magnitudes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3083fc2d-e5fa-48cb-93a8-1728d726785a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7e6f372-158a-4caf-9732-8c338a31cdb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlite3 import connect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "332d1037-646a-45dc-8742-2a27b7774763",
   "metadata": {},
   "outputs": [],
   "source": [
    "opsim_db = connect('baseline_v2.0_10yrs.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc585855-1bf6-4ccc-a028-d0b293e99b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "opsim_file = pd.read_sql('select * from observations limit 1000', opsim_db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "72944be8-ac93-49d2-83df-c35813132bd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['observationId', 'fieldRA', 'fieldDec', 'observationStartMJD',\n",
       "       'flush_by_mjd', 'visitExposureTime', 'filter', 'rotSkyPos',\n",
       "       'numExposures', 'airmass', 'seeingFwhm500', 'seeingFwhmEff',\n",
       "       'seeingFwhmGeom', 'skyBrightness', 'night', 'slewTime', 'visitTime',\n",
       "       'slewDistance', 'fiveSigmaDepth', 'altitude', 'azimuth', 'paraAngle',\n",
       "       'cloud', 'moonAlt', 'sunAlt', 'note', 'fieldId', 'proposalId',\n",
       "       'block_id', 'observationStartLST', 'rotTelPos', 'moonAz', 'sunAz',\n",
       "       'sunRA', 'sunDec', 'moonRA', 'moonDec', 'moonDistance', 'solarElong',\n",
       "       'moonPhase', 'cummTelAz'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opsim_file.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34e53bea-0edb-48e7-95e2-a153709f713e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('observationId',\n",
       " 'fieldRA',\n",
       " 'fieldDec',\n",
       " 'observationStartMJD',\n",
       " 'fieldId',\n",
       " 'filter',\n",
       " 'fiveSigmaDepth')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'observationId', 'fieldRA', 'fieldDec', 'observationStartMJD', 'fieldId', 'filter', 'fiveSigmaDepth'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c863b5eb-9b63-4f2b-91bd-7afca0cce816",
   "metadata": {},
   "outputs": [],
   "source": [
    "band_obs = pd.read_sql_query('SELECT observationId, fieldRA, fieldDec, observationStartMJD, fieldId, filter, fiveSigmaDepth FROM observations', opsim_db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54403856-1534-4415-96f9-5f8382519cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>observationId</th>\n",
       "      <th>fieldRA</th>\n",
       "      <th>fieldDec</th>\n",
       "      <th>observationStartMJD</th>\n",
       "      <th>fieldId</th>\n",
       "      <th>filter</th>\n",
       "      <th>fiveSigmaDepth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>310.024480</td>\n",
       "      <td>-60.812928</td>\n",
       "      <td>60218.001806</td>\n",
       "      <td>-1</td>\n",
       "      <td>y</td>\n",
       "      <td>22.370557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>310.601871</td>\n",
       "      <td>-63.561425</td>\n",
       "      <td>60218.002254</td>\n",
       "      <td>-1</td>\n",
       "      <td>y</td>\n",
       "      <td>22.338327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>311.292611</td>\n",
       "      <td>-66.317774</td>\n",
       "      <td>60218.002703</td>\n",
       "      <td>-1</td>\n",
       "      <td>y</td>\n",
       "      <td>22.295487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>312.140731</td>\n",
       "      <td>-69.082666</td>\n",
       "      <td>60218.003152</td>\n",
       "      <td>-1</td>\n",
       "      <td>y</td>\n",
       "      <td>21.270421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>304.170163</td>\n",
       "      <td>-73.375442</td>\n",
       "      <td>60218.003624</td>\n",
       "      <td>-1</td>\n",
       "      <td>y</td>\n",
       "      <td>21.205276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2086975</th>\n",
       "      <td>2086975</td>\n",
       "      <td>338.528926</td>\n",
       "      <td>-55.399555</td>\n",
       "      <td>63870.104267</td>\n",
       "      <td>-1</td>\n",
       "      <td>y</td>\n",
       "      <td>21.917628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086976</th>\n",
       "      <td>2086976</td>\n",
       "      <td>347.380828</td>\n",
       "      <td>-54.922128</td>\n",
       "      <td>63870.104758</td>\n",
       "      <td>-1</td>\n",
       "      <td>y</td>\n",
       "      <td>21.864586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086977</th>\n",
       "      <td>2086977</td>\n",
       "      <td>350.974688</td>\n",
       "      <td>-52.986827</td>\n",
       "      <td>63870.105219</td>\n",
       "      <td>-1</td>\n",
       "      <td>y</td>\n",
       "      <td>21.871581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086978</th>\n",
       "      <td>2086978</td>\n",
       "      <td>356.062503</td>\n",
       "      <td>-53.778759</td>\n",
       "      <td>63870.105677</td>\n",
       "      <td>-1</td>\n",
       "      <td>y</td>\n",
       "      <td>21.850527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086979</th>\n",
       "      <td>2086979</td>\n",
       "      <td>352.585400</td>\n",
       "      <td>-55.879554</td>\n",
       "      <td>63870.106138</td>\n",
       "      <td>-1</td>\n",
       "      <td>y</td>\n",
       "      <td>21.837416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2086980 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         observationId     fieldRA   fieldDec  observationStartMJD  fieldId  \\\n",
       "0                    0  310.024480 -60.812928         60218.001806       -1   \n",
       "1                    1  310.601871 -63.561425         60218.002254       -1   \n",
       "2                    2  311.292611 -66.317774         60218.002703       -1   \n",
       "3                    3  312.140731 -69.082666         60218.003152       -1   \n",
       "4                    4  304.170163 -73.375442         60218.003624       -1   \n",
       "...                ...         ...        ...                  ...      ...   \n",
       "2086975        2086975  338.528926 -55.399555         63870.104267       -1   \n",
       "2086976        2086976  347.380828 -54.922128         63870.104758       -1   \n",
       "2086977        2086977  350.974688 -52.986827         63870.105219       -1   \n",
       "2086978        2086978  356.062503 -53.778759         63870.105677       -1   \n",
       "2086979        2086979  352.585400 -55.879554         63870.106138       -1   \n",
       "\n",
       "        filter  fiveSigmaDepth  \n",
       "0            y       22.370557  \n",
       "1            y       22.338327  \n",
       "2            y       22.295487  \n",
       "3            y       21.270421  \n",
       "4            y       21.205276  \n",
       "...        ...             ...  \n",
       "2086975      y       21.917628  \n",
       "2086976      y       21.864586  \n",
       "2086977      y       21.871581  \n",
       "2086978      y       21.850527  \n",
       "2086979      y       21.837416  \n",
       "\n",
       "[2086980 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "band_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "af68a082-ca75-4313-942d-38774b87785c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib Inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dee442e8-3eff-4635-bd07-fc3e74d5d307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'DEC')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(band_obs['fieldRA'], band_obs['fieldDec'])\n",
    "plt.xlabel('RA')\n",
    "plt.ylabel('DEC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "52c4cda9-b2aa-4a27-9cd0-269b54d80099",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as npl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "083dccc5-4826-42d5-9f71-dca602162981",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f716a4fe978>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(band_obs['fieldRA'], band_obs['fieldDec'], bins = 50, norm=npl.colors.LogNorm())\n",
    "plt.xlabel('RA')\n",
    "plt.ylabel('DEC')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf41e81a-e8b8-46b7-bdd2-5bb9b4089ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1ea3b215-bbda-48fb-8948-10961bfa2db3",
   "metadata": {},
   "outputs": [],
   "source": [
    "find_max_RA = np.max(band_obs['fieldRA'])\n",
    "find_min_RA = np.min(band_obs['fieldRA'])\n",
    "find_max_DEC = np.max(band_obs['fieldDec'])\n",
    "find_min_DEC = np.min(band_obs['fieldDec'])\n",
    "num_bins = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "e82a01a4-b7b2-47d0-97f5-229cf4df053d",
   "metadata": {},
   "outputs": [],
   "source": [
    "RA_set = np.linspace(find_min_RA, find_max_RA, num_bins)\n",
    "DEC_set = np.linspace(find_min_DEC, find_max_DEC, num_bins)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "3c304fdb-3ee1-4eb7-b005-974cf9e538af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5.90633443e-04, 7.34751493e+00, 1.46944392e+01, 2.20413635e+01,\n",
       "        2.93882878e+01, 3.67352121e+01, 4.40821364e+01, 5.14290607e+01,\n",
       "        5.87759850e+01, 6.61229093e+01, 7.34698336e+01, 8.08167579e+01,\n",
       "        8.81636822e+01, 9.55106065e+01, 1.02857531e+02, 1.10204455e+02,\n",
       "        1.17551379e+02, 1.24898304e+02, 1.32245228e+02, 1.39592152e+02,\n",
       "        1.46939077e+02, 1.54286001e+02, 1.61632925e+02, 1.68979850e+02,\n",
       "        1.76326774e+02, 1.83673698e+02, 1.91020622e+02, 1.98367547e+02,\n",
       "        2.05714471e+02, 2.13061395e+02, 2.20408320e+02, 2.27755244e+02,\n",
       "        2.35102168e+02, 2.42449093e+02, 2.49796017e+02, 2.57142941e+02,\n",
       "        2.64489865e+02, 2.71836790e+02, 2.79183714e+02, 2.86530638e+02,\n",
       "        2.93877563e+02, 3.01224487e+02, 3.08571411e+02, 3.15918336e+02,\n",
       "        3.23265260e+02, 3.30612184e+02, 3.37959108e+02, 3.45306033e+02,\n",
       "        3.52652957e+02, 3.59999881e+02]),\n",
       " array([-89.77809403, -87.24096482, -84.70383561, -82.1667064 ,\n",
       "        -79.62957719, -77.09244797, -74.55531876, -72.01818955,\n",
       "        -69.48106034, -66.94393113, -64.40680192, -61.86967271,\n",
       "        -59.3325435 , -56.79541429, -54.25828508, -51.72115587,\n",
       "        -49.18402666, -46.64689745, -44.10976824, -41.57263903,\n",
       "        -39.03550982, -36.49838061, -33.9612514 , -31.42412219,\n",
       "        -28.88699298, -26.34986377, -23.81273455, -21.27560534,\n",
       "        -18.73847613, -16.20134692, -13.66421771, -11.1270885 ,\n",
       "         -8.58995929,  -6.05283008,  -3.51570087,  -0.97857166,\n",
       "          1.55855755,   4.09568676,   6.63281597,   9.16994518,\n",
       "         11.70707439,  14.2442036 ,  16.78133281,  19.31846202,\n",
       "         21.85559123,  24.39272044,  26.92984965,  29.46697887,\n",
       "         32.00410808,  34.54123729]))"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RA_set, DEC_set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c73a44e0-c21e-4dd8-94dc-fde632dcda4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-61.869672711842924"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DEC_set[11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "3a784692-ea8c-46cd-8b36-097a8138d81c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([43, 43, 43, ..., 48, 49, 48])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RA_bins = np.digitize(band_obs['fieldRA'], RA_set, right=True)\n",
    "RA_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "5989a9d2-6775-4847-9a11-88fdfb6b3036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([43, 43, 43, ..., 48, 49, 48])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.searchsorted(RA_set, band_obs['fieldRA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "c87d2465-c536-4d41-8904-2f2c75f3c271",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 11, 10, ..., 15, 15, 14])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DEC_bins = np.digitize(band_obs['fieldDec'], DEC_set, right=True)\n",
    "DEC_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "ad703853-7a94-4a94-bb0f-85b409261afe",
   "metadata": {},
   "outputs": [],
   "source": [
    "band_obs['RABins'] = RA_bins\n",
    "band_obs['DECBins'] = DEC_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "74dda8af-b411-4f3c-819c-afb84ee6c009",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(DEC_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4df61024-5a58-4133-8621-ebf281ccaa43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>observationId</th>\n",
       "      <th>fieldRA</th>\n",
       "      <th>fieldDec</th>\n",
       "      <th>observationStartMJD</th>\n",
       "      <th>fieldId</th>\n",
       "      <th>filter</th>\n",
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       "      <td>2364</td>\n",
       "      <td>y</td>\n",
       "      <td>21.917628</td>\n",
       "      <td>47</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>2086976</th>\n",
       "      <td>2086976</td>\n",
       "      <td>347.380828</td>\n",
       "      <td>-54.922128</td>\n",
       "      <td>63870.104758</td>\n",
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       "  </tbody>\n",
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       "<p>2086980 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         observationId     fieldRA   fieldDec  observationStartMJD  fieldId  \\\n",
       "0                    0  310.024480 -60.812928         60218.001806     2162   \n",
       "1                    1  310.601871 -63.561425         60218.002254     2161   \n",
       "2                    2  311.292611 -66.317774         60218.002703     2160   \n",
       "3                    3  312.140731 -69.082666         60218.003152     2159   \n",
       "4                    4  304.170163 -73.375442         60218.003624     2107   \n",
       "...                ...         ...        ...                  ...      ...   \n",
       "2086975        2086975  338.528926 -55.399555         63870.104267     2364   \n",
       "2086976        2086976  347.380828 -54.922128         63870.104758     2414   \n",
       "2086977        2086977  350.974688 -52.986827         63870.105219     2415   \n",
       "2086978        2086978  356.062503 -53.778759         63870.105677     2465   \n",
       "2086979        2086979  352.585400 -55.879554         63870.106138     2414   \n",
       "\n",
       "        filter  fiveSigmaDepth  RABins  DECBins  \n",
       "0            y       22.370557      43       12  \n",
       "1            y       22.338327      43       11  \n",
       "2            y       22.295487      43       10  \n",
       "3            y       21.270421      43        9  \n",
       "4            y       21.205276      42        7  \n",
       "...        ...             ...     ...      ...  \n",
       "2086975      y       21.917628      47       14  \n",
       "2086976      y       21.864586      48       14  \n",
       "2086977      y       21.871581      48       15  \n",
       "2086978      y       21.850527      49       15  \n",
       "2086979      y       21.837416      48       14  \n",
       "\n",
       "[2086980 rows x 9 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_field_id = (RA_bins) * 50 + DEC_bins\n",
    "band_obs['fieldId'] = new_field_id\n",
    "band_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f2f2346d-6e7e-46fd-80ab-d63354e73076",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>observationId</th>\n",
       "      <th>fieldRA</th>\n",
       "      <th>fieldDec</th>\n",
       "      <th>observationStartMJD</th>\n",
       "      <th>fieldId</th>\n",
       "      <th>filter</th>\n",
       "      <th>fiveSigmaDepth</th>\n",
       "      <th>RABins</th>\n",
       "      <th>DECBins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [observationId, fieldRA, fieldDec, observationStartMJD, fieldId, filter, fiveSigmaDepth, RABins, DECBins]\n",
       "Index: []"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "band_obs.query(f'fieldId == 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "671961c0-c0f0-4234-9378-c6f6cdbc7669",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         observationId     fieldRA   fieldDec  observationStartMJD  fieldId  \\\n",
      "772                772  349.857034 -82.633511         60218.357890     2403   \n",
      "846124          846124  349.132762 -83.345848         61682.142242     2403   \n",
      "860288          860288  349.770490 -84.147058         61703.077226     2403   \n",
      "1680668        1680668  349.123067 -82.933787         63159.185750     2403   \n",
      "1875208        1875208  346.865503 -83.230118         63536.152786     2403   \n",
      "\n",
      "        filter  fiveSigmaDepth  RABins  DECBins  \n",
      "772          r       22.115278      48        3  \n",
      "846124       r       23.833470      48        3  \n",
      "860288       r       23.336416      48        3  \n",
      "1680668      r       23.423731      48        3  \n",
      "1875208      r       23.918868      48        3            observationId     fieldRA   fieldDec  observationStartMJD  fieldId  \\\n",
      "6825              6825  352.165162 -82.198183         60226.356411     2403   \n",
      "1259256        1259256  351.795196 -83.451359         62442.066218     2403   \n",
      "1478871        1478871  346.629750 -83.641666         62802.056051     2403   \n",
      "1680618        1680618  349.123067 -82.933787         63159.161138     2403   \n",
      "\n",
      "        filter  fiveSigmaDepth  RABins  DECBins  \n",
      "6825         g       24.091112      48        3  \n",
      "1259256      g       24.005959      48        3  \n",
      "1478871      g       23.803613      48        3  \n",
      "1680618      g       23.770814      48        3  \n",
      "2491\n"
     ]
    }
   ],
   "source": [
    "r_filter = band_obs.query(f'fieldId == 2403 and filter == \"r\"')\n",
    "g_filter = band_obs.query(f'fieldId == 2403 and filter == \"g\"')\n",
    "print(r_filter, g_filter)\n",
    "field_id_max = np.max(band_obs['fieldId'])\n",
    "#field_id_min = np.min(band_obs['fieldId'])\n",
    "print(field_id_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "b33aa337-fa4b-4255-844b-3f59bcc0961c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "atmos_10.dat   filter_r.dat    hardware_r.dat  lens3.dat\ttotal_g.dat\n",
      "atmos_std.dat  filter_u.dat    hardware_u.dat  LSSTfilters.png\ttotal_i.dat\n",
      "darksky.dat    filter_y.dat    hardware_y.dat  m1.dat\t\ttotal_r.dat\n",
      "detector.dat   filter_z.dat    hardware_z.dat  m2.dat\t\ttotal_u.dat\n",
      "filter_g.dat   hardware_g.dat  lens1.dat       m3.dat\t\ttotal_y.dat\n",
      "filter_i.dat   hardware_i.dat  lens2.dat       README.md\ttotal_z.dat\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "\n",
    "fdir = os.getenv('RUBIN_SIM_DATA_DIR')\n",
    "if fdir is None:  #environment variable not set\n",
    "    fdir = os.path.join(os.getenv('HOME'), 'rubin_sim_data')\n",
    "\n",
    "fdir = '/home/jovyan/uw-premap-2022-photoz/data/lsst_baseline_throughputs'\n",
    "\n",
    "!ls $fdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1977465f-c747-4eb9-b20f-6584d1aa766b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "atmos_10.dat   filter_r.dat    hardware_r.dat  lens3.dat\ttotal_g.dat\n",
      "atmos_std.dat  filter_u.dat    hardware_u.dat  LSSTfilters.png\ttotal_i.dat\n",
      "darksky.dat    filter_y.dat    hardware_y.dat  m1.dat\t\ttotal_r.dat\n",
      "detector.dat   filter_z.dat    hardware_z.dat  m2.dat\t\ttotal_u.dat\n",
      "filter_g.dat   hardware_g.dat  lens1.dat       m3.dat\t\ttotal_y.dat\n",
      "filter_i.dat   hardware_i.dat  lens2.dat       README.md\ttotal_z.dat\n"
     ]
    }
   ],
   "source": [
    "fdir = os.getenv('RUBIN_SIM_DATA_DIR')\n",
    "if fdir is None:  #environment variable not set\n",
    "    fdir = os.path.join(os.getenv('HOME'), 'rubin_sim_data')\n",
    "\n",
    "fdir = '/home/jovyan/uw-premap-2022-photoz/data/lsst_baseline_throughputs'\n",
    "\n",
    "!ls $fdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "4c8a5811-d93a-4331-8faa-bc13f7b41925",
   "metadata": {},
   "outputs": [],
   "source": [
    "import rubin_sim.photUtils.Bandpass as Bandpass\n",
    "import rubin_sim.photUtils.Sed as Sed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "a54169c6-6b45-4a3b-a0c0-b59964c27e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "sun = Sed()\n",
    "sun.readSED_flambda('/home/jovyan/uw-premap-2022-photoz/data/sed_templates/CWW_KINNEY/CWW_E_ext.sed')\n",
    "sun.wavelen/=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9a5dbd9-5084-4cde-a83d-9331d79a42a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "48411316-9469-4ad5-a0b5-a2a76cbcef5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>-4.150003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>-5.896804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>-6.622219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>-6.974888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>-7.233845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>-7.425142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sun\n",
       "u -4.150003\n",
       "g -5.896804\n",
       "r -6.622219\n",
       "i -6.974888\n",
       "z -7.233845\n",
       "y -7.425142"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filterlist = ['u', 'g', 'r', 'i', 'z', 'y']\n",
    "\n",
    "lsst = {}\n",
    "for f in filterlist:\n",
    "    lsst[f] = Bandpass()\n",
    "    lsst[f].readThroughput(os.path.join(fdir, f'total_{f}.dat'))\n",
    "    \n",
    "mags = {}\n",
    "for f in filterlist:\n",
    "    mags[f] = sun.calcMag(lsst[f])\n",
    "\n",
    "# Make a dataframe just to get a nice output cell.\n",
    "pd.DataFrame(mags, index=['Sun']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "a6580a77-07e6-4098-b202-5fbce7293345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distant Sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>22.472216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>20.725415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>19.647330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>19.388374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>19.197076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Distant Sun\n",
       "u    22.472216\n",
       "g    20.725415\n",
       "r    20.000000\n",
       "i    19.647330\n",
       "z    19.388374\n",
       "y    19.197076"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = 20.0\n",
    "\n",
    "fluxNorm = sun.calcFluxNorm(r, lsst['r'])\n",
    "\n",
    "sun.multiplyFluxNorm(fluxNorm)\n",
    "\n",
    "mags = {}\n",
    "for f in filterlist:\n",
    "    mags[f] = sun.calcMag(lsst[f])\n",
    "    \n",
    "pd.DataFrame(mags, index=['Distant Sun']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "6514feb5-2aed-4fdb-958d-e3cee5f54a3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rubin_sim.photUtils import SignalToNoise\n",
    "from rubin_sim.photUtils import PhotometricParameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "7e0232bc-f13c-44bb-a896-7f39e5eec3cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.013139422508835895, 0.03934261437469505)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SignalToNoise.calcMagError_m5(magnitude=20, bandpass=lsst['r'], m5=24, photParams=PhotometricParameters())\n",
    "SignalToNoise.calcMagError_m5(magnitude=20.725415, bandpass=lsst['g'], m5=24, photParams=PhotometricParameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "86ab8100-e3c6-4d72-844d-8aed63972de0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.031221444804164934\n",
      "0.009322245115585407\n"
     ]
    }
   ],
   "source": [
    "mag_error_r, gamma = SignalToNoise.calcMagError_m5(20, lsst['r'], 22.115278, PhotometricParameters())\n",
    "mag_error_2r, gamma = SignalToNoise.calcMagError_m5(20, lsst['r'], 23.833470, PhotometricParameters())\n",
    "print(mag_error_r)\n",
    "print(mag_error_2r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "98bfb9d4-5f3d-4ac6-8458-58cf5122ac80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.014857161547721024 0.013093479367888855\n"
     ]
    }
   ],
   "source": [
    "mag_error_g, gamma = SignalToNoise.calcMagError_m5(20.725415, lsst['g'], 23.803613 , PhotometricParameters())\n",
    "mag_error_2g, gamma = SignalToNoise.calcMagError_m5(20.725415, lsst['g'], 24.005959, PhotometricParameters())\n",
    "print(mag_error_g, mag_error_2g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "56b74e26-622b-4d38-a81c-62434bb52469",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mag_2 = 20\n",
    "mag_1 = 20.725415\n",
    "def combine_col_err(mag_1, mag_2, err_1, err_2):\n",
    "    return mag_1-mag_2, np.sqrt(mag_error_g**2 + mag_error_r**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "5b0a94c6-e2b5-4924-b5f3-6a418b7cf148",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmr_error = combine_col_err(mag_1, mag_2, mag_error_g, mag_error_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "7b8f5d81-e061-4863-8874-2dc993b7b9c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7254150000000017, 0.0345762037377529)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmr_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "f5ef3db6-fd8f-49be-8abe-bb5586bde2d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "2ff1cea1-b959-4990-a225-bcf01f08c3de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7254152168005632, 0.9092875263745306, 1.21556563554606, 1.5101335745430582, 1.7336961338800663, 1.6247841723966872, 1.564944229071827, 1.6311809361247, 1.8812287964095908, 2.127568839165825, 2.2677011843884642, 2.1867075250048273, 1.8528072436778764, 1.4936730257263982, 1.1240496109998404, 0.8557468597614317, 0.7198391764086622, 0.7096387142844485, 0.747772632412822, 0.7750666481098385, 0.7663830025511693, 0.7227059844917534, 0.6590263915621257, 0.5962345500838815, 0.5445307149382153, 0.508888128898807, 0.4915751575070537, 0.4913180779593205, 0.5034478075371531, 0.5257928424279541, 0.5554684677789687] [0.37017813081659756, 0.4106166880291578, 0.4936703200193052, 0.5935508056397875, 0.6831634000258694, 0.6379932233974491, 0.6144016195935934, 0.6405664346332308, 0.7489732638916033, 0.8707147554809013, 0.9465152678502351, 0.9021312189629442, 0.7358808040627709, 0.5874297653001357, 0.46670527968637987, 0.3981440945213296, 0.3690553798652329, 0.36701680257128677, 0.3747396397974698, 0.3804391732704134, 0.3786101576725795, 0.3696318789018914, 0.3571916595860722, 0.34565910225562496, 0.3366937307361052, 0.3307848939477323, 0.3279931235932389, 0.3279520508453723, 0.3299021477239154, 0.3335601014034936, 0.33855109039526265]\n"
     ]
    }
   ],
   "source": [
    "from copy import copy\n",
    "g_r = []\n",
    "g_r_err = []\n",
    "pdf_list = []\n",
    "r = 24.5\n",
    "for redshift in np.arange(0,3.1,0.1):\n",
    "    sun_copy = copy(sun)\n",
    "    sun_copy.redshiftSED(redshift)\n",
    "    mags = {}\n",
    "    fluxNorm = sun_copy.calcFluxNorm(r, lsst['r'])\n",
    "    sun_copy.multiplyFluxNorm(fluxNorm)\n",
    "    mags = {}\n",
    "    for f in filterlist:\n",
    "        mags[f] = sun_copy.calcMag(lsst[f])\n",
    "        pd.DataFrame(mags, index=['Distant Sun']).T\n",
    "    gr_color = mags['g'] - mags['r']\n",
    "    g_r.append(mags['g'] - mags['r'])\n",
    "    result_g = 1.25 * np.log10(np.sum(10.0 ** (0.8 * g_filter['fiveSigmaDepth'])))\n",
    "    result_r = 1.25 * np.log10(np.sum(10.0 ** (0.8 * r_filter['fiveSigmaDepth'])))\n",
    "    mag_error_g, gamma = SignalToNoise.calcMagError_m5(mags['g'], lsst['g'], result_g , PhotometricParameters())\n",
    "    mag_error_r, gamma = SignalToNoise.calcMagError_m5(mags['r'], lsst['r'], result_r, PhotometricParameters())\n",
    "    g_r_err.append(np.sqrt(mag_error_r**2 + mag_error_g**2))\n",
    "    gr_error = np.sqrt(mag_error_r**2 + mag_error_g**2)\n",
    "    pdf_list.append(norm(loc = gr_color, scale = gr_error))\n",
    "    \n",
    "\n",
    "print(g_r, g_r_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "ab4f2791-9818-4080-838d-550444295296",
   "metadata": {},
   "outputs": [],
   "source": [
    "# g_r_err on x-axis, g_r on y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "a73c9aa6-e3a8-42cb-9e3d-a31a2d0b08c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Color')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "redshift_r = np.arange(0,3.1,0.1)\n",
    "plt.plot(redshift_r, g_r)\n",
    "plt.xlabel(\"Redshift\")\n",
    "plt.ylabel(\"Color\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52236490-384c-4fda-858f-fb82ec9c781c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "6e597be9-53b6-4e2b-bcf1-d4149d5b1ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7254152168005632, 0.37017813081659756)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_r[0], g_r_err[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "b44240f1-2e2f-48a5-a46d-358660ae3408",
   "metadata": {},
   "outputs": [],
   "source": [
    "gal_z_0 = norm(loc=g_r[0], scale=g_r_err[0])\n",
    "gal_z_0p5 = norm(loc=g_r[5], scale=g_r_err[5])\n",
    "gal_z_1p0 = norm(loc=g_r[10], scale=g_r_err[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2f4b3b0-7cde-455f-a3ac-29a15a82cc00",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "62389351-0b1f-409a-b423-3374cb53f296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4913180779593205, 2.2677011843884642)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(g_r), np.max(g_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "4317bf64-f5cd-44a4-8687-1e4b2baa98c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "color_range = np.arange(-4,8, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "98a0c8f1-b864-442f-80a3-d4195663731e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7207d7b4e0>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(color_range, pdf_list[0].pdf(color_range), label = 'z=0')\n",
    "plt.plot(color_range, pdf_list[5].pdf(color_range), label = 'z=0.5')\n",
    "plt.plot(color_range, pdf_list[10].pdf(color_range), label = 'z=1.0')\n",
    "plt.xlabel('g-r color')\n",
    "plt.ylabel('Probability Density')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "8db37547-bb64-424a-8946-440475a2e37d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#combined_pdf = gal_z_0.pdf(color_range) + gal_z_0p5.pdf(color_range) +  gal_z_1p0.pdf(color_range)\n",
    "combined_pdf = np.zeros(len(color_range))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "80d44b31-b4d3-4b29-a172-90ac5b756a96",
   "metadata": {},
   "outputs": [],
   "source": [
    "for gal in pdf_list:\n",
    "    combined_pdf += gal.pdf(color_range)\n",
    "combined_pdf_2 = combined_pdf / len(pdf_list)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "8d52b97f-87aa-47fe-844a-a3c835a6eba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7207792ba8>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(color_range, gal_z_0.pdf(color_range), label = 'z=0')\n",
    "plt.plot(color_range, gal_z_0p5.pdf(color_range), label = 'z=0.5')\n",
    "plt.plot(color_range, gal_z_1p0.pdf(color_range), label = 'z=1.0')\n",
    "plt.plot(color_range, combined_pdf_2 , '--', label = 'All redshifts')\n",
    "plt.xlabel('g-r color')\n",
    "plt.ylabel('Probability Density')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "6847aa1d-2aba-4525-877d-705d1bab47a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.special import kl_div, rel_entr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "20818a10-e13a-43e1-96df-32578cfc8e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7207599ac8>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(color_range, gal_z_0.pdf(color_range), label = 'z=0')\n",
    "#plt.plot(color_range, gal_z_0p5.pdf(color_range), label = 'z=0.5')\n",
    "#plt.plot(color_range, gal_z_1p0.pdf(color_range), label = 'z=1.0')\n",
    "plt.plot(color_range, combined_pdf_2, '--', label = 'All redshifts')\n",
    "plt.xlabel('g-r color')\n",
    "plt.ylabel('Probability Density')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "473eabaa-88e9-45a3-90a7-7f7b53665e69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.010000000000000214"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(0.01*gal_z_0.pdf(color_range)*0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "062d6805-553b-411b-b53d-5da78d920860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2612086986342901"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(kl_div(0.01*gal_z_0.pdf(color_range), 0.01*combined_pdf_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "dc1e6f55-91f0-41c7-b3cc-e1dac214d46d",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_kl = []\n",
    "for gal in pdf_list:\n",
    "    combined_kl.append(np.sum(kl_div(0.01*gal.pdf(color_range), 0.01*combined_pdf_2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "fd793ab6-9791-4a8c-9a91-53b73886d936",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.40359042125279876\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(combined_kl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "4d80a3f3-5772-47fc-9680-5af26a880e93",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_r = 1.25 * np.log10(np.sum(10.0 ** (0.8 * r_filter['fiveSigmaDepth'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "a05cd803-09ae-462c-8a22-655655d21fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_g = 1.25 * np.log10(np.sum(10.0 ** (0.8 * g_filter['fiveSigmaDepth'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "f95003ca-2c6e-49e9-95a4-3c524cea5944",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.444680387674623 24.687150268328377\n"
     ]
    }
   ],
   "source": [
    "print(result_r, result_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "51510e07-a8b6-471f-9298-d072f7c0a2f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_colors_pdf(r_filter, g_filter):\n",
    "    g_r = []\n",
    "    g_r_err = []\n",
    "    pdf_list = []\n",
    "    r = 24.5\n",
    "    for redshift in np.arange(0,3.1,0.1):\n",
    "        sun_copy = copy(sun)\n",
    "        sun_copy.redshiftSED(redshift)\n",
    "        mags = {}\n",
    "        fluxNorm = sun_copy.calcFluxNorm(r, lsst['r'])\n",
    "        sun_copy.multiplyFluxNorm(fluxNorm)\n",
    "        mags = {}\n",
    "        for f in filterlist:\n",
    "            mags[f] = sun_copy.calcMag(lsst[f])\n",
    "            pd.DataFrame(mags, index=['Distant Sun']).T\n",
    "        gr_color = mags['g'] - mags['r']\n",
    "        g_r.append(mags['g'] - mags['r'])\n",
    "        result_g = 1.25 * np.log10(np.sum(10.0 ** (0.8 * g_filter['fiveSigmaDepth'])))\n",
    "        result_r = 1.25 * np.log10(np.sum(10.0 ** (0.8 * r_filter['fiveSigmaDepth'])))\n",
    "        mag_error_g, gamma = SignalToNoise.calcMagError_m5(mags['g'], lsst['g'], result_g , PhotometricParameters())\n",
    "        mag_error_r, gamma = SignalToNoise.calcMagError_m5(mags['r'], lsst['r'], result_r, PhotometricParameters())\n",
    "        g_r_err.append(np.sqrt(mag_error_r**2 + mag_error_g**2))\n",
    "        gr_error = np.sqrt(mag_error_r**2 + mag_error_g**2)\n",
    "        pdf_list.append(norm(loc = gr_color, scale = gr_error))\n",
    "    return g_r, g_r_err, pdf_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "4d7dac36-dd23-44ed-a70d-49c3e24f90eb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def combined_pdfs(pdf_list_1, color_range_1):\n",
    "    combined_pdf = np.zeros(len(color_range_1))\n",
    "    for gal in pdf_list_1:\n",
    "        combined_pdf += gal.pdf(color_range_1)\n",
    "    combined_pdf_2 = combined_pdf / len(pdf_list_1) \n",
    "    return combined_pdf_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "d5f91af7-debd-4c92-a369-365053c396a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#max_sig = np.max(band_obs['fiveSigmaDepth']                 \n",
    "#def combined_sigma(r_filter, g_filter):\n",
    " #   result_r = 1.25 * np.log10(np.sum(10.0 ** (0.8 * r_filter['fiveSigmaDepth']))) \n",
    "  #  result_g = 1.25 * np.log10(np.sum(10.0 ** (0.8 * g_filter['fiveSigmaDepth'])))\n",
    "   # return result_r, result_g\n",
    "    \n",
    "    # IGNORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "45eed8f9-74bd-4fd7-bee2-706adb43f597",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def combined_kl_div(combined_pdf_2, color_range, pdf_list):\n",
    "    combined_kl = []\n",
    "    for gal in pdf_list:\n",
    "        check = kl_div(0.01*gal.pdf(color_range), 0.01*combined_pdf_2)\n",
    "        if np.inf in check:\n",
    "            check_ind = np.where(check == np.inf)\n",
    "            check[check_ind] = 0\n",
    "        combined_kl.append(np.sum(check))\n",
    "    return combined_kl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "541c86ed-9732-4b4d-9dc5-75f45fca7512",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "700\n",
      "800\n",
      "900\n",
      "1000\n",
      "1100\n",
      "1200\n",
      "1300\n",
      "1400\n",
      "1500\n",
      "1600\n",
      "1700\n",
      "1800\n",
      "1900\n",
      "2000\n",
      "2100\n",
      "2200\n",
      "2300\n",
      "2400\n"
     ]
    }
   ],
   "source": [
    "color_range_1 = np.arange(-4,8, 0.01)\n",
    "field_kl = []\n",
    "for fid in range(0,field_id_max+1):\n",
    "    if fid % 100 == 0:\n",
    "        print(fid)\n",
    "    r_filter = band_obs.query(f'fieldId == {fid} and filter == \"r\"')\n",
    "    g_filter = band_obs.query(f'fieldId == {fid} and filter == \"g\"')\n",
    "    if len(r_filter) == 0:\n",
    "        field_kl.append([])\n",
    "        continue\n",
    "    if len(g_filter) == 0:\n",
    "        field_kl.append([])\n",
    "        continue\n",
    "    g_r, g_r_err, pdf_list_1 = calc_colors_pdf(r_filter, g_filter)\n",
    "    combined_pdf_2 = combined_pdfs(pdf_list_1, color_range_1)\n",
    "    combined_kl = combined_kl_div(combined_pdf_2, color_range_1, pdf_list_1)\n",
    "    field_kl.append(combined_kl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "b8f53024-fc44-4bd1-9d0b-2cdcc300503b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2491"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(band_obs['fieldId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "034c2554-66a2-4d14-89a4-bffb5042de0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "0630c58d-1fe9-4224-a0bd-9c6d396a3893",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('baseline_kl_field_values.pkl', 'wb') as f:\n",
    "    pickle.dump(field_kl, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "be489431-39f9-4b74-b0f6-8f5623cf86d6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('baseline_kl_field_values.pkl', 'rb') as f:\n",
    "    field_kl = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaf84442-5a07-47bf-92a2-d0e0cf446728",
   "metadata": {},
   "outputs": [],
   "source": [
    "band_obs.query(['field_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "c6a32428-1360-4399-bab1-62a9f1245b52",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/astro/users/brycek/.local/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/astro/users/brycek/.local/lib/python3.7/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-187-7893c2ef09d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mRA\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mRA_set\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mDEC\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDEC_set\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0mmean_kl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfield_kl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0miters\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mra_dec_kl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mRA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDEC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean_kl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miters\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0miters\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "iters = 0\n",
    "ra_dec_kl = []\n",
    "for RA in RA_set:\n",
    "    for DEC in DEC_set:\n",
    "        mean_kl = np.mean(field_kl[iters])\n",
    "        ra_dec_kl.append([RA, DEC, mean_kl, iters])\n",
    "        iters += 1    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "848cf17b-21a9-4d77-a628-c3ffaa175493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 49)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(RA_bins), np.max(DEC_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "f0f28d9f-9ab5-4f35-8181-f0acf915ffcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2492\n"
     ]
    }
   ],
   "source": [
    "print(iters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "91de17cb-cda4-42ef-acc5-ae579ed72447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0005906334430789996, -89.77809402699343, nan, 0],\n",
       " [0.0005906334430789996, -87.2409648165252, nan, 1],\n",
       " [0.0005906334430789996, -84.70383560605697, nan, 2],\n",
       " [0.0005906334430789996, -82.16670639558875, nan, 3],\n",
       " [0.0005906334430789996, -79.62957718512052, nan, 4],\n",
       " [0.0005906334430789996, -77.09244797465229, nan, 5],\n",
       " [0.0005906334430789996, -74.55531876418407, nan, 6],\n",
       " [0.0005906334430789996, -72.01818955371584, nan, 7],\n",
       " [0.0005906334430789996, -69.4810603432476, nan, 8],\n",
       " [0.0005906334430789996, -66.94393113277937, nan, 9]]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_dec_kl[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "9ef24b99-737a-4923-b3f2-d5e787af969a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.90633443e-04,  5.90633443e-04,  5.90633443e-04, ...,\n",
       "         3.59999881e+02,  3.59999881e+02,  3.59999881e+02],\n",
       "       [-8.97780940e+01, -8.72409648e+01, -8.47038356e+01, ...,\n",
       "         9.16994518e+00,  1.17070744e+01,  1.42442036e+01],\n",
       "       [            nan,             nan,             nan, ...,\n",
       "         1.39026830e+00,  1.13084289e+00,  2.09143799e-01],\n",
       "       [ 0.00000000e+00,  1.00000000e+00,  2.00000000e+00, ...,\n",
       "         2.48900000e+03,  2.49000000e+03,  2.49100000e+03]])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array = np.array(ra_dec_kl)\n",
    "my_array = my_array.T\n",
    "my_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "e7c32626-baf3-4b00-8660-27dd61d5ce79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f72072b7518>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(my_array[0], my_array[1], c=my_array[2],)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "d1d1e0d4-ae52-46ec-8841-c7d5fc6c0325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f72067b4630>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(band_obs['fieldRA'], band_obs['fieldDec'], bins = 50, norm=npl.colors.LogNorm())\n",
    "plt.xlabel('RA')\n",
    "plt.ylabel('DEC')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfb54f4b-bb7e-4c16-9e0d-e6551fe538a3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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