Validating_DC2.ipynb

{
 "cells": [
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import sncosmo",
   "execution_count": 1,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import pandas as pd",
   "execution_count": 2,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "from sqlalchemy import create_engine",
   "execution_count": 3,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "!ls",
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "stream",
     "text": "Validating_DC2.ipynb          sne_cosmoDC2_v1.1.4_MS_DDF.db\r\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "engine = create_engine('sqlite:///sne_cosmoDC2_v1.1.4_MS_DDF.db')",
   "execution_count": 5,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df = pd.read_sql_table('sne_params', con=engine)",
   "execution_count": 6,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "len(df)",
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 7,
     "data": {
      "text/plain": "588212"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df.columns",
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 8,
     "data": {
      "text/plain": "Index(['htmid_level_6', 'galaxy_id', 'c_in', 'mB', 't0_in', 'x0_in', 'x1_in',\n       'z_in', 'snid_in', 'snra_in', 'sndec_in'],\n      dtype='object')"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "%matplotlib inline\nimport matplotlib.pyplot as plt",
   "execution_count": 9,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import seaborn as sns\nsns.set_style('whitegrid')\nsns.set_context('talk')",
   "execution_count": 10,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "fig, ax = plt.subplots()\nax.plot(df.z_in, df.mB, '.')",
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 11,
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x181c925eb8>]"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "fig, ax = plt.subplots()\nax.hist(df.z_in)",
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 12,
     "data": {
      "text/plain": "(array([  3178.,  16846.,  39915.,  69117., 101534., 135151., 167392.,\n         20346.,  16733.,  18000.]),\n array([0.01906788, 0.15578095, 0.29249401, 0.42920707, 0.56592014,\n        0.7026332 , 0.83934627, 0.97605933, 1.11277239, 1.24948546,\n        1.38619852]),\n <a list of 10 Patch objects>)"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "fig, ax = plt.subplots()\nxx = df.query('z_in > 1.0')\nax.scatter(xx.snra_in, xx.sndec_in)",
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 13,
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x181cb1ada0>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "from astropy.cosmology import Planck15",
   "execution_count": 14,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "fig, ax = plt.subplots()\nax.plot(df.z_in, df.mB - Planck15.distmod(df.z_in.values), '.')",
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 15,
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x181cc805f8>]"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "fig, ax = plt.subplots()\nsns.jointplot('c_in', 'x1_in', data=df, kind='scatter', )",
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "stream",
     "text": "/Users/rbiswas/soft/mypython3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
     "name": "stderr"
    },
    {
     "output_type": "execute_result",
     "execution_count": 16,
     "data": {
      "text/plain": "<seaborn.axisgrid.JointGrid at 0x181ccb9470>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "len(df)",
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 17,
     "data": {
      "text/plain": "588212"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "fig, ax = plt.subplots()\nax.scatter(df.snra_in, df.sndec_in)",
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 18,
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x181ec95d30>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df.galaxy_id.unique().size ",
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 19,
     "data": {
      "text/plain": "587861"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df.galaxy_id.size",
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 20,
     "data": {
      "text/plain": "588212"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df.t0_in.apply(lambda x: (x - 59980)/365.).describe()",
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 21,
     "data": {
      "text/plain": "count    588212.000000\nmean          3.904190\nstd           2.884376\nmin          -1.095867\n25%           1.408275\n50%           3.903245\n75%           6.401065\nmax           8.904095\nName: t0_in, dtype: float64"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "40 * 20 * 60 * 60 ",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "2880000"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df.head()",
   "execution_count": 25,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 25,
     "data": {
      "text/plain": "   htmid_level_6    galaxy_id      c_in         mB         t0_in     x0_in  \\\n0           9021  10562502111  0.035201  18.516489  60772.368515  0.000617   \n1           9023  10562500822 -0.086665  17.966773  63060.297448  0.001028   \n2           9021  10562500735  0.037553  19.078334  62832.166508  0.000370   \n3           9021  10562502246  0.076715  19.192019  61400.008116  0.000335   \n4           9023  10562504918 -0.012653  19.388848  61342.657248  0.000278   \n\n      x1_in      z_in     snid_in    snra_in   sndec_in  \n0  1.477374  0.077278  MS_10199_0  66.115587 -40.866055  \n1  0.576378  0.073256  MS_10199_1  66.662435 -42.042877  \n2 -0.177457  0.068469  MS_10199_2  65.212146 -41.416473  \n3 -1.344205  0.080189  MS_10199_3  65.469824 -41.109646  \n4  0.474204  0.115499  MS_10199_5  65.986211 -42.086708  ",
      "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>htmid_level_6</th>\n      <th>galaxy_id</th>\n      <th>c_in</th>\n      <th>mB</th>\n      <th>t0_in</th>\n      <th>x0_in</th>\n      <th>x1_in</th>\n      <th>z_in</th>\n      <th>snid_in</th>\n      <th>snra_in</th>\n      <th>sndec_in</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>9021</td>\n      <td>10562502111</td>\n      <td>0.035201</td>\n      <td>18.516489</td>\n      <td>60772.368515</td>\n      <td>0.000617</td>\n      <td>1.477374</td>\n      <td>0.077278</td>\n      <td>MS_10199_0</td>\n      <td>66.115587</td>\n      <td>-40.866055</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>9023</td>\n      <td>10562500822</td>\n      <td>-0.086665</td>\n      <td>17.966773</td>\n      <td>63060.297448</td>\n      <td>0.001028</td>\n      <td>0.576378</td>\n      <td>0.073256</td>\n      <td>MS_10199_1</td>\n      <td>66.662435</td>\n      <td>-42.042877</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>9021</td>\n      <td>10562500735</td>\n      <td>0.037553</td>\n      <td>19.078334</td>\n      <td>62832.166508</td>\n      <td>0.000370</td>\n      <td>-0.177457</td>\n      <td>0.068469</td>\n      <td>MS_10199_2</td>\n      <td>65.212146</td>\n      <td>-41.416473</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9021</td>\n      <td>10562502246</td>\n      <td>0.076715</td>\n      <td>19.192019</td>\n      <td>61400.008116</td>\n      <td>0.000335</td>\n      <td>-1.344205</td>\n      <td>0.080189</td>\n      <td>MS_10199_3</td>\n      <td>65.469824</td>\n      <td>-41.109646</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9023</td>\n      <td>10562504918</td>\n      <td>-0.012653</td>\n      <td>19.388848</td>\n      <td>61342.657248</td>\n      <td>0.000278</td>\n      <td>0.474204</td>\n      <td>0.115499</td>\n      <td>MS_10199_5</td>\n      <td>65.986211</td>\n      <td>-42.086708</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "uddf_sn = df.query('snra_in < 53.76 and snra_in > 52.48 and sndec_in > -28.66 and sndec_in < -27.53')",
   "execution_count": 30,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "len(uddf_sn)",
   "execution_count": 32,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 32,
     "data": {
      "text/plain": "4549"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  }
 ],
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