{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "67a83cf9-d6a1-408e-a83c-686c4bbbe2c9",
   "metadata": {},
   "source": [
    "## Compare sky coverage between metadected catalog an object catalog\n",
    "\n",
    "This is a simple script to compare the sky coverage of the two different processings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f079097a-27e6-4cd7-8988-f8899ec6429c",
   "metadata": {},
   "source": [
    "#### Standard imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "360b52b2-4540-4630-8b2a-835a61748b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tables_io\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d20cb6-17fb-419d-8832-4bb7e6870812",
   "metadata": {},
   "source": [
    "#### Read the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a8b76d5-95c1-45b7-b189-9236d2377356",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = tables_io.read('test_data/object_10463.pq')\n",
    "t2 = tables_io.read('test_data/shear_wmom_0p01_cleaned_10463_ns.pq')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38693875-ff0b-4035-a0c8-ff13d929c6b6",
   "metadata": {},
   "source": [
    "#### Plot the footprints of the object table and metedetect table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd90f80e-5ecd-47f7-b4b7-f17c3c8a64ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = plt.scatter(t.ra, t.dec, s=1, label=\"Object Table\")\n",
    "_ = plt.scatter(t2.ra, t2.dec, s=1, c=(t2.patch_x + 10*t2.patch_y), cmap='rainbow', label=\"MetaDetect\")\n",
    "_ = plt.colorbar(label=\"Patch number\")\n",
    "_ = plt.xlabel(\"RA [deg]\")\n",
    "_ = plt.ylabel(\"DEC [deg]\")\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e4f190a-0bfd-4ef9-ba25-f7d4aa3abd93",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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