test_merge_subarrays.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge full frame with concurrent subarrays\n",
    "\n",
    "This is a level 3 tool, it assumes the inputs have been already reduced by a level 2 tool (the `image2` pipeline in this case).\n",
    "\n",
    "It takes a pointer to the full frame and the subarrays and combines them back into a single reduced file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import iris_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_pipeline.monkeypatch_jwst_datamodels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/azonca/p/software/iris_pipeline/iris_pipeline/tests'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input files\n",
    "\n",
    "Input files are defined by an association JSON file.\n",
    "We specify the full frame first, then the subarrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting asn_merge_subarrays.json\n"
     ]
    }
   ],
   "source": [
    "%%file asn_merge_subarrays.json\n",
    "\n",
    "{\n",
    "    \"asn_rule\": \"Asn_Image\",\n",
    "    \"asn_pool\": \"pool\",\n",
    "    \"asn_type\": \"image3\",\n",
    "    \"products\": [\n",
    "        {\n",
    "            \"name\": \"test_merge_subarrays_asn\",\n",
    "            \"members\": [\n",
    "                {\n",
    "                    \"expname\": \"data/reduced_science_frame_sci_with_subarrays.fits\",\n",
    "                    \"exptype\": \"science\"\n",
    "                },\n",
    "                {\n",
    "                    \"expname\": \"data/reduced_science_frame_sci_subarray_1.fits\",\n",
    "                    \"exptype\": \"science\"\n",
    "                },\n",
    "                {\n",
    "                    \"expname\": \"data/reduced_science_frame_sci_subarray_2.fits\",\n",
    "                    \"exptype\": \"science\"\n",
    "                }\n",
    "            ]\n",
    "        }\n",
    "    ]   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-04-17 09:42:09,868 - stpipe.MergeSubarraysStep - INFO - MergeSubarraysStep instance created.\n",
      "2020-04-17 09:42:09,871 - stpipe.MergeSubarraysStep - INFO - MergeSubarraysStep instance created.\n",
      "2020-04-17 09:42:09,948 - stpipe.MergeSubarraysStep - INFO - Step MergeSubarraysStep running with args ('asn_merge_subarrays.json',).\n",
      "2020-04-17 09:42:10,816 - stpipe.MergeSubarraysStep - INFO - Step MergeSubarraysStep done\n"
     ]
    }
   ],
   "source": [
    "im = iris_pipeline.MergeSubarraysStep().call(\"asn_merge_subarrays.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "original = iris_pipeline.datamodels.IRISImageModel(\"data/reduced_science_frame_sci_with_subarrays.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "292231.53"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.data.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "vmax = 100000\n",
    "fig, axes = plt.subplots(1,2, figsize=(10,5))\n",
    "axes[0].imshow(original.data, vmin=0, vmax=vmax)\n",
    "axes[0].set_title(\"Original\")\n",
    "axes[1].imshow(im.data, vmin=0, vmax=vmax)\n",
    "axes[1].set_title(\"Merged\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "jwst",
   "language": "python",
   "name": "jwst"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}