MetaDataEval.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QuickLook at BOSS Spectra Meta Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook takes a look at the distribution of metadata and can be used to cross check the metadata compiled in boss_meta_rich.pkl created using boss_meta_rich.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pickle \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.coordinates import EarthLocation\n",
    "from astropy.time import Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meta_rich = np.load('meta_rich.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "meta_df = pd.DataFrame.from_records(meta_rich)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meta_df.drop_duplicates(subset=['PLATE','IMG'], keep='first', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meta_df = meta_df.apply(lambda x: pd.to_numeric(x, errors='ignore'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PLATE', 'IMG', 'TAI-BEG', 'TAI-END', 'RA', 'DEC', 'CAMERAS', 'AIRMASS',\n",
       "       'ALT', 'AZ', 'EXPTIME', 'SEEING20', 'SEEING50', 'SEEING80', 'AIRTEMP',\n",
       "       'DEWPOINT', 'DUSTA', 'DUSTB', 'WINDD25M', 'WINDS25M', 'GUSTD', 'GUSTS',\n",
       "       'HUMIDITY', 'PRESSURE', 'WINDD', 'WINDS', 'MOON_LAT', 'MOON_LON',\n",
       "       'SUN_LAT', 'SUN_LON', 'MOON_ALT', 'MOON_AZ', 'SUN_ALT', 'SUN_AZ',\n",
       "       'DAYS2FULL', 'ECL_LAT', 'ECL_LON', 'GAL_LAT', 'GAL_LON', 'AZ_CALC',\n",
       "       'FLI', 'SOLARFLUX', 'XFOCAL', 'YFOCAL', 'THROUGHPUT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2473"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(meta_df.PLATE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify \"GOOD\" Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This txt file contains all the good plate-days\n",
    "GOOD_FILE = '/global/homes/p/parkerf/BOSS_Sky/good_plates.txt' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Pull info from this file and only use critical info\n",
    "good_data = pd.DataFrame.from_csv(GOOD_FILE, index_col=None, header=0)\n",
    "good_info = good_data[['MJD','QUALITY','PLATE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Need to calculate the MJD of each observation to compare with the plate-day\n",
    "\n",
    "#location and time\n",
    "APACHE = EarthLocation(lat=(32+4.6/60+49.30/3600), lon=(-105+49./60+13.50/3600.), height=2788)\n",
    "def mjd_time(fits_time):\n",
    "    fits_time = float(fits_time)\n",
    "    tt = Time(fits_time/86400, scale='tai', format='mjd', location=APACHE)\n",
    "    return tt.mjd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meta_df['TAI-MJD'] = meta_df['TAI-BEG'].apply(mjd_time)\n",
    "meta_df['MJD'] = np.round(meta_df['TAI-MJD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "good_obs = pd.merge(meta_df,good_info,on=['MJD','PLATE'],how='left')\n",
    "good_meta_df = good_obs[good_obs.QUALITY == 'good']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at Dark Cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dark_df = good_meta_df[(good_meta_df['AIRMASS'] <= 1.4)&(good_meta_df['GAL_LAT'] > 10)&(good_meta_df['MOON_ALT'] <= -18)&(good_meta_df['ECL_LON'] >= 90)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Check Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#INPUTS:\n",
    "ra = 7.947569\n",
    "dec = 19.996900"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xcheck_data = meta_df[(meta_df.RA == ra) & (meta_df.DEC == dec)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PLATE</th>\n",
       "      <th>IMG</th>\n",
       "      <th>TAI-BEG</th>\n",
       "      <th>RA</th>\n",
       "      <th>DEC</th>\n",
       "      <th>MOON_LAT</th>\n",
       "      <th>MOON_LON</th>\n",
       "      <th>SUN_LAT</th>\n",
       "      <th>SUN_LON</th>\n",
       "      <th>MOON_ALT</th>\n",
       "      <th>MOON_AZ</th>\n",
       "      <th>SUN_ALT</th>\n",
       "      <th>SUN_AZ</th>\n",
       "      <th>DAYS2FULL</th>\n",
       "      <th>ECL_LAT</th>\n",
       "      <th>ECL_LON</th>\n",
       "      <th>GAL_LAT</th>\n",
       "      <th>GAL_LON</th>\n",
       "      <th>AZ_CALC</th>\n",
       "      <th>FLI</th>\n",
       "      <th>SOLARFLUX</th>\n",
       "      <th>TAI-MJD</th>\n",
       "      <th>MJD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6187</td>\n",
       "      <td>150697</td>\n",
       "      <td>4.859499e+09</td>\n",
       "      <td>7.947569</td>\n",
       "      <td>19.9969</td>\n",
       "      <td>-0.021724</td>\n",
       "      <td>346.079315</td>\n",
       "      <td>-0.012297</td>\n",
       "      <td>280.691833</td>\n",
       "      <td>-66.381447</td>\n",
       "      <td>308.174225</td>\n",
       "      <td>-59.208748</td>\n",
       "      <td>291.117279</td>\n",
       "      <td>15.41596</td>\n",
       "      <td>15.19384</td>\n",
       "      <td>15.333151</td>\n",
       "      <td>-42.635181</td>\n",
       "      <td>116.652893</td>\n",
       "      <td>219.012589</td>\n",
       "      <td>0.29176</td>\n",
       "      <td>145.766663</td>\n",
       "      <td>56244.199687</td>\n",
       "      <td>56244.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PLATE     IMG       TAI-BEG        RA      DEC  MOON_LAT    MOON_LON  \\\n",
       "97   6187  150697  4.859499e+09  7.947569  19.9969 -0.021724  346.079315   \n",
       "\n",
       "     SUN_LAT     SUN_LON   MOON_ALT     MOON_AZ    SUN_ALT      SUN_AZ  \\\n",
       "97 -0.012297  280.691833 -66.381447  308.174225 -59.208748  291.117279   \n",
       "\n",
       "    DAYS2FULL   ECL_LAT    ECL_LON    GAL_LAT     GAL_LON     AZ_CALC  \\\n",
       "97   15.41596  15.19384  15.333151 -42.635181  116.652893  219.012589   \n",
       "\n",
       "        FLI   SOLARFLUX       TAI-MJD      MJD  \n",
       "97  0.29176  145.766663  56244.199687  56244.0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xcheck_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_hist(name):\n",
    "    meta_df[name].plot.hist(bins=100)\n",
    "    good_meta_df[name].plot.hist(bins=100)\n",
    "    dark_df[name].plot.hist(bins=100)\n",
    "    plt.title(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5mu96Gqf9kuo3RsSRNBC/B1ggaQYwJSK2pdbGhswyZmZWgVG7rdLR/yPp5ogPF1l5WnYH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\neZiZWWFOHmZmVpiTh5mZFfb/AV16ZIpH1V7BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373a8d55f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('MJD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373f599668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('AIRMASS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373f03aba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('ALT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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x+qHD7mnaHmm2F7Yv9n0uAC4AWLt27XiCl1Zi49MBnIorSR1nEiipDyaeFCZ5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\n+EU3+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3746d14c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,6])\n",
    "plt.subplot(1,2,1)\n",
    "plot_hist('MOON_ALT')\n",
    "plt.subplot(1,2,2)\n",
    "plot_hist('MOON_AZ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6R9K3JJ2WyusaL8BFqYniOknHprJaxitpOTAUET8Y9VAt4x0maV36u3s78KFU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\n5GFmZqX9fzHtmgEy/Bf7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3739f47f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('SUN_ALT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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07luB6yS9i6y1MlrSF4FDksZHxIHUJXU4Ld8DTKpZf2Kq60nl+nozMytJs2dbfbJm+ktg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3746d28c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('SUN_AZ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373b083f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('DAYS2FULL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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clmRbkkeaRyn9cTOvF+k8Ru4n53GuNyf5xz1sc3mSHc1IvJmyv+iK7bEkP+h6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373f037160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,10])\n",
    "plt.subplot(3,1,1)\n",
    "plot_hist(\"GAL_LAT\")\n",
    "plt.subplot(3,1,2)\n",
    "plot_hist('ECL_LAT')\n",
    "plt.subplot(3,1,3)\n",
    "plot_hist('ECL_LON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373b0a4240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('AZ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RVn+U7l1+hFq/x+cj4qFSa9aC3LdhZlYzYXikS6o/nC6O2HWBMZZs30bvP95dYU3MzJpv\nwmardKLem5Le3oT6mJlZG8g72upVYKekh4DXhgsj4sZSamVmZi0tb3h8Kz3MzMzGDw9JsyLi2YiY\n7HWszMysA0105HEvtft2IOmbEfHr5Vep/Yw8MdDMrPNN1GGuzPS7y6yImZm1j4nCI8aYNjOzLjZR\ns9Vlko5QOwI5J02T5iMippZauxbj5ikzs5pxwyMipjSrImZm1j7yDtUtTNLZwHeBs9LrfCMibpV0\nEfA1oBfYDyyNiJfTOrcAK4HjwI0R8WAqX8DJOwk+ANyULtRoTZS9PMv+dddNehkza39FbgZV1FHg\nlyPiMuByYLGkRUA/sC0i5gDb0jyS5lK71/k8YDFwR7o0CsCdwCpq9zWfk543M7OKlBYeUfNqmn1L\negSwhJP3P99A7X7opPJNEXE0IvYBe4GFkmYAUyNiezra2JhZx8zMKlDmkQeSpkh6EjgMPBQRjwLT\nI+JQWuQFYHqa7gGey6x+MJX1pOn68tFeb7WkAUkDQ0NDDdwTMzPLKjU8IuJ4RFwOzKR2FDG/7vkT\nN5hq0Outj4i+iOibNm1aozZrZmZ1Suswz4qIH0v639T6Kl6UNCMiDqUmqcNpsUHgksxqM1PZYJqu\nL28Zl86edWJ6575nK6yJmVlzlHbkIWmapAvS9DnANcBTwBZgRVpsBXBfmt4CLJN0lqTZ1DrGd6Qm\nriOSFkkSsDyzjjXR/rM/feJhZt2tzCOPGcCGNGLqDGBzRPyNpO9Ru63tSuAAsBQgInZJ2gzsBo4B\na9K9RABu4ORQ3a3pYVaZEUOSz66wImYVKS08IuIHwAdHKX8JuHqMddYCa0cpHwDmn7pGd/C5E9Zq\n/DdppXaYm5lZZ3J4mJlZYQ4PMzMrzOFhZmaFNeU8DzN3sJp1FoeHNV2rBEmr1MOsHTk8RjHijPEK\n69GORp5A+Epl9TCzcjk8Ooi/STePz7K3bufwmIDPJDYzO5VHW5mZWWE+8rBJcRNZd8s22/X2332y\n3H8LXcPh0QbcCW1mrcbh0UEcMmbWLA4PazqHnFn7c3jYpDgAzLqbR1uZmVlhPvIws47ikYDNUVp4\nSLoE2AhMBwJYHxG3SboI+BrQC+wHlkbEy2mdW4CVwHHgxoh4MJUv4ORtaB8AboqIKKvu1hjZf2Iz\n6yxlNlsdA/59RMwFFgFrJM0F+oFtETEH2JbmSc8tA+YBi4E70v3PAe4EVgFz0mNxifW2Ltfbf/+J\nh5mNrsx7mB8CDqXpn0jaA/QAS4Cr0mIbgEeAm1P5pog4CuyTtBdYKGk/MDUitgNI2ghcD2wtq+7W\nHcY60c3MJtaUDnNJvcAHgUeB6SlYAF6g1qwFtWB5LrPawVTWk6bry0d7ndWSBiQNDA0NNaz+ZmY2\nUunhIeltwDeBz0XEkexzqd+iYX0XEbE+Ivoiom/atGmN2qyZmdUpdbSVpLdQC46/johvpeIXJc2I\niEOSZgCHU/kgcElm9ZmpbDBN15dbi/CVh826T2lHHpIE/DmwJyK+mHlqC7AiTa8A7suUL5N0lqTZ\n1DrGd6QmriOSFqVtLs+sY2ZmFSjzyOPDwL8Gdkp6MpX9IbAO2CxpJXAAWAoQEbskbQZ2UxuptSYi\njqf1buDkUN2tuLO8VB4nb2YTKXO01f8BNMbTV4+xzlpg7SjlA8D8xtXOLB8HqdnofIa5NUWjbtvq\nD3Oz1uDwKJE/6MysUzk8WpRHMJ0eB/fk+XdneTg8zOqMOPP8H9v7zPP6S6w4DKxRHB6TdOnsWVVX\nwcysMg6PNufmLWtVbv7qbA6PCTRqlFAnq+p35OAsnwPAxuLwKCBPU9WIZfY0vg6+TLiZtQKHRxfo\npG+PZXVm+zwUs2IcHiUa+YH0SmX1sNZSRsB0Wmh12v50IoeHnaJRoVfKt/mcfRvl7EN3fAHopKHK\nVh6Hh7WkVuzb6YQO+lb7Rt9q9bH8HB4d6NLZs2DDpQDsXLGz4tpYpzid8MwTEkW/MDh4quXwMKC6\nb9XN/gBo5aMHfxhaO3F4tKiyzp3wB5SZNYLDo81180mM3bzveY3/O2qtAQDuqG8vpYWHpL8APg4c\njoj5qewi4GtAL7AfWBoRL6fnbgFWAseBGyPiwVS+gJN3EXwAuCkioqx6m5WtW0ZwOQw6W5lHHncB\n/w3YmCnrB7ZFxDpJ/Wn+ZklzgWXAPOBdwMOS3pNuQ3snsAp4lFp4LMa3oS2kWf/E2bPrd+57trTX\nqUozj3TK6GCeDAeAjaXM29B+V1JvXfES4Ko0vQF4BLg5lW+KiKPAPkl7gYWS9gNTI2I7gKSNwPU4\nPHLr7b+/5TqGi3LzVPvrlqOtbtLsPo/pEXEoTb8ATE/TPcD2zHIHU9kbabq+fFSSVgOrAWbN8iXT\nrfm68UOyG/fZKuwwj4iQ1NC+i4hYD6wH6Ovr68h+kbG+hU/m/iKteCLesEYebYwcnuujmMny786y\nmh0eL0qaERGHJM0ADqfyQeCSzHIzU9lgmq4vb5puv+nTaOdFdHrfRhnGOr8kT4C30/DqVj6Pxhqr\n2eGxBVgBrEs/78uU3y3pi9Q6zOcAOyLiuKQjkhZR6zBfDvxJk+vcURrdAXq64eomj8bzB7g1Q5lD\ndb9KrXP8YkkHgVuphcZmSSuBA8BSgIjYJWkzsBs4BqxJI60AbuDkUN2tdEBneTt9k2wmf+h1hzxH\nW/5S0frKHG31m2M8dfUYy68F1o5SPgDMb2DVrAnctNX6fS1jfYg7uC0Pn2HeJFV1To/3odWKH2it\noorw8zkV1k4cHmYtKE+QdHvYuGmrWg6PirV6O7+PTspVxu+3Ge+ZmyXN4WGV8QdQ47RKyLdCPXxE\n0hwOjxJlPxz375t8E4M/ZK0q7f6355GN5XF4WEsb6zySqj7IinyYtsK38FaX53dU9CKRp9v868DJ\nx+FRsdP5gOm0s9+r+LA9nW/W7fKtPE89HXRWlMOjAt38j9qowDudI5JOC10rrn7ovI8winN4mE0g\nT9i0wlFI2cHcsG3uafjmrQIOjyYZ68Ol6IeOvzUX1+wjvUYHyXijh5r599Co18rzfjQqbEacC9Pf\nfefClMnh0UWq+nZcVeC1wtFAVtEvEGXXv4xQLbvO2eam898/+mjGserjI57Gcni0kNKbC2xczfz9\nT/a1Wv2kUuseDg8bU6t9c+9mw+/F+fTzkz3rKq6NmcOjEq18NNDob8rN1i717CRF/2aKjpQ7//39\nJ6bHCs4873t2O/XNg5du6D+xfZ+hno/Dw7qCQ6UzZD/YL6Xx7+l4/UA+eXAkh4dZm+nm84Sa6dLZ\ns2DDpcCpRzwOEodH12rl/oxuPkooe99b7XIvWR3xvn/h7Znpzm7yapvwkLQYuA2YAnw5Itxr2CCt\n8E/bCnXoZv79T2zsI77ODomxtEV4SJoC/HfgGuAg8JikLRGxu9qamVk3GNHXUh+0qWlr54qdI8s7\n/CikLcIDWAjsjYgfAUjaBCwBHB7Wdbr5KKHsS7Cc1mjDTFiMFTBjqe9TGdGPkrY7Wh1GbW5sUlC1\nS3j0AM9l5g8C/6x+IUmrgdVp9lVJT0/y9S4G/mGS67Yi70/r6qR9gS7eH42Y+2HBl/n4yG39l9GW\nOXWbGmUp/tOopcPy7M/PT/A80D7hkUtErAfWn+52JA1ERF8DqtQSvD+tq5P2Bbw/ra6R+3NGIzbS\nBIPAJZn5manMzMwq0C7h8RgwR9JsSW8FlgFbKq6TmVnXaotmq4g4JunfAg9SG6r7FxGxq8SXPO2m\nrxbj/WldnbQv4P1pdQ3bH0VEo7ZlZmZdol2arczMrIU4PMzMrLCuDg9JiyU9LWmvpP5Rnpek29Pz\nP5B0RRX1zCPHvrxP0vckHZX0B1XUsYgc+/Nb6T3ZKenvJF1WRT3zyrE/S9L+PClpQNIvVFHPvCba\nn8xyH5J0TNKnmlm/onK8P1dJeiW9P09K+nwV9cwjz3uT9udJSbskfWdSLxQRXfmg1vH+98C7gbcC\n/xeYW7fMtcBWaufiLAIerbrep7Ev7wA+BKwF/qDqOjdgf64ELkzTH2vV96bA/ryNk32QHwCeqrre\np7M/meX+FngA+FTV9T7N9+cq4G+qrmuD9uUCalfnmJXm3zGZ1+rmI48TlzyJiNeB4UueZC0BNkbN\nduACSTOaXdEcJtyXiDgcEY8Bb1RRwYLy7M/fRcTLaXY7tXN/WlWe/Xk10n8ycB7QyiNZ8vzvAHwW\n+CZwuJmVm4S8+9MO8uzLp4FvRcSzUPtsmMwLdXN4jHbJk55JLNMK2qWeeRXdn5XUjhBbVa79kfRJ\nSU8B9wP/pkl1m4wJ90dSD/BJ4M4m1muy8v69XZmaFrdKmtecqhWWZ1/eA1wo6RFJj0taPpkXaovz\nPMzGIukj1MKjpfsI8oiIe4B7JP0S8J+BX6m4SqfjS8DNEfGmNO61ltrFE9SaeV6VdC1wLzCn4jpN\n1pnAAuBq4Bzge5K2R8T/K7qRbpXnkiftclmUdqlnXrn2R9IHgC8DH4uIl5pUt8ko9P5ExHclvVvS\nxRHRihcZzLM/fcCmFBwXA9dKOhYR9zanioVMuD8RcSQz/YCkO1r0/cnz3hwEXoqI14DXJH0XuAwo\nFB6Vd/BU2LF0JvAjYDYnO5bm1S1zHSM7zHdUXe/J7ktm2S/Q+h3med6bWcBe4Mqq69ug/fmnnOww\nvyL9w6vqup/u31ta/i5au8M8z/vzzsz7sxB4thXfn5z78n5gW1r2XGqX651f9LW69sgjxrjkiaTf\nTc//KbVRItdS+5D6KfCZquo7njz7IumdwAAwFXhT0ueojcI4MuaGK5Lzvfk88HPAHenb7bFo0auf\n5tyfXweWS3oD+BnwG5H+01tNzv1pGzn351PA70k6Ru39WdaK70+efYmIPZK+DfwAeJPanVmLXkPe\nlycxM7Piunm0lZmZTZLDw8zMCnN4mJlZYQ4PMzMrzOFhZmaFOTzMzKwwh4eZmRX2/wGg2bh/wvuC\nHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373af6f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('FLI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f37408919e8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IVnZWqz6zV4FPcp5nthNHcsUOb/StSbf7TPTqiUuSFimLYfLv7Yd234dVeMqn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\nSEEIIYTI8v8DGabqTVx5ZiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373eaf9358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(meta_df.MJD, meta_df.SOLARFLUX,'ro')\n",
    "plt.plot(good_meta_df.MJD,good_meta_df.SOLARFLUX,'go')\n",
    "plt.plot(dark_df.MJD,dark_df.SOLARFLUX,'bx')\n",
    "plt.title(\"Solar Flux data (avg. per MJD)\")\n",
    "plt.xlabel(\"MJD\")\n",
    "plt.ylabel(\"2800 MHz Solar Flux\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TpLHU0zDUqvoOcFHzkaSRsdNOOwF8tapeM+y+SFLbTjvtxBlnnMGZZ575pao6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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f374e0a2c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,6])\n",
    "plt.subplot(1,3,1)\n",
    "plot_hist('SEEING20')\n",
    "plt.subplot(1,3,2)\n",
    "plot_hist('SEEING50')\n",
    "plt.subplot(1,3,3)\n",
    "plot_hist('SEEING80')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373894dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('AIRTEMP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tHwtcClwyhr5EREQL6n6d8CeBjwEXltALgC+MVMf27cBjNduxBLjO9tO2NwObgFMkzQYO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\niYiIxvx/09xA8tXyfaUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373ab4ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('DEWPOINT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9WVV7q+q7wIeB09uq7cDxfZse12LbW3t6fJ8+SRYBRwJPDS57SdIksYiUJGke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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373e6c0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,6])\n",
    "plt.subplot(1,2,1)\n",
    "plot_hist('DUSTA')\n",
    "plt.subplot(1,2,2)\n",
    "plot_hist('DUSTB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJEnqMFmUJEmSJHWYLEqSJEmSOtaNuwJSv6MPP+zB5evHWA9JkiRprTNZlCRJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373e7cee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,6])\n",
    "plt.subplot(1,2,1)\n",
    "plot_hist('WINDD25M')\n",
    "plt.subplot(1,2,2)\n",
    "plot_hist('WINDS25M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B1wJXAbuTXAzcD1wIUFV3JdkN3A08BlxeVY83u7qM3syqJwI3Nw+AtwBvS7KX3kQ628b1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\nkiRJkjTg/we9Nshe4UR23AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f374015e080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,6])\n",
    "plt.subplot(1,2,1)\n",
    "plot_hist('GUSTD')\n",
    "plt.subplot(1,2,2)\n",
    "plot_hist('GUSTS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2VtXDzfIjwMZmeRNwR99+h5uyrzbL88s1Qw6tv7S3sKsp2PX5aYUiaUbZsihJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f363573ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,6])\n",
    "plt.subplot(1,2,1)\n",
    "plot_hist('WINDD')\n",
    "plt.subplot(1,2,2)\n",
    "plot_hist('WINDS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f373ea91780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('HUMIDITY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w3t9nS7vOMwd4dJj3JEnq3rCnv5bQe0rxu4EfAy4H/nxfHarq4qo6oaoW0LsAf2tV/QKw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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3741157c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('PRESSURE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at location of sky fibers on plate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def focal_rad(x,y):\n",
    "    r = np.sqrt(x**2+y**2)\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = meta_df['XFOCAL']\n",
    "Y = meta_df['YFOCAL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r=[]\n",
    "for i, x in enumerate(X):\n",
    "    r.append(focal_rad(x,Y[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "meta_df['RFOCAL'] = r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "meta_df['THROUGHPUT'] = meta_df['THROUGHPUT']/65535."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6bfc7738d0>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zxzKz0iBrtZmVh50nHjO7z8y2mtnqmLYeZrbAzF4PfnePmRf3OQiDmfU3s8Vm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c054f8d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}