166\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:22.613)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  48/  59) Add exposure jw01074002004_02103_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80787  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.158\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:24.994)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:37:27,063 - stpipe - WARNING - ! 9025 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  49/  59) Add exposure jw01074002004_02103_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78498  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.175\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:27.369)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  50/  59) Add exposure jw01074002004_02103_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80559  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.159\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:29.755)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:37:31,821 - stpipe - WARNING - ! 30939 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  51/  59) Add exposure jw01074002004_02103_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 78612  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.175\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:32.129)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  52/  59) Add exposure jw01473014001_06101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 85526  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.152\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:34.508)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  53/  59) Add exposure jw01473014001_06101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 84798  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.162\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:37.264)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  54/  59) Add exposure jw01473014001_06101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 85946  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.148\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:40.016)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  55/  59) Add exposure jw01473014001_06101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 84787  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.160\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:42.768)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  56/  59) Add exposure jw01473014001_06101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 86135  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.149\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:45.531)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  57/  59) Add exposure jw01473014001_06101_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 84798  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.160\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:48.282)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  58/  59) Add exposure jw01473014001_06101_00004_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 85586  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.152\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:51.037)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  59/  59) Add exposure jw01473014001_06101_00004_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 86254  ( 2e+00 %)\n",
      "  0    PHOTFLAM=2.19e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.157\n",
      "       PHOTFLAM=2.19e-21, scale=1.0\n",
      "# (2022-08-26 13:37:53.791)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.107 to 10.0 nJy\n",
      "============ lmc-astrometric-f444w-clear ============\n",
      "\n",
      "(   1/  67) Add exposure jw01069001001_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40250  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.282\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:37:58.646)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:38:01,469 - stpipe - WARNING - ! 86779 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(   2/  67) Add exposure jw01069001001_02101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39881  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.292\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:01.829)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/  67) Add exposure jw01069001001_02101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40072  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.280\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:04.487)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:38:06,861 - stpipe - WARNING - ! 47817 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(   4/  67) Add exposure jw01069001001_02101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39781  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.271\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:07.220)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/  67) Add exposure jw01069001002_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40270  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.289\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:09.939)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:38:12,141 - stpipe - WARNING - ! 335344 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(   6/  67) Add exposure jw01069001002_02101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39610  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.281\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:12.445)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/  67) Add exposure jw01069001002_02101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40292  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.285\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:15.101)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:38:17,289 - stpipe - WARNING - ! 406881 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(   8/  67) Add exposure jw01069001002_02101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39534  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.282\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:17.592)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/  67) Add exposure jw01069001003_02101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39577  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.282\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:20.280)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  10/  67) Add exposure jw01069001003_02101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39913  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.297\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:22.942)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/  67) Add exposure jw01069001004_02101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39666  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.278\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:25.626)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/  67) Add exposure jw01069001004_02101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39715  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.277\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:28.293)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/  67) Add exposure jw01069002002_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39987  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.286\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:30.981)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/  67) Add exposure jw01069002002_02101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40200  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.287\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:33.680)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/  67) Add exposure jw01069002003_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40114  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.283\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:36.367)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/  67) Add exposure jw01069002003_02101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39537  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.276\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:39.058)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:38:41,188 - stpipe - WARNING - ! 410409 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  17/  67) Add exposure jw01069002003_02101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40124  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.279\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:41.491)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/  67) Add exposure jw01069002003_02101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39519  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.281\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:44.195)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:38:46,330 - stpipe - WARNING - ! 498865 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  19/  67) Add exposure jw01069002004_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40097  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.295\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:46.637)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  20/  67) Add exposure jw01069002004_02101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39722  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.291\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:49.321)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:38:51,400 - stpipe - WARNING - ! 629304 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  21/  67) Add exposure jw01069002004_02101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40225  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.281\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:51.701)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  22/  67) Add exposure jw01069002004_02101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 39588  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.277\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:54.405)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:38:56,557 - stpipe - WARNING - ! 537995 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  23/  67) Add exposure jw01069021001_02101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40189  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.284\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:56.862)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  24/  67) Add exposure jw01069021001_02101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 40180  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.290\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:38:59.574)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  25/  67) Add exposure jw01072001001_02105_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51379  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.300\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:02.294)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  26/  67) Add exposure jw01072001001_02105_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51713  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.295\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:05.054)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  27/  67) Add exposure jw01072001001_02105_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51332  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.315\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:07.775)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  28/  67) Add exposure jw01072001001_02105_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 51675  ( 1e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.325\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:10.497)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  29/  67) Add exposure jw01074001001_08101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80614  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.275\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:13.215)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  30/  67) Add exposure jw01074001001_08101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80849  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.272\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:15.594)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  31/  67) Add exposure jw01074001001_08101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80334  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.277\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:17.980)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  32/  67) Add exposure jw01074001002_08101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80266  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.278\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:20.364)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  33/  67) Add exposure jw01074001002_08101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77436  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.287\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:22.754)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  34/  67) Add exposure jw01074001002_08101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80485  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.272\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:25.134)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  35/  67) Add exposure jw01074001002_08101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77519  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.282\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:27.521)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  36/  67) Add exposure jw01074001002_08101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80377  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.281\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:29.914)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  37/  67) Add exposure jw01074001002_08101_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77357  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.277\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:32.349)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  38/  67) Add exposure jw01074002001_02105_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80375  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.264\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:34.786)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  39/  67) Add exposure jw01074002001_02105_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80306  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.273\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:37.175)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  40/  67) Add exposure jw01074002001_02105_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80195  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.275\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:39.558)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  41/  67) Add exposure jw01074002002_02105_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80007  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.269\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:41.943)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  42/  67) Add exposure jw01074002002_02105_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 76695  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.273\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:44.337)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:39:46,397 - stpipe - WARNING - ! 28358 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  43/  67) Add exposure jw01074002002_02105_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 79844  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.268\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:46.702)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  44/  67) Add exposure jw01074002002_02105_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 76592  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.274\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:49.083)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:39:51,147 - stpipe - WARNING - ! 6993 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  45/  67) Add exposure jw01074002002_02105_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80255  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.272\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:51.456)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  46/  67) Add exposure jw01074002002_02105_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 76720  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.273\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:53.846)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  47/  67) Add exposure jw01074002003_02105_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80505  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.272\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:56.224)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:39:58,214 - stpipe - WARNING - ! 196083 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  48/  67) Add exposure jw01074002003_02105_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 76634  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.280\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:39:58.521)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  49/  67) Add exposure jw01074002003_02105_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80614  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.275\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:00.904)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:40:02,875 - stpipe - WARNING - ! 275409 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  50/  67) Add exposure jw01074002003_02105_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 76834  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.274\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:03.185)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  51/  67) Add exposure jw01074002003_02105_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80772  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.284\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:05.555)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:40:07,464 - stpipe - WARNING - ! 368368 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  52/  67) Add exposure jw01074002003_02105_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77196  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.269\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:07.770)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  53/  67) Add exposure jw01074002004_02105_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80626  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.264\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:10.157)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:40:12,242 - stpipe - WARNING - ! 193 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  54/  67) Add exposure jw01074002004_02105_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77470  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.273\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:12.556)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  55/  67) Add exposure jw01074002004_02105_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80017  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.274\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:14.946)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:40:17,020 - stpipe - WARNING - ! 8935 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  56/  67) Add exposure jw01074002004_02105_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77369  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.278\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:17.331)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  57/  67) Add exposure jw01074002004_02105_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80307  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.286\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:19.718)\n",
      "Drizzle array 1/1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:40:21,792 - stpipe - WARNING - ! 30767 points were outside the output image.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(  58/  67) Add exposure jw01074002004_02105_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77422  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.276\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:22.112)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  59/  67) Add exposure jw01074003001_08101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80548  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.273\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:24.498)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  60/  67) Add exposure jw01074003001_08101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80706  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.269\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:26.502)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  61/  67) Add exposure jw01074003001_08101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80223  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.275\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:28.498)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  62/  67) Add exposure jw01074003002_08101_00001_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80224  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.269\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:30.494)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  63/  67) Add exposure jw01074003002_08101_00001_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77489  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.272\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:32.504)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  64/  67) Add exposure jw01074003002_08101_00002_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80894  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.277\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:34.502)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  65/  67) Add exposure jw01074003002_08101_00002_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77520  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.274\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:36.503)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  66/  67) Add exposure jw01074003002_08101_00003_nrcalong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCALONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 80726  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.272\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:38.516)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  67/  67) Add exposure jw01074003002_08101_00003_nrcblong_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCBLONG.fits.gz\n",
      "Extra -5 sigma low pixels: N= 77273  ( 2e+00 %)\n",
      "  0    PHOTFLAM=1.44e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.281\n",
      "       PHOTFLAM=1.44e-21, scale=1.0\n",
      "# (2022-08-26 13:40:40.514)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.107 to 10.0 nJy\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><i>GTable length=878</i>\n",
       "<table id=\"table140605075996624\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>dataset</th><th>extension</th><th>sciext</th><th>assoc</th><th>filter</th><th>pupil</th><th>exptime</th><th>footprint</th><th>detector</th></tr></thead>\n",
       "<thead><tr><th>str34</th><th>str4</th><th>int64</th><th>str6</th><th>str11</th><th>str5</th><th>float64</th><th>str93</th><th>str8</th></tr></thead>\n",
       "<tr><td>jw01018003001_02101_00001_nrca1</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>CLEAR</td><td>236.209</td><td>((80.466308,-69.519605),(80.496384,-69.533968),(80.537223,-69.523694),(80.507634,-69.509160))</td><td>NRCA1</td></tr>\n",
       "<tr><td>jw01018003001_02101_00001_nrca2</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>CLEAR</td><td>236.209</td><td>((80.498554,-69.534828),(80.527880,-69.549039),(80.568375,-69.538931),(80.539408,-69.524584))</td><td>NRCA2</td></tr>\n",
       "<tr><td>jw01018003001_02101_00001_nrca3</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>CLEAR</td><td>236.209</td><td>((80.510687,-69.508345),(80.540243,-69.522893),(80.581538,-69.512716),(80.552387,-69.497953))</td><td>NRCA3</td></tr>\n",
       "<tr><td>jw01018003001_02101_00001_nrcalong</td><td>rate</td><td>1</td><td>manual</td><td>F356W-CLEAR</td><td>CLEAR</td><td>236.209</td><td>((80.468221,-69.519647),(80.528524,-69.548647),(80.611152,-69.528206),(80.552341,-69.498525))</td><td>NRCALONG</td></tr>\n",
       "<tr><td>jw01018004001_02101_00001_nrca1</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>CLEAR</td><td>236.209</td><td>((80.466233,-69.519643),(80.496300,-69.534008),(80.537145,-69.523737),(80.507564,-69.509200))</td><td>NRCA1</td></tr>\n",
       "<tr><td>jw01018004001_02101_00001_nrca2</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>CLEAR</td><td>236.209</td><td>((80.498470,-69.534868),(80.527788,-69.549081),(80.568288,-69.538976),(80.539329,-69.524627))</td><td>NRCA2</td></tr>\n",
       "<tr><td>jw01018004001_02101_00001_nrca3</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>CLEAR</td><td>236.209</td><td>((80.510618,-69.508386),(80.540165,-69.522936),(80.581467,-69.512763),(80.552325,-69.497997))</td><td>NRCA3</td></tr>\n",
       "<tr><td>jw01018004001_02101_00001_nrcalong</td><td>rate</td><td>1</td><td>manual</td><td>F356W-CLEAR</td><td>CLEAR</td><td>236.209</td><td>((80.468146,-69.519685),(80.528431,-69.548690),(80.611071,-69.528254),(80.552279,-69.498569))</td><td>NRCALONG</td></tr>\n",
       "<tr><td>jw01069001001_02101_00001_nrcalong</td><td>rate</td><td>1</td><td>manual</td><td>F444W-CLEAR</td><td>CLEAR</td><td>96.631</td><td>((80.346319,-69.528440),(80.413146,-69.555646),(80.490808,-69.532939),(80.425334,-69.505011))</td><td>NRCALONG</td></tr>\n",
       "<tr><td>jw01069001001_02101_00001_nrcblong</td><td>rate</td><td>1</td><td>manual</td><td>F444W-CLEAR</td><td>CLEAR</td><td>96.631</td><td>((80.453639,-69.497461),(80.521006,-69.524901),(80.598355,-69.502029),(80.533351,-69.474271))</td><td>NRCBLONG</td></tr>\n",
       "<tr><td>jw01069001001_02101_00002_nrcalong</td><td>rate</td><td>1</td><td>manual</td><td>F444W-CLEAR</td><td>CLEAR</td><td>96.631</td><td>((80.343385,-69.527130),(80.410204,-69.554336),(80.487864,-69.531631),(80.422398,-69.503703))</td><td>NRCALONG</td></tr>\n",
       "<tr><td>jw01069001001_02101_00002_nrcblong</td><td>rate</td><td>1</td><td>manual</td><td>F444W-CLEAR</td><td>CLEAR</td><td>96.631</td><td>((80.450701,-69.496153),(80.518062,-69.523593),(80.595408,-69.500723),(80.530411,-69.472963))</td><td>NRCBLONG</td></tr>\n",
       "<tr><td>jw01069001001_04101_00001_nrca4</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>96.631</td><td>((80.421109,-69.530627),(80.453530,-69.544049),(80.491933,-69.532870),(80.459747,-69.519260))</td><td>NRCA4</td></tr>\n",
       "<tr><td>jw01069001001_04101_00001_nrcalong</td><td>rate</td><td>1</td><td>manual</td><td>F277W-CLEAR</td><td>CLEAR</td><td>96.631</td><td>((80.346329,-69.528440),(80.413153,-69.555645),(80.490815,-69.532939),(80.425343,-69.505012))</td><td>NRCALONG</td></tr>\n",
       "<tr><td>jw01069001001_04101_00001_nrcb1</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>96.631</td><td>((80.528615,-69.499802),(80.560837,-69.513234),(80.598997,-69.502127),(80.567256,-69.488612))</td><td>NRCB1</td></tr>\n",
       "<tr><td>jw01069001001_04101_00001_nrcb2</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>96.631</td><td>((80.493714,-69.485215),(80.526129,-69.498952),(80.564931,-69.487808),(80.533124,-69.473954))</td><td>NRCB2</td></tr>\n",
       "<tr><td>jw01069001001_04101_00001_nrcb3</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>96.631</td><td>((80.487160,-69.512099),(80.520086,-69.525454),(80.557931,-69.514095),(80.525582,-69.500694))</td><td>NRCB3</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>jw01473015001_04201_00004_nrcb3</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>CLEAR</td><td>128.841</td><td>((80.525816,-69.487089),(80.563134,-69.498929),(80.596667,-69.486030),(80.559914,-69.474120))</td><td>NRCB3</td></tr>\n",
       "<tr><td>jw01473015001_04201_00004_nrcb4</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>CLEAR</td><td>128.841</td><td>((80.485620,-69.474210),(80.523480,-69.486255),(80.557624,-69.473282),(80.520444,-69.461125))</td><td>NRCB4</td></tr>\n",
       "<tr><td>jw01477001001_02103_00001_nrca1</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>1610.515</td><td>((80.461246,-69.474261),(80.437872,-69.490065),(80.482253,-69.498302),(80.506279,-69.482569))</td><td>NRCA1</td></tr>\n",
       "<tr><td>jw01477001001_02103_00001_nrca2</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>1610.515</td><td>((80.436635,-69.491128),(80.413250,-69.506617),(80.457051,-69.514823),(80.480943,-69.499382))</td><td>NRCA2</td></tr>\n",
       "<tr><td>jw01477001001_02103_00001_nrca3</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>1610.515</td><td>((80.509720,-69.483163),(80.485641,-69.498892),(80.530004,-69.507322),(80.554814,-69.491619))</td><td>NRCA3</td></tr>\n",
       "<tr><td>jw01477001001_02103_00001_nrca4</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>1610.515</td><td>((80.483885,-69.499935),(80.460232,-69.515429),(80.504096,-69.523775),(80.528330,-69.508282))</td><td>NRCA4</td></tr>\n",
       "<tr><td>jw01477001001_02103_00001_nrcalong</td><td>rate</td><td>1</td><td>manual</td><td>F277W-CLEAR</td><td>CLEAR</td><td>1610.515</td><td>((80.462017,-69.474835),(80.414563,-69.506603),(80.503504,-69.523438),(80.553367,-69.491815))</td><td>NRCALONG</td></tr>\n",
       "<tr><td>jw01477001001_02103_00002_nrca1</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>1610.515</td><td>((80.453295,-69.465025),(80.429928,-69.480828),(80.474286,-69.489065),(80.498305,-69.473334))</td><td>NRCA1</td></tr>\n",
       "<tr><td>jw01477001001_02103_00002_nrca2</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>1610.515</td><td>((80.428689,-69.481889),(80.405305,-69.497377),(80.449086,-69.505587),(80.472977,-69.490146))</td><td>NRCA2</td></tr>\n",
       "<tr><td>jw01477001001_02103_00002_nrca3</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>1610.515</td><td>((80.501744,-69.473929),(80.477671,-69.489657),(80.522012,-69.498088),(80.546816,-69.482386))</td><td>NRCA3</td></tr>\n",
       "<tr><td>jw01477001001_02103_00002_nrca4</td><td>rate</td><td>1</td><td>manual</td><td>F150W-CLEAR</td><td>CLEAR</td><td>1610.515</td><td>((80.475914,-69.490699),(80.452269,-69.506192),(80.496109,-69.514539),(80.520336,-69.499048))</td><td>NRCA4</td></tr>\n",
       "<tr><td>jw01477002001_02103_00001_nrcblong</td><td>rate</td><td>1</td><td>manual</td><td>F277W-CLEAR</td><td>CLEAR</td><td>1610.515</td><td>((80.508725,-69.476218),(80.418201,-69.493611),(80.465983,-69.524854),(80.557086,-69.508329))</td><td>NRCBLONG</td></tr>\n",
       "<tr><td>jw01477002001_02103_00002_nrcblong</td><td>rate</td><td>1</td><td>manual</td><td>F277W-CLEAR</td><td>CLEAR</td><td>1610.515</td><td>((80.516663,-69.466997),(80.426231,-69.484402),(80.474029,-69.515627),(80.565040,-69.499088))</td><td>NRCBLONG</td></tr>\n",
       "<tr><td>jw01515001001_03101_00001_nis</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>F200W</td><td>128.841</td><td>((80.490740,-69.471919),(80.411008,-69.496764),(80.481849,-69.524386),(80.561564,-69.499533))</td><td>NIS</td></tr>\n",
       "<tr><td>jw01515001001_03101_00002_nis</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>F200W</td><td>128.841</td><td>((80.490629,-69.471809),(80.410898,-69.496653),(80.481736,-69.524275),(80.561451,-69.499424))</td><td>NIS</td></tr>\n",
       "<tr><td>jw01515001001_03101_00003_nis</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>F200W</td><td>128.841</td><td>((80.490926,-69.471801),(80.411193,-69.496646),(80.482033,-69.524269),(80.561749,-69.499417))</td><td>NIS</td></tr>\n",
       "<tr><td>jw01515001001_03101_00004_nis</td><td>rate</td><td>1</td><td>manual</td><td>F200W-CLEAR</td><td>F200W</td><td>128.841</td><td>((80.491035,-69.471913),(80.411304,-69.496757),(80.482143,-69.524380),(80.561859,-69.499528))</td><td>NIS</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<GTable length=878>\n",
       "             dataset               extension ...                                           footprint                                           detector\n",
       "              str34                   str4   ...                                             str93                                               str8  \n",
       "---------------------------------- --------- ... --------------------------------------------------------------------------------------------- --------\n",
       "   jw01018003001_02101_00001_nrca1      rate ... ((80.466308,-69.519605),(80.496384,-69.533968),(80.537223,-69.523694),(80.507634,-69.509160))    NRCA1\n",
       "   jw01018003001_02101_00001_nrca2      rate ... ((80.498554,-69.534828),(80.527880,-69.549039),(80.568375,-69.538931),(80.539408,-69.524584))    NRCA2\n",
       "   jw01018003001_02101_00001_nrca3      rate ... ((80.510687,-69.508345),(80.540243,-69.522893),(80.581538,-69.512716),(80.552387,-69.497953))    NRCA3\n",
       "jw01018003001_02101_00001_nrcalong      rate ... ((80.468221,-69.519647),(80.528524,-69.548647),(80.611152,-69.528206),(80.552341,-69.498525)) NRCALONG\n",
       "   jw01018004001_02101_00001_nrca1      rate ... ((80.466233,-69.519643),(80.496300,-69.534008),(80.537145,-69.523737),(80.507564,-69.509200))    NRCA1\n",
       "   jw01018004001_02101_00001_nrca2      rate ... ((80.498470,-69.534868),(80.527788,-69.549081),(80.568288,-69.538976),(80.539329,-69.524627))    NRCA2\n",
       "   jw01018004001_02101_00001_nrca3      rate ... ((80.510618,-69.508386),(80.540165,-69.522936),(80.581467,-69.512763),(80.552325,-69.497997))    NRCA3\n",
       "jw01018004001_02101_00001_nrcalong      rate ... ((80.468146,-69.519685),(80.528431,-69.548690),(80.611071,-69.528254),(80.552279,-69.498569)) NRCALONG\n",
       "jw01069001001_02101_00001_nrcalong      rate ... ((80.346319,-69.528440),(80.413146,-69.555646),(80.490808,-69.532939),(80.425334,-69.505011)) NRCALONG\n",
       "jw01069001001_02101_00001_nrcblong      rate ... ((80.453639,-69.497461),(80.521006,-69.524901),(80.598355,-69.502029),(80.533351,-69.474271)) NRCBLONG\n",
       "jw01069001001_02101_00002_nrcalong      rate ... ((80.343385,-69.527130),(80.410204,-69.554336),(80.487864,-69.531631),(80.422398,-69.503703)) NRCALONG\n",
       "jw01069001001_02101_00002_nrcblong      rate ... ((80.450701,-69.496153),(80.518062,-69.523593),(80.595408,-69.500723),(80.530411,-69.472963)) NRCBLONG\n",
       "   jw01069001001_04101_00001_nrca4      rate ... ((80.421109,-69.530627),(80.453530,-69.544049),(80.491933,-69.532870),(80.459747,-69.519260))    NRCA4\n",
       "jw01069001001_04101_00001_nrcalong      rate ... ((80.346329,-69.528440),(80.413153,-69.555645),(80.490815,-69.532939),(80.425343,-69.505012)) NRCALONG\n",
       "   jw01069001001_04101_00001_nrcb1      rate ... ((80.528615,-69.499802),(80.560837,-69.513234),(80.598997,-69.502127),(80.567256,-69.488612))    NRCB1\n",
       "   jw01069001001_04101_00001_nrcb2      rate ... ((80.493714,-69.485215),(80.526129,-69.498952),(80.564931,-69.487808),(80.533124,-69.473954))    NRCB2\n",
       "   jw01069001001_04101_00001_nrcb3      rate ... ((80.487160,-69.512099),(80.520086,-69.525454),(80.557931,-69.514095),(80.525582,-69.500694))    NRCB3\n",
       "                               ...       ... ...                                                                                           ...      ...\n",
       "   jw01473015001_04201_00004_nrcb3      rate ... ((80.525816,-69.487089),(80.563134,-69.498929),(80.596667,-69.486030),(80.559914,-69.474120))    NRCB3\n",
       "   jw01473015001_04201_00004_nrcb4      rate ... ((80.485620,-69.474210),(80.523480,-69.486255),(80.557624,-69.473282),(80.520444,-69.461125))    NRCB4\n",
       "   jw01477001001_02103_00001_nrca1      rate ... ((80.461246,-69.474261),(80.437872,-69.490065),(80.482253,-69.498302),(80.506279,-69.482569))    NRCA1\n",
       "   jw01477001001_02103_00001_nrca2      rate ... ((80.436635,-69.491128),(80.413250,-69.506617),(80.457051,-69.514823),(80.480943,-69.499382))    NRCA2\n",
       "   jw01477001001_02103_00001_nrca3      rate ... ((80.509720,-69.483163),(80.485641,-69.498892),(80.530004,-69.507322),(80.554814,-69.491619))    NRCA3\n",
       "   jw01477001001_02103_00001_nrca4      rate ... ((80.483885,-69.499935),(80.460232,-69.515429),(80.504096,-69.523775),(80.528330,-69.508282))    NRCA4\n",
       "jw01477001001_02103_00001_nrcalong      rate ... ((80.462017,-69.474835),(80.414563,-69.506603),(80.503504,-69.523438),(80.553367,-69.491815)) NRCALONG\n",
       "   jw01477001001_02103_00002_nrca1      rate ... ((80.453295,-69.465025),(80.429928,-69.480828),(80.474286,-69.489065),(80.498305,-69.473334))    NRCA1\n",
       "   jw01477001001_02103_00002_nrca2      rate ... ((80.428689,-69.481889),(80.405305,-69.497377),(80.449086,-69.505587),(80.472977,-69.490146))    NRCA2\n",
       "   jw01477001001_02103_00002_nrca3      rate ... ((80.501744,-69.473929),(80.477671,-69.489657),(80.522012,-69.498088),(80.546816,-69.482386))    NRCA3\n",
       "   jw01477001001_02103_00002_nrca4      rate ... ((80.475914,-69.490699),(80.452269,-69.506192),(80.496109,-69.514539),(80.520336,-69.499048))    NRCA4\n",
       "jw01477002001_02103_00001_nrcblong      rate ... ((80.508725,-69.476218),(80.418201,-69.493611),(80.465983,-69.524854),(80.557086,-69.508329)) NRCBLONG\n",
       "jw01477002001_02103_00002_nrcblong      rate ... ((80.516663,-69.466997),(80.426231,-69.484402),(80.474029,-69.515627),(80.565040,-69.499088)) NRCBLONG\n",
       "     jw01515001001_03101_00001_nis      rate ... ((80.490740,-69.471919),(80.411008,-69.496764),(80.481849,-69.524386),(80.561564,-69.499533))      NIS\n",
       "     jw01515001001_03101_00002_nis      rate ... ((80.490629,-69.471809),(80.410898,-69.496653),(80.481736,-69.524275),(80.561451,-69.499424))      NIS\n",
       "     jw01515001001_03101_00003_nis      rate ... ((80.490926,-69.471801),(80.411193,-69.496646),(80.482033,-69.524269),(80.561749,-69.499417))      NIS\n",
       "     jw01515001001_03101_00004_nis      rate ... ((80.491035,-69.471913),(80.411304,-69.496757),(80.482143,-69.524380),(80.561859,-69.499528))      NIS"
      ]
     },
     "execution_count": 1069,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from grizli.aws import visit_processor, db\n",
    "from grizli import utils\n",
    "utils.set_warnings()\n",
    "\n",
    "visit_processor.cutout_mosaic(rootname='lmc-astrometric', \n",
    "                              ra=ra, dec=dec,\n",
    "                              size=360,\n",
    "                              ir_scale=0.025, \n",
    "                              half_optical=False, \n",
    "                              clean_flt=False, \n",
    "                              s3output=None, \n",
    "                              gzip_output=False, \n",
    "                              kernel='square',\n",
    "                              pixfrac=0.6,\n",
    "                              skip_existing=True,\n",
    "                                  res=res)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d8dee63-55f8-4589-a39b-9832f16633ea",
   "metadata": {},
   "source": [
    "# mosaics by detector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1033,
   "id": "0cf0ea0c-cb00-45d5-b265-e1243707119d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   N  value     \n",
      "====  ==========\n",
      "  52  NRCA1     \n",
      "  52  NRCB1     \n",
      "  72  NRCA2     \n",
      "  72  NRCB2     \n",
      "  77  NIS       \n",
      "  84  NRCA4     \n",
      "  87  NRCB4     \n",
      "  87  NRCBLONG  \n",
      "  91  NRCB3     \n",
      "  95  NRCA3     \n",
      " 109  NRCALONG  \n",
      "Fix NIRISS: filters = F090WN-CLEAR F115WN-CLEAR F150WN-CLEAR F200WN-CLEAR\n",
      "Skip lmc-astrometric-nis-f090wn-clear\n",
      "Skip lmc-astrometric-nis-f115wn-clear\n",
      "Skip lmc-astrometric-nis-f150wn-clear\n",
      "Skip lmc-astrometric-nis-f200wn-clear\n",
      "Skip lmc-astrometric-nrca1-f090w-clear\n",
      "Skip lmc-astrometric-nrca1-f115w-clear\n",
      "Skip lmc-astrometric-nrca1-f150w-clear\n",
      "Skip lmc-astrometric-nrca1-f200w-clear\n",
      "Skip lmc-astrometric-nrca2-f090w-clear\n",
      "Skip lmc-astrometric-nrca2-f115w-clear\n",
      "Skip lmc-astrometric-nrca2-f150w-clear\n",
      "Skip lmc-astrometric-nrca2-f200w-clear\n",
      "Skip lmc-astrometric-nrca3-f090w-clear\n",
      "Skip lmc-astrometric-nrca3-f115w-clear\n",
      "Skip lmc-astrometric-nrca3-f150w-clear\n",
      "Skip lmc-astrometric-nrca3-f200w-clear\n",
      "Skip lmc-astrometric-nrca4-f090w-clear\n",
      "Skip lmc-astrometric-nrca4-f115w-clear\n",
      "Skip lmc-astrometric-nrca4-f150w-clear\n",
      "Skip lmc-astrometric-nrca4-f200w-clear\n",
      "Skip lmc-astrometric-nrcalong-f277w-clear\n",
      "Skip lmc-astrometric-nrcalong-f356w-clear\n",
      "Skip lmc-astrometric-nrcalong-f444w-clear\n",
      "Skip lmc-astrometric-nrcb1-f090w-clear\n",
      "Skip lmc-astrometric-nrcb1-f115w-clear\n",
      "Skip lmc-astrometric-nrcb1-f150w-clear\n",
      "Skip lmc-astrometric-nrcb1-f200w-clear\n",
      "Skip lmc-astrometric-nrcb2-f090w-clear\n",
      "Skip lmc-astrometric-nrcb2-f115w-clear\n",
      "Skip lmc-astrometric-nrcb2-f150w-clear\n",
      "============ lmc-astrometric-nrcb2-f200w-clear ============\n",
      "\n",
      "(   1/  20) Add exposure jw01069001001_06101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33833  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.381\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:48:59.656)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   2/  20) Add exposure jw01069001001_06101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33777  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.350\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:01.968)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   3/  20) Add exposure jw01069001002_06101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33897  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.352\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:03.718)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   4/  20) Add exposure jw01069001002_06101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33840  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.394\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:05.475)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   5/  20) Add exposure jw01069001003_06101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33796  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.333\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:07.203)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   6/  20) Add exposure jw01069001003_06101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33729  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.394\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:09.007)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   7/  20) Add exposure jw01069001004_06101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33741  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.341\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:10.734)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   8/  20) Add exposure jw01069001004_06101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 33775  ( 0.8 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.438\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:12.525)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(   9/  20) Add exposure jw01074001002_08101_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37047  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.396\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:14.253)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  10/  20) Add exposure jw01074002003_02107_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36618  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.312\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:15.938)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  11/  20) Add exposure jw01074002003_02107_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36598  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.380\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:17.604)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  12/  20) Add exposure jw01074002004_02107_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36751  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.289\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:19.267)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  13/  20) Add exposure jw01074002004_02107_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36937  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.400\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:20.913)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  14/  20) Add exposure jw01074002004_02107_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 36799  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.328\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:22.568)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  15/  20) Add exposure jw01473013001_04101_00001_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37216  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.367\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:24.200)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  16/  20) Add exposure jw01473013001_04101_00002_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37215  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.348\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:26.029)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  17/  20) Add exposure jw01473013001_04101_00003_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37284  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.378\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:27.788)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  18/  20) Add exposure jw01473013001_04101_00004_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37308  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.284\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:29.589)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  19/  20) Add exposure jw01473013001_04101_00005_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37331  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.374\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:31.361)\n",
      "Drizzle array 1/1\n",
      "\n",
      "(  20/  20) Add exposure jw01473013001_04101_00006_nrcb2_rate.fits\n",
      "\n",
      "Use extra badpix in /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/grizli/data/nrc_lowpix_0916_NRCB2.fits.gz\n",
      "Extra -5 sigma low pixels: N= 37402  ( 0.9 %)\n",
      "  0    PHOTFLAM=1.73e-21, scale=1.0\n",
      "  ext (SCI,1), sky=0.345\n",
      "       PHOTFLAM=1.73e-21, scale=1.0\n",
      "# (2022-08-26 12:49:33.143)\n",
      "Drizzle array 1/1\n",
      "Scale PHOTFNU x 0.437 to 10.0 nJy\n",
      "Skip lmc-astrometric-nrcb3-f090w-clear\n",
      "Skip lmc-astrometric-nrcb3-f115w-clear\n",
      "Skip lmc-astrometric-nrcb3-f150w-clear\n",
      "Skip lmc-astrometric-nrcb3-f200w-clear\n",
      "Skip lmc-astrometric-nrcb4-f090w-clear\n",
      "Skip lmc-astrometric-nrcb4-f115w-clear\n",
      "Skip lmc-astrometric-nrcb4-f150w-clear\n",
      "Skip lmc-astrometric-nrcb4-f200w-clear\n",
      "Skip lmc-astrometric-nrcblong-f277w-clear\n",
      "Skip lmc-astrometric-nrcblong-f356w-clear\n",
      "Skip lmc-astrometric-nrcblong-f444w-clear\n"
     ]
    }
   ],
   "source": [
    "un = utils.Unique(res['detector'])\n",
    "\n",
    "for det in un.values:\n",
    "    _res = res[un[det]]\n",
    "    _root = f'lmc-astrometric-{det.lower()}'\n",
    "    # Filters where A/B modules overlap\n",
    "    _ = visit_processor.cutout_mosaic(rootname=_root, \n",
    "                                      res=_res,\n",
    "                              ra=ra, dec=dec,\n",
    "                              size=360,\n",
    "                              ir_scale=0.025, \n",
    "                                  half_optical=False, \n",
    "                                  filters=['F200W-CLEAR',\n",
    "                                           'F150W-CLEAR',\n",
    "                                           'F115W-CLEAR',\n",
    "                                           'F090W-CLEAR',\n",
    "                                           'F277W-CLEAR',\n",
    "                                           'F356W-CLEAR',\n",
    "                                           'F444W-CLEAR'], \n",
    "                                  pixfrac=0.75, kernel='square', \n",
    "                                  gzip=False,\n",
    "                                  s3output=None, \n",
    "                                  clean_flt=False, \n",
    "                                  skip_existing=True,\n",
    "                                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1034,
   "id": "26c61522-f2f4-418c-b1b5-468fb76b4806",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lmc-astrometric-nis-f200wn-clear_drc_sci.fits 05:21:56.490 -69:30:02.11 J2000 6.014mx6.007m -0.0250/0.0250s/pix  14400x14400 pix\n",
      "lmc-astrometric-nrca1-f200w-clear_drc_sci.fits 05:21:56.490 -69:30:02.11 J2000 6.014mx6.007m -0.0250/0.0250s/pix  14400x14400 pix\n",
      "lmc-astrometric-nrca2-f200w-clear_drc_sci.fits 05:21:56.490 -69:30:02.11 J2000 6.014mx6.007m -0.0250/0.0250s/pix  14400x14400 pix\n",
      "lmc-astrometric-nrca3-f200w-clear_drc_sci.fits 05:21:56.490 -69:30:02.11 J2000 6.014mx6.007m -0.0250/0.0250s/pix  14400x14400 pix\n",
      "lmc-astrometric-nrca4-f200w-clear_drc_sci.fits 05:21:56.490 -69:30:02.11 J2000 6.014mx6.007m -0.0250/0.0250s/pix  14400x14400 pix\n",
      "lmc-astrometric-nrcb1-f200w-clear_drc_sci.fits 05:21:56.490 -69:30:02.11 J2000 6.014mx6.007m -0.0250/0.0250s/pix  14400x14400 pix\n",
      "lmc-astrometric-nrcb2-f200w-clear_drc_sci.fits 05:21:56.490 -69:30:02.11 J2000 6.014mx6.007m -0.0250/0.0250s/pix  14400x14400 pix\n",
      "lmc-astrometric-nrcb3-f200w-clear_drc_sci.fits 05:21:56.490 -69:30:02.11 J2000 6.014mx6.007m -0.0250/0.0250s/pix  14400x14400 pix\n",
      "lmc-astrometric-nrcb4-f200w-clear_drc_sci.fits 05:21:56.490 -69:30:02.11 J2000 6.014mx6.007m -0.0250/0.0250s/pix  14400x14400 pix\n"
     ]
    }
   ],
   "source": [
    "!imsize *f200*sci.fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1035,
   "id": "484c6814-0357-4f02-88a3-fed3a8a335be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10567"
      ]
     },
     "execution_count": 1035,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1022,
   "id": "bc9b5d75-c4d7-4ee4-bbc5-8b24bf503e35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "make_SEP_catalog: sep version = 1.2.0\n",
      "# (2022-08-26 12:42:08.480)\n",
      "sep: Image AB zeropoint =  28.900\n",
      "# (2022-08-26 12:42:14.630)\n",
      "SEP: Get background {'bw': 32.0, 'bh': 32.0, 'fw': 3, 'fh': 3}\n",
      "SEP: err_scale=0.927\n",
      "   SEP: Extract...\n",
      "    Done.\n",
      "compute_SEP_auto_params: sep version = 1.2.0\n",
      "compute_SEP_auto_params: autoparams=[2.5, <Quantity 0.35 arcsec>, 2.4, 3.8]; pixel_scale=0.02499999999999985; subpix=0; flux_radii=[0.2, 0.5, 0.9]\n",
      "# SEP lmc-astrometric-nis-f090wn-clear.cat.fits: 28175 objects\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "28175"
      ]
     },
     "execution_count": 1022,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcat = prep.make_SEP_catalog('lmc-astrometric-nis-f090wn-clear', threshold=30)\n",
    "pcat['ra'], pcat['dec'] = pcat['X_WORLD'], pcat['Y_WORLD']\n",
    "len(pcat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1039,
   "id": "0e5b03a4-c4fe-46f0-806e-b352a53dbcac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 5.0)"
      ]
     },
     "execution_count": 1039,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dp = np.sqrt((pcat['X'] - pcat['XPEAK'])**2 +  (pcat['Y'] - pcat['YPEAK'])**2)\n",
    "# _cat = _cat[dp < 1]\n",
    "plt.scatter(pcat['MAG_AUTO'], dp, alpha=0.1)\n",
    "plt.ylim(0,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1036,
   "id": "e8753df3-db61-4441-a47f-df9ee74ff54a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NIS-F090WN 0.02499999999999985\n",
      "NIS-F115WN 0.02499999999999985\n",
      "NIS-F150WN 0.02499999999999985\n",
      "NIS-F200WN 0.02499999999999985\n",
      "NRCA1-F090W 0.02499999999999985\n",
      "NRCA1-F115W 0.02499999999999985\n",
      "NRCA1-F150W 0.02499999999999985\n",
      "NRCA1-F200W 0.02499999999999985\n",
      "NRCA2-F090W 0.02499999999999985\n",
      "NRCA2-F115W 0.02499999999999985\n",
      "NRCA2-F150W 0.02499999999999985\n",
      "NRCA2-F200W 0.02499999999999985\n",
      "NRCA3-F090W 0.02499999999999985\n",
      "NRCA3-F115W 0.02499999999999985\n",
      "NRCA3-F150W 0.02499999999999985\n",
      "NRCA3-F200W 0.02499999999999985\n",
      "NRCA4-F090W 0.02499999999999985\n",
      "NRCA4-F115W 0.02499999999999985\n",
      "NRCA4-F150W 0.02499999999999985\n",
      "NRCA4-F200W 0.02499999999999985\n",
      "NRCALONG-F277W 0.02499999999999985\n",
      "NRCALONG-F356W 0.02499999999999985\n",
      "NRCALONG-F444W 0.02499999999999985\n",
      "NRCB1-F090W 0.02499999999999985\n",
      "NRCB1-F115W 0.02499999999999985\n",
      "NRCB1-F150W 0.02499999999999985\n",
      "NRCB1-F200W 0.02499999999999985\n",
      "NRCB2-F090W 0.02499999999999985\n",
      "NRCB2-F115W 0.02499999999999985\n",
      "NRCB2-F150W 0.02499999999999985\n",
      "NRCB2-F200W 0.02499999999999985\n",
      "NRCB3-F090W 0.02499999999999985\n",
      "NRCB3-F115W 0.02499999999999985\n",
      "NRCB3-F150W 0.02499999999999985\n",
      "NRCB3-F200W 0.02499999999999985\n",
      "NRCB4-F090W 0.02499999999999985\n",
      "NRCB4-F115W 0.02499999999999985\n",
      "NRCB4-F150W 0.02499999999999985\n",
      "NRCB4-F200W 0.02499999999999985\n",
      "NRCBLONG-F277W 0.02499999999999985\n",
      "NRCBLONG-F356W 0.02499999999999985\n",
      "NRCBLONG-F444W 0.02499999999999985\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "files = glob.glob('lmc-astrometric-n**sci.fits')\n",
    "files.sort()\n",
    "\n",
    "mos_phot = {}\n",
    "\n",
    "ap_asec = 0.25\n",
    "\n",
    "for _i, file in enumerate(files):\n",
    "    _im = pyfits.open(file)\n",
    "    wcs = pywcs.WCS(_im[0].header, relax=True)\n",
    "    wcs.pscale = utils.get_wcs_pscale(wcs)\n",
    "    \n",
    "    key = '-'.join(file.split('-')[2:4]).upper()\n",
    "    \n",
    "    print(key, wcs.pscale)\n",
    "    mos_phot[key] = sep.sum_circle(_im[0].data.astype(np.float32),\n",
    "                                   pcat['X'], pcat['Y'], ap_asec/wcs.pscale)[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1041,
   "id": "d8dcc3d7-df28-4c11-a618-5a9eaa4b7eb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7107\n",
      "lmc_clip0.reg: x = X_WORLD, y=Y_WORLD, ellipse=False\n",
      "lmc_clip1.reg: x = X_WORLD, y=Y_WORLD, ellipse=False\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dp = np.sqrt((pcat['X'] - pcat['XPEAK'])**2 +  (pcat['Y'] - pcat['YPEAK'])**2)\n",
    "\n",
    "plt.scatter(pcat['MAG_AUTO'], pcat['FLUX_RADIUS'], c=dp, vmin=0, vmax=3, alpha=0.1)\n",
    "plt.ylim(0,10)\n",
    "clip = (pcat['FLUX_RADIUS'] < 2) & (pcat['MAG_AUTO'] < 21.5) # 50 mas\n",
    "clip = (pcat['FLUX_RADIUS'] < 3) & (pcat['MAG_AUTO'] < 21.5) # 25 mas\n",
    "clip &= dp < 2\n",
    "\n",
    "print(clip.sum())\n",
    "prep.table_to_regions(pcat[~clip], 'lmc_clip0.reg',size=0.25)\n",
    "prep.table_to_regions(pcat[clip], 'lmc_clip1.reg', size=0.25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1045,
   "id": "2f517eb6-2830-4524-8d39-5cf4c9c251f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NRCB3-F200W-CLEAR: 1.0\n",
      "NRCA2-F200W-CLEAR: 1.001\n",
      "NRCB1-F200W-CLEAR: 1.0\n",
      "NRCA4-F200W-CLEAR: 1.088\n",
      "NRCB4-F200W-CLEAR: 1.0\n",
      "NRCA1-F200W-CLEAR: 0.941\n",
      "NRCB2-F200W-CLEAR: 1.0\n",
      "NRCA3-F200W-CLEAR: 1.025\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairs = [['A2','B3'], \n",
    "         ['A4','B1'],\n",
    "         ['A1','B4'],\n",
    "         ['A3','B2']]\n",
    "\n",
    "for filt in ['F200W']:\n",
    "    fig, ax = plt.subplots(1,1,figsize=(8,4))\n",
    "    for p in pairs:\n",
    "        pa, pb = p\n",
    "        key = f'NRC{pa}-{filt}'\n",
    "        kb = f'NRC{pb}-{filt}'\n",
    "        if (key not in mos_phot) | (kb not in mos_phot):\n",
    "            continue\n",
    "            \n",
    "        ratio = mos_phot[key]/mos_phot[kb]\n",
    "        ratio[~np.isfinite(ratio) | (ratio == 0)] = np.nan\n",
    "        medc = np.nanmedian(ratio[clip])\n",
    "        med = np.nanmedian(ratio)\n",
    "\n",
    "        ax.hist(ratio[clip], bins=np.linspace(0.7, 1.3, 128), alpha=0.5,\n",
    "                label=f'{filt}  {pa}/{pb} = {medc:.3f} ({med:.3f})')\n",
    "\n",
    "        print(f'NRC{pb}-{filt}-CLEAR: 1.0')\n",
    "        print(f'NRC{pa}-{filt}-CLEAR: {medc:.3f}')\n",
    "\n",
    "    ax.grid()\n",
    "    ax.legend()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1049,
   "id": "cdd867a7-10ae-4f74-863e-09a0a18146ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NRCA1-F150W-CLEAR: 1.005\n",
      "NRCA2-F150W-CLEAR: 0.982\n",
      "NRCA3-F150W-CLEAR: 1.057\n",
      "NRCA4-F150W-CLEAR: 1.097\n",
      "NRCB1-F150W-CLEAR: 1.028\n",
      "NRCB2-F150W-CLEAR: 1.079\n",
      "NRCB3-F150W-CLEAR: 0.979\n",
      "NRCB4-F150W-CLEAR: 1.117\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairs = [['A2','B3'], \n",
    "         ['A4','B1'],\n",
    "         ['A1','B4'],\n",
    "         ['A3','B2']]\n",
    "\n",
    "dets = []\n",
    "for m in 'AB':\n",
    "    for d in range(4):\n",
    "        dets.append(f'{m}{d+1}')\n",
    "        \n",
    "for filt in ['F150W']:\n",
    "    fig, axes = plt.subplots(2,1,figsize=(8,8))\n",
    "    for pa in dets:\n",
    "        key = f'NRC{pa}-{filt}'\n",
    "        if key not in mos_phot:\n",
    "            continue\n",
    "            \n",
    "        ratio = mos_phot[key]/mos_phot[f'NIS-{filt}N']\n",
    "        ratio[~np.isfinite(ratio) | (ratio == 0)] = np.nan\n",
    "        medc = np.nanmedian(ratio[clip])\n",
    "        med = np.nanmedian(ratio)\n",
    "\n",
    "        ax = axes[0] if pa.startswith('A') else axes[1]\n",
    "        \n",
    "        ax.hist(ratio[clip], bins=np.linspace(0.7, 1.3, 128), alpha=0.5,\n",
    "                label=f'{filt}-{pa} = {medc:.3f} ({med:.3f})')\n",
    "\n",
    "        print(f'NRC{pa}-{filt}-CLEAR: {medc:.3f}')\n",
    "\n",
    "    for ax in axes:\n",
    "        ax.grid()\n",
    "        ax.legend()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1044,
   "id": "e093886c-04ba-4ddf-b883-22b5188651e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx, dr, dx, dy = pcat.match_to_catalog_sky(t, get_2d_offset=True)\n",
    "plt.scatter(dx, dy, alpha=0.01)\n",
    "plt.xlim(-0.03, 0.03)\n",
    "plt.ylim(-0.03, 0.03)\n",
    "plt.grid()\n",
    "\n",
    "cat = pcat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1550da84-e278-441e-b87d-9844317e902e",
   "metadata": {},
   "source": [
    "# Photometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1050,
   "id": "43d41719-2a85-4cff-90fe-91f1f0d68efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28175, 28175)"
      ]
     },
     "execution_count": 1050,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clip), len(pcat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1052,
   "id": "80d37472-b4be-42d2-b0e3-85884aea2ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = t\n",
    "cat = pcat[clip]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1053,
   "id": "eeb688a3-e2f6-490b-b7e4-1a52dd391938",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 jw01018003001_02101_00001_nrca1 F200W-CLEAR\n",
      "1 jw01018003001_02101_00001_nrca2 F200W-CLEAR\n",
      "2 jw01018003001_02101_00001_nrca3 F200W-CLEAR\n",
      "3 jw01018003001_02101_00001_nrcalong F356W-CLEAR\n",
      "4 jw01018004001_02101_00001_nrca1 F200W-CLEAR\n",
      "5 jw01018004001_02101_00001_nrca2 F200W-CLEAR\n",
      "6 jw01018004001_02101_00001_nrca3 F200W-CLEAR\n",
      "7 jw01018004001_02101_00001_nrcalong F356W-CLEAR\n",
      "8 jw01069001001_02101_00001_nrcalong F444W-CLEAR\n",
      "9 jw01069001001_02101_00001_nrcblong F444W-CLEAR\n",
      "10 jw01069001001_02101_00002_nrcalong F444W-CLEAR\n",
      "11 jw01069001001_02101_00002_nrcblong F444W-CLEAR\n",
      "12 jw01069001001_04101_00001_nrca4 F150W-CLEAR\n",
      "13 jw01069001001_04101_00001_nrcalong F277W-CLEAR\n",
      "14 jw01069001001_04101_00001_nrcb1 F150W-CLEAR\n",
      "15 jw01069001001_04101_00001_nrcb2 F150W-CLEAR\n",
      "16 jw01069001001_04101_00001_nrcb3 F150W-CLEAR\n",
      "17 jw01069001001_04101_00001_nrcb4 F150W-CLEAR\n",
      "18 jw01069001001_04101_00001_nrcblong F277W-CLEAR\n",
      "19 jw01069001001_04101_00002_nrca4 F150W-CLEAR\n",
      "20 jw01069001001_04101_00002_nrcalong F277W-CLEAR\n",
      "21 jw01069001001_04101_00002_nrcb1 F150W-CLEAR\n",
      "22 jw01069001001_04101_00002_nrcb2 F150W-CLEAR\n",
      "23 jw01069001001_04101_00002_nrcb3 F150W-CLEAR\n",
      "24 jw01069001001_04101_00002_nrcb4 F150W-CLEAR\n",
      "25 jw01069001001_04101_00002_nrcblong F277W-CLEAR\n",
      "26 jw01069001001_06101_00001_nrca4 F200W-CLEAR\n",
      "27 jw01069001001_06101_00001_nrcalong F356W-CLEAR\n",
      "28 jw01069001001_06101_00001_nrcb1 F200W-CLEAR\n",
      "29 jw01069001001_06101_00001_nrcb2 F200W-CLEAR\n",
      "30 jw01069001001_06101_00001_nrcb3 F200W-CLEAR\n",
      "31 jw01069001001_06101_00001_nrcb4 F200W-CLEAR\n",
      "32 jw01069001001_06101_00001_nrcblong F356W-CLEAR\n",
      "33 jw01069001001_06101_00002_nrca4 F200W-CLEAR\n",
      "34 jw01069001001_06101_00002_nrcalong F356W-CLEAR\n",
      "35 jw01069001001_06101_00002_nrcb1 F200W-CLEAR\n",
      "36 jw01069001001_06101_00002_nrcb2 F200W-CLEAR\n",
      "37 jw01069001001_06101_00002_nrcb3 F200W-CLEAR\n",
      "38 jw01069001001_06101_00002_nrcb4 F200W-CLEAR\n",
      "39 jw01069001001_06101_00002_nrcblong F356W-CLEAR\n",
      "40 jw01069001002_02101_00001_nrcalong F444W-CLEAR\n",
      "41 jw01069001002_02101_00001_nrcblong F444W-CLEAR\n",
      "42 jw01069001002_02101_00002_nrcalong F444W-CLEAR\n",
      "43 jw01069001002_02101_00002_nrcblong F444W-CLEAR\n",
      "44 jw01069001002_04101_00001_nrcalong F277W-CLEAR\n",
      "45 jw01069001002_04101_00001_nrcb1 F150W-CLEAR\n",
      "46 jw01069001002_04101_00001_nrcb2 F150W-CLEAR\n",
      "47 jw01069001002_04101_00001_nrcb3 F150W-CLEAR\n",
      "48 jw01069001002_04101_00001_nrcb4 F150W-CLEAR\n",
      "49 jw01069001002_04101_00001_nrcblong F277W-CLEAR\n",
      "50 jw01069001002_04101_00002_nrcalong F277W-CLEAR\n",
      "51 jw01069001002_04101_00002_nrcb1 F150W-CLEAR\n",
      "52 jw01069001002_04101_00002_nrcb2 F150W-CLEAR\n",
      "53 jw01069001002_04101_00002_nrcb3 F150W-CLEAR\n",
      "54 jw01069001002_04101_00002_nrcb4 F150W-CLEAR\n",
      "55 jw01069001002_04101_00002_nrcblong F277W-CLEAR\n",
      "56 jw01069001002_06101_00001_nrcalong F356W-CLEAR\n",
      "57 jw01069001002_06101_00001_nrcb1 F200W-CLEAR\n",
      "58 jw01069001002_06101_00001_nrcb2 F200W-CLEAR\n",
      "59 jw01069001002_06101_00001_nrcb3 F200W-CLEAR\n",
      "60 jw01069001002_06101_00001_nrcb4 F200W-CLEAR\n",
      "61 jw01069001002_06101_00001_nrcblong F356W-CLEAR\n",
      "62 jw01069001002_06101_00002_nrcalong F356W-CLEAR\n",
      "63 jw01069001002_06101_00002_nrcb1 F200W-CLEAR\n",
      "64 jw01069001002_06101_00002_nrcb2 F200W-CLEAR\n",
      "65 jw01069001002_06101_00002_nrcb3 F200W-CLEAR\n",
      "66 jw01069001002_06101_00002_nrcb4 F200W-CLEAR\n",
      "67 jw01069001002_06101_00002_nrcblong F356W-CLEAR\n",
      "68 jw01069001003_02101_00001_nrcblong F444W-CLEAR\n",
      "69 jw01069001003_02101_00002_nrcblong F444W-CLEAR\n",
      "70 jw01069001003_04101_00001_nrcb1 F150W-CLEAR\n",
      "71 jw01069001003_04101_00001_nrcb2 F150W-CLEAR\n",
      "72 jw01069001003_04101_00001_nrcb3 F150W-CLEAR\n",
      "73 jw01069001003_04101_00001_nrcblong F277W-CLEAR\n",
      "74 jw01069001003_04101_00002_nrcb1 F150W-CLEAR\n",
      "75 jw01069001003_04101_00002_nrcb2 F150W-CLEAR\n",
      "76 jw01069001003_04101_00002_nrcb3 F150W-CLEAR\n",
      "77 jw01069001003_04101_00002_nrcblong F277W-CLEAR\n",
      "78 jw01069001003_06101_00001_nrcb1 F200W-CLEAR\n",
      "79 jw01069001003_06101_00001_nrcb2 F200W-CLEAR\n",
      "80 jw01069001003_06101_00001_nrcb3 F200W-CLEAR\n",
      "81 jw01069001003_06101_00001_nrcblong F356W-CLEAR\n",
      "82 jw01069001003_06101_00002_nrcb1 F200W-CLEAR\n",
      "83 jw01069001003_06101_00002_nrcb2 F200W-CLEAR\n",
      "84 jw01069001003_06101_00002_nrcb3 F200W-CLEAR\n",
      "85 jw01069001003_06101_00002_nrcblong F356W-CLEAR\n",
      "86 jw01069001004_02101_00001_nrcblong F444W-CLEAR\n",
      "87 jw01069001004_02101_00002_nrcblong F444W-CLEAR\n",
      "88 jw01069001004_04101_00001_nrcb1 F150W-CLEAR\n",
      "89 jw01069001004_04101_00001_nrcb2 F150W-CLEAR\n",
      "90 jw01069001004_04101_00001_nrcb3 F150W-CLEAR\n",
      "91 jw01069001004_04101_00001_nrcb4 F150W-CLEAR\n",
      "92 jw01069001004_04101_00001_nrcblong F277W-CLEAR\n",
      "93 jw01069001004_04101_00002_nrcb1 F150W-CLEAR\n",
      "94 jw01069001004_04101_00002_nrcb2 F150W-CLEAR\n",
      "95 jw01069001004_04101_00002_nrcb3 F150W-CLEAR\n",
      "96 jw01069001004_04101_00002_nrcb4 F150W-CLEAR\n",
      "97 jw01069001004_04101_00002_nrcblong F277W-CLEAR\n",
      "98 jw01069001004_06101_00001_nrcb1 F200W-CLEAR\n",
      "99 jw01069001004_06101_00001_nrcb2 F200W-CLEAR\n",
      "100 jw01069001004_06101_00001_nrcb3 F200W-CLEAR\n",
      "101 jw01069001004_06101_00001_nrcb4 F200W-CLEAR\n",
      "102 jw01069001004_06101_00001_nrcblong F356W-CLEAR\n",
      "103 jw01069001004_06101_00002_nrcb1 F200W-CLEAR\n",
      "104 jw01069001004_06101_00002_nrcb2 F200W-CLEAR\n",
      "105 jw01069001004_06101_00002_nrcb3 F200W-CLEAR\n",
      "106 jw01069001004_06101_00002_nrcb4 F200W-CLEAR\n",
      "107 jw01069001004_06101_00002_nrcblong F356W-CLEAR\n",
      "108 jw01069002002_02101_00001_nrcalong F444W-CLEAR\n",
      "109 jw01069002002_02101_00002_nrcalong F444W-CLEAR\n",
      "110 jw01069002002_04101_00001_nrca1 F150W-CLEAR\n",
      "111 jw01069002002_04101_00001_nrca2 F150W-CLEAR\n",
      "112 jw01069002002_04101_00001_nrca3 F150W-CLEAR\n",
      "113 jw01069002002_04101_00001_nrca4 F150W-CLEAR\n",
      "114 jw01069002002_04101_00001_nrcalong F277W-CLEAR\n",
      "115 jw01069002002_04101_00002_nrca1 F150W-CLEAR\n",
      "116 jw01069002002_04101_00002_nrca2 F150W-CLEAR\n",
      "117 jw01069002002_04101_00002_nrca3 F150W-CLEAR\n",
      "118 jw01069002002_04101_00002_nrca4 F150W-CLEAR\n",
      "119 jw01069002002_04101_00002_nrcalong F277W-CLEAR\n",
      "120 jw01069002002_06101_00001_nrca1 F200W-CLEAR\n",
      "121 jw01069002002_06101_00001_nrca2 F200W-CLEAR\n",
      "122 jw01069002002_06101_00001_nrca3 F200W-CLEAR\n",
      "123 jw01069002002_06101_00001_nrca4 F200W-CLEAR\n",
      "124 jw01069002002_06101_00001_nrcalong F356W-CLEAR\n",
      "125 jw01069002002_06101_00002_nrca1 F200W-CLEAR\n",
      "126 jw01069002002_06101_00002_nrca2 F200W-CLEAR\n",
      "127 jw01069002002_06101_00002_nrca3 F200W-CLEAR\n",
      "128 jw01069002002_06101_00002_nrca4 F200W-CLEAR\n",
      "129 jw01069002002_06101_00002_nrcalong F356W-CLEAR\n",
      "130 jw01069002003_02101_00001_nrcalong F444W-CLEAR\n",
      "131 jw01069002003_02101_00001_nrcblong F444W-CLEAR\n",
      "132 jw01069002003_02101_00002_nrcalong F444W-CLEAR\n",
      "133 jw01069002003_02101_00002_nrcblong F444W-CLEAR\n",
      "134 jw01069002003_04101_00001_nrca2 F150W-CLEAR\n",
      "135 jw01069002003_04101_00001_nrca3 F150W-CLEAR\n",
      "136 jw01069002003_04101_00001_nrca4 F150W-CLEAR\n",
      "137 jw01069002003_04101_00001_nrcalong F277W-CLEAR\n",
      "138 jw01069002003_04101_00001_nrcb3 F150W-CLEAR\n",
      "139 jw01069002003_04101_00001_nrcb4 F150W-CLEAR\n",
      "140 jw01069002003_04101_00001_nrcblong F277W-CLEAR\n",
      "141 jw01069002003_04101_00002_nrca2 F150W-CLEAR\n",
      "142 jw01069002003_04101_00002_nrca3 F150W-CLEAR\n",
      "143 jw01069002003_04101_00002_nrca4 F150W-CLEAR\n",
      "144 jw01069002003_04101_00002_nrcalong F277W-CLEAR\n",
      "145 jw01069002003_04101_00002_nrcb3 F150W-CLEAR\n",
      "146 jw01069002003_04101_00002_nrcb4 F150W-CLEAR\n",
      "147 jw01069002003_04101_00002_nrcblong F277W-CLEAR\n",
      "148 jw01069002003_06101_00001_nrca2 F200W-CLEAR\n",
      "149 jw01069002003_06101_00001_nrca3 F200W-CLEAR\n",
      "150 jw01069002003_06101_00001_nrca4 F200W-CLEAR\n",
      "151 jw01069002003_06101_00001_nrcalong F356W-CLEAR\n",
      "152 jw01069002003_06101_00001_nrcb3 F200W-CLEAR\n",
      "153 jw01069002003_06101_00001_nrcb4 F200W-CLEAR\n",
      "154 jw01069002003_06101_00001_nrcblong F356W-CLEAR\n",
      "155 jw01069002003_06101_00002_nrca2 F200W-CLEAR\n",
      "156 jw01069002003_06101_00002_nrca3 F200W-CLEAR\n",
      "157 jw01069002003_06101_00002_nrca4 F200W-CLEAR\n",
      "158 jw01069002003_06101_00002_nrcalong F356W-CLEAR\n",
      "159 jw01069002003_06101_00002_nrcb3 F200W-CLEAR\n",
      "160 jw01069002003_06101_00002_nrcb4 F200W-CLEAR\n",
      "161 jw01069002003_06101_00002_nrcblong F356W-CLEAR\n",
      "162 jw01069002004_02101_00001_nrcalong F444W-CLEAR\n",
      "163 jw01069002004_02101_00001_nrcblong F444W-CLEAR\n",
      "164 jw01069002004_02101_00002_nrcalong F444W-CLEAR\n",
      "165 jw01069002004_02101_00002_nrcblong F444W-CLEAR\n",
      "166 jw01069002004_04101_00001_nrca1 F150W-CLEAR\n",
      "167 jw01069002004_04101_00001_nrca2 F150W-CLEAR\n",
      "168 jw01069002004_04101_00001_nrca3 F150W-CLEAR\n",
      "169 jw01069002004_04101_00001_nrca4 F150W-CLEAR\n",
      "170 jw01069002004_04101_00001_nrcalong F277W-CLEAR\n",
      "171 jw01069002004_04101_00001_nrcb4 F150W-CLEAR\n",
      "172 jw01069002004_04101_00001_nrcblong F277W-CLEAR\n",
      "173 jw01069002004_04101_00002_nrca1 F150W-CLEAR\n",
      "174 jw01069002004_04101_00002_nrca2 F150W-CLEAR\n",
      "175 jw01069002004_04101_00002_nrca3 F150W-CLEAR\n",
      "176 jw01069002004_04101_00002_nrca4 F150W-CLEAR\n",
      "177 jw01069002004_04101_00002_nrcalong F277W-CLEAR\n",
      "178 jw01069002004_04101_00002_nrcb4 F150W-CLEAR\n",
      "179 jw01069002004_04101_00002_nrcblong F277W-CLEAR\n",
      "180 jw01069002004_06101_00001_nrca1 F200W-CLEAR\n",
      "181 jw01069002004_06101_00001_nrca2 F200W-CLEAR\n",
      "182 jw01069002004_06101_00001_nrca3 F200W-CLEAR\n",
      "183 jw01069002004_06101_00001_nrca4 F200W-CLEAR\n",
      "184 jw01069002004_06101_00001_nrcalong F356W-CLEAR\n",
      "185 jw01069002004_06101_00001_nrcb4 F200W-CLEAR\n",
      "186 jw01069002004_06101_00001_nrcblong F356W-CLEAR\n",
      "187 jw01069002004_06101_00002_nrca1 F200W-CLEAR\n",
      "188 jw01069002004_06101_00002_nrca2 F200W-CLEAR\n",
      "189 jw01069002004_06101_00002_nrca3 F200W-CLEAR\n",
      "190 jw01069002004_06101_00002_nrca4 F200W-CLEAR\n",
      "191 jw01069002004_06101_00002_nrcalong F356W-CLEAR\n",
      "192 jw01069002004_06101_00002_nrcb4 F200W-CLEAR\n",
      "193 jw01069002004_06101_00002_nrcblong F356W-CLEAR\n",
      "194 jw01069021001_02101_00001_nrcalong F444W-CLEAR\n",
      "195 jw01069021001_02101_00002_nrcalong F444W-CLEAR\n",
      "196 jw01069021001_02103_00001_nrca1 F150W-CLEAR\n",
      "197 jw01069021001_02103_00001_nrca2 F150W-CLEAR\n",
      "198 jw01069021001_02103_00001_nrca3 F150W-CLEAR\n",
      "199 jw01069021001_02103_00001_nrca4 F150W-CLEAR\n",
      "200 jw01069021001_02103_00001_nrcalong F277W-CLEAR\n",
      "201 jw01069021001_02103_00002_nrca1 F150W-CLEAR\n",
      "202 jw01069021001_02103_00002_nrca2 F150W-CLEAR\n",
      "203 jw01069021001_02103_00002_nrca3 F150W-CLEAR\n",
      "204 jw01069021001_02103_00002_nrca4 F150W-CLEAR\n",
      "205 jw01069021001_02103_00002_nrcalong F277W-CLEAR\n",
      "206 jw01069021001_02105_00001_nrca1 F200W-CLEAR\n",
      "207 jw01069021001_02105_00001_nrca2 F200W-CLEAR\n",
      "208 jw01069021001_02105_00001_nrca3 F200W-CLEAR\n",
      "209 jw01069021001_02105_00001_nrca4 F200W-CLEAR\n",
      "210 jw01069021001_02105_00001_nrcalong F356W-CLEAR\n",
      "211 jw01069021001_02105_00002_nrca1 F200W-CLEAR\n",
      "212 jw01069021001_02105_00002_nrca2 F200W-CLEAR\n",
      "213 jw01069021001_02105_00002_nrca3 F200W-CLEAR\n",
      "214 jw01069021001_02105_00002_nrca4 F200W-CLEAR\n",
      "215 jw01069021001_02105_00002_nrcalong F356W-CLEAR\n",
      "216 jw01072001001_02101_00001_nrcalong F277W-CLEAR\n",
      "217 jw01072001001_02101_00001_nrcblong F277W-CLEAR\n",
      "218 jw01072001001_02101_00002_nrcalong F277W-CLEAR\n",
      "219 jw01072001001_02101_00002_nrcblong F277W-CLEAR\n",
      "220 jw01072001001_02103_00001_nrca1 F090W-CLEAR\n",
      "221 jw01072001001_02103_00001_nrca2 F090W-CLEAR\n",
      "222 jw01072001001_02103_00001_nrca3 F090W-CLEAR\n",
      "223 jw01072001001_02103_00001_nrcalong F356W-CLEAR\n",
      "224 jw01072001001_02103_00001_nrcb1 F090W-CLEAR\n",
      "225 jw01072001001_02103_00001_nrcb2 F090W-CLEAR\n",
      "226 jw01072001001_02103_00001_nrcb3 F090W-CLEAR\n",
      "227 jw01072001001_02103_00001_nrcblong F356W-CLEAR\n",
      "228 jw01072001001_02103_00002_nrca1 F090W-CLEAR\n",
      "229 jw01072001001_02103_00002_nrca2 F090W-CLEAR\n",
      "230 jw01072001001_02103_00002_nrca3 F090W-CLEAR\n",
      "231 jw01072001001_02103_00002_nrcalong F356W-CLEAR\n",
      "232 jw01072001001_02103_00002_nrcb1 F090W-CLEAR\n",
      "233 jw01072001001_02103_00002_nrcb2 F090W-CLEAR\n",
      "234 jw01072001001_02103_00002_nrcb3 F090W-CLEAR\n",
      "235 jw01072001001_02103_00002_nrcblong F356W-CLEAR\n",
      "236 jw01072001001_02105_00001_nrca1 F115W-CLEAR\n",
      "237 jw01072001001_02105_00001_nrca2 F115W-CLEAR\n",
      "238 jw01072001001_02105_00001_nrca3 F115W-CLEAR\n",
      "239 jw01072001001_02105_00001_nrcalong F444W-CLEAR\n",
      "240 jw01072001001_02105_00001_nrcb1 F115W-CLEAR\n",
      "241 jw01072001001_02105_00001_nrcb2 F115W-CLEAR\n",
      "242 jw01072001001_02105_00001_nrcb3 F115W-CLEAR\n",
      "243 jw01072001001_02105_00001_nrcblong F444W-CLEAR\n",
      "244 jw01072001001_02105_00002_nrca1 F115W-CLEAR\n",
      "245 jw01072001001_02105_00002_nrca3 F115W-CLEAR\n",
      "246 jw01072001001_02105_00002_nrcalong F444W-CLEAR\n",
      "247 jw01072001001_02105_00002_nrcb1 F115W-CLEAR\n",
      "248 jw01072001001_02105_00002_nrcb2 F115W-CLEAR\n",
      "249 jw01072001001_02105_00002_nrcb3 F115W-CLEAR\n",
      "250 jw01072001001_02105_00002_nrcblong F444W-CLEAR\n",
      "251 jw01073001001_02101_00001_nrca1 F150W-CLEAR\n",
      "252 jw01073001001_02101_00001_nrca2 F150W-CLEAR\n",
      "253 jw01073001001_02101_00001_nrca4 F150W-CLEAR\n",
      "254 jw01073001001_02101_00001_nrcb2 F150W-CLEAR\n",
      "255 jw01073001001_02101_00001_nrcb4 F150W-CLEAR\n",
      "256 jw01073001001_02101_00002_nrca1 F150W-CLEAR\n",
      "257 jw01073001001_02101_00002_nrca3 F150W-CLEAR\n",
      "258 jw01073001001_02101_00002_nrcb4 F150W-CLEAR\n",
      "259 jw01073001002_02101_00001_nrca1 F150W-CLEAR\n",
      "260 jw01073001002_02101_00001_nrca3 F150W-CLEAR\n",
      "261 jw01073001002_02101_00001_nrcb2 F150W-CLEAR\n",
      "262 jw01073001002_02101_00001_nrcb4 F150W-CLEAR\n",
      "263 jw01073001002_02101_00002_nrca1 F150W-CLEAR\n",
      "264 jw01073001002_02101_00002_nrca2 F150W-CLEAR\n",
      "265 jw01073001002_02101_00002_nrca3 F150W-CLEAR\n",
      "266 jw01073001002_02101_00002_nrca4 F150W-CLEAR\n",
      "267 jw01073001002_02101_00002_nrcb2 F150W-CLEAR\n",
      "268 jw01073001002_02101_00002_nrcb3 F150W-CLEAR\n",
      "269 jw01073001002_02101_00002_nrcb4 F150W-CLEAR\n",
      "270 jw01073001003_02101_00001_nrca4 F150W-CLEAR\n",
      "271 jw01073001003_02101_00001_nrcb1 F150W-CLEAR\n",
      "272 jw01073001003_02101_00001_nrcb3 F150W-CLEAR\n",
      "273 jw01073001003_02101_00002_nrcb1 F150W-CLEAR\n",
      "274 jw01073001003_02101_00002_nrcb3 F150W-CLEAR\n",
      "275 jw01073002001_02101_00001_nrca1 F150W-CLEAR\n",
      "276 jw01073002001_02101_00001_nrca2 F150W-CLEAR\n",
      "277 jw01073002001_02101_00001_nrca3 F150W-CLEAR\n",
      "278 jw01073002001_02101_00001_nrca4 F150W-CLEAR\n",
      "279 jw01073002001_02101_00001_nrcb2 F150W-CLEAR\n",
      "280 jw01073002001_02101_00001_nrcb3 F150W-CLEAR\n",
      "281 jw01073002001_02101_00001_nrcb4 F150W-CLEAR\n",
      "282 jw01073002001_02101_00002_nrca2 F150W-CLEAR\n",
      "283 jw01073002001_02101_00002_nrca3 F150W-CLEAR\n",
      "284 jw01073002001_02101_00002_nrca4 F150W-CLEAR\n",
      "285 jw01073002001_02101_00002_nrcb1 F150W-CLEAR\n",
      "286 jw01073002001_02101_00002_nrcb2 F150W-CLEAR\n",
      "287 jw01073002001_02101_00002_nrcb3 F150W-CLEAR\n",
      "288 jw01073002001_02101_00002_nrcb4 F150W-CLEAR\n",
      "289 jw01073002001_02101_00003_nrca2 F150W-CLEAR\n",
      "290 jw01073002001_02101_00003_nrca3 F150W-CLEAR\n",
      "291 jw01073002001_02101_00003_nrca4 F150W-CLEAR\n",
      "292 jw01073002001_02101_00003_nrcb1 F150W-CLEAR\n",
      "293 jw01073002001_02101_00003_nrcb2 F150W-CLEAR\n",
      "294 jw01073002001_02101_00003_nrcb3 F150W-CLEAR\n",
      "295 jw01073002001_02101_00003_nrcb4 F150W-CLEAR\n",
      "296 jw01073002001_02101_00004_nrca2 F150W-CLEAR\n",
      "297 jw01073002001_02101_00004_nrca3 F150W-CLEAR\n",
      "298 jw01073002001_02101_00004_nrca4 F150W-CLEAR\n",
      "299 jw01073002001_02101_00004_nrcb1 F150W-CLEAR\n",
      "300 jw01073002001_02101_00004_nrcb2 F150W-CLEAR\n",
      "301 jw01073002001_02101_00004_nrcb3 F150W-CLEAR\n",
      "302 jw01073002001_02101_00004_nrcb4 F150W-CLEAR\n",
      "303 jw01073003001_02101_00001_nrca2 F150W-CLEAR\n",
      "304 jw01073003001_02101_00001_nrca3 F150W-CLEAR\n",
      "305 jw01073003001_02101_00001_nrcb1 F150W-CLEAR\n",
      "306 jw01073003001_02101_00001_nrcb2 F150W-CLEAR\n",
      "307 jw01073003001_02101_00001_nrcb3 F150W-CLEAR\n",
      "308 jw01073003001_02101_00001_nrcb4 F150W-CLEAR\n",
      "309 jw01073003001_02101_00002_nrca2 F150W-CLEAR\n",
      "310 jw01073003001_02101_00002_nrca3 F150W-CLEAR\n",
      "311 jw01073003001_02101_00002_nrca4 F150W-CLEAR\n",
      "312 jw01073003001_02101_00002_nrcb1 F150W-CLEAR\n",
      "313 jw01073003001_02101_00002_nrcb2 F150W-CLEAR\n",
      "314 jw01073003001_02101_00002_nrcb3 F150W-CLEAR\n",
      "315 jw01073003001_02101_00002_nrcb4 F150W-CLEAR\n",
      "316 jw01073003001_02101_00003_nrca2 F150W-CLEAR\n",
      "317 jw01073003001_02101_00003_nrca3 F150W-CLEAR\n",
      "318 jw01073003001_02101_00003_nrca4 F150W-CLEAR\n",
      "319 jw01073003001_02101_00003_nrcb1 F150W-CLEAR\n",
      "320 jw01073003001_02101_00003_nrcb2 F150W-CLEAR\n",
      "321 jw01073003001_02101_00003_nrcb3 F150W-CLEAR\n",
      "322 jw01073003001_02101_00003_nrcb4 F150W-CLEAR\n",
      "323 jw01073003001_02101_00004_nrca2 F150W-CLEAR\n",
      "324 jw01073003001_02101_00004_nrca3 F150W-CLEAR\n",
      "325 jw01073003001_02101_00004_nrca4 F150W-CLEAR\n",
      "326 jw01073003001_02101_00004_nrcb1 F150W-CLEAR\n",
      "327 jw01073003001_02101_00004_nrcb2 F150W-CLEAR\n",
      "328 jw01073003001_02101_00004_nrcb3 F150W-CLEAR\n",
      "329 jw01073003001_02101_00004_nrcb4 F150W-CLEAR\n",
      "330 jw01074001001_04101_00001_nrcalong F277W-CLEAR\n",
      "331 jw01074001001_04101_00002_nrcalong F277W-CLEAR\n",
      "332 jw01074001001_04101_00003_nrcalong F277W-CLEAR\n",
      "333 jw01074001001_06101_00001_nrca1 F150W-CLEAR\n",
      "334 jw01074001001_06101_00001_nrca2 F150W-CLEAR\n",
      "335 jw01074001001_06101_00002_nrca1 F150W-CLEAR\n",
      "336 jw01074001001_06101_00002_nrca2 F150W-CLEAR\n",
      "337 jw01074001001_06101_00003_nrca2 F150W-CLEAR\n",
      "338 jw01074001001_06101_00003_nrca3 F150W-CLEAR\n",
      "339 jw01074001001_08101_00001_nrca1 F200W-CLEAR\n",
      "340 jw01074001001_08101_00001_nrca2 F200W-CLEAR\n",
      "341 jw01074001001_08101_00001_nrcalong F444W-CLEAR\n",
      "342 jw01074001001_08101_00002_nrca1 F200W-CLEAR\n",
      "343 jw01074001001_08101_00002_nrca2 F200W-CLEAR\n",
      "344 jw01074001001_08101_00002_nrcalong F444W-CLEAR\n",
      "345 jw01074001001_08101_00003_nrca1 F200W-CLEAR\n",
      "346 jw01074001001_08101_00003_nrca2 F200W-CLEAR\n",
      "347 jw01074001001_08101_00003_nrca3 F200W-CLEAR\n",
      "348 jw01074001001_08101_00003_nrcalong F444W-CLEAR\n",
      "349 jw01074001002_04101_00001_nrcalong F277W-CLEAR\n",
      "350 jw01074001002_04101_00001_nrcblong F277W-CLEAR\n",
      "351 jw01074001002_04101_00002_nrcalong F277W-CLEAR\n",
      "352 jw01074001002_04101_00002_nrcblong F277W-CLEAR\n",
      "353 jw01074001002_04101_00003_nrcalong F277W-CLEAR\n",
      "354 jw01074001002_04101_00003_nrcblong F277W-CLEAR\n",
      "355 jw01074001002_06101_00001_nrca3 F150W-CLEAR\n",
      "356 jw01074001002_06101_00001_nrcb3 F150W-CLEAR\n",
      "357 jw01074001002_06101_00001_nrcb4 F150W-CLEAR\n",
      "358 jw01074001002_06101_00002_nrca3 F150W-CLEAR\n",
      "359 jw01074001002_06101_00002_nrca4 F150W-CLEAR\n",
      "360 jw01074001002_06101_00002_nrcb3 F150W-CLEAR\n",
      "361 jw01074001002_06101_00002_nrcb4 F150W-CLEAR\n",
      "362 jw01074001002_06101_00003_nrca3 F150W-CLEAR\n",
      "363 jw01074001002_06101_00003_nrca4 F150W-CLEAR\n",
      "364 jw01074001002_06101_00003_nrcb2 F150W-CLEAR\n",
      "365 jw01074001002_06101_00003_nrcb3 F150W-CLEAR\n",
      "366 jw01074001002_06101_00003_nrcb4 F150W-CLEAR\n",
      "367 jw01074001002_08101_00001_nrca3 F200W-CLEAR\n",
      "368 jw01074001002_08101_00001_nrca4 F200W-CLEAR\n",
      "369 jw01074001002_08101_00001_nrcalong F444W-CLEAR\n",
      "370 jw01074001002_08101_00001_nrcb3 F200W-CLEAR\n",
      "371 jw01074001002_08101_00001_nrcb4 F200W-CLEAR\n",
      "372 jw01074001002_08101_00001_nrcblong F444W-CLEAR\n",
      "373 jw01074001002_08101_00002_nrca3 F200W-CLEAR\n",
      "374 jw01074001002_08101_00002_nrca4 F200W-CLEAR\n",
      "375 jw01074001002_08101_00002_nrcalong F444W-CLEAR\n",
      "376 jw01074001002_08101_00002_nrcb3 F200W-CLEAR\n",
      "377 jw01074001002_08101_00002_nrcb4 F200W-CLEAR\n",
      "378 jw01074001002_08101_00002_nrcblong F444W-CLEAR\n",
      "379 jw01074001002_08101_00003_nrca3 F200W-CLEAR\n",
      "380 jw01074001002_08101_00003_nrca4 F200W-CLEAR\n",
      "381 jw01074001002_08101_00003_nrcalong F444W-CLEAR\n",
      "382 jw01074001002_08101_00003_nrcb2 F200W-CLEAR\n",
      "383 jw01074001002_08101_00003_nrcb3 F200W-CLEAR\n",
      "384 jw01074001002_08101_00003_nrcb4 F200W-CLEAR\n",
      "385 jw01074001002_08101_00003_nrcblong F444W-CLEAR\n",
      "386 jw01074002001_02101_00001_nrcalong F277W-CLEAR\n",
      "387 jw01074002001_02101_00002_nrcalong F277W-CLEAR\n",
      "388 jw01074002001_02101_00003_nrcalong F277W-CLEAR\n",
      "389 jw01074002001_02103_00001_nrca1 F115W-CLEAR\n",
      "390 jw01074002001_02103_00001_nrca4 F115W-CLEAR\n",
      "391 jw01074002001_02103_00001_nrcalong F356W-CLEAR\n",
      "392 jw01074002001_02103_00002_nrcalong F356W-CLEAR\n",
      "393 jw01074002001_02103_00003_nrca3 F115W-CLEAR\n",
      "394 jw01074002001_02103_00003_nrcalong F356W-CLEAR\n",
      "395 jw01074002001_02105_00001_nrcalong F444W-CLEAR\n",
      "396 jw01074002001_02105_00002_nrca3 F150W-CLEAR\n",
      "397 jw01074002001_02105_00002_nrcalong F444W-CLEAR\n",
      "398 jw01074002001_02105_00003_nrca3 F150W-CLEAR\n",
      "399 jw01074002001_02105_00003_nrcalong F444W-CLEAR\n",
      "400 jw01074002001_02107_00002_nrca3 F200W-CLEAR\n",
      "401 jw01074002001_02107_00003_nrca3 F200W-CLEAR\n",
      "402 jw01074002001_02107_00003_nrca4 F200W-CLEAR\n",
      "403 jw01074002002_02101_00001_nrcalong F277W-CLEAR\n",
      "404 jw01074002002_02101_00001_nrcblong F277W-CLEAR\n",
      "405 jw01074002002_02101_00002_nrcalong F277W-CLEAR\n",
      "406 jw01074002002_02101_00002_nrcblong F277W-CLEAR\n",
      "407 jw01074002002_02101_00003_nrcalong F277W-CLEAR\n",
      "408 jw01074002002_02101_00003_nrcblong F277W-CLEAR\n",
      "409 jw01074002002_02103_00001_nrca1 F115W-CLEAR\n",
      "410 jw01074002002_02103_00001_nrca2 F115W-CLEAR\n",
      "411 jw01074002002_02103_00001_nrca3 F115W-CLEAR\n",
      "412 jw01074002002_02103_00001_nrca4 F115W-CLEAR\n",
      "413 jw01074002002_02103_00001_nrcalong F356W-CLEAR\n",
      "414 jw01074002002_02103_00001_nrcb3 F115W-CLEAR\n",
      "415 jw01074002002_02103_00001_nrcb4 F115W-CLEAR\n",
      "416 jw01074002002_02103_00001_nrcblong F356W-CLEAR\n",
      "417 jw01074002002_02103_00002_nrca1 F115W-CLEAR\n",
      "418 jw01074002002_02103_00002_nrca2 F115W-CLEAR\n",
      "419 jw01074002002_02103_00002_nrca3 F115W-CLEAR\n",
      "420 jw01074002002_02103_00002_nrca4 F115W-CLEAR\n",
      "421 jw01074002002_02103_00002_nrcalong F356W-CLEAR\n",
      "422 jw01074002002_02103_00002_nrcb3 F115W-CLEAR\n",
      "423 jw01074002002_02103_00002_nrcb4 F115W-CLEAR\n",
      "424 jw01074002002_02103_00002_nrcblong F356W-CLEAR\n",
      "425 jw01074002002_02103_00003_nrca1 F115W-CLEAR\n",
      "426 jw01074002002_02103_00003_nrca2 F115W-CLEAR\n",
      "427 jw01074002002_02103_00003_nrca3 F115W-CLEAR\n",
      "428 jw01074002002_02103_00003_nrca4 F115W-CLEAR\n",
      "429 jw01074002002_02103_00003_nrcalong F356W-CLEAR\n",
      "430 jw01074002002_02103_00003_nrcb3 F115W-CLEAR\n",
      "431 jw01074002002_02103_00003_nrcb4 F115W-CLEAR\n",
      "432 jw01074002002_02103_00003_nrcblong F356W-CLEAR\n",
      "433 jw01074002002_02105_00001_nrca3 F150W-CLEAR\n",
      "434 jw01074002002_02105_00001_nrca4 F150W-CLEAR\n",
      "435 jw01074002002_02105_00001_nrcalong F444W-CLEAR\n",
      "436 jw01074002002_02105_00001_nrcb3 F150W-CLEAR\n",
      "437 jw01074002002_02105_00001_nrcb4 F150W-CLEAR\n",
      "438 jw01074002002_02105_00001_nrcblong F444W-CLEAR\n",
      "439 jw01074002002_02105_00002_nrca3 F150W-CLEAR\n",
      "440 jw01074002002_02105_00002_nrca4 F150W-CLEAR\n",
      "441 jw01074002002_02105_00002_nrcalong F444W-CLEAR\n",
      "442 jw01074002002_02105_00002_nrcb3 F150W-CLEAR\n",
      "443 jw01074002002_02105_00002_nrcb4 F150W-CLEAR\n",
      "444 jw01074002002_02105_00002_nrcblong F444W-CLEAR\n",
      "445 jw01074002002_02105_00003_nrca3 F150W-CLEAR\n",
      "446 jw01074002002_02105_00003_nrca4 F150W-CLEAR\n",
      "447 jw01074002002_02105_00003_nrcalong F444W-CLEAR\n",
      "448 jw01074002002_02105_00003_nrcb3 F150W-CLEAR\n",
      "449 jw01074002002_02105_00003_nrcb4 F150W-CLEAR\n",
      "450 jw01074002002_02105_00003_nrcblong F444W-CLEAR\n",
      "451 jw01074002002_02107_00001_nrca3 F200W-CLEAR\n",
      "452 jw01074002002_02107_00001_nrca4 F200W-CLEAR\n",
      "453 jw01074002002_02107_00001_nrcb3 F200W-CLEAR\n",
      "454 jw01074002002_02107_00001_nrcb4 F200W-CLEAR\n",
      "455 jw01074002002_02107_00002_nrca3 F200W-CLEAR\n",
      "456 jw01074002002_02107_00002_nrca4 F200W-CLEAR\n",
      "457 jw01074002002_02107_00002_nrcb3 F200W-CLEAR\n",
      "458 jw01074002002_02107_00002_nrcb4 F200W-CLEAR\n",
      "459 jw01074002002_02107_00003_nrca3 F200W-CLEAR\n",
      "460 jw01074002002_02107_00003_nrca4 F200W-CLEAR\n",
      "461 jw01074002002_02107_00003_nrcb3 F200W-CLEAR\n",
      "462 jw01074002002_02107_00003_nrcb4 F200W-CLEAR\n",
      "463 jw01074002003_02101_00001_nrcalong F277W-CLEAR\n",
      "464 jw01074002003_02101_00001_nrcblong F277W-CLEAR\n",
      "465 jw01074002003_02101_00002_nrcalong F277W-CLEAR\n",
      "466 jw01074002003_02101_00002_nrcblong F277W-CLEAR\n",
      "467 jw01074002003_02101_00003_nrcalong F277W-CLEAR\n",
      "468 jw01074002003_02101_00003_nrcblong F277W-CLEAR\n",
      "469 jw01074002003_02103_00001_nrca3 F115W-CLEAR\n",
      "470 jw01074002003_02103_00001_nrca4 F115W-CLEAR\n",
      "471 jw01074002003_02103_00001_nrcalong F356W-CLEAR\n",
      "472 jw01074002003_02103_00001_nrcb1 F115W-CLEAR\n",
      "473 jw01074002003_02103_00001_nrcb2 F115W-CLEAR\n",
      "474 jw01074002003_02103_00001_nrcb4 F115W-CLEAR\n",
      "475 jw01074002003_02103_00001_nrcblong F356W-CLEAR\n",
      "476 jw01074002003_02103_00002_nrca3 F115W-CLEAR\n",
      "477 jw01074002003_02103_00002_nrca4 F115W-CLEAR\n",
      "478 jw01074002003_02103_00002_nrcalong F356W-CLEAR\n",
      "479 jw01074002003_02103_00002_nrcb1 F115W-CLEAR\n",
      "480 jw01074002003_02103_00002_nrcb2 F115W-CLEAR\n",
      "481 jw01074002003_02103_00002_nrcb4 F115W-CLEAR\n",
      "482 jw01074002003_02103_00002_nrcblong F356W-CLEAR\n",
      "483 jw01074002003_02103_00003_nrca3 F115W-CLEAR\n",
      "484 jw01074002003_02103_00003_nrca4 F115W-CLEAR\n",
      "485 jw01074002003_02103_00003_nrcalong F356W-CLEAR\n",
      "486 jw01074002003_02103_00003_nrcb1 F115W-CLEAR\n",
      "487 jw01074002003_02103_00003_nrcb2 F115W-CLEAR\n",
      "488 jw01074002003_02103_00003_nrcb4 F115W-CLEAR\n",
      "489 jw01074002003_02103_00003_nrcblong F356W-CLEAR\n",
      "490 jw01074002003_02105_00001_nrcalong F444W-CLEAR\n",
      "491 jw01074002003_02105_00001_nrcb1 F150W-CLEAR\n",
      "492 jw01074002003_02105_00001_nrcb2 F150W-CLEAR\n",
      "493 jw01074002003_02105_00001_nrcb4 F150W-CLEAR\n",
      "494 jw01074002003_02105_00001_nrcblong F444W-CLEAR\n",
      "495 jw01074002003_02105_00002_nrcalong F444W-CLEAR\n",
      "496 jw01074002003_02105_00002_nrcb1 F150W-CLEAR\n",
      "497 jw01074002003_02105_00002_nrcb2 F150W-CLEAR\n",
      "498 jw01074002003_02105_00002_nrcb4 F150W-CLEAR\n",
      "499 jw01074002003_02105_00002_nrcblong F444W-CLEAR\n",
      "500 jw01074002003_02105_00003_nrcalong F444W-CLEAR\n",
      "501 jw01074002003_02105_00003_nrcb1 F150W-CLEAR\n",
      "502 jw01074002003_02105_00003_nrcb2 F150W-CLEAR\n",
      "503 jw01074002003_02105_00003_nrcb4 F150W-CLEAR\n",
      "504 jw01074002003_02105_00003_nrcblong F444W-CLEAR\n",
      "505 jw01074002003_02107_00001_nrcb1 F200W-CLEAR\n",
      "506 jw01074002003_02107_00001_nrcb2 F200W-CLEAR\n",
      "507 jw01074002003_02107_00001_nrcb4 F200W-CLEAR\n",
      "508 jw01074002003_02107_00002_nrcb1 F200W-CLEAR\n",
      "509 jw01074002003_02107_00002_nrcb2 F200W-CLEAR\n",
      "510 jw01074002003_02107_00002_nrcb4 F200W-CLEAR\n",
      "511 jw01074002004_02101_00001_nrcalong F277W-CLEAR\n",
      "512 jw01074002004_02101_00001_nrcblong F277W-CLEAR\n",
      "513 jw01074002004_02101_00002_nrcalong F277W-CLEAR\n",
      "514 jw01074002004_02101_00002_nrcblong F277W-CLEAR\n",
      "515 jw01074002004_02101_00003_nrcalong F277W-CLEAR\n",
      "516 jw01074002004_02101_00003_nrcblong F277W-CLEAR\n",
      "517 jw01074002004_02103_00001_nrca3 F115W-CLEAR\n",
      "518 jw01074002004_02103_00001_nrca4 F115W-CLEAR\n",
      "519 jw01074002004_02103_00001_nrcalong F356W-CLEAR\n",
      "520 jw01074002004_02103_00001_nrcb1 F115W-CLEAR\n",
      "521 jw01074002004_02103_00001_nrcb2 F115W-CLEAR\n",
      "522 jw01074002004_02103_00001_nrcb3 F115W-CLEAR\n",
      "523 jw01074002004_02103_00001_nrcblong F356W-CLEAR\n",
      "524 jw01074002004_02103_00002_nrca3 F115W-CLEAR\n",
      "525 jw01074002004_02103_00002_nrca4 F115W-CLEAR\n",
      "526 jw01074002004_02103_00002_nrcalong F356W-CLEAR\n",
      "527 jw01074002004_02103_00002_nrcb1 F115W-CLEAR\n",
      "528 jw01074002004_02103_00002_nrcb2 F115W-CLEAR\n",
      "529 jw01074002004_02103_00002_nrcb3 F115W-CLEAR\n",
      "530 jw01074002004_02103_00002_nrcblong F356W-CLEAR\n",
      "531 jw01074002004_02103_00003_nrca3 F115W-CLEAR\n",
      "532 jw01074002004_02103_00003_nrca4 F115W-CLEAR\n",
      "533 jw01074002004_02103_00003_nrcalong F356W-CLEAR\n",
      "534 jw01074002004_02103_00003_nrcb1 F115W-CLEAR\n",
      "535 jw01074002004_02103_00003_nrcb2 F115W-CLEAR\n",
      "536 jw01074002004_02103_00003_nrcb3 F115W-CLEAR\n",
      "537 jw01074002004_02103_00003_nrcblong F356W-CLEAR\n",
      "538 jw01074002004_02105_00001_nrca4 F150W-CLEAR\n",
      "539 jw01074002004_02105_00001_nrcalong F444W-CLEAR\n",
      "540 jw01074002004_02105_00001_nrcb2 F150W-CLEAR\n",
      "541 jw01074002004_02105_00001_nrcb3 F150W-CLEAR\n",
      "542 jw01074002004_02105_00001_nrcblong F444W-CLEAR\n",
      "543 jw01074002004_02105_00002_nrcalong F444W-CLEAR\n",
      "544 jw01074002004_02105_00002_nrcb1 F150W-CLEAR\n",
      "545 jw01074002004_02105_00002_nrcb2 F150W-CLEAR\n",
      "546 jw01074002004_02105_00002_nrcb3 F150W-CLEAR\n",
      "547 jw01074002004_02105_00002_nrcblong F444W-CLEAR\n",
      "548 jw01074002004_02105_00003_nrcalong F444W-CLEAR\n",
      "549 jw01074002004_02105_00003_nrcb1 F150W-CLEAR\n",
      "550 jw01074002004_02105_00003_nrcb2 F150W-CLEAR\n",
      "551 jw01074002004_02105_00003_nrcb3 F150W-CLEAR\n",
      "552 jw01074002004_02105_00003_nrcblong F444W-CLEAR\n",
      "553 jw01074002004_02107_00001_nrca4 F200W-CLEAR\n",
      "554 jw01074002004_02107_00001_nrcb2 F200W-CLEAR\n",
      "555 jw01074002004_02107_00001_nrcb3 F200W-CLEAR\n",
      "556 jw01074002004_02107_00002_nrcb1 F200W-CLEAR\n",
      "557 jw01074002004_02107_00002_nrcb2 F200W-CLEAR\n",
      "558 jw01074002004_02107_00002_nrcb3 F200W-CLEAR\n",
      "559 jw01074002004_02107_00003_nrcb1 F200W-CLEAR\n",
      "560 jw01074002004_02107_00003_nrcb2 F200W-CLEAR\n",
      "561 jw01074002004_02107_00003_nrcb3 F200W-CLEAR\n",
      "562 jw01074003001_04101_00001_nrcalong F277W-CLEAR\n",
      "563 jw01074003001_04101_00002_nrcalong F277W-CLEAR\n",
      "564 jw01074003001_04101_00003_nrcalong F277W-CLEAR\n",
      "565 jw01074003001_06101_00001_nrca1 F150W-CLEAR\n",
      "566 jw01074003001_06101_00001_nrca2 F150W-CLEAR\n",
      "567 jw01074003001_06101_00002_nrca1 F150W-CLEAR\n",
      "568 jw01074003001_06101_00002_nrca2 F150W-CLEAR\n",
      "569 jw01074003001_06101_00003_nrca1 F150W-CLEAR\n",
      "570 jw01074003001_06101_00003_nrca2 F150W-CLEAR\n",
      "571 jw01074003001_08101_00001_nrca1 F200W-CLEAR\n",
      "572 jw01074003001_08101_00001_nrca2 F200W-CLEAR\n",
      "573 jw01074003001_08101_00001_nrcalong F444W-CLEAR\n",
      "574 jw01074003001_08101_00002_nrca1 F200W-CLEAR\n",
      "575 jw01074003001_08101_00002_nrca2 F200W-CLEAR\n",
      "576 jw01074003001_08101_00002_nrcalong F444W-CLEAR\n",
      "577 jw01074003001_08101_00003_nrca1 F200W-CLEAR\n",
      "578 jw01074003001_08101_00003_nrca2 F200W-CLEAR\n",
      "579 jw01074003001_08101_00003_nrcalong F444W-CLEAR\n",
      "580 jw01074003002_04101_00001_nrcalong F277W-CLEAR\n",
      "581 jw01074003002_04101_00001_nrcblong F277W-CLEAR\n",
      "582 jw01074003002_04101_00002_nrcalong F277W-CLEAR\n",
      "583 jw01074003002_04101_00002_nrcblong F277W-CLEAR\n",
      "584 jw01074003002_04101_00003_nrcalong F277W-CLEAR\n",
      "585 jw01074003002_04101_00003_nrcblong F277W-CLEAR\n",
      "586 jw01074003002_06101_00001_nrca3 F150W-CLEAR\n",
      "587 jw01074003002_06101_00001_nrca4 F150W-CLEAR\n",
      "588 jw01074003002_06101_00001_nrcb3 F150W-CLEAR\n",
      "589 jw01074003002_06101_00001_nrcb4 F150W-CLEAR\n",
      "590 jw01074003002_06101_00002_nrca3 F150W-CLEAR\n",
      "591 jw01074003002_06101_00002_nrca4 F150W-CLEAR\n",
      "592 jw01074003002_06101_00002_nrcb3 F150W-CLEAR\n",
      "593 jw01074003002_06101_00002_nrcb4 F150W-CLEAR\n",
      "594 jw01074003002_06101_00003_nrca3 F150W-CLEAR\n",
      "595 jw01074003002_06101_00003_nrca4 F150W-CLEAR\n",
      "596 jw01074003002_06101_00003_nrcb3 F150W-CLEAR\n",
      "597 jw01074003002_06101_00003_nrcb4 F150W-CLEAR\n",
      "598 jw01074003002_08101_00001_nrca3 F200W-CLEAR\n",
      "599 jw01074003002_08101_00001_nrca4 F200W-CLEAR\n",
      "600 jw01074003002_08101_00001_nrcalong F444W-CLEAR\n",
      "601 jw01074003002_08101_00001_nrcb3 F200W-CLEAR\n",
      "602 jw01074003002_08101_00001_nrcb4 F200W-CLEAR\n",
      "603 jw01074003002_08101_00001_nrcblong F444W-CLEAR\n",
      "604 jw01074003002_08101_00002_nrca3 F200W-CLEAR\n",
      "605 jw01074003002_08101_00002_nrca4 F200W-CLEAR\n",
      "606 jw01074003002_08101_00002_nrcalong F444W-CLEAR\n",
      "607 jw01074003002_08101_00002_nrcb3 F200W-CLEAR\n",
      "608 jw01074003002_08101_00002_nrcb4 F200W-CLEAR\n",
      "609 jw01074003002_08101_00002_nrcblong F444W-CLEAR\n",
      "610 jw01074003002_08101_00003_nrca3 F200W-CLEAR\n",
      "611 jw01074003002_08101_00003_nrca4 F200W-CLEAR\n",
      "612 jw01074003002_08101_00003_nrcalong F444W-CLEAR\n",
      "613 jw01074003002_08101_00003_nrcb3 F200W-CLEAR\n",
      "614 jw01074003002_08101_00003_nrcb4 F200W-CLEAR\n",
      "615 jw01074003002_08101_00003_nrcblong F444W-CLEAR\n",
      "616 jw01086001001_14101_00001_nis F090W-CLEAR\n",
      "617 jw01086001001_14101_00002_nis F090W-CLEAR\n",
      "618 jw01086001001_16101_00001_nis F115W-CLEAR\n",
      "619 jw01086001001_16101_00002_nis F115W-CLEAR\n",
      "620 jw01086001001_22101_00001_nis F150W-CLEAR\n",
      "621 jw01086001001_22101_00002_nis F150W-CLEAR\n",
      "622 jw01086001001_24101_00001_nis F200W-CLEAR\n",
      "623 jw01086001001_24101_00002_nis F200W-CLEAR\n",
      "624 jw01086001001_38101_00001_nis F090W-CLEAR\n",
      "625 jw01086001001_38101_00002_nis F090W-CLEAR\n",
      "626 jw01086001001_40101_00001_nis F115W-CLEAR\n",
      "627 jw01086001001_40101_00002_nis F115W-CLEAR\n",
      "628 jw01086001001_46101_00001_nis F150W-CLEAR\n",
      "629 jw01086001001_46101_00002_nis F150W-CLEAR\n",
      "630 jw01086001001_48101_00001_nis F200W-CLEAR\n",
      "631 jw01086001001_48101_00002_nis F200W-CLEAR\n",
      "632 jw01086001001_62101_00001_nis F090W-CLEAR\n",
      "633 jw01086001001_62101_00002_nis F090W-CLEAR\n",
      "634 jw01086001001_64101_00001_nis F115W-CLEAR\n",
      "635 jw01086001001_64101_00002_nis F115W-CLEAR\n",
      "636 jw01086001001_70101_00001_nis F150W-CLEAR\n",
      "637 jw01086001001_70101_00002_nis F150W-CLEAR\n",
      "638 jw01086001001_72101_00001_nis F200W-CLEAR\n",
      "639 jw01086001001_72101_00002_nis F200W-CLEAR\n",
      "640 jw01086001001_86101_00001_nis F090W-CLEAR\n",
      "641 jw01086001001_86101_00002_nis F090W-CLEAR\n",
      "642 jw01086001001_88101_00001_nis F115W-CLEAR\n",
      "643 jw01086001001_88101_00002_nis F115W-CLEAR\n",
      "644 jw01086001001_94101_00001_nis F150W-CLEAR\n",
      "645 jw01086001001_94101_00002_nis F150W-CLEAR\n",
      "646 jw01086001001_96101_00001_nis F200W-CLEAR\n",
      "647 jw01086001001_96101_00002_nis F200W-CLEAR\n",
      "648 jw01086001002_14101_00001_nis F090W-CLEAR\n",
      "649 jw01086001002_14101_00002_nis F090W-CLEAR\n",
      "650 jw01086001002_16101_00001_nis F115W-CLEAR\n",
      "651 jw01086001002_16101_00002_nis F115W-CLEAR\n",
      "652 jw01086001002_22101_00001_nis F150W-CLEAR\n",
      "653 jw01086001002_22101_00002_nis F150W-CLEAR\n",
      "654 jw01086001002_24101_00001_nis F200W-CLEAR\n",
      "655 jw01086001002_24101_00002_nis F200W-CLEAR\n",
      "656 jw01086001002_38101_00001_nis F090W-CLEAR\n",
      "657 jw01086001002_38101_00002_nis F090W-CLEAR\n",
      "658 jw01086001002_40101_00001_nis F115W-CLEAR\n",
      "659 jw01086001002_40101_00002_nis F115W-CLEAR\n",
      "660 jw01086001002_46101_00001_nis F150W-CLEAR\n",
      "661 jw01086001002_46101_00002_nis F150W-CLEAR\n",
      "662 jw01086001002_48101_00001_nis F200W-CLEAR\n",
      "663 jw01086001002_48101_00002_nis F200W-CLEAR\n",
      "664 jw01086001003_14101_00001_nis F090W-CLEAR\n",
      "665 jw01086001003_14101_00002_nis F090W-CLEAR\n",
      "666 jw01086001003_16101_00001_nis F115W-CLEAR\n",
      "667 jw01086001003_16101_00002_nis F115W-CLEAR\n",
      "668 jw01086001003_22101_00001_nis F150W-CLEAR\n",
      "669 jw01086001003_22101_00002_nis F150W-CLEAR\n",
      "670 jw01086001003_24101_00001_nis F200W-CLEAR\n",
      "671 jw01086001003_24101_00002_nis F200W-CLEAR\n",
      "672 jw01086001003_38101_00001_nis F090W-CLEAR\n",
      "673 jw01086001003_38101_00002_nis F090W-CLEAR\n",
      "674 jw01086001003_40101_00001_nis F115W-CLEAR\n",
      "675 jw01086001003_40101_00002_nis F115W-CLEAR\n",
      "676 jw01086001003_46101_00001_nis F150W-CLEAR\n",
      "677 jw01086001003_46101_00002_nis F150W-CLEAR\n",
      "678 jw01086001003_48101_00001_nis F200W-CLEAR\n",
      "679 jw01086001003_48101_00002_nis F200W-CLEAR\n",
      "680 jw01086001004_14101_00001_nis F090W-CLEAR\n",
      "681 jw01086001004_14101_00002_nis F090W-CLEAR\n",
      "682 jw01086001004_16101_00001_nis F115W-CLEAR\n",
      "683 jw01086001004_16101_00002_nis F115W-CLEAR\n",
      "684 jw01086001004_22101_00001_nis F150W-CLEAR\n",
      "685 jw01086001004_22101_00002_nis F150W-CLEAR\n",
      "686 jw01086001004_24101_00001_nis F200W-CLEAR\n",
      "687 jw01086001004_24101_00002_nis F200W-CLEAR\n",
      "688 jw01086002001_04101_00001_nis F200W-CLEAR\n",
      "689 jw01473011001_02101_00001_nrca2 F150W-CLEAR\n",
      "690 jw01473011001_02101_00001_nrca3 F150W-CLEAR\n",
      "691 jw01473011001_02101_00001_nrca4 F150W-CLEAR\n",
      "692 jw01473011001_02101_00001_nrcb2 F150W-CLEAR\n",
      "693 jw01473011001_02101_00001_nrcb3 F150W-CLEAR\n",
      "694 jw01473011001_02101_00001_nrcb4 F150W-CLEAR\n",
      "695 jw01473011001_02101_00002_nrca2 F150W-CLEAR\n",
      "696 jw01473011001_02101_00002_nrca3 F150W-CLEAR\n",
      "697 jw01473011001_02101_00002_nrca4 F150W-CLEAR\n",
      "698 jw01473011001_02101_00002_nrcb2 F150W-CLEAR\n",
      "699 jw01473011001_02101_00002_nrcb3 F150W-CLEAR\n",
      "700 jw01473011001_02101_00002_nrcb4 F150W-CLEAR\n",
      "701 jw01473011001_02101_00003_nrca2 F150W-CLEAR\n",
      "702 jw01473011001_02101_00003_nrca3 F150W-CLEAR\n",
      "703 jw01473011001_02101_00003_nrca4 F150W-CLEAR\n",
      "704 jw01473011001_02101_00003_nrcb2 F150W-CLEAR\n",
      "705 jw01473011001_02101_00003_nrcb3 F150W-CLEAR\n",
      "706 jw01473011001_02101_00003_nrcb4 F150W-CLEAR\n",
      "707 jw01473011001_02101_00004_nrca3 F150W-CLEAR\n",
      "708 jw01473011001_02101_00004_nrca4 F150W-CLEAR\n",
      "709 jw01473011001_02101_00004_nrcb2 F150W-CLEAR\n",
      "710 jw01473011001_02101_00004_nrcb3 F150W-CLEAR\n",
      "711 jw01473011001_02101_00004_nrcb4 F150W-CLEAR\n",
      "712 jw01473012001_02101_00001_nrca2 F150W-CLEAR\n",
      "713 jw01473012001_02101_00001_nrca3 F150W-CLEAR\n",
      "714 jw01473012001_02101_00001_nrca4 F150W-CLEAR\n",
      "715 jw01473012001_02101_00001_nrcb2 F150W-CLEAR\n",
      "716 jw01473012001_02101_00001_nrcb3 F150W-CLEAR\n",
      "717 jw01473012001_02101_00001_nrcb4 F150W-CLEAR\n",
      "718 jw01473012001_02101_00002_nrca2 F150W-CLEAR\n",
      "719 jw01473012001_02101_00002_nrca3 F150W-CLEAR\n",
      "720 jw01473012001_02101_00002_nrca4 F150W-CLEAR\n",
      "721 jw01473012001_02101_00002_nrcb2 F150W-CLEAR\n",
      "722 jw01473012001_02101_00002_nrcb3 F150W-CLEAR\n",
      "723 jw01473012001_02101_00002_nrcb4 F150W-CLEAR\n",
      "724 jw01473012001_02101_00003_nrca2 F150W-CLEAR\n",
      "725 jw01473012001_02101_00003_nrca3 F150W-CLEAR\n",
      "726 jw01473012001_02101_00003_nrca4 F150W-CLEAR\n",
      "727 jw01473012001_02101_00003_nrcb2 F150W-CLEAR\n",
      "728 jw01473012001_02101_00003_nrcb3 F150W-CLEAR\n",
      "729 jw01473012001_02101_00003_nrcb4 F150W-CLEAR\n",
      "730 jw01473012001_02101_00004_nrca3 F150W-CLEAR\n",
      "731 jw01473012001_02101_00004_nrca4 F150W-CLEAR\n",
      "732 jw01473012001_02101_00004_nrcb2 F150W-CLEAR\n",
      "733 jw01473012001_02101_00004_nrcb3 F150W-CLEAR\n",
      "734 jw01473012001_02101_00004_nrcb4 F150W-CLEAR\n",
      "735 jw01473013001_04101_00001_nrca2 F200W-CLEAR\n",
      "736 jw01473013001_04101_00001_nrca3 F200W-CLEAR\n",
      "737 jw01473013001_04101_00001_nrca4 F200W-CLEAR\n",
      "738 jw01473013001_04101_00001_nrcb2 F200W-CLEAR\n",
      "739 jw01473013001_04101_00001_nrcb3 F200W-CLEAR\n",
      "740 jw01473013001_04101_00001_nrcb4 F200W-CLEAR\n",
      "741 jw01473013001_04101_00002_nrca2 F200W-CLEAR\n",
      "742 jw01473013001_04101_00002_nrca3 F200W-CLEAR\n",
      "743 jw01473013001_04101_00002_nrca4 F200W-CLEAR\n",
      "744 jw01473013001_04101_00002_nrcb2 F200W-CLEAR\n",
      "745 jw01473013001_04101_00002_nrcb3 F200W-CLEAR\n",
      "746 jw01473013001_04101_00002_nrcb4 F200W-CLEAR\n",
      "747 jw01473013001_04101_00003_nrca2 F200W-CLEAR\n",
      "748 jw01473013001_04101_00003_nrca3 F200W-CLEAR\n",
      "749 jw01473013001_04101_00003_nrca4 F200W-CLEAR\n",
      "750 jw01473013001_04101_00003_nrcb2 F200W-CLEAR\n",
      "751 jw01473013001_04101_00003_nrcb3 F200W-CLEAR\n",
      "752 jw01473013001_04101_00003_nrcb4 F200W-CLEAR\n",
      "753 jw01473013001_04101_00004_nrca2 F200W-CLEAR\n",
      "754 jw01473013001_04101_00004_nrca3 F200W-CLEAR\n",
      "755 jw01473013001_04101_00004_nrca4 F200W-CLEAR\n",
      "756 jw01473013001_04101_00004_nrcb2 F200W-CLEAR\n",
      "757 jw01473013001_04101_00004_nrcb3 F200W-CLEAR\n",
      "758 jw01473013001_04101_00004_nrcb4 F200W-CLEAR\n",
      "759 jw01473013001_04101_00005_nrca2 F200W-CLEAR\n",
      "760 jw01473013001_04101_00005_nrca3 F200W-CLEAR\n",
      "761 jw01473013001_04101_00005_nrca4 F200W-CLEAR\n",
      "762 jw01473013001_04101_00005_nrcb2 F200W-CLEAR\n",
      "763 jw01473013001_04101_00005_nrcb3 F200W-CLEAR\n",
      "764 jw01473013001_04101_00005_nrcb4 F200W-CLEAR\n",
      "765 jw01473013001_04101_00006_nrca2 F200W-CLEAR\n",
      "766 jw01473013001_04101_00006_nrca3 F200W-CLEAR\n",
      "767 jw01473013001_04101_00006_nrca4 F200W-CLEAR\n",
      "768 jw01473013001_04101_00006_nrcb2 F200W-CLEAR\n",
      "769 jw01473013001_04101_00006_nrcb3 F200W-CLEAR\n",
      "770 jw01473013001_04101_00006_nrcb4 F200W-CLEAR\n",
      "771 jw01473014001_04101_00001_nrca1 F115W-CLEAR\n",
      "772 jw01473014001_04101_00001_nrca2 F115W-CLEAR\n",
      "773 jw01473014001_04101_00001_nrca3 F115W-CLEAR\n",
      "774 jw01473014001_04101_00001_nrca4 F115W-CLEAR\n",
      "775 jw01473014001_04101_00001_nrcb1 F115W-CLEAR\n",
      "776 jw01473014001_04101_00001_nrcb2 F115W-CLEAR\n",
      "777 jw01473014001_04101_00001_nrcb3 F115W-CLEAR\n",
      "778 jw01473014001_04101_00001_nrcb4 F115W-CLEAR\n",
      "779 jw01473014001_04101_00002_nrca1 F115W-CLEAR\n",
      "780 jw01473014001_04101_00002_nrca2 F115W-CLEAR\n",
      "781 jw01473014001_04101_00002_nrca3 F115W-CLEAR\n",
      "782 jw01473014001_04101_00002_nrca4 F115W-CLEAR\n",
      "783 jw01473014001_04101_00002_nrcb1 F115W-CLEAR\n",
      "784 jw01473014001_04101_00002_nrcb2 F115W-CLEAR\n",
      "785 jw01473014001_04101_00002_nrcb3 F115W-CLEAR\n",
      "786 jw01473014001_04101_00002_nrcb4 F115W-CLEAR\n",
      "787 jw01473014001_04101_00003_nrca1 F115W-CLEAR\n",
      "788 jw01473014001_04101_00003_nrca2 F115W-CLEAR\n",
      "789 jw01473014001_04101_00003_nrca3 F115W-CLEAR\n",
      "790 jw01473014001_04101_00003_nrca4 F115W-CLEAR\n",
      "791 jw01473014001_04101_00003_nrcb1 F115W-CLEAR\n",
      "792 jw01473014001_04101_00003_nrcb2 F115W-CLEAR\n",
      "793 jw01473014001_04101_00003_nrcb3 F115W-CLEAR\n",
      "794 jw01473014001_04101_00003_nrcb4 F115W-CLEAR\n",
      "795 jw01473014001_04101_00004_nrca1 F115W-CLEAR\n",
      "796 jw01473014001_04101_00004_nrca2 F115W-CLEAR\n",
      "797 jw01473014001_04101_00004_nrca3 F115W-CLEAR\n",
      "798 jw01473014001_04101_00004_nrca4 F115W-CLEAR\n",
      "799 jw01473014001_04101_00004_nrcb1 F115W-CLEAR\n",
      "800 jw01473014001_04101_00004_nrcb2 F115W-CLEAR\n",
      "801 jw01473014001_04101_00004_nrcb3 F115W-CLEAR\n",
      "802 jw01473014001_04101_00004_nrcb4 F115W-CLEAR\n",
      "803 jw01473014001_06101_00001_nrca1 F090W-CLEAR\n",
      "804 jw01473014001_06101_00001_nrca2 F090W-CLEAR\n",
      "805 jw01473014001_06101_00001_nrca3 F090W-CLEAR\n",
      "806 jw01473014001_06101_00001_nrca4 F090W-CLEAR\n",
      "807 jw01473014001_06101_00001_nrcalong F356W-CLEAR\n",
      "808 jw01473014001_06101_00001_nrcb1 F090W-CLEAR\n",
      "809 jw01473014001_06101_00001_nrcb2 F090W-CLEAR\n",
      "810 jw01473014001_06101_00001_nrcb3 F090W-CLEAR\n",
      "811 jw01473014001_06101_00001_nrcb4 F090W-CLEAR\n",
      "812 jw01473014001_06101_00001_nrcblong F356W-CLEAR\n",
      "813 jw01473014001_06101_00002_nrca1 F090W-CLEAR\n",
      "814 jw01473014001_06101_00002_nrca2 F090W-CLEAR\n",
      "815 jw01473014001_06101_00002_nrca3 F090W-CLEAR\n",
      "816 jw01473014001_06101_00002_nrca4 F090W-CLEAR\n",
      "817 jw01473014001_06101_00002_nrcalong F356W-CLEAR\n",
      "818 jw01473014001_06101_00002_nrcb1 F090W-CLEAR\n",
      "819 jw01473014001_06101_00002_nrcb2 F090W-CLEAR\n",
      "820 jw01473014001_06101_00002_nrcb3 F090W-CLEAR\n",
      "821 jw01473014001_06101_00002_nrcb4 F090W-CLEAR\n",
      "822 jw01473014001_06101_00002_nrcblong F356W-CLEAR\n",
      "823 jw01473014001_06101_00003_nrca1 F090W-CLEAR\n",
      "824 jw01473014001_06101_00003_nrca2 F090W-CLEAR\n",
      "825 jw01473014001_06101_00003_nrca3 F090W-CLEAR\n",
      "826 jw01473014001_06101_00003_nrca4 F090W-CLEAR\n",
      "827 jw01473014001_06101_00003_nrcalong F356W-CLEAR\n",
      "828 jw01473014001_06101_00003_nrcb1 F090W-CLEAR\n",
      "829 jw01473014001_06101_00003_nrcb2 F090W-CLEAR\n",
      "830 jw01473014001_06101_00003_nrcb3 F090W-CLEAR\n",
      "831 jw01473014001_06101_00003_nrcb4 F090W-CLEAR\n",
      "832 jw01473014001_06101_00003_nrcblong F356W-CLEAR\n",
      "833 jw01473014001_06101_00004_nrca1 F090W-CLEAR\n",
      "834 jw01473014001_06101_00004_nrca2 F090W-CLEAR\n",
      "835 jw01473014001_06101_00004_nrca3 F090W-CLEAR\n",
      "836 jw01473014001_06101_00004_nrca4 F090W-CLEAR\n",
      "837 jw01473014001_06101_00004_nrcalong F356W-CLEAR\n",
      "838 jw01473014001_06101_00004_nrcb1 F090W-CLEAR\n",
      "839 jw01473014001_06101_00004_nrcb2 F090W-CLEAR\n",
      "840 jw01473014001_06101_00004_nrcb3 F090W-CLEAR\n",
      "841 jw01473014001_06101_00004_nrcb4 F090W-CLEAR\n",
      "842 jw01473014001_06101_00004_nrcblong F356W-CLEAR\n",
      "843 jw01473015001_04201_00001_nrca1 F200W-CLEAR\n",
      "844 jw01473015001_04201_00001_nrca2 F200W-CLEAR\n",
      "845 jw01473015001_04201_00001_nrca3 F200W-CLEAR\n",
      "846 jw01473015001_04201_00001_nrcb3 F200W-CLEAR\n",
      "847 jw01473015001_04201_00001_nrcb4 F200W-CLEAR\n",
      "848 jw01473015001_04201_00002_nrca1 F200W-CLEAR\n",
      "849 jw01473015001_04201_00002_nrca2 F200W-CLEAR\n",
      "850 jw01473015001_04201_00002_nrca3 F200W-CLEAR\n",
      "851 jw01473015001_04201_00002_nrcb3 F200W-CLEAR\n",
      "852 jw01473015001_04201_00002_nrcb4 F200W-CLEAR\n",
      "853 jw01473015001_04201_00003_nrca1 F200W-CLEAR\n",
      "854 jw01473015001_04201_00003_nrca2 F200W-CLEAR\n",
      "855 jw01473015001_04201_00003_nrca3 F200W-CLEAR\n",
      "856 jw01473015001_04201_00003_nrcb3 F200W-CLEAR\n",
      "857 jw01473015001_04201_00003_nrcb4 F200W-CLEAR\n",
      "858 jw01473015001_04201_00004_nrca1 F200W-CLEAR\n",
      "859 jw01473015001_04201_00004_nrca2 F200W-CLEAR\n",
      "860 jw01473015001_04201_00004_nrca3 F200W-CLEAR\n",
      "861 jw01473015001_04201_00004_nrcb3 F200W-CLEAR\n",
      "862 jw01473015001_04201_00004_nrcb4 F200W-CLEAR\n",
      "863 jw01477001001_02103_00001_nrca1 F150W-CLEAR\n",
      "864 jw01477001001_02103_00001_nrca2 F150W-CLEAR\n",
      "865 jw01477001001_02103_00001_nrca3 F150W-CLEAR\n",
      "866 jw01477001001_02103_00001_nrca4 F150W-CLEAR\n",
      "867 jw01477001001_02103_00001_nrcalong F277W-CLEAR\n",
      "868 jw01477001001_02103_00002_nrca1 F150W-CLEAR\n",
      "869 jw01477001001_02103_00002_nrca2 F150W-CLEAR\n",
      "870 jw01477001001_02103_00002_nrca3 F150W-CLEAR\n",
      "871 jw01477001001_02103_00002_nrca4 F150W-CLEAR\n",
      "872 jw01477002001_02103_00001_nrcblong F277W-CLEAR\n",
      "873 jw01477002001_02103_00002_nrcblong F277W-CLEAR\n",
      "874 jw01515001001_03101_00001_nis F200W-CLEAR\n",
      "875 jw01515001001_03101_00002_nis F200W-CLEAR\n",
      "876 jw01515001001_03101_00003_nis F200W-CLEAR\n",
      "877 jw01515001001_03101_00004_nis F200W-CLEAR\n"
     ]
    }
   ],
   "source": [
    "import astropy.io.fits as pyfits\n",
    "import astropy.wcs as pywcs\n",
    "import sep\n",
    "from grizli import utils\n",
    "\n",
    "_data = np.zeros((len(res), len(cat)), dtype=np.float32)*np.nan\n",
    "_x = np.zeros((len(res), len(cat)), dtype=np.float32)*np.nan\n",
    "_y = np.zeros((len(res), len(cat)), dtype=np.float32)*np.nan\n",
    "\n",
    "coo = np.array([cat['ra'], cat['dec']]).T\n",
    "\n",
    "for i in range(len(res)):\n",
    "    print(i, res['dataset'][i], res['filter'][i])\n",
    "    im = pyfits.open(f\"{res['dataset'][i]}_rate.fits\")\n",
    "    wcs = pywcs.WCS(im['SCI'].header, relax=True)\n",
    "    wcs.pscale = utils.get_wcs_pscale(wcs)\n",
    "\n",
    "    tofnu = im['SCI'].header['PHOTFNU']\n",
    "    \n",
    "    _sci = (im['SCI'].data - im['SCI'].header['MDRIZSKY']*0.8).astype(np.float32)\n",
    "    \n",
    "    if res['filter'][i] < 'F260':\n",
    "        ap_asec = 0.25\n",
    "    else:\n",
    "        ap_asec = 0.4\n",
    "    \n",
    "    sr = utils.SRegion(wcs.calc_footprint())\n",
    "    in_wcs = sr.path[0].contains_points(coo)\n",
    "    \n",
    "    xi, yi = wcs.all_world2pix(cat['ra'][in_wcs], cat['dec'][in_wcs], 0)\n",
    "    dq = utils.unset_dq_bits(im['DQ'].data, 4)\n",
    "    _sci[dq > 0] = np.nan\n",
    "    \n",
    "    _ap = sep.sum_circle(_sci, xi, yi, ap_asec/wcs.pscale)\n",
    "    row = _data[i,:]*1\n",
    "    row[in_wcs] = _ap[0]*tofnu\n",
    "    _data[i,:] = row*1\n",
    "    row *= 0.\n",
    "    row[in_wcs] = xi\n",
    "    _x[i,:] = row\n",
    "    \n",
    "    row *= 0.\n",
    "    row[in_wcs] = yi\n",
    "    _y[i,:] = row\n",
    "                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1054,
   "id": "d82fc841-1275-4d9c-b0c7-3b48043f95d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "_data[_data == 0] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1055,
   "id": "d788d977-303b-4dd1-85ff-db72ec1cd4e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   N  value     \n",
      "====  ==========\n",
      "  42  NRCB4-F150W-CLEAR\n",
      "  41  NRCA3-F150W-CLEAR\n",
      "  41  NRCB3-F150W-CLEAR\n",
      "  39  NRCALONG-F277W-CLEAR\n",
      "  39  NRCA4-F150W-CLEAR\n",
      "  38  NRCALONG-F444W-CLEAR\n",
      "  34  NRCB2-F150W-CLEAR\n",
      "  32  NRCA2-F150W-CLEAR\n",
      "  32  NRCB3-F200W-CLEAR\n",
      "  32  NRCA3-F200W-CLEAR\n",
      "  32  NRCALONG-F356W-CLEAR\n",
      "  31  NRCBLONG-F277W-CLEAR\n",
      "  31  NRCB4-F200W-CLEAR\n",
      "  29  NRCBLONG-F444W-CLEAR\n",
      "  27  NRCBLONG-F356W-CLEAR\n",
      "  27  NRCA4-F200W-CLEAR\n",
      "  26  NRCA2-F200W-CLEAR\n",
      "  23  NIS-F200W-CLEAR\n",
      "  22  NRCB1-F150W-CLEAR\n",
      "  20  NRCB2-F200W-CLEAR\n",
      "  18  NIS-F090W-CLEAR\n",
      "  18  NIS-F115W-CLEAR\n",
      "  18  NIS-F150W-CLEAR\n",
      "  18  NRCA1-F150W-CLEAR\n",
      "  18  NRCA1-F200W-CLEAR\n",
      "  16  NRCA3-F115W-CLEAR\n",
      "  14  NRCA4-F115W-CLEAR\n",
      "  12  NRCB3-F115W-CLEAR\n",
      "  12  NRCB1-F200W-CLEAR\n",
      "  12  NRCB2-F115W-CLEAR\n",
      "  12  NRCB1-F115W-CLEAR\n",
      "  10  NRCB4-F115W-CLEAR\n",
      "  10  NRCA1-F115W-CLEAR\n",
      "   8  NRCA2-F115W-CLEAR\n",
      "   6  NRCA3-F090W-CLEAR\n",
      "   6  NRCA2-F090W-CLEAR\n",
      "   6  NRCB3-F090W-CLEAR\n",
      "   6  NRCA1-F090W-CLEAR\n",
      "   6  NRCB1-F090W-CLEAR\n",
      "   6  NRCB2-F090W-CLEAR\n",
      "   4  NRCB4-F090W-CLEAR\n",
      "   4  NRCA4-F090W-CLEAR\n"
     ]
    }
   ],
   "source": [
    "det_filter = np.array(['{detector}-{filter}'.format(**row)\n",
    "                       for row in res])\n",
    "un = utils.Unique(det_filter, verbose=False)\n",
    "un.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1056,
   "id": "265e4523-48d9-498b-a3c6-91b31916015e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NIS-F090W-CLEAR\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-26 13:05:33,207 - stpipe - WARNING - /home/ec2-user/miniconda3/envs/grizli-dev/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1120: RuntimeWarning: All-NaN slice encountered\n",
      "  overwrite_input=overwrite_input)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NIS-F115W-CLEAR\n",
      "NIS-F150W-CLEAR\n",
      "NIS-F200W-CLEAR\n",
      "NRCA1-F090W-CLEAR\n",
      "NRCA1-F115W-CLEAR\n",
      "NRCA1-F150W-CLEAR\n",
      "NRCA1-F200W-CLEAR\n",
      "NRCA2-F090W-CLEAR\n",
      "NRCA2-F115W-CLEAR\n",
      "NRCA2-F150W-CLEAR\n",
      "NRCA2-F200W-CLEAR\n",
      "NRCA3-F090W-CLEAR\n",
      "NRCA3-F115W-CLEAR\n",
      "NRCA3-F150W-CLEAR\n",
      "NRCA3-F200W-CLEAR\n",
      "NRCA4-F090W-CLEAR\n",
      "NRCA4-F115W-CLEAR\n",
      "NRCA4-F150W-CLEAR\n",
      "NRCA4-F200W-CLEAR\n",
      "NRCALONG-F277W-CLEAR\n",
      "NRCALONG-F356W-CLEAR\n",
      "NRCALONG-F444W-CLEAR\n",
      "NRCB1-F090W-CLEAR\n",
      "NRCB1-F115W-CLEAR\n",
      "NRCB1-F150W-CLEAR\n",
      "NRCB1-F200W-CLEAR\n",
      "NRCB2-F090W-CLEAR\n",
      "NRCB2-F115W-CLEAR\n",
      "NRCB2-F150W-CLEAR\n",
      "NRCB2-F200W-CLEAR\n",
      "NRCB3-F090W-CLEAR\n",
      "NRCB3-F115W-CLEAR\n",
      "NRCB3-F150W-CLEAR\n",
      "NRCB3-F200W-CLEAR\n",
      "NRCB4-F090W-CLEAR\n",
      "NRCB4-F115W-CLEAR\n",
      "NRCB4-F150W-CLEAR\n",
      "NRCB4-F200W-CLEAR\n",
      "NRCBLONG-F277W-CLEAR\n",
      "NRCBLONG-F356W-CLEAR\n",
      "NRCBLONG-F444W-CLEAR\n"
     ]
    }
   ],
   "source": [
    "stats = {}\n",
    "for v in un.values:\n",
    "    print(v)\n",
    "    stats[v.replace('-CLEAR','')] = np.nanmedian(_data[un[v],:], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1059,
   "id": "d049dbf0-6437-43dd-ad5c-8cf058ad9b42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3230\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.8, 1.4)"
      ]
     },
     "execution_count": 1059,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "perc = np.nanpercentile(stats['NIS-F150W'], [40, 50, 90])\n",
    "clip = (stats['NIS-F150W'] > perc[0]) & (stats['NIS-F150W'] < perc[2])\n",
    "# clip &= stats['NIS-F150W'] < 1.3*perc[1]\n",
    "\n",
    "print(clip.sum())\n",
    "\n",
    "plt.scatter(stats['NIS-F150W'], stats['NRCB4-F150W'] / stats['NIS-F150W'], alpha=0.5)\n",
    "plt.scatter(stats['NIS-F150W'][clip], (stats['NRCB4-F150W'] / stats['NIS-F150W'])[clip], alpha=0.5)\n",
    "\n",
    "plt.ylim(0.8, 1.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1060,
   "id": "fc5d517b-8e8b-461d-82c7-0e2f3813e063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe196a49690>"
      ]
     },
     "execution_count": 1060,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#perc = np.nanpercentile(stats['NIS-F200W'], [5, 50, 95])\n",
    "# clip = (stats['NIS-F200W'] > perc[0]) & (stats['NIS-F200W'] < perc[2])\n",
    "# clip &= stats['NIS-F200W'] < 1.5*perc[1]\n",
    "\n",
    "#print(clip.sum())\n",
    "      \n",
    "plt.scatter(stats['NIS-F200W'], stats['NRCB4-F200W'] / stats['NIS-F200W'], alpha=0.5)\n",
    "plt.scatter(stats['NIS-F200W'][clip], (stats['NRCB4-F200W'] / stats['NIS-F200W'])[clip], alpha=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1071,
   "id": "60238247-b9c8-4f93-8ef0-2da5af98352f",
   "metadata": {},
   "outputs": [],
   "source": [
    "scl = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1072,
   "id": "ce2d36c6-d042-45e3-b71f-f87a5c8d2b77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LW\n",
    "fig, ax = plt.subplots(1,1,figsize=(8,4), sharex=True)\n",
    "for filt in ['F277W','F356W','F444W']:\n",
    "\n",
    "    _ratio = (stats[f'NRCALONG-{filt}'] / stats[f'NRCBLONG-{filt}'])[clip]\n",
    "    med = np.nanmedian(_ratio)\n",
    "    \n",
    "    scl[f'NRCBLONG-{filt}'] = 1.0\n",
    "    scl[f'NRCALONG-{filt}'] = float(med)\n",
    "    \n",
    "    _h = ax.hist(_ratio, bins=100, range=(0.8,1.2), alpha=0.5, label=f'{filt} A/B - {med*1.:.3f}')\n",
    "\n",
    "ax.vlines(1.0, *ax.get_ylim(), color='k', linestyle='--')\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "fig.savefig(f'nircam_LW_astrometric_field.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1007,
   "id": "d068b296-7be1-4c0b-ba64-7a98f0549c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filt = 'F090W'\n",
    "\n",
    "for filt in ['F090W','F115W','F150W','F200W']:\n",
    "    fig, axes = plt.subplots(2,1,figsize=(8,8), sharex=True)\n",
    "    for k in stats:\n",
    "        if filt not in k:\n",
    "            continue\n",
    "\n",
    "        if 'NRC' in k:\n",
    "            _ratio = (stats[f'{k}'] / stats[f'NIS-{filt}'])[clip]\n",
    "            med = np.nanmedian(_ratio)\n",
    "\n",
    "            ax = axes[0] if 'NRCA' in k else axes[1]\n",
    "\n",
    "            _h = ax.hist(_ratio, \n",
    "                  bins=100, range=(0.8,1.5), alpha=0.5, label=f'{k} - {med*1.:.3f}')\n",
    "\n",
    "    for ax in axes:\n",
    "        ax.grid()\n",
    "        ax.legend()\n",
    "\n",
    "    #axes[0].set_title(filt)\n",
    "\n",
    "# _h = plt.hist((stats['NRCALONG-F277W'] / stats['NRCBLONG-F277W']), \n",
    "#               bins=100, range=(0.5,1.5), alpha=0.5)\n",
    "\n",
    "    fig.savefig(f'nircam_to_niriss-{filt}_astrometric_field.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1066,
   "id": "369f4544-53ed-4484-a438-666f692bef02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['NIS-F090W', 'NIS-F115W', 'NIS-F150W', 'NIS-F200W', 'NRCA1-F090W', 'NRCA1-F115W', 'NRCA1-F150W', 'NRCA1-F200W', 'NRCA2-F090W', 'NRCA2-F115W', 'NRCA2-F150W', 'NRCA2-F200W', 'NRCA3-F090W', 'NRCA3-F115W', 'NRCA3-F150W', 'NRCA3-F200W', 'NRCA4-F090W', 'NRCA4-F115W', 'NRCA4-F150W', 'NRCA4-F200W', 'NRCALONG-F277W', 'NRCALONG-F356W', 'NRCALONG-F444W', 'NRCB1-F090W', 'NRCB1-F115W', 'NRCB1-F150W', 'NRCB1-F200W', 'NRCB2-F090W', 'NRCB2-F115W', 'NRCB2-F150W', 'NRCB2-F200W', 'NRCB3-F090W', 'NRCB3-F115W', 'NRCB3-F150W', 'NRCB3-F200W', 'NRCB4-F090W', 'NRCB4-F115W', 'NRCB4-F150W', 'NRCB4-F200W', 'NRCBLONG-F277W', 'NRCBLONG-F356W', 'NRCBLONG-F444W'])"
      ]
     },
     "execution_count": 1066,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1067,
   "id": "8935447b-9969-451e-836f-e1a1feb04a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s2rWrf9nHQuPHj2fLli08/vjj/PnPfw7ahk8++YQLLrigRPlbb71FSkoKJ510Etdccw1vvPEGx48f5/vvv+fVV1/lzjvvLHHO8OHDS03K4fjss8+oX78+33//Pdu2bePpp59m69atvPXWW7Rt25a+fftG9P0qlZjN7HLgPmC4c+5IkV2pwCgza2xmHYEuwGfhN1NERILx8rKPhUaNGsUbb7xRonzr1q2cccYZNGrUqMS+OXPm8P777xMXF0ffvn3JzMzkgw8+YO3atWzevJn4+Hji4uI4cuSIf0g/XEWXh8zPz+eHH36gVatW/Pvf/+bSSy+lYcOGtG3bloEDB5Kens4nn3xCamoqcXFxjBo1ig8++ID//M//DLsdoTwuNYeCm7fOMrMMM7sZmAo0A5aY2TozewHAOfcV8AqwAVgEjHfOHQ+7lSIiUoKXl3389ttv/ce9/fbb/mHnokpbXerQoUN89NFH/N///Z+/7ueee445c+ZwxRVXsHv3bn9506ZN2bx5c2U+vhKKfjavvfYal1xyCWbGmWeeyYcffgjA4cOHWbVqFWeffTaPPfYYGRkZbN++nblz53LJJZfwz3/+M+x2hHJX9uggxTPKOP5R4NFwGiV1Q+A157sHh7+OqUhdUnTZx8Ih3MWLF9O2bVumTZvGDTfcwNGjRxk6dGixZR+vu+46ZsyYwc9+9jNeeeUVf30DBw7k2WefLbHsY7ClFMtb9vHf//4377//Pg0bNqRly5b+hFfUokWL+J//+Z8S5QsWLOCSSy6hceOfrtFfddVV3HfffeTl5RUrL6qsa8wXXnghX3/9NTk5OcTGxjJjxgyGDBnCgw8+SFJSEsOHD+fmm2/m+uuvJz4+nlNPPdX/R8b48eO5/vrr6d69O845brzxRnr16hW0DZGgZR9ruJoUZ2AiDlRWYq4xcVb05q+Ax2RqTJxhqmlxatnH0lU2zry8PP+QcE1Qlcs+ahELERGpco0bN64xSbmqKTGLiIh4iBKziIiIhygxi4iIeIgSs4iIiIcoMYuIiHiIErNIFE3L+qLYl0hl1bRlHwvNnz8fMyvzDmyvL/t47NgxbrvtNnr27Env3r2LLUZx7Ngxxo0bR9euXTn77LOZP39+SO0pSyRWlxIRqVOmrZtW7jFlTYQR6PaE28s9pqYt+wgFz/4+++yz/lWhgqkJyz7+/e9/B+DLL79k7969DB06lM8//5x69erx6KOP0rZtWzZt2sSJEyc4cOBAyG0qjXrMIiI1VJ8+fTj99NMB6N69O0ePHiUvL49du3Zx6NAh/1SaY8aM8c9VvXDhQsaOHQvA2LFj/eUDBgxgxYoVAKxYsYK7776blStX+reDLWIB8Oabb3LllVcyatQo/0xZhR544AEmTpxIkyZNSo2hrMlm5syZw29+8xvOPPNMf1ugYNnHe++9lyeeeKLsD6iIQYMGlTtBSNHP5tprr2Xp0qU459iwYQMXXXQRAG3btqVFixb+EYCZM2dy//0FEwXVq1eP1q1bh9ym0igxS9RMWbKp2JeIVN7Ro0f9w9gjRowosX/+/PkkJibSuHFjdu7cWawHGhsb6+9J79mzh/bt2wNw2mmnsWfPHqBgOs7CxPzZZ58xYsQI/4IOK1asYMCAAUHb9dprrzF69GhGjx5dbAGLNWvWsGPHDq644ooy4yptvuzc3Fzef/99rrzyyhJ1T506leHDh/vjKJSamsqDDz5Y5vuVZefOnZxxRsECiQ0aNKB58+ZkZmbSu3dv3n33XfLz89m2bRurV69mx44dZGVlAQV/gCQmJjJy5Ej/5xkOJWYRkRqgJi37eOLECe655x6efvrpcttQE5Z9vOmmmzj99NNJSkrit7/9LQMGDKB+/frk5+eTkZHBgAEDWLNmDf3792fChAlhv5+uMYtUo2kLiq8R063lr4vvD7iWGcq1SKlbKrvsY/v27SO67CMUrAo1Z84c7rvvPtavX+8fot69ezfDhw8nNTWVpKSfpoYub9nHjz/+mLi4OAD/so/Hjh3zL/sI+Jd9jMQKU4XLPsbGxhZb9tHMmDx5sn8ofMCAAXTt2pVWrVrRtGlTrr76agBGjhzJjBmlrvEUMvWYRURqKK8u+9i8eXP279/vL+/Xr1+JpAw1Z9nHI0eOcPjwYaDghrQGDRrQrVs3zIwrr7zSf5f20qVL6datW9jtUGIWEamhii77WHj9ee/evQBMmzaNW265hfj4eDp37lxs2cclS5bQpUsX3n///WKPIg0cOJCtW7eWWPYx2PXl8pZ9DMWiRYuCJubSln188803ycvLK7W+sq4xX3jhhYwcOZKlS5cSGxvLe++9B8CDDz5IamoqADfffDOZmZnEx8fzzDPPMHnyZAD27t3LhRdeyDnnnMPjjz/Oyy+/7K/38ccf56GHHqJXr168/PLLIQ3fl0fLPtZwXo6zojd81cZlHwOfXb69Ra8y93dr+eticdbWoewa8/300bKPpdOyj6HRso8iIuJpWvaxdErMIiIiHqLELCIi4iEhJWYzm2lme81sfZGyU81siZl96/u3pa/czOyvZrbZzL4ws8RoNV5EpKp44X4cqZkq+rMTao/5JSDw1rlJwFLnXBdgqW8bYCjQxfc1Dni+Qi0SEfGYJk2akJmZqeQsFeacIzMzs8xpSQOFNMGIc+5DM4sLKL4KSPa9ngWkARN95bNdwU/wKjNrYWbtnXO7Qm6ViIiHxMbGkpGRwb59+0I+Jzc3t0K/jGsqxVm+Jk2aBF2kozThzPzVrkiy3Q20873uAOwoclyGr0yJWURqpIYNG/pntwpVWloaffr0iVKLvENxRl7IzzH7esxvOed6+LaznHMtiuw/6JxraWZvAZOdcx/7ypcCE51z6QH1jaNgqJt27dr1DVyVJFw5OTkhr9NZk3k5zr3ZpU8EEEzbZqUvkeflOIvJ3l1sc9/xoxU6/aQGbYrFue9o8R5am5PaVL5tHlJjvp9hqAsxguIMR0pKStDnmMPpMe8pHKI2s/bAXl/5TuCMIsfF+sqKcc5NB6ZDwQQjkZ5soKZNYFBZXo6zohOMXJdcAycYCZhQhID/t4ETiJSnW0zZE4yMTBhZofq8yrPfzwiqCzGC4oyGcB6XSgXG+l6PBRYWKR/juzu7H/CDri+LiIiEJqQes5nNoeBGr9ZmlgH8FzAZeMXMbga+A67zHf4OMAzYDBwBboxwm0VERGqtUO/KHl3KrkFBjnXA+HAaJSIiUldp5i8REREPCefmL5GICrxZrKzVpkREaiv1mEVERDxEiVlERMRDlJhFREQ8RIlZRETEQ5SYRUREPESJWURExEOUmEVERDxEiVlERMRDlJhFREQ8RIlZRETEQ5SYRUREPESJWURExEOUmEVERDxEq0uJeMi+o/uYtm5adTdDRKqRErOIl+TlwLaPftrueGH1tUVEqoWGskVERDxEiVlERMRDlJhFREQ8JKzEbGZ3m9lXZrbezOaYWRMz62hmn5rZZjObZ2aNItVYERGR2q7SidnMOgB3AUnOuR5AfWAU8DgwxTkXDxwEbo5EQ0VEROqCcIeyGwAnmVkDoCmwC7gEeM23fxbw8zDfQ0REpM4w51zlTzb7DfAocBRYDPwGWOXrLWNmZwDv+nrUgeeOA8YBtGvXru/cuXMr3Y5gcnJyiImJiWidXuTlOPdm54V1fttmjf2vPRtn9u5im/uOHw2rugacQj6HfipoXDzmNie1Cat+r/Ds9zOC6kKMoDjDkZKSsto5lxRYXunnmM2sJXAV0BHIAl4FLg/1fOfcdGA6QFJSkktOTq5sU4JKS0sj0nV6kZfjnLJkU1jnX5fc1f/as3Eue6zY5rSsL8Kqro1dxj63+KeC9sWfYx6ZMDKs+r3Cs9/PCKoLMYLijIZwJhi5FNjmnNsHYGavAwOBFmbWwDmXD8QCO8NvptQE4SZiEREJLzH/H9DPzJpSMJQ9CEgHlgHXAnOBscDCcBsp4hkBPWQRkUir9M1fzrlPKbjJaw3wpa+u6cBE4B4z2wy0AmZEoJ0iIiJ1QlhzZTvn/gv4r4DircB54dQrIsEFLnBxe8Lt1dQSEYkWzfwlIiLiIUrMIiIiHqJlH6XSdBe2iEjkqccsIiLiIUrMIiIiHqLELCIi4iFKzCIiIh6im79EwhDu3NgiIoHUYxYREfEQJWYREREPUWIWERHxECVmERERD1FiFhER8RAlZhEREQ/R41IiNZiWgRSpfdRjFhER8RD1mEUqQBOKiEi0qccsIiLiIUrMIiIiHhLWULaZtQBeBHoADrgJ+AaYB8QB24HrnHMHw3kfqZumLNnkf90hN68aWyIiUnXC7TE/Cyxyzp0N9AY2ApOApc65LsBS37aIiIiEoNKJ2cyaAxcBMwCcc8ecc1nAVcAs32GzgJ+H10QREZG6I5yh7I7APuAfZtYbWA38BmjnnNvlO2Y30C68JorUYds+Kr7d8cLqaYeIVBlzzlXuRLMkYBUw0Dn3qZk9CxwC7nTOtShy3EHnXMsg548DxgG0a9eu79y5cyvVjtLk5OQQExMT0Tq9qDrj3Jtdddd9G57Io2XzU6rs/UqzL2tbVOtvwCnkc6j0AxqX/b1uc1KbCLcoOurC/8+6ECMoznCkpKSsds4lBZaH02POADKcc5/6tl+j4HryHjNr75zbZWbtgb3BTnbOTQemAyQlJbnk5OQwmlJSWloaka7Ti6ozzqI3Z0Vbh9xtnvh+Tlvwt6jW38YuY59bXPoB7cvuMY9MGBnhFkVHXfj/WRdiBMUZDZW+xuyc2w3sMLOzfEWDgA1AKjDWVzYWWBhWC0VEROqQcGf+uhP4l5k1ArYCN1KQ7F8xs5uB74DrwnwPkeqz7LHqboGI1DFhJWbn3DqgxPg4Bb1nERERqSDN/CUiIuIhWsRCQlaVN3uJiNRV6jGLiIh4iBKziIiIh2goW6Qo3YUtItVMPWYREREPUY9ZpBaZtm5amftvT7i9iloiIpWlHrOIiIiHqMcsUpNotSmRWk+JWaQM07K+qO4miEgdo6FsERERD1GPWWqMwJnH7h7ctZpaIiISPeoxi4iIeIgSs4iIiIcoMYuIiHiIErOIiIiHKDGLiIh4iBKziIiIh+hxKZEiNKGIiFQ39ZhFREQ8JOzEbGb1zWytmb3l2+5oZp+a2WYzm2dmjcJvpoiISN0QiR7zb4CNRbYfB6Y45+KBg8DNEXgPERGROiGsxGxmscAVwIu+bQMuAV7zHTIL+Hk47yEiZdj2UfEvEanxwu0x/wW4Dzjh224FZDnn8n3bGUCHMN9DRESkzjDnXOVONPsPYJhz7nYzSwYmADcAq3zD2JjZGcC7zrkeQc4fB4wDaNeuXd+5c+dWqh2lycnJISYmJqJ1elFVxrk3O69K3ieYhify+LFe42JlbZs1LuXoytuXtS3idVZEA04hn0OVr6Bx2T8LbU5qU/m6I6gu/P+sCzGC4gxHSkrKaudcUmB5OI9LDQSGm9kwoAlwCvAs0MLMGvh6zbHAzmAnO+emA9MBkpKSXHJychhNKSktLY1I1+lFVRln4OpOValD7jZ2NulYrOy65MivLjVtwd8iXmdFtLHL2OcWV76C9heWuXtkwsjK1x1BdeH/Z12IERRnNFR6KNs5d79zLtY5FweMAj5wzv0/YBlwre+wscDCsFspIiJSR0TjOeaJwD1mtpmCa84zovAeIiIitVJEZv5yzqUBab7XW4HzIlGviIhIXaOZv0RERDxEiVlERMRDtIiFlKo678IWEamrlJjFT4lYRKT6aShbRETEQ5SYRUREPESJWURExEOUmEVERDxEiVlERMRDdFd2Haa7sIFlj1V3C6rUtHXTSpTdnnB7NbREREqjxCx1SonElPVF9TRERKQUGsoWERHxEPWYpW7Z9lF1t8BzAkcRNLQtUr2UmEVqk8A/PDpeWD3tEJFK01C2iIiIh6jHLFKbqQctUuMoMUuNFfi4192Du1ZTS0REIkdD2SIiIh6ixCwiIuIhlU7MZnaGmS0zsw1m9pWZ/cZXfqqZLTGzb33/toxcc0VERGq3cHrM+cDvnHPdgH7AeDPrBkwCljrnugBLfdsiIiISgkonZufcLufcGt/rbGAj0AG4CpjlO2wW8PMw2ygiIlJnROQas5nFAX2AT4F2zrldvl27gXaReA8REZG6wJxz4VVgFgMsBx51zr1uZlnOuRZF9h90zpW4zmxm44BxAO3ates7d+7csNoRKCcnh5iYmIjW6UXhxLk3Oy/CrYmehify+LFe4zKPsQaHim23OalNiWP2ZW2LaLsirQGnkM+h8g+srMbl/6wE+9wirS78/6wLMYLiDEdKSspq51xSYHlYzzGbWUNgPvAv59zrvuI9ZtbeObfLzNoDe4Od65ybDkwHSEpKcsnJyeE0pYS0tDQiXacXhRNnTVr2sUPuNnY26VjmMY3bvF9se+TBzSWOmea8vZpUG7uMfW5x9N6gffkTjIxMGBm99/epC/8/60KMoDijIZy7sg2YAWx0zj1TZFcqMNb3eiywsPLNExERqVvC6TEPBK4HvjSzdb6y3wOTgVfM7GbgO+C6sFooEVOTesih6Pd/04ttr23TqZpaIiISOZVOzM65jwErZfegytYrUmkB80JPK+UwqRgtCylStTTzl4iIiIdoEQuRuiRwtSkoseJUYA9ZRKqWErOIhEVD3SKRpaFsERERD1GPuZYIdsd1bVufOPAu7EA7so4W2z6jxUnRbI6ISFSoxywiIuIhSswiIiIeosQsIiLiIbrGXEMVXlPukJtX62b0EhGpy5SYazElbAlJ4LPNHctf6EJEokdD2SIiIh6iHrOIVIhmBhOJLiVmqTZrDs0rtp14yi8iWn/gc82gZ5tFxPs0lC0iIuIh6jGLZxWd6etA84QS+1Prba5wnZodLPo0d7ZIeJSYxbOKJt4edg6nVmNb6pQI36WtRC1SMUrMNYQefapcD1lqHyV6qe10jVlERMRD1GP2iMAecW1bGSoUgatHpVbBn4265hwBGvoWiSglZo+qiqHrwMeVbs/6odj2qjPHRfX9aoM6mdgDE3F5+wMS9bR102hztI0/AVd14lXiF6+LWp/EzC43s2/MbLOZTYrW+4iIiNQmUekxm1l94DlgMJABfG5mqc65DdF4Py+qjX+VB/Z4Yw+tLvP4trFPlLk/8PyqGLquqDrZIw5UXg+5oudXcKi7vJnGytsfjf970xaMLv4eI+aEV+Gyx4pvp9wfXn1eVBdijJBoDWWfB2x2zm0FMLO5wFVAlSXmvdnFV126u8H8Yvun5F9T/PiM+4ptZ5zSt9j2y9c8ULEGBPwyCvzVsXJLZrHtwFmvAtsTmOQK97c95Ur27n8+aBIsL6byEmPbEjWGp7xE7gXBZguryP6qFok/HKr8j49tH4Fd9tP/kYPFL6GQ9UXZ5wcm9vL+cCgnMYcyxWhgcg/8zILVUZXD9eH+oVCi/UE+03DrLPcT8FjiDmx/N7pV2XtHq4/SAdhRZDvDVyYiIiJlMOdc5Cs1uxa43Dl3i2/7euB859wdRY4ZBxTeXXQW8E2Em9Ea2B/hOr1IcdYuirP2qAsxguIMx8+cc20CC6M1lL0TOKPIdqyvzM85Nx0o/nxMBJlZunMuKVr1e4XirF0UZ+1RF2IExRkN0RrK/hzoYmYdzawRMApIjdJ7iYiI1BpR6TE75/LN7A7gPaA+MNM591U03ktERKQ2idoEI865d4B3olV/CKI2TO4xirN2UZy1R12IERRnxEXl5i8RERGpHA9O6SAiIlJ3KTGLiIh4iBKziIiIhygxi4iIeIgSs4iIiIcoMYuIiHiIErOIiIiHKDGLiIh4iBKziIiIhygxi4iIeIgSs4iIiIcoMYuIiHiIErOIiIiHKDGLiIh4iBKziIiIhygxi4iIeIgSs4iIiIcoMYuIiHiIErOIiIiHKDGLiIh4iBKziIiIhygxi4iIeIgSs4iIiIcoMYuIiHiIErOIiIiHKDGLiIh4iBKziIiIhygxi4iIeIgSs4iIiIcoMYuIiHiIErOIiIiHKDGLiIh4iBKziIiIhzSo7gYAtG7d2sXFxUW0zsOHD3PyySdHtE4vUpy1i+KsPepCjKA4w7F69er9zrk2geWeSMxxcXGkp6dHtM60tDSSk5MjWqcXKc7aRXHWHnUhRlCc4TCz74KVayhbRETEQ5SYRUREPESJWURExEM8cY1ZRMTLfvzxRzIyMsjNzQ35nObNm7Nx48YotsobFGf5mjRpQmxsLA0bNgzpeCVmEZFyZGRk0KxZM+Li4jCzkM7Jzs6mWbNmUW5Z9VOcZXPOkZmZSUZGBh07dgzpHA1li4iUIzc3l1atWoWclEUKmRmtWrWq0GhLuYnZzGaa2V4zW1+kbJ6ZrfN9bTezdb7yODM7WmTfC5UJRETEa5SUpbIq+rMTSo/5JeDyogXOuV845xKccwnAfOD1Iru3FO5zzt1aodaIiEhQ9evXJyEhwf+1fft2lixZQt++fenZsyd9+/blgw8+8B+/evVqevbsSXx8PHfddRfOOQAOHDjA4MGD6dKlC4MHD+bgwYM452jdujUHDx4EYNeuXZgZH3/8sb++Nm3akJmZWaxNL730Eh07diQhIYHu3btz7bXXcuTIEQCeeeYZunXrRq9evRg0aBDffRf0kV0Abr31Vj755JNiZQ899BAdOnQgISGBs88+m9tuu40TJ04UO+bpp5/GzNi/f3+5n99NN91E27Zt6dGjR6nHOOe46667iI+Pp1evXqxZs6bY/kOHDhEbG8sdd9zhLzt27Bjjxo2ja9eunH322cyfP7/ctpSn3GvMzrkPzSwu2D4r+DPgOuCSsFsiNd+yx4pvp9xfPe0QibIpSzaVe8yxY3k0atQ4pPruHty13GNOOukk1q1bV6zs4MGDvPnmm5x++umsX7+eIUOGsHPnTgBuu+02/v73v3P++eczbNgwFi1axNChQ5k8eTKDBg1i0qRJTJ48mcmTJ/P444/Tr18/Vq5cybBhw1ixYgV9+vRhxYoVXHDBBXzzzTe0atWKVq1alWjX1VdfzfTp0wH45S9/ybx587jxxhvp06cP6enpNG3alOeff5777ruPefPmBY1t1apVPPfccyU/l7vvZsKECZw4cYKLLrqI5cuXk5KSAsCOHTtYvHgxZ555ZrmfHcANN9zAHXfcwZgxY0o95t133+Xbb7/l22+/5dNPP+W2227j008/9e9/4IEHuOiii4qd8+ijj9K2bVs2bdrEiRMnOHDgQEjtKUu415gvBPY4574tUtbRzNaa2XIzuzDM+kVEpBR9+vTh9NNPB6B79+4cPXqUvLw8du3axaFDh+jXrx9mxpgxY3jjjTcAWLhwIWPHjgVg7Nix/vIBAwawYsUKAFasWMHdd9/NypUr/dsDBw4ssy35+fkcPnyYli1bApCSkkLTpk0B6NevHxkZGUHP27hxI127dqV+/fql1n3s2DFyc3P9dUNB0n7iiSdCHia+6KKLOPXUU8s8ZuHChYwZMwYzo1+/fmRlZbFr1y4A1q5dy549e7jsssuKnTNz5kzuv7+gE1KvXj1at24dUnvKEu5d2aOBOUW2dwFnOucyzawv8IaZdXfOHQo80czGAeMA2rVrR1paWphNKS4nJyfidXqRp+LMCbjjMILt8lScUaQ4val58+ZkZ2f7t48dyyv3nBMnXEjHAcXqLs3Ro0fp1asXAD/72c/497//XWz/G2+8Qe/evTl27BibNm2iffv2/npPPfVUvvvuO7Kzs9mzZw8xMTFkZ2dz8skns2fPHrKzs+nTpw+TJ08mOzublStXMmHCBKZMmUJ2djbLly/n3HPPLdHO3NxcXn/9dVatWsXu3buJj48nOTm5xHHPP/88l1xySdA433jjjaDn5OXlMX36dGbPns2OHTu49NJL6dy5M9nZ2bz99tu0adOGTp064ZwjJyeHxo0bs2bNGmbOnMnUqVODfoY5OTmcOHGi1M/7u+++o1WrVv797du3Z9OmTTRt2pTf//73vPjii6SlpXHs2DGys7PJysrCOcfEiRP5+OOP6dixI0899RRt27YtUXdubm7IP/OVTsxm1gC4GuhbWOacywPyfK9Xm9kWoCtQYiJs59x0YDpAUlKSi/QcpJq/tRoEDmUnj4pY1Z6KM4oUpzdt3Lix2KMyoQxRV2QoO5THcE466SS++OKLoPu++uorHnroIRYvXkyzZs04+eSTqV+/vr/epk2b0qBBA/920fczM5o1a8bFF1/ML3/5S+rVq8eJEydo37498fHx7Nmzh88//5xJkyaVaGeTJk38Q9nOOcaPH88LL7zApEmT/Mf885//5IsvvmD58uU0blzy80hLS+Mf//hHibobN27MPffcw4QJE/jxxx+59tprefvttxk+fDhTpkzxx2pmxMTE+GO4+OKLS/0MY2JiqFevXqmfd4MGDWjatKl/f/369Tn55JN5+eWXGTJkCGeffTarVq2iUaNGNGvWjLy8PHbu3ElycjJTp07lmWee4aGHHuLll18uUXeTJk3o06dPqW0rKpyh7EuBr51z/vEJM2tjZvV9rzsBXYCtYbyHiIiUISMjgxEjRjB79mw6d+4MQIcOHYoNHWdkZNChQwegYISycHh2165d/t5d06ZN6dKlCzNnziQxMREoGIJ+55132Lt3L2eddVaZ7TAzrrzySj788EN/2fvvv8+jjz5Kampq0KR85MgRsrKy/MPxpWnYsCGXX345H374IVu2bGHbtm307t2buLg4MjIySExMZPfu3eV9VOXq0KEDO3bs8G8Xfm4rV65k+vTpxMXFMWHCBGbPns2kSZNo1aoVTZs25eqrrwZg5MiRJW4Yq4xQHpeaA6wEzjKzDDO72bdrFMWHsQEuAr7wPT71GnCrcy78K+EiIlJCVlYWV1xxBZMnTy52Dbh9+/accsoprFq1Cuccs2fP5qqrrgJg+PDhzJo1C4BZs2b5y6HgOvNf/vIX+vfvD0D//v159tln/deqy/Pxxx/7/zhYu3Ytv/71r0lNTQ06tAuwbNky/81cZXHO8cknn9C5c2d69uzJ3r172b59O9u3byc2NpY1a9Zw2mmnlVtPeYYPH87s2bNxzrFq1SqaN29O+/bt+de//sWGDRvYvn07Tz31FGPGjGHy5Mn+P0YKh6iXLl1Kt27dwm5HuYnZOTfaOdfeOdfQORfrnJvhK7/BOfdCwLHznXPdfY9KJTrn3gy7hSIiEtTUqVPZvHkzjzzyiP8xqr179wIwbdo0brnlFuLj4+ncuTNDhw4FYNKkSSxZsoQuXbrw/vvvFxt2HjhwIFu3bvUn5sTERDIyMhgwYECpbXj99ddJSEigV69erF27lgceeACAe++9l5ycHEaOHElCQgLDhw8vce67777L5ZdfXqK80JQpU0hISKBHjx4cP36c22+/vczPIz09nVtuuSXovtGjR9O/f3+++eYbYmNjmTFjBgAvvPACL7xQkMqGDRtGp06diI+P51e/+hXTpk0r8/0AHn/8cR566CF69erFyy+/zNNPP13uOeWxwmfbqlNSUpLTesyV46k4o/i4lKfijCLF6U0bN27knHPOqdA5mqqyfImJiXz66achzyFdncL9fgb7GTKz1c65pMBjNVe2iIhUi0hcj62NNFe2iIiIhygxi4iIeIgSs4iIiIcoMYuIiHiIErOIiIiHKDGLiNQANW3ZxxdeeIGePXuSkJDABRdcwIYNG0qNbejQoSUWubjhhhv8dZ999tk8/PDDJc676667iImJCenzmzVrFl26dKFLly7+CVYC/e///i/9+/enZ8+eXHnllRw69NMyD+vXr6d///50796dnj17kpubC1TTso8iIhIg8Jn9IBody4MQ58oO5Zn/mrbs4y9/+UtuvfVWAFJTU7nnnntYtGhRifOPHj1KZmYmsbGxJfY9+eSTXHvtteTm5tKtWzfGjBlDx44Fi+Wkp6f7/5Aoz4EDB3j44YdJT0/HzOjbty/Dhw8vtloVwC233MJTTz3FxRdfzMyZM3nyySf505/+RH5+Pr/61a/417/+Re/evcnMzPQ/e+3FZR9FRKSaeHnZx1NOOcW/7/Dhw6VO6RnKZDOFvdOTTz4ZgOPHj3PvvffyxBNPlHleoffee4/Bgwdz6qmn0rJlSwYPHhz0j4RNmzb511sePHiwv/e7ePFiunfvTu/evQFo1aqVf5nKaCz7qMQsIlIDHD161D+MPWLEiBL758+fT2JiIo0bN2bnzp3FeqCxsbH+nvSePXto3749AKeddhp79uwBCqbjLEzMn332GSNGjPAv6LBixYpSp+UsnJKzQ4cOHDhwgCuvvNK/77nnnqNz587cd999/PWvfw16flnTct57770kJCQQGxvLqFGj/HNuT506leHDh/vjKJSamsqDDz5Yop6dO3dyxhlnBP08iurevTsLFy4E4NVXX/XHv2nTJsyMIUOGkJiY6P+DICsrC4AHHniAxMRERo4c6f88w6HELCJSAxQOZa9bt44FCxYU2/fVV18xceJE/va3v1WoTjPz92TPPfdc1q5dy+HDh/nxxx+JiYmhU6dObN68ucwe89VXX826devYvXs3PXv25Mknn/TvGz9+PFu2bOHxxx/nz3/+c9DzP/nkEy644IKg+5588kl/3UuXLmXFihV8//33vPrqq9x5550ljh8+fDiPPPJIhT6DombOnMm0adPo27cv2dnZNGrUCCgYDVi1ahX/+te/+Pjjj1mwYAFLly4lPz/fP5f4mjVr6N+/PxMmTKj0+xdSYhYRqcG8vOxjoVGjRvmHzIvaunUrZ5xxhj8BliYmJobk5GQ+/vhj1q5dy+bNm4mPjycuLo4jR44QHx9f5vmlLecY6Oyzz2bx4sWsXr2a0aNH+z/P2NhYBgwYQOvWrWnatCnDhg1jzZo11bfso4iIeJOXl3389ttv/eVvv/02Xbp0KXF8eatLFcrPz+fTTz+lc+fOXHHFFezevdu/7GPTpk3ZvHlzmecPGTKExYsXc/DgQQ4ePMjixYsZMmRIieMKV+Y6ceIEf/7zn/03rw0ZMoQNGzZw5MgR8vPzWb58Od26dau+ZR9FRMSbvLzs49SpU+nevTsJCQk888wzQR9RWrRoUZmJufAac69evejZs6e/Z1qa0q4xn3rqqTzwwAOce+65nHvuuTz44IOceuqpQMGd2IWrG86ZM8f/2NPpp5/OjTfeCEDLli0ZP3485557LgkJCSQmJnLFFVcAWvaxQmrasnKV5ak4texj2BSnN2nZx9JVNs68vDwGDhxIpH/3R0tVLvuoHrOIiFS5xo0b15ikXNWUmEVERDyk3MRsZjPNbK+ZrS9S9pCZ7TSzdb6vYUX23W9mm83sGzMreXVdREREShVKj/klINjV+SnOuQTf1zsAZtYNGAV0950zzczqR6qxIiIitV25c2U75z40s7gQ67sKmOucywO2mdlm4DxgZeWbKDVWFG8GExGprcJZxOIOMxsDpAO/c84dBDoAq4ock+ErK8HMxgHjoOCB98LnwCIlJycn4nV6kafizOlY9v4w2umpOKNIcXpT8+bNyc7OrtA5x48fr/A5NZHiDE1ubm7oP/POuXK/gDhgfZHtdkB9CobCHwVm+sqnAv9Z5LgZwLXl1d+3b18XacuWLYt4nV7kqTg/+O+yv8LgqTijSHF604YNGyp8zqFDhyLahnr16rnevXv7v7Zt2+YWL17sEhMTXY8ePVxiYqJbunSp//j09HTXo0cP17lzZ3fnnXe6EydOOOecy8zMdJdeeqmLj493l156qTtw4IA7ceKEa9WqlTtw4IBzzrnvv//eAe6jjz7y19e6dWu3f//+Ym36xz/+4Vq1auV69+7tunXr5q655hp3+PDhYse89tprDnCff/55qbFdfvnlbseOHcXKxo4d6+Li4lzv3r3dWWed5R566KES5915553u5JNPDunzGzJkiGvevLm74oorSj0mNzfXXXfdda5z587uvPPOc9u2bfPvW7FihevXr5/r1q2b69Gjhzt69Kg7fPiwGzZsmDvrrLNct27d3MSJE0utO9jPEJDuguTESvWYnXP+WbrN7O/AW77NncAZRQ6N9ZWJiNQa09ZNK/eYvLw8GjcObdnH2xNuL/eYmrbsIxQ8+/vss89y/vnnlxpXVSz7CAWTlRw5cqTM+cRnzJhBy5Yt2bx5M3PnzmXixInMmzev1GUf8/LymDBhAikpKRw7doxBgwbx7rvv+idzqaxKPS5lZkWX9BgBFN6xnQqMMrPGZtYR6AJ8FlYLRUQkKC8v+wgFqy5NnDiRJk2alHpeVSz7CDBo0KByJwgp+tlce+21LF26FOdcqcs+Nm3alJSUFAAaNWrknyktXKE8LjWHgpu3zjKzDDO7GXjCzL40sy+AFOBuAOfcV8ArwAZgETDeOXc87FaKiNRxNW3ZxzVr1rBjxw7/1JWlqYplH0NVdHnIBg0a0Lx5czIzM0td9rGorKws3nzzTQYNGlTp9y8Uyl3Zo4MUzyjj+EcpuO4sIiIREmwou1Dhso+LFy+uUJ0VWfbxd7/7XdA6CoeynXOMHz+eJ598kvvuu4977rmHl156qdw2fPLJJzz11FNB9xUOZefk5DBo0CBWrFhBXFwcr776atAbqYYPH87w4cNDjj9Uhcs+pqen07RpUwYNGkTfvn39STg/P5/Ro0dz11130alTp7DfTzN/iYjUYF5c9jE7O5v169eTnJxMXFwcq1atYvjw4SWm4KyqZR9DVXR5yPz8fH744QdatWpV6rKPhcaNG0eXLl347W9/G5F2KDGLiNRQXl32sXnz5uzfv9+/NGO/fv1ITU0lKan4eg1VtexjqIp+Nq+99hqXXHKJfwg72LKPAH/84x/54Ycf+Mtf/hKRNoASs4hIjeXlZR9DUVXLPgJceOGFjBw5kqVLlxIbG8t7770HwIMPPkhqaioAN998M5mZmcTHx/PMM88wefJkoPRlHzMyMnj00UfZsGEDiYmJJCQk8OKLL4Ycf2m07GMN56k4A2f6ChTGzF+eijOKFKc3adnH0mnZx9Bo2UcREfE0LftYOiVmERERD1FiFhER8RAlZhGREHjhfhypmSr6s6PELCJSjiZNmpCZmankLBXmnCMzM7PMaUkDhbPso4hInRAbG0tGRgb79u0L+Zzc3NwK/TKuqRRn+Zo0aRJ0kY7SKDGLiJSjYcOG/lWNQpWWlkafPn2i1CLvUJyRp6FsERERD1FiFhER8RAlZhEREQ9RYhYREfEQJWYREREPUWIWERHxkHITs5nNNLO9Zra+SNmTZva1mX1hZgvMrIWvPM7MjprZOt/XC1Fsu4iISK0TSo/5JSBwwcwlQA/nXC9gE1B0Pb8tzrkE39etkWmmiIhI3VBuYnbOfQgcCChb7JzL922uAkKf0kRERERKFYlrzDcB7xbZ7mhma81suZldGIH6RURE6gwLZVJ2M4sD3nLO9Qgo/wOQBFztnHNm1hiIcc5lmllf4A2gu3PuUJA6xwHjANq1a9d37ty54cZSTE5ODjExMRGt04s8FWf27rL3Nzut0lV7Ks4oUpy1R12IERRnOFJSUlY755ICyys9V7aZ3QD8BzDI+bK7cy4PyPO9Xm1mW4CuQHrg+c656cB0gKSkJJecnFzZpgSVlpZGpOv0Ik/Fueyxsvcnj6p01Z6KM4oUZ+1RF2IExRkNlRrKNrPLgfuA4c65I0XK25hZfd/rTkAXYGskGioiIlIXlNtjNrM5QDLQ2swygP+i4C7sxsASMwNY5bsD+yLgETP7ETgB3OqcOxC0YhERESmh3MTsnBsdpHhGKcfOB+aH2ygREZG6SusxS+gCryGn3B/8OBERqTQlZqm88m72EhGRClNiFiliypJNxbbvHty1mloiInWVFrEQERHxECVmERERD1FiFhER8RAlZhEREQ9RYhYREfEQJWYREREPUWIWERHxECVmERERD1FiFhER8RAlZhEREQ9RYhYREfEQJWYREREPUWIWERHxEK0uJVWnBq7nrNWmRKSqqccsIiLiISElZjObaWZ7zWx9kbJTzWyJmX3r+7elr9zM7K9mttnMvjCzxGg1XkREpLYJtcf8EnB5QNkkYKlzrguw1LcNMBTo4vsaBzwffjNFRETqhpASs3PuQ+BAQPFVwCzf61nAz4uUz3YFVgEtzKx9BNoqIiJS65lzLrQDzeKAt5xzPXzbWc65Fr7XBhx0zrUws7eAyc65j337lgITnXPpAfWNo6BHTbt27frOnTs3MhH55OTkEBMTE9E6vahK48zeHdn6mp0W8qFVFefe7Lwy97dt1jiq76+f29qjLsQIijMcKSkpq51zSYHlEbkr2znnzCy0DP/TOdOB6QBJSUkuOTk5Ek3xS0tLI9J1elGVxhl4V3W4kkeFfGhVxRl4F3ag65Kje1e2fm5rj7oQIyjOaAjnruw9hUPUvn/3+sp3AmcUOS7WVyYiIiLlCCcxpwJjfa/HAguLlI/x3Z3dD/jBObcrjPcRERGpM0IayjazOUAy0NrMMoD/AiYDr5jZzcB3wHW+w98BhgGbgSPAjRFus4iISK0VUmJ2zo0uZdegIMc6YHw4jRIREamrNPOXiIiIhygxi4iIeIgWsZA6rbzHo0REqpp6zCIiIh6iHrPUKeH2kKetm1Zs+/aE28OqT0QkkHrMIiIiHqLELCIi4iFKzCIiIh6ixCwiIuIhSswiIiIeosQsIiLiIXpcSqpP4PrOKfdXTztERDxEPWYREREPUWIWERHxECVmERERD1FiFhER8RDd/CVSxJpD84ptJ57yi2pqiYjUVZVOzGZ2FlD0t1gn4EGgBfArYJ+v/PfOuXcq+z4iIiJ1SaUTs3PuGyABwMzqAzuBBcCNwBTn3FORaKCIiEhdEqmh7EHAFufcd2YWoSpFql/g0Hb/Nq2qqSUiUldE6uavUcCcItt3mNkXZjbTzFpG6D1ERERqPXPOhVeBWSPge6C7c26PmbUD9gMO+BPQ3jl3U5DzxgHjANq1a9d37ty5YbUjUE5ODjExMRGt04uqNM7s3dGtv9lppe6KVJx7s/PK3H/4+IEy98c0Lj7I1OakNmG3qSj93NYedSFGUJzhSElJWe2cSwosj8RQ9lBgjXNuD0DhvwBm9nfgrWAnOeemA9MBkpKSXHJycgSa8pO0tDQiXacXVWmcgVNoRlryqFJ3RSrOKUs2lbl/zaHPytzfv33xoeyRCSPDblNR+rmtPepCjKA4oyESQ9mjKTKMbWbti+wbAayPwHuIiIjUCWH1mM3sZGAw8OsixU+YWQIFQ9nbA/aJiIhIGcJKzM65w0CrgLLrw2qRiIet3JJZbPv2hOpph4jUXpqSU0RExEOUmEVERDxEc2VLnRY4gUhFTVs3rdj27Qm3h1WfiIh6zCIiIh6ixCwiIuIhGsoW7wicwCTl/upph4hINVJillqrvFm+RES8SEPZIiIiHqLELCIi4iEaypbSRXvRijpAj1OJSEWpxywiIuIh6jHLT7zWQy7anpyO1dcOEZEqpB6ziIiIhygxi4iIeIgSs4iIiIcoMYuIiHiIErOIiIiHKDGLiIh4SNiPS5nZdiAbOA7kO+eSzOxUYB4QB2wHrnPOHQz3vUS8LnBCERGRiopUjznFOZfgnEvybU8CljrnugBLfdsiIiJSjmgNZV8FzPK9ngX8PErvIyIiUqtEIjE7YLGZrTazcb6yds65Xb7Xu4F2EXgfERGRWs+cc+FVYNbBObfTzNoCS4A7gVTnXIsixxx0zrUMOG8cMA6gXbt2fefOnRtWOwLl5OQQExMT0Tq9KKJxZu+OTD1RkHOiMTHNW5Z/YBF7s/PKPebw8QOVbRIAMY0rdptGm5PalLlfP7e1R12IERRnOFJSUlYXuQTsF/bNX865nb5/95rZAuA8YI+ZtXfO7TKz9sDeIOdNB6YDJCUlueTk5HCbUkxaWhqRrtOLIhqn1+bKLiItp2OF45yyZFO5x6w59FklW1Sgf/tWFTp+ZMLIMvfr57b2qAsxguKMhrASs5mdDNRzzmX7Xl8GPAKkAmOByb5/F4bbUJFIWHNoXnU3QUSkTOH2mNsBC8yssK5/O+cWmdnnwCtmdjPwHXBdmO8jUq5QesgiIl4XVmJ2zm0FegcpzwQGhVO3VAEPD12LiNRVmvlLRETEQ5SYRUREPESJWURExEPCflxKREIXOJf27Qm3V1NLRMSrlJhFwrByS2ax7f6dK/Zcs4hIICVmqTkC7yJPub962iEiEkW6xiwiIuIh6jGLVKPAa87d6FZNLRERr1CPWURExEOUmEVERDxEQ9lSa2nBChGpidRjFhER8RD1mEUiSM81i0i41GMWERHxECVmERERD1FiFhER8RBdY5Yaa8qSTdXdBBGRiKt0YjazM4DZQDvAAdOdc8+a2UPAr4B9vkN/75x7J9yGSgQEzjUtIiKeE06POR/4nXNujZk1A1ab2RLfvinOuafCb56IiEjdUunE7JzbBezyvc42s41Ah0g1TEREpC6KyM1fZhYH9AE+9RXdYWZfmNlMM2sZifcQERGpC8w5F14FZjHAcuBR59zrZtYO2E/Bdec/Ae2dczcFOW8cMA6gXbt2fefOnRtWOwLl5OQQExMT0Tq9qEJxZu+ObmOiKOdEY2Lq5RUr20vZf/MdPn4gmk0KSUzjig1KnXT8JP3c1hJ1IUZQnOFISUlZ7ZxLCiwPKzGbWUPgLeA959wzQfbHAW8553qUVU9SUpJLT0+vdDuCSUtLIzk5OaJ1elGF4qzBN3+l5XQkOWZbsbIp+deUeY4X5squ6Mxf3bK66ee2lqgLMYLiDIeZBU3MlR7KNjMDZgAbiyZlM2tf5LARwPrKvoeIiEhdE85d2QOB64EvzWydr+z3wGgzS6BgKHs78Osw3kNERKROCeeu7I8BC7JLzyyLiIhUkqbkFBER8RBNySm1hhdu9hIRCZcSs0gUVXR95n1H9zFt3TT/9u0Jt0elXSLiXUrMIh5WNEmDErVIXaBrzCIiIh6ixCwiIuIhGsqWGmPl1uLXa9e00M1eIlL7KDFLjZFab3NASd9qaYeISDQpMdcWwebBTrm/6tsRQdOyvvC/bmOnldgfe2h1se2MU2p/otbNYCK1nxKzVJvAoen+nYo/SrQj66j/dfNTTlRJm6pa4ONUw5u2qdD5StQitY8Sc21Wg1eTEhGpq5SYpdaoCUPbgT1kEZFAelxKRETEQ9RjrikCh6Vr+I1dIiISnBJzTVWYqHM66lqy+OlmMJGaT4lZaq3Aa87gzevOReXk5Re7Dl3eohciUvsoMUu1CZwwpD9Vn4TKu2Gsum8oK+9mMSVukdpHN3+JiIh4SNR6zGZ2OfAsUB940Tk3OVrvVSvVwevG92//NOrvEWx4W8qm69YiVSsqidnM6gPPAYOBDOBzM0t1zm2IxvvVSHUg8QYm2sfizq+mloSuvMRd3UPbFXX9/D8V2375mgfKPScwEYtI1YpWj/k8YLNzbiuAmc0FrgK8k5j1+FGVq4oecV0TeA26JlxznrZuGm2OtvH/AVBeD1w9dqlropWYOwA7imxnAFXbXcreXSz5VrT3VuKXwcEfim0HzvO89tSdxSvoeGGZ50da0QUf/O/ZoleZ55Q3V3V5+5Voo9+DrujNaSu3lP3+lekNl7wBrexEWdH3KHH8to+Kbwf8X4q0KUs2lSi7e3DXsOvskJvnr7txm/eL7Y/2HxcV/WNGf/x4iznnIl+p2bXA5c65W3zb1wPnO+fuKHLMOGCcb/Ms4JsIN6M1sD/CdXqR4qxdFGftURdiBMUZjp8550qsXBOtHvNO4Iwi27G+Mj/n3HRgepTeHzNLd84lRat+r1CctYvirD3qQoygOKMhWo9LfQ50MbOOZtYIGAWkRum9REREao2o9Jidc/lmdgfwHgWPS810zn0VjfcSERGpTaL2HLNz7h3gnWjVH4KoDZN7jOKsXRRn7VEXYgTFGXFRuflLREREKkdTcoqIiHhIjU7MZna5mX1jZpvNbFKQ/Wea2TIzW2tmX5jZsOpoZ7hCiPNnZrbUF2OamcVWRzvDZWYzzWyvma0vZb+Z2V99n8MXZpZY1W2MhBDiPNvMVppZnplNqOr2RUoIcf4/3/fxSzNbYWa9q7qNkRBCnFf54lxnZulmdkFVtzESyouzyHHnmlm+77HZGieE72eymf3g+36uM7MHI94I51yN/KLgprItQCegEfC/QLeAY6YDt/ledwO2V3e7oxTnq8BY3+tLgJeru92VjPUiIBFYX8r+YcC7gAH9gE+ru81RirMtcC7wKDChutsbxTgHAC19r4fW4u9nDD9dNuwFfF3dbY5GnL5j6gMfUHB/0bXV3eYofT+Tgbei2Yaa3GP2T/vpnDsGFE77WZQDTvG9bg58X4Xti5RQ4uxGwX8GgGVB9tcIzrkPgQNlHHIVMNsVWAW0MLP2VdO6yCkvTufcXufc58CPVdeqyAshzhXOuYO+zVUUzHdQ44QQZ47z/UYHTqbg91KNE8L/T4A7gfnA3ui3KDpCjDOqanJiDjbtZ4eAYx4C/tPMMij4C+7OqmlaRIUS5/8CV/tejwCamZn3J02uuFA+C6mZbqZgNKRWMrMRZvY18DZwU3W3JxrMrAMFv3+er+62VIH+Zva/ZvaumXWPdOU1OTGHYjTwknMuloJh0JfNrDbGPAG42MzWAhdTMMva8eptkkhozCyFgsQ8sbrbEi3OuQXOubOBnwN/KufwmuovwETn3InqbkiUraFgKs3ewP8Ab0T6DaL2HHMVKHfaTwr+s18O4JxbaWZNKJjvtCYNs4Qyven3+HrMZhYDXOOcy6qqBlahUL7nUoOYWS/gRWCocy5wtYxaxzn3oZl1MrPWzrnaNr90EjDXzKDg9+wwM8t3zr1Rra2KMOfcoSKv3zGzaZH+ftbk3mMo037+HzAIwMzOAZoA+6q0leErN04za11kJOB+YGYVt7GqpAJjfHdn9wN+cM7tqu5GSeWY2ZnA68D1zrmSSzzVEmYWb75s5XuSoDFQ6/4Icc51dM7FOefigNeA22tbUgYws9OKfD/PoyCPRvT7WWN7zK6UaT/N7BEg3TmXCvwO+LuZ3U3BDRc3FLkJo0YIMc5k4DEzc8CHwPhqa3AYzGwOBbG09t0X8F9AQwDn3AsU3CcwDNgMHAFurJ6Whqe8OM3sNCCdghsXT5jZbym4E/9Q8Bq9KYTv54NAK2Ca7/dcvquBiyGEEOc1FPxB+SNwFPhFTfs9BCHFWSuEEOe1wG1mlk/B93NUpL+fmvlLRETEQ2ryULaIiEito8QsIiLiIUrMIiIiHqLELCIi4iFKzCIiIh6ixCwiIuIhSswiIiIeosQsIiLiIf8fXuGQbrWEBVUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filt = 'F200W'\n",
    "\n",
    "fig, axes = plt.subplots(2,1,figsize=(8,8), sharex=True)\n",
    "\n",
    "for i, m in enumerate('AB'):\n",
    "    ax = axes[i]\n",
    "    dref = 1\n",
    "    #scl[f'NRC{m}{dref}-{filt}'] = 1.0\n",
    "    \n",
    "    for d in [2,3,4]:\n",
    "        k1 = f'NRC{m}{d}-{filt}'\n",
    "        k2 = f'NRC{m}{dref}-{filt}'\n",
    "        _ratio = (stats[k1] / stats[k2])[clip]\n",
    "        \n",
    "        if k1 in scl:\n",
    "            _ratio /= scl[k1]\n",
    "        \n",
    "        if k2 in scl:\n",
    "            _ratio *= scl[k2]\n",
    "            \n",
    "        med = np.nanmedian(_ratio)\n",
    "        #scl[f'NRC{m}{d}-{filt}'] = float(med)\n",
    "\n",
    "        _h = ax.hist(_ratio, bins=100, range=(0.8,1.5), alpha=0.5, label=f'{filt} {m}{d} / {m}4: {med:.3f}')\n",
    "\n",
    "for ax in axes:\n",
    "    ax.grid()\n",
    "    ax.legend()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e925db8e-b7c9-4d99-b047-c55295193bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "filt = 'F200W'\n",
    "\n",
    "fig, axes = plt.subplots(2,1,figsize=(8,8), sharex=True)\n",
    "\n",
    "for i, m in enumerate('AB'):\n",
    "    ax = axes[i]\n",
    "    dref = 1\n",
    "    #scl[f'NRC{m}{dref}-{filt}'] = 1.0\n",
    "    \n",
    "    for d in [2,3,4]:\n",
    "        k1 = f'NRC{m}{d}-{filt}'\n",
    "        k2 = f'NRC{m}{dref}-{filt}'\n",
    "        _ratio = (stats[k1] / stats[k2])[clip]\n",
    "        \n",
    "        if k1 in scl:\n",
    "            _ratio /= scl[k1]\n",
    "        \n",
    "        if k2 in scl:\n",
    "            _ratio *= scl[k2]\n",
    "            \n",
    "        med = np.nanmedian(_ratio)\n",
    "        #scl[f'NRC{m}{d}-{filt}'] = float(med)\n",
    "\n",
    "        _h = ax.hist(_ratio, bins=100, range=(0.8,1.5), alpha=0.5, label=f'{filt} {m}{d} / {m}4: {med:.3f}')\n",
    "\n",
    "for ax in axes:\n",
    "    ax.grid()\n",
    "    ax.legend()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1019,
   "id": "ddb67629-8562-427a-af75-06b9ce72b5db",
   "metadata": {},
   "outputs": [],
   "source": [
    "scl = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1073,
   "id": "f17ec55f-d41a-496b-809a-38604f67a000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filt = 'F200W'\n",
    "\n",
    "for filt in ['F090W','F115W','F150W','F200W']:\n",
    "    fig, axes = plt.subplots(2,1,figsize=(8,8), sharex=True)\n",
    "    for k in stats:\n",
    "        if filt not in k:\n",
    "            continue\n",
    "\n",
    "        if 'NRC' in k:\n",
    "            _ratio = (stats[f'{k}'] / stats[f'NIS-{filt}'])[clip]\n",
    "            if k in scl:\n",
    "                _ratio /= scl[k]\n",
    "                print(k, scl[k])\n",
    "\n",
    "            med = np.nanmedian(_ratio)\n",
    "            if k in scl:\n",
    "                scl[k] = float(med*scl[k])\n",
    "            else:\n",
    "                scl[k] = float(med)\n",
    "\n",
    "            ax = axes[0] if 'NRCA' in k else axes[1]\n",
    "\n",
    "            _h = ax.hist(_ratio, \n",
    "                  bins=100, range=(0.9,1.4), alpha=0.5, label=f'{k} - {med*1.:.3f}')\n",
    "        \n",
    "    for ax in axes:\n",
    "        ax.vlines(1, *ax.get_ylim(), color='k', linestyle='--')\n",
    "        ax.grid()\n",
    "        ax.legend()\n",
    "    \n",
    "    fig.savefig(f'nircam_to_niriss-{filt}_astrometric_field.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 850,
   "id": "12248f31-de41-4f14-a62e-127b13dd6b80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((9589,), (682, 9589), (682, 9589))"
      ]
     },
     "execution_count": 850,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats['NRCB1-F200W'].shape, _x.shape, _data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 881,
   "id": "2e7c8594-db6c-4d8a-9567-a88640651a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "913288"
      ]
     },
     "execution_count": 881,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isfinite(_x).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 885,
   "id": "dcb505b6-f06d-4e07-a8ab-ee5e4e369ee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NIS-F090W-CLEAR',\n",
       " 'NIS-F115W-CLEAR',\n",
       " 'NIS-F150W-CLEAR',\n",
       " 'NIS-F200W-CLEAR',\n",
       " 'NRCA1-F090W-CLEAR',\n",
       " 'NRCA1-F115W-CLEAR',\n",
       " 'NRCA1-F150W-CLEAR',\n",
       " 'NRCA1-F200W-CLEAR',\n",
       " 'NRCA2-F090W-CLEAR',\n",
       " 'NRCA2-F115W-CLEAR',\n",
       " 'NRCA2-F150W-CLEAR',\n",
       " 'NRCA2-F200W-CLEAR',\n",
       " 'NRCA3-F090W-CLEAR',\n",
       " 'NRCA3-F115W-CLEAR',\n",
       " 'NRCA3-F150W-CLEAR',\n",
       " 'NRCA3-F200W-CLEAR',\n",
       " 'NRCA4-F090W-CLEAR',\n",
       " 'NRCA4-F115W-CLEAR',\n",
       " 'NRCA4-F150W-CLEAR',\n",
       " 'NRCA4-F200W-CLEAR',\n",
       " 'NRCB1-F090W-CLEAR',\n",
       " 'NRCB1-F115W-CLEAR',\n",
       " 'NRCB1-F150W-CLEAR',\n",
       " 'NRCB1-F200W-CLEAR',\n",
       " 'NRCB2-F090W-CLEAR',\n",
       " 'NRCB2-F115W-CLEAR',\n",
       " 'NRCB2-F150W-CLEAR',\n",
       " 'NRCB2-F200W-CLEAR',\n",
       " 'NRCB3-F090W-CLEAR',\n",
       " 'NRCB3-F115W-CLEAR',\n",
       " 'NRCB3-F150W-CLEAR',\n",
       " 'NRCB3-F200W-CLEAR',\n",
       " 'NRCB4-F090W-CLEAR',\n",
       " 'NRCB4-F115W-CLEAR',\n",
       " 'NRCB4-F150W-CLEAR',\n",
       " 'NRCB4-F200W-CLEAR']"
      ]
     },
     "execution_count": 885,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "un.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "895b70e0-b891-4a8b-a515-9822464c1e30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 911,
   "id": "89a69792-b356-4cfd-b1c4-83c2aea19dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7fe0eabeab50>"
      ]
     },
     "execution_count": 911,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ix = np.where(un[key+'-CLEAR'])[0]\n",
    "\n",
    "_X = _x[_ix,:][:,clip]\n",
    "_Y = _y[_ix,:][:,clip]\n",
    "_C = (_data[_ix,:] / stats[key])[:,clip]\n",
    "\n",
    "ok = np.isfinite(_X + _Y + _C)\n",
    "_X = _X[ok]\n",
    "_Y = _Y[ok]\n",
    "_C = _C[ok]\n",
    "\n",
    "plt.hexbin(_X, _Y, C=_C, reduce_C_function=np.median, gridsize=32, vmin=0.95, vmax=1.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 917,
   "id": "31ccb779-7fa8-4da8-9959-238b54040d9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['NIS-F090W', 'NIS-F115W', 'NIS-F150W', 'NIS-F200W', 'NRCA1-F090W', 'NRCA1-F115W', 'NRCA1-F150W', 'NRCA1-F200W', 'NRCA2-F090W', 'NRCA2-F115W', 'NRCA2-F150W', 'NRCA2-F200W', 'NRCA3-F090W', 'NRCA3-F115W', 'NRCA3-F150W', 'NRCA3-F200W', 'NRCA4-F090W', 'NRCA4-F115W', 'NRCA4-F150W', 'NRCA4-F200W', 'NRCB1-F090W', 'NRCB1-F115W', 'NRCB1-F150W', 'NRCB1-F200W', 'NRCB2-F090W', 'NRCB2-F115W', 'NRCB2-F150W', 'NRCB2-F200W', 'NRCB3-F090W', 'NRCB3-F115W', 'NRCB3-F150W', 'NRCB3-F200W', 'NRCB4-F090W', 'NRCB4-F115W', 'NRCB4-F150W', 'NRCB4-F200W'])"
      ]
     },
     "execution_count": 917,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 935,
   "id": "7fbf7463-c785-47a5-82cd-99072f56a461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,4, squeeze=False, sharex=True, sharey=True, figsize=(8,4))\n",
    "\n",
    "dets = [['A2','A4','B3','B1'],\n",
    "        ['A1','A3','B4','B2']]\n",
    "\n",
    "filt = 'F200W'\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(4):\n",
    "        #axes[i, j].text(0.5, 0.5, dets[i][j], ha='center', va='center')\n",
    "        ax = axes[i,j]\n",
    "        key = f'NRC{dets[i][j]}-F200W'\n",
    "        \n",
    "        _ix = np.where(un[key+'-CLEAR'])[0]\n",
    "\n",
    "        _X = _x[_ix,:][:,clip]\n",
    "        _Y = _y[_ix,:][:,clip]\n",
    "        _C = (_data[_ix,:] / stats[key])[:,clip]\n",
    "\n",
    "        ok = np.isfinite(_X + _Y + _C)\n",
    "        _X = _X[ok]\n",
    "        _Y = _Y[ok]\n",
    "        _C = _C[ok]\n",
    "\n",
    "        ax.hexbin(_X, _Y, C=_C, reduce_C_function=np.median,\n",
    "                  gridsize=48, vmin=0.95, vmax=1.05)\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "        ax.set_aspect(1)\n",
    "        \n",
    "fig.tight_layout(pad=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 918,
   "id": "eb0ea933-dbcd-41ed-a6ed-bd22546f9a55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7fe12d746e10>"
      ]
     },
     "execution_count": 918,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "key = 'NRCB4-F200W'\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(8,8))\n",
    "\n",
    "_ix = np.where(un[key+'-CLEAR'])[0]\n",
    "\n",
    "_X = _x[_ix,:][:,clip]\n",
    "_Y = _y[_ix,:][:,clip]\n",
    "_C = (_data[_ix,:] / stats[key])[:,clip]\n",
    "\n",
    "ok = np.isfinite(_X + _Y + _C)\n",
    "_X = _X[ok]\n",
    "_Y = _Y[ok]\n",
    "_C = _C[ok]\n",
    "\n",
    "ax.hexbin(_X, _Y, C=_C, reduce_C_function=np.median, gridsize=32, vmin=0.95, vmax=1.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1078,
   "id": "90012f74-201e-468f-9ecd-0c7b72174acc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scale F444W\n"
     ]
    }
   ],
   "source": [
    "if 'NIS-F200W' not in scl:\n",
    "    print('Scale F200W')\n",
    "    for k in scl:\n",
    "        if 'F200W' in k:\n",
    "            scl[k] *= 1.04\n",
    "    scl['NIS-F200W'] = 1.04\n",
    "\n",
    "if scl['NRCBLONG-F444W'] == 1.0:\n",
    "    print('Scale F444W')\n",
    "    scl['NRCBLONG-F444W'] = 0.912\n",
    "    scl['NRCALONG-F444W'] *= scl['NRCBLONG-F444W']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1079,
   "id": "578c5fd7-7fb9-409a-9a15-59c9bf666cfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NIS-F200W: 1.04\n",
      "NRCA1-F090W: 1.2104910612106323\n",
      "NRCA1-F115W: 1.0944347381591797\n",
      "NRCA1-F150W: 1.068378210067749\n",
      "NRCA1-F200W: 1.1555598258972168\n",
      "NRCA2-F090W: 1.20111083984375\n",
      "NRCA2-F115W: 1.1008034944534302\n",
      "NRCA2-F150W: 1.067354440689087\n",
      "NRCA2-F200W: 1.1246335983276368\n",
      "NRCA3-F090W: 1.3130921125411987\n",
      "NRCA3-F115W: 1.1598023176193237\n",
      "NRCA3-F150W: 1.139467716217041\n",
      "NRCA3-F200W: 1.2506070518493653\n",
      "NRCA4-F090W: 1.3281090259552002\n",
      "NRCA4-F115W: 1.2294491529464722\n",
      "NRCA4-F150W: 1.1948838233947754\n",
      "NRCA4-F200W: 1.2642295932769776\n",
      "NRCALONG-F277W: 0.9036391973495483\n",
      "NRCALONG-F356W: 0.9188557863235474\n",
      "NRCALONG-F444W: 0.880343999862671\n",
      "NRCB1-F090W: 1.1186286211013794\n",
      "NRCB1-F115W: 1.0426808595657349\n",
      "NRCB1-F150W: 1.0576807260513306\n",
      "NRCB1-F200W: 1.1453625392913818\n",
      "NRCB2-F090W: 1.1873260736465454\n",
      "NRCB2-F115W: 1.1144301891326904\n",
      "NRCB2-F150W: 1.1165666580200195\n",
      "NRCB2-F200W: 1.2063575649261475\n",
      "NRCB3-F090W: 1.0946354866027832\n",
      "NRCB3-F115W: 1.0048294067382812\n",
      "NRCB3-F150W: 1.0147753953933716\n",
      "NRCB3-F200W: 1.109879264831543\n",
      "NRCB4-F090W: 1.221938967704773\n",
      "NRCB4-F115W: 1.203657865524292\n",
      "NRCB4-F150W: 1.1781944036483765\n",
      "NRCB4-F200W: 1.231037425994873\n",
      "NRCBLONG-F277W: 1.0\n",
      "NRCBLONG-F356W: 1.0\n",
      "NRCBLONG-F444W: 0.912\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import yaml\n",
    "print(yaml.dump(scl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b148de56-e30a-4176-8c53-8784b4c2f17a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}