Example uncertainty.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from chaplinfilter import filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading file: /home/davies/Projects/ChaplinFilter/chaplinfilter/data/jitterdata_box4.txt\n"
     ]
    }
   ],
   "source": [
    "f = filter(verbose=True)\n",
    "f.read_train_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>ID</th>\n",
       "      <th>teff</th>\n",
       "      <th>teff_err</th>\n",
       "      <th>logg</th>\n",
       "      <th>logg_err</th>\n",
       "      <th>L</th>\n",
       "      <th>L_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70 OphA</td>\n",
       "      <td>5300</td>\n",
       "      <td>60</td>\n",
       "      <td>4.555</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alpha Cen B</td>\n",
       "      <td>5145</td>\n",
       "      <td>60</td>\n",
       "      <td>4.530</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tau Cet</td>\n",
       "      <td>5290</td>\n",
       "      <td>60</td>\n",
       "      <td>4.533</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sun</td>\n",
       "      <td>5777</td>\n",
       "      <td>60</td>\n",
       "      <td>4.438</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>del Pav</td>\n",
       "      <td>5550</td>\n",
       "      <td>60</td>\n",
       "      <td>4.306</td>\n",
       "      <td>0.034</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  teff  teff_err   logg  logg_err     L  L_err\n",
       "0      70 OphA  5300        60  4.555     0.023  0.59   0.02\n",
       "1  alpha Cen B  5145        60  4.530     0.018  0.51   0.02\n",
       "2      tau Cet  5290        60  4.533     0.018  0.50   0.02\n",
       "3          Sun  5777        60  4.438     0.000  1.00   0.00\n",
       "4      del Pav  5550        60  4.306     0.034  1.22   0.04"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('examples.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70 OphA\n",
      "Predicting ...\n",
      "Means : [3.80883589 4.81577349] Std : [0.42844712 0.46485922]\n",
      "Mean tpE : 3.808835893025007 Std : 0.4284471190035417\n",
      "alpha Cen B\n",
      "Predicting ...\n",
      "Means : [3.22742306 3.59844365] Std : [0.15629858 0.29805341]\n",
      "Mean tpE : 3.2274230617749953 Std : 0.15629857525212917\n",
      "tau Cet\n",
      "Predicting ...\n",
      "Means : [3.29173135 3.65250991] Std : [0.19448031 0.39595339]\n",
      "Mean tpE : 3.291731351450002 Std : 0.19448030616273976\n",
      "Sun\n",
      "Predicting ...\n",
      "Means : [12.33227658 10.72861884] Std : [0.25770375 0.0597345 ]\n",
      "Mean tpE : 12.332276581274986 Std : 0.2577037522479628\n",
      "del Pav\n",
      "Predicting ...\n",
      "Means : [20.49814527 18.58569134] Std : [1.00637952 1.07754683]\n",
      "Mean tpE : 20.498145273299947 Std : 1.0063795151243113\n",
      "alpha Cen A\n",
      "Predicting ...\n",
      "Means : [28.42588566 22.53916815] Std : [1.7026588  0.96699627]\n",
      "Mean tpE : 28.42588565849992 Std : 1.7026587994398956\n",
      "iota Hor\n",
      "Predicting ...\n",
      "Means : [29.8465762  22.30082654] Std : [0.82376666 0.51528186]\n",
      "Mean tpE : 29.846576195274903 Std : 0.8237666620239671\n",
      "mu Ara\n",
      "Predicting ...\n",
      "Means : [37.66191243 28.58353819] Std : [3.56555286 2.41941982]\n",
      "Mean tpE : 37.66191242975001 Std : 3.5655528585673477\n",
      "beta Vir\n",
      "Predicting ...\n",
      "Means : [109.13975662  64.92709975] Std : [5.89277567 3.08777597]\n",
      "Mean tpE : 109.1397566197498 Std : 5.892775666114878\n",
      "beta Hyi\n",
      "Predicting ...\n",
      "Means : [149.14913637  88.97648537] Std : [4.2384096  2.07602595]\n",
      "Mean tpE : 149.14913637000032 Std : 4.238409598408843\n",
      "alpha CMi\n",
      "Predicting ...\n",
      "Means : [301.90857647 140.71061723] Std : [14.50352237  6.14999539]\n",
      "Mean tpE : 301.9085764700008 Std : 14.50352237393997\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "npts = 1000\n",
    "ts = np.zeros([len(df), npts, 2])\n",
    "for iter, row in df.iterrows():\n",
    "    print(row.ID)\n",
    "    teff = row.teff + np.random.randn(npts) * row.teff_err\n",
    "    logg = row.logg + np.random.randn(npts) * row.logg_err\n",
    "    logL = row.L + np.random.randn(npts) * row.L_err\n",
    "    results = f(teff, logg, logL)\n",
    "    ts[iter, :, :] = results\n",
    "\n",
    "    print(f'Means : {np.mean(results, axis=0)} Std : {np.std(results, axis=0)}')\n",
    "    print(f'Mean tpE : {np.mean(results[:,0])} Std : {np.std(results[:,0])}')\n",
    "df['tpE'] = np.mean(ts[:, :, 0], axis=1)\n",
    "df['tpE_err'] = np.std(ts[:, :, 0], axis=1)\n",
    "df['tp1'] = np.mean(ts[:, :, 1], axis=1)\n",
    "df['tp1_err'] = np.std(ts[:, :, 1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('example_results.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/davies/.local/lib/python3.6/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",
      "/home/davies/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3d56d093c8>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "for i in range(len(df)):\n",
    "    ax = sns.distplot(ts[i,:,0], label=df.loc[i].ID)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/davies/.local/lib/python3.6/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",
      "/home/davies/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3d56cc9cc0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "for i in range(len(df)):\n",
    "    ax = sns.distplot((ts[i,:,0] - np.mean(ts[i,:,0])) / np.std(ts[i,:,0]),  label=df.loc[i].ID)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}