DESC DC2 minion_1016.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28398f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import rubin_sim.maf as maf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0744dcf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "runName = 'minion_1016_dc2'\n",
    "opsdb_file = 'minion_1016_desc_dithered_v4_trimmed.db'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ec7d819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('FWHMeff', 'expMJD')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The minion_1016 database is old -- it's opsim v3\n",
    "# The default observations table is 'Summary'\n",
    "# and the schema uses different column names - exposure MJD is 'expMJD' and the seeing is \"FWHMeff\", etc\n",
    "# The other consideration is that Summary may contain duplicates of a given exposure .. however the DESC version \n",
    "# is already only unique visits, so no problem there\n",
    "defaultTable = 'Summary'\n",
    "colmap = maf.ColMapDict('opsimV3')\n",
    "# colmap doesn't contain every column -- just the ones which change between versions\n",
    "colmap['seeingEff'], colmap['mjd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "046d820c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# And let's identify the DESC dither columns\n",
    "dithRA = 'descDitheredRA'\n",
    "dithDec = 'descDitheredDec'\n",
    "dithRotTelPos = 'descDitheredRotTelPos'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2368d2e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healpix slicer using NSIDE=128, approximate resolution 27.483891 arcminutes\n"
     ]
    }
   ],
   "source": [
    "# Set up a healpix slicer .. \n",
    "# (we could do this more efficiently at higher resolution that only covers the DC2 patch, \n",
    "# but I'm going with this for now)\n",
    "\n",
    "# HMM - DESC added a dithered rotational position, but we can't use that easily here so going with original rotations\n",
    "s = maf.HealpixSlicer(lonCol=dithRA, latCol=dithDec, latLonDeg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a945fd53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Mean airmass', 'Median airmass', 'Min airmass', 'Max airmass']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate a bunch of metrics at each of these points to evaluate the range of airmasses at each point\n",
    "metrics = maf.standardMetrics('airmass')\n",
    "[m.name for m in metrics]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c08bf3d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<rubin_sim.maf.metrics.simpleMetrics.MeanMetric at 0x19dd0e820>,\n",
       " <rubin_sim.maf.metrics.simpleMetrics.RmsMetric at 0x19dd0e2e0>,\n",
       " <rubin_sim.maf.metrics.simpleMetrics.MedianMetric at 0x19dd0e670>,\n",
       " <rubin_sim.maf.metrics.simpleMetrics.MaxMetric at 0x19dd0e7f0>,\n",
       " <rubin_sim.maf.metrics.simpleMetrics.MinMetric at 0x19dd0e460>,\n",
       " <rubin_sim.maf.metrics.simpleMetrics.NoutliersNsigmaMetric at 0x19dd0e100>,\n",
       " <rubin_sim.maf.metrics.simpleMetrics.NoutliersNsigmaMetric at 0x19dd0e0d0>,\n",
       " <rubin_sim.maf.metrics.simpleMetrics.CountMetric at 0x19dd0e9a0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And then for each of these, we can calculate the mean/max, etc. over the sky \n",
    "# (this means we could calculate the minimum airmass at each point, then figure out the median value of those)\n",
    "summarystats = maf.standardSummary()\n",
    "summarystats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd480320",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put them together in the metric bundle and run -- could add subselections per filter, etc.\n",
    "sql = ''\n",
    "\n",
    "bundleDict = {}\n",
    "for m in metrics:\n",
    "    bundleDict[m.name] = maf.MetricBundle(m, s, sql, summaryMetrics=summarystats, runName=runName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "40f878bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Querying table Summary with no constraint for columns ['airmass', 'rotSkyPos', 'descDitheredRA', 'descDitheredDec'].\n",
      "Found 68446 visits\n",
      "Running:  ['Mean airmass', 'Median airmass', 'Min airmass', 'Max airmass']\n",
      "Completed metric generation.\n",
      "Running reduce methods.\n",
      "Running summary statistics.\n",
      "Completed.\n"
     ]
    }
   ],
   "source": [
    "# Now run it\n",
    "outDir = 'desc_maf'\n",
    "resultsDb = maf.ResultsDb(outDir)\n",
    "g = maf.MetricBundleGroup(bundleDict, opsdb_file, outDir=outDir, resultsDb=resultsDb, dbTable=defaultTable)\n",
    "g.runAll()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2947b1fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0.0 180.0 -180.0 180.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'SkyMap': 1, 'Histogram': 2}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bundleDict['Median airmass'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f0f434e8",
   "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>Mean</th>\n",
       "      <th>Rms</th>\n",
       "      <th>Median</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "      <th>N(+3Sigma)</th>\n",
       "      <th>N(-3Sigma)</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean airmass</th>\n",
       "      <td>1.196626</td>\n",
       "      <td>0.020968</td>\n",
       "      <td>1.196974</td>\n",
       "      <td>1.412943</td>\n",
       "      <td>1.002749</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>2822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median airmass</th>\n",
       "      <td>1.177193</td>\n",
       "      <td>0.027106</td>\n",
       "      <td>1.178857</td>\n",
       "      <td>1.416064</td>\n",
       "      <td>1.002749</td>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "      <td>2822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min airmass</th>\n",
       "      <td>1.012093</td>\n",
       "      <td>0.024586</td>\n",
       "      <td>1.003979</td>\n",
       "      <td>1.403240</td>\n",
       "      <td>1.001530</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max airmass</th>\n",
       "      <td>1.552623</td>\n",
       "      <td>0.216229</td>\n",
       "      <td>1.519328</td>\n",
       "      <td>2.709891</td>\n",
       "      <td>1.002749</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>2822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Mean       Rms    Median       Max       Min  N(+3Sigma)  \\\n",
       "Mean airmass    1.196626  0.020968  1.196974  1.412943  1.002749          26   \n",
       "Median airmass  1.177193  0.027106  1.178857  1.416064  1.002749          34   \n",
       "Min airmass     1.012093  0.024586  1.003979  1.403240  1.001530          28   \n",
       "Max airmass     1.552623  0.216229  1.519328  2.709891  1.002749          92   \n",
       "\n",
       "                N(-3Sigma)  Count  \n",
       "Mean airmass            30   2822  \n",
       "Median airmass          33   2822  \n",
       "Min airmass              0   2822  \n",
       "Max airmass              0   2822  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at those summary stats\n",
    "pd.DataFrame([bundleDict[k].summaryValues for k in bundleDict], index=list(bundleDict.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd59a970",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median minimum airmass 1.00397942475\n",
      "Median median airmass 1.178856905385\n",
      "Median maximum airmass 1.51932788444\n"
     ]
    }
   ],
   "source": [
    "# Another way to look at some of them ..\n",
    "print('Median minimum airmass', bundleDict['Min airmass'].summaryValues['Median'])\n",
    "print('Median median airmass', bundleDict['Median airmass'].summaryValues['Median'])\n",
    "print('Median maximum airmass', bundleDict['Max airmass'].summaryValues['Median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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