iris_example_dq_init.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jwst import datamodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = datamodels.TMTRampModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.pixeldq = np.zeros((4096, 4096))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.meta.observation.time = \"10:00:00\"\n",
    "f.meta.instrument.name = \"IRIS\"\n",
    "f.meta.instrument.detector = \"IRIS1\"\n",
    "f.meta.filter = \"K\"\n",
    "f.meta.exposure_type = \"IRIS_IMAGE\"\n",
    "f.meta.observation.date = \"01/01/2019\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flat-fielding also requires the subarray properties to be defined\n",
    "f.meta.subarray.name = 'FULL'\n",
    "f.meta.subarray.xsize = 4096\n",
    "f.meta.subarray.xstart = 1\n",
    "f.meta.subarray.ysize = 4096\n",
    "f.meta.subarray.ystart = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([       0,        0,        0,        0,        0, 16777216,\n",
       "               0,        0,        0,        0]),\n",
       " array([-0.5, -0.4, -0.3, -0.2, -0.1,  0. ,  0.1,  0.2,  0.3,  0.4,  0.5]))"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(f.pixeldq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.data = np.zeros((1, 4, 4096, 4096))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.groupdq = np.zeros((1, 4, 4096, 4096))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "        [[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "        [[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "        [[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]]]], dtype=uint8)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.groupdq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"test_ramp.fits\"\n",
    "f.write(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jwst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"CRDS_PATH\"]= str(Path.home() / \"crds_cache\")\n",
    "os.environ[\"CRDS_CONTEXT\"]=\"tmt_0001.pmap\"\n",
    "os.environ[\"CRDS_SERVER_URL\"]=\"https://crds-serverless-mode.stsci.edu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from iris_pipeline import dq_init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TMTRampModel(1, 4, 4096, 4096) from test_ramp.fits>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datamodels.open(\"test_ramp.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-04-18 17:28:44,121 - stpipe.DQInitStep - INFO - DQInitStep instance created.\n",
      "2019-04-18 17:28:44,190 - stpipe.DQInitStep - INFO - Step DQInitStep running with args ('test_ramp.fits',).\n",
      "2019-04-18 17:28:44,312 - stpipe.DQInitStep - INFO - Using MASK reference file /home/azonca/crds_cache/references/tmt/iris/tmt_iris_mask_0001.fits\n",
      "2019-04-18 17:28:44,626 - stpipe.DQInitStep - INFO - Step DQInitStep done\n"
     ]
    }
   ],
   "source": [
    "out = dq_init.DQInitStep.call(\"test_ramp.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([16777016,        0,        0,        0,        0,      100,\n",
       "               0,        0,        0,      100]),\n",
       " array([   0. ,  204.8,  409.6,  614.4,  819.2, 1024. , 1228.8, 1433.6,\n",
       "        1638.4, 1843.2, 2048. ]))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(out.pixeldq)"
   ]
  }
 ],
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