{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FRB example\n",
    "\n",
    "## v2 -- Using PATH object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "from importlib import reload\n",
    "import os\n",
    "from pkg_resources import resource_filename\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pandas\n",
    "\n",
    "from astropy.coordinates import SkyCoord\n",
    "#from astropy.io import fits\n",
    "\n",
    "from astropath import path\n",
    "from astropath import localization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummy FRB (based on 180924)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Path = path.PATH()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Localization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Here is the full data model for the localization options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': {'dtype': str,\n",
       "  'options': ['eellipse', 'wcs', 'healpix'],\n",
       "  'help': 'Localization type.'},\n",
       " 'center_coord': {'dtype': astropy.coordinates.sky_coordinate.SkyCoord,\n",
       "  'help': '\"Central\" coordinate'},\n",
       " 'eellipse': {'dtype': dict,\n",
       "  'help': 'Error ellipse with keys \"a [arcsec]\", \"b [arcsec]\", \"theta [deg]\"'},\n",
       " 'healpix_data': {'dtype': (numpy.ndarray, astropy.table.table.Table),\n",
       "  'help': 'Data containing the healpix information. Input either as a simple numpy array for a full NESTED array or an astropy Table for NUNIQ format with columns UNIQ and PROBDENSITY.'},\n",
       " 'healpix_nside': {'dtype': int, 'help': 'NSIDE value of healpix map.'},\n",
       " 'healpix_ordering': {'dtype': str,\n",
       "  'options': ['NESTED', 'NUNIQ'],\n",
       "  'help': 'Ordering of the healpix information.'},\n",
       " 'healpix_coord': {'dtype': str,\n",
       "  'options': ['C'],\n",
       "  'help': 'Coordinate system of the healpix.'},\n",
       " 'wcs_data': {'dtype': numpy.ndarray, 'help': 'PDF of localization.'},\n",
       " 'wcs_WCS': {'dtype': astropy.wcs.wcs.WCS, 'help': 'WCS of the localization.'}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "localization.localization_dmodel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will use the Error Ellipse type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (ICRS): (ra, dec) in deg\n",
       "    (326.10522917, -40.90002778)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## FRB Coord\n",
    "frb_coord = SkyCoord('21h44m25.255s -40d54m00.10s', frame='icrs')\n",
    "frb_coord"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error ellipse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#eellipse = dict(a=0.3, b=0.1, theta=45.)\n",
    "#eellipse = dict(a=3, b=3, theta=90.)\n",
    "eellipse = dict(a=0.1, b=0.1, theta=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Path.init_localization('eellipse', center_coord=frb_coord, eellipse=eellipse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'eellipse',\n",
       " 'center_coord': <SkyCoord (ICRS): (ra, dec) in deg\n",
       "     (326.10522917, -40.90002778)>,\n",
       " 'eellipse': {'a': 0.1, 'b': 0.1, 'theta': 0.0}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Path.localiz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Candidates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cand_file = os.path.join(resource_filename('astropath', 'data'), 'frb_example', 'frb180924_candidates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>half_light</th>\n",
       "      <th>VLT_FORS2_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>326.101738</td>\n",
       "      <td>-40.899771</td>\n",
       "      <td>0.458869</td>\n",
       "      <td>25.295416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>326.105365</td>\n",
       "      <td>-40.900239</td>\n",
       "      <td>1.308629</td>\n",
       "      <td>21.319569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>326.104186</td>\n",
       "      <td>-40.900180</td>\n",
       "      <td>0.814683</td>\n",
       "      <td>24.272838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>326.106237</td>\n",
       "      <td>-40.899279</td>\n",
       "      <td>0.501247</td>\n",
       "      <td>25.474828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ra        dec  half_light  VLT_FORS2_g\n",
       "3  326.101738 -40.899771    0.458869    25.295416\n",
       "7  326.105365 -40.900239    1.308629    21.319569\n",
       "8  326.104186 -40.900180    0.814683    24.272838\n",
       "9  326.106237 -40.899279    0.501247    25.474828"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates = pandas.read_csv(cand_file, index_col=0)\n",
    "candidates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Init in PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Path.init_candidates(candidates.ra.values,\n",
    "                     candidates.dec.values,\n",
    "                     candidates.half_light.values,\n",
    "                     mag=candidates.VLT_FORS2_g.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>ang_size</th>\n",
       "      <th>mag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>326.101738</td>\n",
       "      <td>-40.899771</td>\n",
       "      <td>0.458869</td>\n",
       "      <td>25.295416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>326.105365</td>\n",
       "      <td>-40.900239</td>\n",
       "      <td>1.308629</td>\n",
       "      <td>21.319569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>326.104186</td>\n",
       "      <td>-40.900180</td>\n",
       "      <td>0.814683</td>\n",
       "      <td>24.272838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>326.106237</td>\n",
       "      <td>-40.899279</td>\n",
       "      <td>0.501247</td>\n",
       "      <td>25.474828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ra        dec  ang_size        mag\n",
       "0  326.101738 -40.899771  0.458869  25.295416\n",
       "1  326.105365 -40.900239  1.308629  21.319569\n",
       "2  326.104186 -40.900180  0.814683  24.272838\n",
       "3  326.106237 -40.899279  0.501247  25.474828"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Path.candidates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Priors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Candidate prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Path.init_cand_prior('inverse', P_U=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Offset prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Path.init_theta_prior('exp', 6.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0316325 , 0.87225473, 0.06831713, 0.02779563])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Path.calc_priors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>ang_size</th>\n",
       "      <th>mag</th>\n",
       "      <th>P_O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>326.101738</td>\n",
       "      <td>-40.899771</td>\n",
       "      <td>0.458869</td>\n",
       "      <td>25.295416</td>\n",
       "      <td>0.031633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>326.105365</td>\n",
       "      <td>-40.900239</td>\n",
       "      <td>1.308629</td>\n",
       "      <td>21.319569</td>\n",
       "      <td>0.872255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>326.104186</td>\n",
       "      <td>-40.900180</td>\n",
       "      <td>0.814683</td>\n",
       "      <td>24.272838</td>\n",
       "      <td>0.068317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>326.106237</td>\n",
       "      <td>-40.899279</td>\n",
       "      <td>0.501247</td>\n",
       "      <td>25.474828</td>\n",
       "      <td>0.027796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ra        dec  ang_size        mag       P_O\n",
       "0  326.101738 -40.899771  0.458869  25.295416  0.031633\n",
       "1  326.105365 -40.900239  1.308629  21.319569  0.872255\n",
       "2  326.104186 -40.900180  0.814683  24.272838  0.068317\n",
       "3  326.106237 -40.899279  0.501247  25.474828  0.027796"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Path.candidates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Posteriors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "P_Ox, P_Ux = Path.calc_posteriors('fixed', box_hwidth=30.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>ang_size</th>\n",
       "      <th>mag</th>\n",
       "      <th>P_O</th>\n",
       "      <th>p_xO</th>\n",
       "      <th>P_Ox</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>326.101738</td>\n",
       "      <td>-40.899771</td>\n",
       "      <td>0.458869</td>\n",
       "      <td>25.295416</td>\n",
       "      <td>0.031633</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>326.105365</td>\n",
       "      <td>-40.900239</td>\n",
       "      <td>1.308629</td>\n",
       "      <td>21.319569</td>\n",
       "      <td>0.872255</td>\n",
       "      <td>5.156772e-02</td>\n",
       "      <td>9.895195e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>326.104186</td>\n",
       "      <td>-40.900180</td>\n",
       "      <td>0.814683</td>\n",
       "      <td>24.272838</td>\n",
       "      <td>0.068317</td>\n",
       "      <td>6.973468e-03</td>\n",
       "      <td>1.048049e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>326.106237</td>\n",
       "      <td>-40.899279</td>\n",
       "      <td>0.501247</td>\n",
       "      <td>25.474828</td>\n",
       "      <td>0.027796</td>\n",
       "      <td>9.296108e-20</td>\n",
       "      <td>5.684350e-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ra        dec  ang_size        mag       P_O          p_xO  \\\n",
       "0  326.101738 -40.899771  0.458869  25.295416  0.031633  0.000000e+00   \n",
       "1  326.105365 -40.900239  1.308629  21.319569  0.872255  5.156772e-02   \n",
       "2  326.104186 -40.900180  0.814683  24.272838  0.068317  6.973468e-03   \n",
       "3  326.106237 -40.899279  0.501247  25.474828  0.027796  9.296108e-20   \n",
       "\n",
       "           P_Ox  \n",
       "0  0.000000e+00  \n",
       "1  9.895195e-01  \n",
       "2  1.048049e-02  \n",
       "3  5.684350e-20  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Path.candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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