Evaluating_consolidated_galactic_plane_survey_footprints.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c0b6c974",
   "metadata": {},
   "source": [
    "# Evaluating Consolidated Galactic Plane Survey Footprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28e23087",
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import path\n",
    "import numpy as np\n",
    "import healpy as hp\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from astropy.coordinates import SkyCoord, Galactic\n",
    "from astropy import units as u\n",
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b3c1f4",
   "metadata": {},
   "source": [
    "The procedure for generating the sky maps is as follows, using code from https://github.com/LSST-TVSSC/software_tools.\n",
    "1. Footprint maps for each of the individual science cases are generated using generate_sky_maps.py.  The file footprint_maps/footprint_map_config.json provides the configuration for all of these programs. \n",
    "2. The combined footprint map including all science cases is generated using the generate_priority_map.py.  This produces a data table of the priority per HEALpixel in FITS table format. \n",
    "3. The combined footprint map HEALpixel data table is converted to a Mollweide map projection in FITS image format, using the code project_galmap_mollweide.py. \n",
    "4. The program objdetect_galplane_map.py, uses an object detection routine to detect centers of high priority, and then apply a clustering algorithm to condense the footprint as much as possible. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6e6f61",
   "metadata": {},
   "source": [
    "Object_galplane_map now accepts a range of priority threshold values to calculate for, and generates the datafile survey_area_priority_data.dat.  Here we explore those data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fc2e9c97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.00000000e+00 3.41844302e+03 5.17605071e+03]\n",
      " [2.02000000e+00 3.15238693e+03 4.87826154e+03]\n",
      " [2.04000000e+00 2.79736572e+03 4.50650794e+03]\n",
      " [2.06000000e+00 2.84520546e+03 4.53089110e+03]\n",
      " [2.08000000e+00 2.93249200e+03 4.57142308e+03]\n",
      " [2.10000000e+00 2.80072289e+03 4.46201893e+03]\n",
      " [2.12000000e+00 2.78813349e+03 4.44793329e+03]\n",
      " [2.14000000e+00 2.82002665e+03 4.47348463e+03]\n",
      " [2.16000000e+00 2.49186283e+03 4.13625278e+03]\n",
      " [2.18000000e+00 2.66391803e+03 4.28786823e+03]\n",
      " [2.20000000e+00 2.63286417e+03 4.22893484e+03]\n",
      " [2.22000000e+00 2.55984562e+03 4.15083635e+03]\n",
      " [2.24000000e+00 2.60264959e+03 4.16855418e+03]\n",
      " [2.26000000e+00 2.49270213e+03 4.03370370e+03]\n",
      " [2.28000000e+00 2.31561117e+03 3.79890949e+03]\n",
      " [2.30000000e+00 2.34582574e+03 3.81876700e+03]\n",
      " [2.32000000e+00 2.31980764e+03 3.77921253e+03]\n",
      " [2.34000000e+00 2.15530608e+03 3.57479124e+03]\n",
      " [2.36000000e+00 2.15782396e+03 3.57763008e+03]\n",
      " [2.38000000e+00 2.17041337e+03 3.60163169e+03]\n",
      " [2.40000000e+00 1.99919746e+03 3.38885299e+03]\n",
      " [2.42000000e+00 2.03192992e+03 3.40933516e+03]\n",
      " [2.44000000e+00 1.91862527e+03 3.26493361e+03]\n",
      " [2.46000000e+00 1.88589282e+03 3.21996083e+03]\n",
      " [2.48000000e+00 1.81287427e+03 3.08918499e+03]\n",
      " [2.50000000e+00 1.71383762e+03 3.02263857e+03]\n",
      " [2.52000000e+00 1.82126721e+03 3.10233545e+03]\n",
      " [2.54000000e+00 1.54849678e+03 2.62808153e+03]\n",
      " [2.56000000e+00 1.56696124e+03 2.63867069e+03]\n",
      " [2.58000000e+00 1.27740493e+03 2.28804091e+03]\n",
      " [2.60000000e+00 1.25474400e+03 2.24610443e+03]\n",
      " [2.62000000e+00 1.14983229e+03 2.14681835e+03]\n",
      " [2.64000000e+00 1.05667070e+03 1.91030603e+03]\n",
      " [2.66000000e+00 8.67829629e+02 1.69114532e+03]\n",
      " [2.68000000e+00 2.98788538e+02 8.17452513e+02]\n",
      " [2.70000000e+00 2.86199133e+02 7.91743928e+02]\n",
      " [2.72000000e+00 2.82002665e+02 7.76398751e+02]\n",
      " [2.74000000e+00 2.70252554e+02 7.50816381e+02]\n",
      " [2.76000000e+00 2.66056086e+02 7.40778964e+02]\n",
      " [2.78000000e+00 2.68573966e+02 7.47517314e+02]\n",
      " [2.80000000e+00 2.55145268e+02 7.21479313e+02]\n",
      " [2.82000000e+00 2.53466681e+02 7.18426052e+02]\n",
      " [2.84000000e+00 2.35002221e+02 6.81395456e+02]\n",
      " [2.86000000e+00 2.27448578e+02 6.62166548e+02]\n",
      " [2.88000000e+00 2.12341292e+02 6.27796264e+02]\n",
      " [2.90000000e+00 2.03948356e+02 6.15901127e+02]\n",
      " [2.92000000e+00 2.02269768e+02 6.09367764e+02]\n",
      " [2.94000000e+00 1.82966015e+02 5.67506502e+02]\n",
      " [2.96000000e+00 1.69537316e+02 5.33668497e+02]\n",
      " [2.98000000e+00 1.62822967e+02 5.21793419e+02]\n",
      " [3.00000000e+00 1.51072856e+02 4.97970733e+02]\n",
      " [3.02000000e+00 1.47715682e+02 4.90764028e+02]\n",
      " [3.04000000e+00 1.44358507e+02 4.84537792e+02]\n",
      " [3.06000000e+00 1.46037094e+02 4.87474778e+02]\n",
      " [3.08000000e+00 1.44358507e+02 4.85680655e+02]\n",
      " [3.10000000e+00 1.39322745e+02 4.72355356e+02]\n",
      " [3.12000000e+00 1.32608396e+02 4.58563314e+02]\n",
      " [3.14000000e+00 1.26733340e+02 4.44887475e+02]\n",
      " [3.16000000e+00 1.22536872e+02 4.34546267e+02]\n",
      " [3.18000000e+00 1.18340404e+02 4.24591644e+02]\n",
      " [3.20000000e+00 1.15822523e+02 4.12049623e+02]\n",
      " [3.22000000e+00 1.15822523e+02 4.12049623e+02]\n",
      " [3.24000000e+00 1.15822523e+02 4.12049623e+02]\n",
      " [3.26000000e+00 1.14983229e+02 4.10642263e+02]\n",
      " [3.28000000e+00 1.13304642e+02 4.05615629e+02]\n",
      " [3.30000000e+00 1.03233118e+02 3.78710701e+02]\n",
      " [3.32000000e+00 1.01554531e+02 3.72506806e+02]\n",
      " [3.34000000e+00 9.81973565e+01 3.63146445e+02]\n",
      " [3.36000000e+00 9.73580628e+01 3.61791945e+02]\n",
      " [3.38000000e+00 9.56794756e+01 3.56491791e+02]\n",
      " [3.40000000e+00 9.40008883e+01 3.54371835e+02]\n",
      " [3.42000000e+00 9.23223010e+01 3.49669336e+02]\n",
      " [3.44000000e+00 9.06437137e+01 3.47455720e+02]\n",
      " [3.46000000e+00 8.81258327e+01 3.41778994e+02]\n",
      " [3.48000000e+00 8.64472455e+01 3.40441114e+02]]\n"
     ]
    }
   ],
   "source": [
    "data_file = '/Users/rstreet1/software/LSST-TVS_software_tools/GalPlaneSurvey/HighCadenceZone/survey_area_priority_data.dat'\n",
    "data = np.loadtxt(data_file)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "32d298a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Priority threshold')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is the nominal survey area within the Galactic Plane assigned in the baseline_v2.0 opsim.  \n",
    "# See notebook Rubin_baseline_footprint.\n",
    "# The two different survey areas are produced by considering the Galactic Plane to be defined as \n",
    "# |b| <= 10.0 and |b| <= 15.0deg\n",
    "baseline2_galplane_area = 1483.87\n",
    "baseline2_galplane_area = 2797.37\n",
    "\n",
    "(fig, ax1) = plt.subplots(1, 1, figsize=(10, 10))\n",
    "ax1.plot(data[:,1], data[:,2],'g.')\n",
    "#ax1.plot(data[:,1], data[:,2],'g-')\n",
    "(xmin,xmax,ymin,ymax) = plt.axis()\n",
    "ax1.plot([baseline2_galplane_area]*2, [ymin,ymax], 'g--')\n",
    "ax1.set_xlabel('Survey area [sq. deg.]', fontsize=16, color='g')\n",
    "ax1.set_ylabel('Sum(priority) within survey region', fontsize=16)\n",
    "ax1.tick_params(axis='x', labelsize=16)\n",
    "ax1.tick_params(axis='y', labelsize=16)\n",
    "\n",
    "ax2 = ax1.twiny()\n",
    "ax2.plot(data[:,0], data[:,2], 'md')\n",
    "#ax2.plot(data[:,0], data[:,2], 'm-')\n",
    "ax2.invert_xaxis()\n",
    "ax2.tick_params(axis='x', labelsize=16)\n",
    "ax2.tick_params(axis='y', labelsize=16)\n",
    "ax2.set_xlabel('Priority threshold', fontsize=16, color='m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d9d0606",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a69ea78",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}