prepare_APOKASC2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy import constants as const\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_palette('colorblind',10)\n",
    "sns.set_context('notebook')\n",
    "import matplotlib\n",
    "matplotlib.rc('xtick', labelsize=20) \n",
    "matplotlib.rc('ytick', labelsize=20)\n",
    "matplotlib.rc('axes',labelsize=20) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>KICID</th>\n",
       "      <th>kepmag_x</th>\n",
       "      <th>Length_Quarters</th>\n",
       "      <th>Length_days</th>\n",
       "      <th>numax</th>\n",
       "      <th>numax_err</th>\n",
       "      <th>dnu</th>\n",
       "      <th>dnu_err</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>err.2_x</th>\n",
       "      <th>...</th>\n",
       "      <th>Ebv</th>\n",
       "      <th>Aks</th>\n",
       "      <th>Aj</th>\n",
       "      <th>Ah</th>\n",
       "      <th>H17_Ag</th>\n",
       "      <th>L</th>\n",
       "      <th>L_err</th>\n",
       "      <th>Mbol</th>\n",
       "      <th>Mbol_err</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [KICID, kepmag_x, Length_Quarters, Length_days, numax, numax_err, dnu, dnu_err, amplitude, err.2_x, fwhm, err.3_x, maxbg, err.4_x, Unnamed: 14, Yu_Teff, Yu_Teff_err, logg, logg_err, [Fe/H], err.2_y, M_noCorrection, M_noCorrection_err, R_noCorrection, R_noCorrection_err, M_RGB, M_RGB_err, R_RGB, R_RGB_err, M_Clump, M_Clump_err, R_Clump, R_Clump_err, stage, Unnamed: 20, designation, source_id, gaia_ref_epoch, ra, ra_error, dec, dec_error, parallax, parallax_error, parallax_over_error, pmra, pmra_error, pmdec, pmdec_error, ra_dec_corr, ra_parallax_corr, ra_pmra_corr, ra_pmdec_corr, dec_parallax_corr, dec_pmra_corr, dec_pmdec_corr, parallax_pmra_corr, parallax_pmdec_corr, pmra_pmdec_corr, astrometric_chi2_al, astrometric_excess_noise, astrometric_excess_noise_sig, astrometric_primary_flag, duplicated_source, phot_g_mean_flux, phot_g_mean_flux_error, GAIAmag, phot_bp_mean_flux, phot_bp_mean_flux_error, phot_bp_mean_mag, phot_rp_mean_flux, phot_rp_mean_flux_error, phot_rp_mean_mag, bp_rp, bp_g, g_rp, radial_velocity, radial_velocity_error, phot_variable_flag, l, b, ecl_lon, ecl_lat, teff_val, teff_percentile_lower, teff_percentile_upper, Ag, a_g_percentile_lower, a_g_percentile_upper, e_bp_min_rp_val, e_bp_min_rp_percentile_lower, e_bp_min_rp_percentile_upper, radius_val, radius_percentile_lower, radius_percentile_upper, lum_val, lum_percentile_lower, lum_percentile_upper, r_est, r_lo, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 165 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h18 = pd.read_csv('/home/oliver/PhD/Gaia_Project/data/KepxDR2/rcxyu18.csv')\n",
    "h18.rename(columns={'Teff':'Yu_Teff', 'Teff_err':'Yu_Teff_err'},inplace=True)\n",
    "h18.head(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5576"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(h18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1637"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check = pd.read_csv('/home/oliver/PhD/Gaia_Project/data/KepxDR2/rcxyuxapokasc2.csv')\n",
    "len(check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "a2 = pd.read_csv('/home/oliver/PhD/Catalogues/APOKASC-2/APOKASC2_Table5.txt',sep='\\t')\n",
    "a2.rename(columns={'KIC':'KICID',\n",
    "                   'S Teff':'Teff_err',\n",
    "                   'Nmax':'A_numax',\n",
    "                   'S Nmax':'A_numax_err',\n",
    "                   'Dnu':'A_dnu',\n",
    "                   'S Dnu':'A_dnu_err',\n",
    "                   'Fdnu':'A_fdnu',\n",
    "                   'S Fdnu':'A_fdnu_err'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(h18, a2, on='KICID', how='left')\n",
    "columns=['KICID','numax', 'numax_err', 'dnu', 'dnu_err','Yu_Teff','Yu_Teff_err','ra','dec','parallax','parallax_error',\n",
    "        'GAIAmag','Ag','e_GAIAmag','Kmag','e_Kmag','ccd','b17','Ebv','Aks','Z', 'Teff','Teff_err',\n",
    "         'M_Clump','M_Clump_err','R_Clump','R_Clump_err',\n",
    "        'A_numax','A_numax_err','A_dnu','A_dnu_err','A_fdnu','A_fdnu_err','[Fe/H]','Log Age']\n",
    "df = df[columns].dropna().reset_index(drop=True)\n",
    "df.drop(df[df.Teff == '...'].index.values, inplace=True)\n",
    "df.drop(df[df.A_fdnu == '...'].index.values, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['A_numax'] = df.A_numax.astype(float)\n",
    "df['A_numax_err'] = df.A_numax_err.astype(float)\n",
    "df['A_dnu'] = df.A_dnu.astype(float)\n",
    "df['A_dnu_err'] = df.A_dnu_err.astype(float)\n",
    "df['A_fdnu'] = df.A_fdnu.astype(float)\n",
    "df['A_fdnu_err'] = df.A_fdnu_err.astype(float)\n",
    "df['Log Age'] = df['Log Age'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Teff'] = df.Teff.astype(float)\n",
    "df['Teff_err'] = df.Teff_err.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1637"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('/home/oliver/PhD/Gaia_Project/data/KepxDR2/rcxyuxapokasc2.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets do some quick comparisons for these stars to the Yu values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams['text.usetex']=False\n",
    "\n",
    "sns.jointplot(df.numax, df.A_numax)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(df.dnu, df.A_dnu)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(df.Teff, df.A_Teff)\n",
    "plt.plot(df.Teff, df.Teff,c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['3.889', '3.639', '3.786', ..., '3.476', '3.645', '3.469'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Log Age'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5fe8665bd0>"
      ]
     },
     "execution_count": 22,
     "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": [
    "sns.distplot(df['Log Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,0,'Age (Myr?)')]"
      ]
     },
     "execution_count": 25,
     "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": [
    "sns.distplot(10**(df['Log Age'])).set(xlabel='Age (Myr?)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}