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  {
   "cell_type": "markdown",
   "id": "986421ef-f460-416f-a30e-99bfe5377c68",
   "metadata": {},
   "source": [
    "## Compare metadetect catalog types\n",
    "\n",
    "This is a simple script to measure the match efficiency between different metadectect shear catalogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a0d82a6-62a3-411d-bea8-3dc7ca19f763",
   "metadata": {},
   "source": [
    "#### Standard imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e5633bc-cfde-4a82-9501-5da113eeb2f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import hpmcm\n",
    "import glob\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d91c8fdb-de5d-4e58-a261-94e9978cba58",
   "metadata": {},
   "source": [
    "#### Set up the configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02ae089e-f779-4070-af62-97f9e2c7b876",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = \"test_data\"   # Input data directory\n",
    "shear_st = \"0p01\"       # Applied shear as a string\n",
    "shear = 0.01            # Decimal version of applied shear\n",
    "shear_type = \"wmom\"     # which object characterization to use \n",
    "tract = 10463           # which tract to study\n",
    "\n",
    "SOURCE_TABLEFILES = sorted(glob.glob(os.path.join(DATADIR, f\"shear_{shear_type}_{shear_st}_uncleaned_{tract}_*.pq\")))\n",
    "SOURCE_TABLEFILES.reverse()\n",
    "VISIT_IDS = np.arange(len(SOURCE_TABLEFILES))\n",
    "\n",
    "PIXEL_R2CUT = 4.         # Cut at distance**2 = 4 pixels\n",
    "PIXEL_MATCH_SCALE = 1    # Use pixel scale to do matching"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3004f6-2c3c-4b82-b021-baa2271a30c0",
   "metadata": {},
   "source": [
    "#### Make the matcher, reduce the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c5e34c0-17cd-493d-a76e-3a17a41046f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "matcher = hpmcm.ShearMatch.createShearMatch(pixel_r2_cut=PIXEL_R2CUT, pixel_match_scale=PIXEL_MATCH_SCALE)\n",
    "matcher.reduceData(SOURCE_TABLEFILES, VISIT_IDS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df1f0291-6f6d-45c2-bd9c-16fcc9daf84b",
   "metadata": {},
   "source": [
    "#### Run the data\n",
    "\n",
    "Note the option to run all the cells.  By default we only run a small subset for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18c03bad-473f-40af-86a1-4732ec7e5755",
   "metadata": {},
   "outputs": [],
   "source": [
    "do_partial = True\n",
    "if do_partial:\n",
    "    x_range = range(50, 70)\n",
    "    y_range = range(170, 190)\n",
    "    matcher.analysisLoop(x_range, y_range)\n",
    "else:\n",
    "    matcher.analysisLoop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50776edd-185b-42b3-bf9d-f548ad42fbb1",
   "metadata": {},
   "source": [
    "#### Classify the objects by match type\n",
    "\n",
    "This looks at the characteristics of the matched objects and categorizes them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d8f6046-ab29-4bd4-9fa1-5c7680dee150",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_lists = hpmcm.classify.classifyObjects(matcher)\n",
    "hpmcm.classify.printObjectTypes(obj_lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d401578-ae4f-4ed8-9c37-bb34cb28cf21",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_good = len(obj_lists['ideal'])\n",
    "bad_list = ['edge_mixed', 'edge_missing', 'edge_extra', 'orphan', 'missing', 'two_missing', 'many_missing', 'extra', 'caught']\n",
    "n_bad = np.sum([len(obj_lists[x]) for x in bad_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37417087-9be8-4652-9da0-47f4d6187d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_good/(n_good+n_bad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2d0eeec-4ea9-42eb-a96c-e3bebccd762b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e8857e9-ba17-4c2a-8c9f-a177a7457f81",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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