Notebook_Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pdb\n",
    "from math import log10, floor\n",
    "\n",
    "import numpy as np\n",
    "import scipy.optimize as op\n",
    "import scipy.special as spec\n",
    "from scipy import stats\n",
    "from scipy import io\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter, MaxNLocator, LogFormatter, ScalarFormatter\n",
    "\n",
    "import seaborn as sns\n",
    "import corner\n",
    "\n",
    "import astropy\n",
    "from astropy import stats as astrostats\n",
    "from astropy.timeseries import LombScargle\n",
    "\n",
    "import emcee\n",
    "import radvel\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assuming a polished list of known planets and candidates in the legacy sample, explore and visualize properties such as mass, host star metallicity, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('legacy_tables/system_props.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "specmatch = pd.read_csv('/Users/lee/Academics/Astronomy/Planets/CLSI/legacy_tables/legacy_specmatch_medians.csv')\n",
    "smsyn = specmatch.query('teff_emp > 4700')\n",
    "smemp = specmatch.query('teff_emp <= 4700')\n",
    "fe_select = specmatch['fe_c'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "specmatch_pub = specmatch[['name', 'teff_c', 'teff_err_c', 'fe_c', 'fe_err_c', \n",
    "                           'logg_c', 'logg_err_c', 'radius_c', 'radius_err_c', \n",
    "                           'mass_c', 'mass_err_c', 'star', 'mv', 'bvcolor']]\n",
    "specmatch_pub['parallax'] = specmatch['iso_parallax']\n",
    "specmatch_pub.to_csv('stellar_parameters.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>iso_mass</th>\n",
       "      <th>iso_logage</th>\n",
       "      <th>iso_logage_err1</th>\n",
       "      <th>iso_logage_err2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>219134</td>\n",
       "      <td>0.788484</td>\n",
       "      <td>9.845051</td>\n",
       "      <td>0.217643</td>\n",
       "      <td>-0.455149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>75732</td>\n",
       "      <td>0.974708</td>\n",
       "      <td>9.786790</td>\n",
       "      <td>0.208371</td>\n",
       "      <td>-0.354344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  iso_mass  iso_logage  iso_logage_err1  iso_logage_err2\n",
       "303  219134  0.788484    9.845051         0.217643        -0.455149\n",
       "466   75732  0.974708    9.786790         0.208371        -0.354344"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specmatch.query('name == \"75732\" or name == \"219134\"')[['name', 'iso_mass', 'iso_logage', 'iso_logage_err1', 'iso_logage_err2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.028870729944700007 +/- 0.28368027269138\n"
     ]
    }
   ],
   "source": [
    "print(np.median(specmatch.fe_c), '+/-', np.std(specmatch.fe_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "objects = pd.read_csv('legacy_tables/planet_list.csv')\n",
    "trends = pd.read_csv('legacy_tables/trend_list.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sort the planets into real, new candidates, known."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "objects_real   = objects.query('status != \"N\" and status != \"A\" and status != \"R\"').reset_index(drop=True)\n",
    "objects_good   = objects_real.query('mass != \"nan\"').reset_index(drop=True)\n",
    "planets_s      = objects_good.query('status == \"S\" or status == \"SS\"').reset_index(drop=True)\n",
    "planets        = objects_good.query('status != \"S\" and status != \"SS\"').reset_index(drop=True)\n",
    "planets_old    = planets.query('status == \"K\"').reset_index(drop=True)\n",
    "planets_new    = planets.query('status == \"C\" or status == \"J\"').reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'hostname', 'status', 'pl_index', 'mass', 'mass_med',\n",
       "       'mass_minus', 'mass_plus', 'axis', 'axis_med', 'axis_minus',\n",
       "       'axis_plus', 'per', 'per_med', 'per_minus', 'per_plus', 'tc', 'tc_med',\n",
       "       'tc_minus', 'tc_plus', 'w', 'w_med', 'w_minus', 'w_plus', 'k', 'k_med',\n",
       "       'k_minus', 'k_plus', 'e', 'e_med', 'e_minus', 'e_plus', 'e_mode',\n",
       "       'e_68', 'insol', 'insol_med', 'insols_minus', 'insols_plus', 'teq',\n",
       "       'teq_med', 'teq_minus', 'teq_plus', 'post_path'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(planets.query('mass < 30/317.8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>hostname</th>\n",
       "      <th>status</th>\n",
       "      <th>pl_index</th>\n",
       "      <th>mass</th>\n",
       "      <th>mass_med</th>\n",
       "      <th>mass_minus</th>\n",
       "      <th>mass_plus</th>\n",
       "      <th>axis</th>\n",
       "      <th>axis_med</th>\n",
       "      <th>...</th>\n",
       "      <th>e_68</th>\n",
       "      <th>insol</th>\n",
       "      <th>insol_med</th>\n",
       "      <th>insols_minus</th>\n",
       "      <th>insols_plus</th>\n",
       "      <th>teq</th>\n",
       "      <th>teq_med</th>\n",
       "      <th>teq_minus</th>\n",
       "      <th>teq_plus</th>\n",
       "      <th>post_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>107148</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.067845</td>\n",
       "      <td>0.062633</td>\n",
       "      <td>0.052797</td>\n",
       "      <td>0.072335</td>\n",
       "      <td>0.140652</td>\n",
       "      <td>0.140650</td>\n",
       "      <td>...</td>\n",
       "      <td>0.401911</td>\n",
       "      <td>67.907955</td>\n",
       "      <td>6.792773e+01</td>\n",
       "      <td>6.268559e+01</td>\n",
       "      <td>7.345871e+01</td>\n",
       "      <td>0.225309</td>\n",
       "      <td>0.225293</td>\n",
       "      <td>6.268559e+01</td>\n",
       "      <td>7.345871e+01</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/107148/10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>136925</td>\n",
       "      <td>C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.872652</td>\n",
       "      <td>0.840422</td>\n",
       "      <td>0.766177</td>\n",
       "      <td>0.918395</td>\n",
       "      <td>5.209309</td>\n",
       "      <td>5.130608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146360</td>\n",
       "      <td>0.052280</td>\n",
       "      <td>5.385304e-02</td>\n",
       "      <td>4.980845e-02</td>\n",
       "      <td>5.813147e-02</td>\n",
       "      <td>1.352622</td>\n",
       "      <td>1.342632</td>\n",
       "      <td>4.980845e-02</td>\n",
       "      <td>5.813147e-02</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/136925/13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>141004</td>\n",
       "      <td>C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.043896</td>\n",
       "      <td>0.042869</td>\n",
       "      <td>0.038311</td>\n",
       "      <td>0.047486</td>\n",
       "      <td>0.123777</td>\n",
       "      <td>0.123790</td>\n",
       "      <td>...</td>\n",
       "      <td>0.215525</td>\n",
       "      <td>135.627013</td>\n",
       "      <td>1.356329e+02</td>\n",
       "      <td>1.243914e+02</td>\n",
       "      <td>1.476890e+02</td>\n",
       "      <td>0.189528</td>\n",
       "      <td>0.189526</td>\n",
       "      <td>1.243914e+02</td>\n",
       "      <td>1.476890e+02</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/141004/14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>145675</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.691600</td>\n",
       "      <td>5.842973</td>\n",
       "      <td>4.813248</td>\n",
       "      <td>7.200666</td>\n",
       "      <td>15.949545</td>\n",
       "      <td>16.419784</td>\n",
       "      <td>...</td>\n",
       "      <td>0.530049</td>\n",
       "      <td>0.002791</td>\n",
       "      <td>2.636649e-03</td>\n",
       "      <td>1.071866e-03</td>\n",
       "      <td>4.873996e-03</td>\n",
       "      <td>2.813989</td>\n",
       "      <td>2.854279</td>\n",
       "      <td>1.071866e-03</td>\n",
       "      <td>4.873996e-03</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/145675/14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>156668</td>\n",
       "      <td>C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.101324</td>\n",
       "      <td>0.099085</td>\n",
       "      <td>0.091433</td>\n",
       "      <td>0.106949</td>\n",
       "      <td>1.570776</td>\n",
       "      <td>1.570311</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127107</td>\n",
       "      <td>0.126132</td>\n",
       "      <td>1.261865e-01</td>\n",
       "      <td>1.185650e-01</td>\n",
       "      <td>1.342235e-01</td>\n",
       "      <td>1.085308</td>\n",
       "      <td>1.085191</td>\n",
       "      <td>1.185650e-01</td>\n",
       "      <td>1.342235e-01</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/156668/15...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>82</td>\n",
       "      <td>164922</td>\n",
       "      <td>C</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.034078</td>\n",
       "      <td>0.033118</td>\n",
       "      <td>0.030034</td>\n",
       "      <td>0.036210</td>\n",
       "      <td>0.229199</td>\n",
       "      <td>0.229221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.123719</td>\n",
       "      <td>14.504370</td>\n",
       "      <td>1.450174e+01</td>\n",
       "      <td>1.349452e+01</td>\n",
       "      <td>1.555400e+01</td>\n",
       "      <td>0.331425</td>\n",
       "      <td>0.331440</td>\n",
       "      <td>1.349452e+01</td>\n",
       "      <td>1.555400e+01</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/164922/16...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>88</td>\n",
       "      <td>168009</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.030927</td>\n",
       "      <td>0.030019</td>\n",
       "      <td>0.026336</td>\n",
       "      <td>0.033793</td>\n",
       "      <td>0.119264</td>\n",
       "      <td>0.119246</td>\n",
       "      <td>...</td>\n",
       "      <td>0.172219</td>\n",
       "      <td>102.871384</td>\n",
       "      <td>1.029570e+02</td>\n",
       "      <td>9.439693e+01</td>\n",
       "      <td>1.119207e+02</td>\n",
       "      <td>0.203089</td>\n",
       "      <td>0.203047</td>\n",
       "      <td>9.439693e+01</td>\n",
       "      <td>1.119207e+02</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/168009/16...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>136</td>\n",
       "      <td>213472</td>\n",
       "      <td>C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.479000</td>\n",
       "      <td>3.479000</td>\n",
       "      <td>2.885000</td>\n",
       "      <td>4.553000</td>\n",
       "      <td>12.990000</td>\n",
       "      <td>12.990000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.649000</td>\n",
       "      <td>0.005693</td>\n",
       "      <td>6.770000e-22</td>\n",
       "      <td>1.480000e-22</td>\n",
       "      <td>2.460000e-12</td>\n",
       "      <td>2.354657</td>\n",
       "      <td>126802.763000</td>\n",
       "      <td>1.480000e-22</td>\n",
       "      <td>2.460000e-12</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/213472/21...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>163</td>\n",
       "      <td>24040</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.206749</td>\n",
       "      <td>0.201043</td>\n",
       "      <td>0.173883</td>\n",
       "      <td>0.228451</td>\n",
       "      <td>1.301112</td>\n",
       "      <td>1.300030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.166341</td>\n",
       "      <td>1.135675</td>\n",
       "      <td>1.137315e+00</td>\n",
       "      <td>1.049463e+00</td>\n",
       "      <td>1.232663e+00</td>\n",
       "      <td>0.626536</td>\n",
       "      <td>0.626310</td>\n",
       "      <td>1.049463e+00</td>\n",
       "      <td>1.232663e+00</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/24040/240...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>167</td>\n",
       "      <td>26161</td>\n",
       "      <td>C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.510000</td>\n",
       "      <td>13.520000</td>\n",
       "      <td>9.840000</td>\n",
       "      <td>21.990000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>20.450000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.881000</td>\n",
       "      <td>0.015781</td>\n",
       "      <td>7.918622e-03</td>\n",
       "      <td>2.553594e-03</td>\n",
       "      <td>1.668076e-02</td>\n",
       "      <td>1.824835</td>\n",
       "      <td>2.168189</td>\n",
       "      <td>2.553594e-03</td>\n",
       "      <td>1.668076e-02</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/26161/261...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>188</td>\n",
       "      <td>3765</td>\n",
       "      <td>C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.174505</td>\n",
       "      <td>0.172514</td>\n",
       "      <td>0.159399</td>\n",
       "      <td>0.186072</td>\n",
       "      <td>2.136434</td>\n",
       "      <td>2.107524</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333186</td>\n",
       "      <td>0.088187</td>\n",
       "      <td>9.062822e-02</td>\n",
       "      <td>8.437785e-02</td>\n",
       "      <td>9.726919e-02</td>\n",
       "      <td>1.186886</td>\n",
       "      <td>1.178810</td>\n",
       "      <td>8.437785e-02</td>\n",
       "      <td>9.726919e-02</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/3765/3765...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>223</td>\n",
       "      <td>66428</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.812772</td>\n",
       "      <td>27.183267</td>\n",
       "      <td>9.770447</td>\n",
       "      <td>48.865305</td>\n",
       "      <td>10.059953</td>\n",
       "      <td>23.006122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.404989</td>\n",
       "      <td>0.012327</td>\n",
       "      <td>2.365470e-03</td>\n",
       "      <td>7.118295e-04</td>\n",
       "      <td>5.232605e-03</td>\n",
       "      <td>1.941072</td>\n",
       "      <td>2.932785</td>\n",
       "      <td>7.118295e-04</td>\n",
       "      <td>5.232605e-03</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/66428/664...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>227</td>\n",
       "      <td>68988</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.048702</td>\n",
       "      <td>14.965098</td>\n",
       "      <td>13.439914</td>\n",
       "      <td>17.798917</td>\n",
       "      <td>13.425650</td>\n",
       "      <td>13.150699</td>\n",
       "      <td>...</td>\n",
       "      <td>0.517329</td>\n",
       "      <td>0.009404</td>\n",
       "      <td>9.749950e-03</td>\n",
       "      <td>4.989125e-03</td>\n",
       "      <td>1.366507e-02</td>\n",
       "      <td>2.076955</td>\n",
       "      <td>2.058301</td>\n",
       "      <td>4.989125e-03</td>\n",
       "      <td>1.366507e-02</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/68988/689...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>269</td>\n",
       "      <td>95735</td>\n",
       "      <td>C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.057407</td>\n",
       "      <td>0.056775</td>\n",
       "      <td>0.048474</td>\n",
       "      <td>0.065837</td>\n",
       "      <td>2.994804</td>\n",
       "      <td>3.103780</td>\n",
       "      <td>...</td>\n",
       "      <td>0.202083</td>\n",
       "      <td>0.002609</td>\n",
       "      <td>2.424993e-03</td>\n",
       "      <td>2.202391e-03</td>\n",
       "      <td>2.645472e-03</td>\n",
       "      <td>2.861688</td>\n",
       "      <td>2.914620</td>\n",
       "      <td>2.202391e-03</td>\n",
       "      <td>2.645472e-03</td>\n",
       "      <td>/data/user/lrosenth/legacy/final_run/95735/957...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0 hostname status  pl_index       mass   mass_med  mass_minus  \\\n",
       "0            6   107148      C       2.0   0.067845   0.062633    0.052797   \n",
       "1           40   136925      C       1.0   0.872652   0.840422    0.766177   \n",
       "2           45   141004      C       1.0   0.043896   0.042869    0.038311   \n",
       "3           58   145675      C       2.0   5.691600   5.842973    4.813248   \n",
       "4           72   156668      C       1.0   0.101324   0.099085    0.091433   \n",
       "5           82   164922      C       3.0   0.034078   0.033118    0.030034   \n",
       "6           88   168009      C       2.0   0.030927   0.030019    0.026336   \n",
       "7          136   213472      C       1.0   3.479000   3.479000    2.885000   \n",
       "8          163    24040      C       2.0   0.206749   0.201043    0.173883   \n",
       "9          167    26161      C       1.0  13.510000  13.520000    9.840000   \n",
       "10         188     3765      C       1.0   0.174505   0.172514    0.159399   \n",
       "11         223    66428      C       2.0   3.812772  27.183267    9.770447   \n",
       "12         227    68988      C       2.0  15.048702  14.965098   13.439914   \n",
       "13         269    95735      C       1.0   0.057407   0.056775    0.048474   \n",
       "\n",
       "    mass_plus       axis   axis_med  ...      e_68       insol     insol_med  \\\n",
       "0    0.072335   0.140652   0.140650  ...  0.401911   67.907955  6.792773e+01   \n",
       "1    0.918395   5.209309   5.130608  ...  0.146360    0.052280  5.385304e-02   \n",
       "2    0.047486   0.123777   0.123790  ...  0.215525  135.627013  1.356329e+02   \n",
       "3    7.200666  15.949545  16.419784  ...  0.530049    0.002791  2.636649e-03   \n",
       "4    0.106949   1.570776   1.570311  ...  0.127107    0.126132  1.261865e-01   \n",
       "5    0.036210   0.229199   0.229221  ...  0.123719   14.504370  1.450174e+01   \n",
       "6    0.033793   0.119264   0.119246  ...  0.172219  102.871384  1.029570e+02   \n",
       "7    4.553000  12.990000  12.990000  ...  0.649000    0.005693  6.770000e-22   \n",
       "8    0.228451   1.301112   1.300030  ...  0.166341    1.135675  1.137315e+00   \n",
       "9   21.990000  20.400000  20.450000  ...  0.881000    0.015781  7.918622e-03   \n",
       "10   0.186072   2.136434   2.107524  ...  0.333186    0.088187  9.062822e-02   \n",
       "11  48.865305  10.059953  23.006122  ...  0.404989    0.012327  2.365470e-03   \n",
       "12  17.798917  13.425650  13.150699  ...  0.517329    0.009404  9.749950e-03   \n",
       "13   0.065837   2.994804   3.103780  ...  0.202083    0.002609  2.424993e-03   \n",
       "\n",
       "    insols_minus   insols_plus       teq        teq_med     teq_minus  \\\n",
       "0   6.268559e+01  7.345871e+01  0.225309       0.225293  6.268559e+01   \n",
       "1   4.980845e-02  5.813147e-02  1.352622       1.342632  4.980845e-02   \n",
       "2   1.243914e+02  1.476890e+02  0.189528       0.189526  1.243914e+02   \n",
       "3   1.071866e-03  4.873996e-03  2.813989       2.854279  1.071866e-03   \n",
       "4   1.185650e-01  1.342235e-01  1.085308       1.085191  1.185650e-01   \n",
       "5   1.349452e+01  1.555400e+01  0.331425       0.331440  1.349452e+01   \n",
       "6   9.439693e+01  1.119207e+02  0.203089       0.203047  9.439693e+01   \n",
       "7   1.480000e-22  2.460000e-12  2.354657  126802.763000  1.480000e-22   \n",
       "8   1.049463e+00  1.232663e+00  0.626536       0.626310  1.049463e+00   \n",
       "9   2.553594e-03  1.668076e-02  1.824835       2.168189  2.553594e-03   \n",
       "10  8.437785e-02  9.726919e-02  1.186886       1.178810  8.437785e-02   \n",
       "11  7.118295e-04  5.232605e-03  1.941072       2.932785  7.118295e-04   \n",
       "12  4.989125e-03  1.366507e-02  2.076955       2.058301  4.989125e-03   \n",
       "13  2.202391e-03  2.645472e-03  2.861688       2.914620  2.202391e-03   \n",
       "\n",
       "        teq_plus                                          post_path  \n",
       "0   7.345871e+01  /data/user/lrosenth/legacy/final_run/107148/10...  \n",
       "1   5.813147e-02  /data/user/lrosenth/legacy/final_run/136925/13...  \n",
       "2   1.476890e+02  /data/user/lrosenth/legacy/final_run/141004/14...  \n",
       "3   4.873996e-03  /data/user/lrosenth/legacy/final_run/145675/14...  \n",
       "4   1.342235e-01  /data/user/lrosenth/legacy/final_run/156668/15...  \n",
       "5   1.555400e+01  /data/user/lrosenth/legacy/final_run/164922/16...  \n",
       "6   1.119207e+02  /data/user/lrosenth/legacy/final_run/168009/16...  \n",
       "7   2.460000e-12  /data/user/lrosenth/legacy/final_run/213472/21...  \n",
       "8   1.232663e+00  /data/user/lrosenth/legacy/final_run/24040/240...  \n",
       "9   1.668076e-02  /data/user/lrosenth/legacy/final_run/26161/261...  \n",
       "10  9.726919e-02  /data/user/lrosenth/legacy/final_run/3765/3765...  \n",
       "11  5.232605e-03  /data/user/lrosenth/legacy/final_run/66428/664...  \n",
       "12  1.366507e-02  /data/user/lrosenth/legacy/final_run/68988/689...  \n",
       "13  2.645472e-03  /data/user/lrosenth/legacy/final_run/95735/957...  \n",
       "\n",
       "[14 rows x 43 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "false_ps = objects.query('status == \"N\" or status == \"A\" or status == \"R\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "163"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(planets_old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hostname</th>\n",
       "      <th>mass_med</th>\n",
       "      <th>axis</th>\n",
       "      <th>axis_plus</th>\n",
       "      <th>per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>120066</td>\n",
       "      <td>3.155188</td>\n",
       "      <td>15.721033</td>\n",
       "      <td>43.307454</td>\n",
       "      <td>21712.99221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>145675</td>\n",
       "      <td>5.842973</td>\n",
       "      <td>15.949545</td>\n",
       "      <td>25.720437</td>\n",
       "      <td>23632.68993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>213472</td>\n",
       "      <td>3.479000</td>\n",
       "      <td>12.990000</td>\n",
       "      <td>18.740000</td>\n",
       "      <td>16745.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>26161</td>\n",
       "      <td>13.520000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>28.320000</td>\n",
       "      <td>31733.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>66428</td>\n",
       "      <td>27.183267</td>\n",
       "      <td>10.059953</td>\n",
       "      <td>41.605342</td>\n",
       "      <td>11370.48258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>68988</td>\n",
       "      <td>14.965098</td>\n",
       "      <td>13.425650</td>\n",
       "      <td>18.425483</td>\n",
       "      <td>16599.67834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>95128</td>\n",
       "      <td>1.514914</td>\n",
       "      <td>13.365341</td>\n",
       "      <td>18.622327</td>\n",
       "      <td>17800.98644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hostname   mass_med       axis  axis_plus          per\n",
       "15    120066   3.155188  15.721033  43.307454  21712.99221\n",
       "36    145675   5.842973  15.949545  25.720437  23632.68993\n",
       "76    213472   3.479000  12.990000  18.740000  16745.00000\n",
       "91     26161  13.520000  20.400000  28.320000  31733.00000\n",
       "117    66428  27.183267  10.059953  41.605342  11370.48258\n",
       "119    68988  14.965098  13.425650  18.425483  16599.67834\n",
       "144    95128   1.514914  13.365341  18.622327  17800.98644"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.query('axis > 10')[['hostname', 'mass_med', 'axis', 'axis_plus', 'per']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hostname</th>\n",
       "      <th>mass</th>\n",
       "      <th>axis</th>\n",
       "      <th>per</th>\n",
       "      <th>e</th>\n",
       "      <th>mass_minus</th>\n",
       "      <th>mass_med</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>136925</td>\n",
       "      <td>0.872652</td>\n",
       "      <td>5.209309</td>\n",
       "      <td>4642.360748</td>\n",
       "      <td>0.155105</td>\n",
       "      <td>0.766177</td>\n",
       "      <td>0.840422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>145675</td>\n",
       "      <td>5.691600</td>\n",
       "      <td>15.949545</td>\n",
       "      <td>23632.689930</td>\n",
       "      <td>0.440759</td>\n",
       "      <td>4.813248</td>\n",
       "      <td>5.842973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>213472</td>\n",
       "      <td>3.479000</td>\n",
       "      <td>12.990000</td>\n",
       "      <td>16745.000000</td>\n",
       "      <td>0.529000</td>\n",
       "      <td>2.885000</td>\n",
       "      <td>3.479000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26161</td>\n",
       "      <td>13.510000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>31733.000000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>9.840000</td>\n",
       "      <td>13.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>66428</td>\n",
       "      <td>3.812772</td>\n",
       "      <td>10.059953</td>\n",
       "      <td>11370.482580</td>\n",
       "      <td>0.131577</td>\n",
       "      <td>9.770447</td>\n",
       "      <td>27.183267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>68988</td>\n",
       "      <td>15.048702</td>\n",
       "      <td>13.425650</td>\n",
       "      <td>16599.678340</td>\n",
       "      <td>0.458257</td>\n",
       "      <td>13.439914</td>\n",
       "      <td>14.965098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hostname       mass       axis           per         e  mass_minus  \\\n",
       "1    136925   0.872652   5.209309   4642.360748  0.155105    0.766177   \n",
       "3    145675   5.691600  15.949545  23632.689930  0.440759    4.813248   \n",
       "7    213472   3.479000  12.990000  16745.000000  0.529000    2.885000   \n",
       "9     26161  13.510000  20.400000  31733.000000  0.820000    9.840000   \n",
       "11    66428   3.812772  10.059953  11370.482580  0.131577    9.770447   \n",
       "12    68988  15.048702  13.425650  16599.678340  0.458257   13.439914   \n",
       "\n",
       "     mass_med  \n",
       "1    0.840422  \n",
       "3    5.842973  \n",
       "7    3.479000  \n",
       "9   13.520000  \n",
       "11  27.183267  \n",
       "12  14.965098  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets_new[['hostname', 'mass', 'axis', 'per', 'e', 'mass_minus', 'mass_med']].query('axis > 5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "163 14\n"
     ]
    }
   ],
   "source": [
    "print(len(planets_old), len(planets_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hostname</th>\n",
       "      <th>mass_minus</th>\n",
       "      <th>mass_med</th>\n",
       "      <th>mass_plus</th>\n",
       "      <th>axis_minus</th>\n",
       "      <th>axis_med</th>\n",
       "      <th>axis_plus</th>\n",
       "      <th>e_minus</th>\n",
       "      <th>e_med</th>\n",
       "      <th>e_plus</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>126614</td>\n",
       "      <td>22.518464</td>\n",
       "      <td>27.635242</td>\n",
       "      <td>34.055481</td>\n",
       "      <td>14.985698</td>\n",
       "      <td>16.495014</td>\n",
       "      <td>18.152885</td>\n",
       "      <td>0.011902</td>\n",
       "      <td>0.042616</td>\n",
       "      <td>0.090068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>167215</td>\n",
       "      <td>20.079095</td>\n",
       "      <td>24.359442</td>\n",
       "      <td>43.316096</td>\n",
       "      <td>7.479448</td>\n",
       "      <td>7.990628</td>\n",
       "      <td>9.010023</td>\n",
       "      <td>0.099420</td>\n",
       "      <td>0.299263</td>\n",
       "      <td>0.571366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>168443</td>\n",
       "      <td>17.408982</td>\n",
       "      <td>17.758193</td>\n",
       "      <td>18.104664</td>\n",
       "      <td>2.852956</td>\n",
       "      <td>2.881388</td>\n",
       "      <td>2.909322</td>\n",
       "      <td>0.209952</td>\n",
       "      <td>0.211245</td>\n",
       "      <td>0.212563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18445</td>\n",
       "      <td>29.472635</td>\n",
       "      <td>33.738929</td>\n",
       "      <td>40.852173</td>\n",
       "      <td>1.192552</td>\n",
       "      <td>1.208903</td>\n",
       "      <td>1.224649</td>\n",
       "      <td>0.567954</td>\n",
       "      <td>0.665781</td>\n",
       "      <td>0.781128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38529</td>\n",
       "      <td>13.112682</td>\n",
       "      <td>13.214594</td>\n",
       "      <td>13.315972</td>\n",
       "      <td>3.726559</td>\n",
       "      <td>3.736452</td>\n",
       "      <td>3.746310</td>\n",
       "      <td>0.349835</td>\n",
       "      <td>0.354501</td>\n",
       "      <td>0.359306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4747</td>\n",
       "      <td>47.546300</td>\n",
       "      <td>49.156142</td>\n",
       "      <td>50.815651</td>\n",
       "      <td>9.678957</td>\n",
       "      <td>9.844308</td>\n",
       "      <td>10.012856</td>\n",
       "      <td>0.729473</td>\n",
       "      <td>0.730979</td>\n",
       "      <td>0.732513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>68017</td>\n",
       "      <td>27.999495</td>\n",
       "      <td>33.788880</td>\n",
       "      <td>39.467197</td>\n",
       "      <td>16.723536</td>\n",
       "      <td>21.175386</td>\n",
       "      <td>25.961902</td>\n",
       "      <td>0.341956</td>\n",
       "      <td>0.432068</td>\n",
       "      <td>0.507253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68988</td>\n",
       "      <td>13.439914</td>\n",
       "      <td>14.965098</td>\n",
       "      <td>17.798917</td>\n",
       "      <td>11.167620</td>\n",
       "      <td>13.150699</td>\n",
       "      <td>18.425483</td>\n",
       "      <td>0.372318</td>\n",
       "      <td>0.453138</td>\n",
       "      <td>0.587606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8765</td>\n",
       "      <td>41.490140</td>\n",
       "      <td>42.957214</td>\n",
       "      <td>44.440522</td>\n",
       "      <td>3.302516</td>\n",
       "      <td>3.355164</td>\n",
       "      <td>3.405652</td>\n",
       "      <td>0.384035</td>\n",
       "      <td>0.393565</td>\n",
       "      <td>0.403421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>167665</td>\n",
       "      <td>47.006376</td>\n",
       "      <td>48.447361</td>\n",
       "      <td>49.868030</td>\n",
       "      <td>5.310911</td>\n",
       "      <td>5.390377</td>\n",
       "      <td>5.468173</td>\n",
       "      <td>0.337721</td>\n",
       "      <td>0.342080</td>\n",
       "      <td>0.346124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  hostname  mass_minus   mass_med  mass_plus  axis_minus   axis_med  \\\n",
       "0   126614   22.518464  27.635242  34.055481   14.985698  16.495014   \n",
       "1   167215   20.079095  24.359442  43.316096    7.479448   7.990628   \n",
       "2   168443   17.408982  17.758193  18.104664    2.852956   2.881388   \n",
       "3    18445   29.472635  33.738929  40.852173    1.192552   1.208903   \n",
       "4    38529   13.112682  13.214594  13.315972    3.726559   3.736452   \n",
       "5     4747   47.546300  49.156142  50.815651    9.678957   9.844308   \n",
       "6    68017   27.999495  33.788880  39.467197   16.723536  21.175386   \n",
       "7    68988   13.439914  14.965098  17.798917   11.167620  13.150699   \n",
       "8     8765   41.490140  42.957214  44.440522    3.302516   3.355164   \n",
       "9   167665   47.006376  48.447361  49.868030    5.310911   5.390377   \n",
       "\n",
       "   axis_plus   e_minus     e_med    e_plus  \n",
       "0  18.152885  0.011902  0.042616  0.090068  \n",
       "1   9.010023  0.099420  0.299263  0.571366  \n",
       "2   2.909322  0.209952  0.211245  0.212563  \n",
       "3   1.224649  0.567954  0.665781  0.781128  \n",
       "4   3.746310  0.349835  0.354501  0.359306  \n",
       "5  10.012856  0.729473  0.730979  0.732513  \n",
       "6  25.961902  0.341956  0.432068  0.507253  \n",
       "7  18.425483  0.372318  0.453138  0.587606  \n",
       "8   3.405652  0.384035  0.393565  0.403421  \n",
       "9   5.468173  0.337721  0.342080  0.346124  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "substellars = objects_good.query('mass_minus > 12 and mass_plus < 60 and axis_plus < 30').reset_index(drop=True)\n",
    "substellars[['hostname', 'mass_minus', 'mass_med', 'mass_plus', \n",
    "             'axis_minus', 'axis_med', 'axis_plus', \n",
    "             'e_minus', 'e_med', 'e_plus']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 18})\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xticks((0.1, 1, 10))\n",
    "ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax.get_xaxis().set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
    "ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax.get_yaxis().set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.set_yscale('log')\n",
    "ax2.set_yticks((0.1, 1, 10))\n",
    "ax2.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_xaxis().set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 18})\n",
    "ax.scatter(planets_old.axis, 317.8*planets_old.mass, c='b', s=50, alpha=0.75, \n",
    "           label='{} Known'.format(len(planets_old)))\n",
    "ax.scatter(planets_new.axis, 317.8*planets_new.mass, s=400, c='g', alpha=0.75, \n",
    "           label='{} New'.format(len(planets_new)))\n",
    "ax.set_xlim([2.5*10**-2, 30])\n",
    "ax.set_ylim([2, 9000])\n",
    "\n",
    "ymin, ymax = ax.get_ylim()\n",
    "ax2.set_ylim((ymin/317.8,ymax/317.8))\n",
    "ax2.plot([],[])\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "#ax.set_xlabel('Semi-major axis (AU)')\n",
    "ax.set_xlabel(r'$a$ (AU)')\n",
    "ax.set_ylabel(r'$M$sin$i$ ($M_{\\oplus}$)')\n",
    "ax2.set_ylabel(r'$M$sin$i$ ($M_J$)')\n",
    "ax.legend(loc=4, facecolor='gray', framealpha=0.2)\n",
    "\n",
    "fig.savefig('plots/m_a_scatter.png', dpi=1000, bbox_inches='tight')\n",
    "fig.savefig('plots/m_a_scatter.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvkQinOhz2tiwGlhdVpoqmgjWBjuds/vs+Kv8+Kv8wxmuEEIMKUkUhRCiF1ue2EKkLdIx1dwXpRRWh5WmXU1oU3c6riyKSFtkOMMVQoghJYmiEEL0IBqIsvvF3TiznDCAGWNlUZgJk1BTqPNxUseFEGKskERRCCF6cODNAyTjSazOgdf9KYui7WDntYoajd1jH6rwhBBi2EmiKIQQPWja3YRhNbC5bShDoZP9H1a02CzEArGO75OxJIbVIGOCVKUKIcYOSRSFEKIHsfYYhmFgWAx8xamdVvpN0SmxDDWGKLumDJtL+igKIcYOSRSFEKIHjgwHZjK1pjCjOAOF6nd7G601hjX1EatNjdaamVfOHLZYhRBiOEiiKIQQPcg/Kb+jctnmtuEd7yURSfQrWUzGkjiyHGitCdQEmHrJVDJLM4c7ZCGEGFKyM4sQQvSgdGEpNpct1WjbYSV3Zi6JSIJwUxiNJhFOpKaXFVgcFmxuG4b1SBsdEzJKMgjUBCg8pZBzvnXOaL8dIYQYMBlRFEKIHlgdVmYtnUWoIdXmxrAY5EzPIRlPEvPHUtPSBqAgEU0QaY4QD8ZJRBMdieTERRO55L5LsDrk93IhxNgjn1xCCNGLkz5zEvte30d7bTvOHCd1m+qwuqzYPXZioRjJaBIApVPrF6OBKBabhbmfm8sZ/3YGBScX9KtRtxBCpCMZURRCiF44Mhxc+otL8RR4aNzRSCKSmoY2bAbOTCfucW4cGQ7sGXZsbhs2t428OXlc/pvLKTylUJJEIcSYJomiEEL0wVvo5Yr/uSK117OGRDhBMprEjJupnVaO1LZ4CjypdY0eGzXra0Y3aCGEGAIy9SyEEP3gzHTiyHRQuqiUcFOYUFMIM26iLAq7145vvC+VSJLa+i/UGOrjiUIIkf4kURRCiH46WpDiK/LhK/J1HDeTJtG2KLFgrGOfZ4vdMlphCiHEkEmbqWel1GKl1EqlVK1SKqqUOqSUekopNbsf92YrpR5WSjUqpdqVUq8ppeaORNxCiBPHpPMnEWmJdHyfiCRormjm4NsHqf2glvqt9dR+UEvjjkYqX6ukeW/zKEYrhBDHLm0SRSAHeB/4N+AS4DvAHGCNUmpiTzep1ErxfwCXAl8DlgI24HWlVMlwBy2EOHGULSlDJ3WqPU4wRvV71bQdbMOwGlidVqxOK0opPAUeat6r4R9f+gcHVx8c7bCFEGLQ0iZR1Fo/qbW+Q2v9N631m1rrx4BrAR/wqV5uvQpYCNx05BkvHzlmAN8a9sCFECeM3Om5nPrPp+Kv8lPzXg06qbG6rCgj1RonEU5gsVvIm5WHt9CL3Wdn5XdX0rizcbRDF0KIQUmbRLEHTUe+xnu55iqgRmv9+tEDWus24Hng6mGMTQhxAjrl5lMoWVCCmTDRpiYejhOPxElGkrjz3BSVF2F1ptYy2j12lEWx4eENoxy1EEIMTtoVsyilLIAFmAj8BKgF/trLLXOArd0c3wbcrJTyaq2DQx6oEOKEZMZNmnc1M2HhBBLhBIloAmUonJnOjgTxo9y5bqrXVROoCXQqgBFCiLEg7RJFYC1w2pG/7wEu1FrX93J9DrC/m+NHV5FnA10SRaXUl4EvA5SWlg42ViHECaZ5TzPxcBxvpheby9bn9UenpWs31UqiKIQYc9Jx6vkm4CzgBsAPLFdKTerlekVHu9sux3uktf6d1rpca12el5c3yFCFECeaeDg+4N1WtKmJh3pbQSOEEOkp7RJFrfUOrfVarfWTwEWAF7izl1uaSY0qflz2ka8tQxyiEOIEZnPZ0Lq73017ZhhGv0YfhRAi3aRdovhRWutWUtPP03q5bBupdYofNxs4KOsThRBDKXtqNlaHlUQ00a/rtanRaApOLhjmyIQQYuildaKolCoAyoC9vVz2D6BYKXXeR+7LAK48ck4IIYaM1WGlbGkZ4aZwv64PN4cpOq2IjJKMYY5MCCGGXtokikqpvyulvq+UulopdYFS6ivAm0ACuO/INecppRJKqZs/cus/gHeBx5VSn1FKLT5yTAH/3wi/DSHECWDWklnYvfZOu7R0Jx6Ok4wnmf8v80coMiGEGFppkygCa4BrgD8DLwL/QSpRnKe13n3kGkWqdU5H3FprE7gCWA78Bvg7kAQu0FpXjVj0QogThifPw+JfLEYZikBNgGQ82em8mTRpr2sn0hrh/LvPJ39O/ihFKoQQx0YNdFH28ai8vFyvX79+tMMQQowxgZoAW/6yhYqXKlJrEU2d+jVWQ+k5pZxy8ymMKxs32mEKIY4zSqn3tdblI/JakihKoiiEODbRQJS6zXXE2+NYHKkt/Dz5ntEOSwhxnBrJRDEdG24LIcSY4vA5KD1HGvcLIY4/6bRGUQghhBBCpBFJFIUQQgghRLdk6lkIMWqi/igVL1Ww87mdRNuiZE7IZM6n51B6bikWm2W0wxNCiBOeJIpCiFERrA3y0r+9RLAuiDPTidVppXV/K69//3VKzi7honsuwmJPv2TxaAHgQPd7FkKIsUgSRSHEiNNa88bdbxBqCpFR/OGOJRa7BUeWg6p3q9j82GZO/dKpoxhlZ817mtn8+Gb2v7EfbWrGnzaeU24+hfGnjh/t0IQQYtjIGkUhxIhrrmimYXtDty1klFJ48jxs+9u2fu+nPNxq3q/hH//8Dw68eQD3ODfeQi/1W+pZ9rVl7H5hd98PEEKIMUoSRSHEiGve04zWusfpW6vTSiKcIHg4OMKRdZWMJ3nzh29i89jwFHgwrAbKULjHuXHnuVl932rCzf3b91kIIcYaSRSFECNOGX2v79Na9+u64Va7sZZIWwSHz9HlnNVhRSc1+97YNwqRCSHE8JNEUQgx4gpOLkApldryrhux9hjuXDe+Yt8IR9ZVqClEbztYKaUI1oz+yKcQQgyHQRWzKKXOAi4FzgKKABfQCOwC3gSe1Vq3DFWQQojji6/Ix8RFE9n/5n58Rb5OU9BmwiTcFGbhnQsxLKP/u6wrx4Wi55FNbWq8Rd4RjEgIIUbOgBJFpdTngduBOYAf2AxUAGEgBzgTuAl4UCn1FPBDrbXMyQghulh450IirRHqNtVh2AwsdguJcALTNJl7w1xmXDljxGJpO9jGvtf3EawNYnVZGT9vPCVnl2CxWRg/fzyODAfRQLTL9HMimkBZFJPPnzxisQohxEhSvU2pdLpQqU1APvAo8FfgA93NzUqpTOAK4EbgfOCLWuv/HaqAh0N5eblev379aIchxAnHTJoc3nCYPS/tIdQUIntqNjMun0HOtJwRef1gbZC3732b2o21oMGwGWhTo02N3Wun/JZyZlw5g5r1NSy/YzkocI9zo5Qi3BImEU6w4I4FlF1dNiLxCiEEgFLqfa11+Yi81gASxW8Av9VaR/r9cKVOAQq11q8MMr4RIYmiEGNTsC5IoCaAYTHInpqN3WPvck00EGXva3vZv3I/geoAhtXAlesib3YeFS9UkIwlcee5uxTOxMNxQo0hTvvKacy7eR6NuxrZ/PhmDrx1AJ3UjJ+f6qNYVF40Um9XCCGANE0Uj2eSKAoxtjTubGTD7zdQ/V41hsVAozEMgxlXzGDeF+fhzHICqSnlF776AvVb6om2RUGDsigySjJor28nEUmQf1I+GSUZ3b5OMp6kvb6dy39zOQVzC4AjO7Po/lVuCyHEcBjJRFF2ZhFCjCk179fw6m2voiwKb6G3I2FLxpNsf2Y7Ve9WcflDl+PKcfHWj9+icXsj8fY4dp89VWmtNf5DfgBsHhuNOxsxrAbewq4FKRabBcNqsO2pbR2JolKKXmpbhBDiuDL6JYVCCNFP0UCUld9did1rx5Pn6TSqZ7FZyChOjRSuumcV/io/tR/UEg1EsbqsHZXVSimSsSTJeBLDkiqiaa5o7rFVj3ucm4NvHSTqj47IexRCiHTSZ6KolJqslFqhlKpUSt2vlHJ+5Ny64Q1PCCE+tG/lPuLhOHZv17WIR3nyPVS/V01TRRPh1jAK1WUHmI8mhYbVIBFLEGnpfvm1YTHAQHZfEUKckPozovgb4BngOlItcFYopY52wbUNV2BCCPFxe1/Zi83d+8eOMlKNvNvr20mEE2Dp/jrD2vnjLx6J9/xQ3fV6IYQ4EfTnk69Aa/2g1vp9rfUXgBdIJYuZgFTCCCFGTCwYw7D1L2HTSU3uzFySkWSnnVWS0SRWh7VLM++e9p1ORBJYXVY8+Z7BBy6EEGNUfz5xO3WY1VrfCzwFrABGf38tIcQJw1voTY0S9kEphXucm9O/ejruXDfJWJJEJEEinMDmtVF8ZnFqfaOmI4l0ZHbdyxkg3BRm9rWzsdh7GJoUQojjWH+qnncrpS7WWi8/ekBr/XOllAn8fPhCE0KIzmZePZND7x7q9RozYWJYDSYsmADA+knrsTgsHQ21j05dZxRn0FbVhjIUrmxXtz0YI20RbB4bM6+eOfRvRgghxoD+jCh+Bnjr4we11vcDE4Y8IiGE6EHxGcVkTEhVNndHm5rA4QBl15bhyHDgyHCw8LsLiQaiaDTxcJymiiaaKppw5bqwuW0k40l8xb7O09PxJIGaANrULL5/sUw7CyFOWH2OKGqtO/WEUEoVa62rj5yrHq7AhBDi4yw2C5fcdwkv//vL+Kv9OLOc2D12tNZEWiLE2+NMOn8S5bd82Id2ykVTwIAXvvwC7fXtqCNNEFtoIWtKFufdfR77V+6n7WAbypI6ZxgGs5bMYs6n5+ArkhU2QogT12Aabm8kteezEEKMON94H1f98Sr2vrKXLX/Z0tE8u2BuAbOvn00ilmD5HctJRBJMXDSR6ZdPp/rdarwFXnKm5pCIJECB3Wsn0hIh1hZjyWNLaN3fSqQ1gsVmIWtSVq8teIQQ4kQx4C38lFINWuu8YYpnVMgWfkKMTVprkrFU42ytNSu+u4JDqw9h89hQFkUsGMOV4yLUEMJX5OvS4iYZTxJpifDZ5z/b7RpFIYRIR+m+hZ+0xBFCpAWlFFZH6mNsz6t7OLT6EL4SX0erG2emk+Y9zYSbw2SWZna532KzoLUm1BiSRFEIIbohHWSFEMeFPS/uSY0kfqwforfQS7g5jJk0u9xjJk10UuPMdHY5J4QQYnAjikIIkXYS0URHMcpH2dw2bG4bofoQ3vHeTudCDSEmnDMBZ9bIJYpaaxp3NLLz2Z007GjAYrcw5RNTmLZ4Gq4c14jFIYQQ/SEjikKI48LERROJB7tuwxduDjPtk9NwZDnwV/uJ+qNE/VH81X5cuS7O+sZZIxajNjVrfrGG57/yPHtf3UukNULwcJD1D63nb5/5G4c3Hh6xWIQQoj8GM6JYNeRRCCHEMZp++XS2P72dQE0AT74HZajUlHPCZMFtC/AV+ah4qYLKFZUYFoPJF01m+ien48jofkeW4bD1r1vZ/vT2VGHNR7YQdGQ4iAaiLL9jOUseXSIteYQQaWPAVc/HI6l6FiK9BOuCtB1sQ5saX5GPzAldC1G6017fzoaHN7D31b2YCZPxp42n/Cvl5M0e/UYNiWiCv17zV6wOK1Zn97+jB2oCnPSZkzj9q6ePcHRCiLEk3aueh4VSajHwbWA2kA00AKuBu7XW23u5bxKwr4fT2Vrr1qGNVAgxXBp3NrLxkY0cWnMoVZSiQCc0+Sfnc+oXT6WovKjX+z35Hs797rmc8+1z0KbGYhu5/Zn91X4OvnOQeHucnGk5lJxV0un1G7Y3kAgnei2cceW4qFhWIYmiECJtDCpRVEoZgKG1Tnzk2GLgJGCl1nrjIB6bA7wP/IZUklgK3AmsUUrN1Vof6OP+e4F/fOxYYBBxCCFGwf4397PizhUoi8I73tsxNatNTfPuZl7+xsssuH0BZdeU9fksw2LACOWI2tSs++91bP/bdtCg0SilcOW6uORnl5AzLQego9F3bwyrQTQQ7f0iIYQYQYMdUXwSiAI3AyilbiGV4AHElVKXa61fG8gDtdZPHnluB6XUOmAn8Cngvj4eUam1XjOQ1xRCjL5Ye4z3f/s+b9/zNhqNYTFoqWzBlePC5ko1znZkOvDkeVh932qyJmdReErhaIfdYev/bmXrX7fiK+687jDcHOblb77M0r8sxeFLxa+TGq11lxY+R8VDcTKKMkYqdCGE6NNgE8WzSE1Nt/bvAAAgAElEQVQTH3UH8DBwG/A74C5gQIliD5qOfO1ayijECcpMmtRurKVxdyM6ockszaTk7JKOxtNjSTQQZdm/L+PAmwfQSmN320nGkoSbw4QaQiirwu6xpxpru6xkFGew5fEtaZMoJuNJNj+2GXeeu1OSCKlpZH+1n30r9lF2TRnZU7PJnppNoCaAK7trGxytNVF/lNP/VaadhRDpY7D/Z8kHqgGUUtOAycB/a60DSqlHgL8MNiCllIXUpNFE4CdALfDXftx6r1Lqt0A78CZwl9Z6y2DjECIdHVx9kHd//i6hxhDaTBWiKUNhc9mY90/zmHP9nB5Hq9LRugfW0VzRTDwUx+a0kYylttRThkLZVGoELqmx+VLnmiubMZMmocYQ7nHu0Q6fQHWAeCiOt9Db7Xmr00r12mrKrilDKcXZ/3E2y762jKg/2qnaWpuaYG2Q3Bm5TL5w8kiFL4QQfRpsougHco/8/XygUWu9+cj3SeBYuteuBU478vc9wIVa6/pero8C/wO8SmptYxnwXWC1UuoMrfWOY4hFiLRRuaKSN+5+A0eGo0v7lEQ0wdoH1hJuClN+a/mYSBbDLWH2Lt+LM9uJNjXKUMQCMZShOtbyKYsiHo5j99qx2C0kIgmCdUHa69uPKVGseb+GjQ9vpH5bPY4MB7OWzmLuZ+f2WI3cE2VRaN3LdLIJhu3DkcaCuQVccv8lrLhzBXWb69Cmxua2YffYmbBwAud+51xsLtug35cQQgy1wTbcXg3cqZS6AvgG8NJHzk0DDh1DTDeRmtq+gVRCuvxIZXO3tNaHtda3aK2f0Vq/rbX+PbCI1J7Ud/V0n1Lqy0qp9Uqp9Q0NDccQrhDDL9IW4e173saV48Lh69r3z+qw4hvvY8uTW6jf2tvvVemjfms96NR+y2gw4yZmwuxa8KEhFoqlikGAaGv0mAo+9r+5n5e/8TLNe5vxFnpRFsWGhzew/NvLu93mrzcZJRl4C7zEgrFuzydjSSZf9OEIYbg5zMY/bCQeindURCdjSbxFXs782pkj2tNRCCH6Y7CJ4rdIVSn/g9To4d0fOfdp4N3BBqS13qG1XnukuOUiwEuq+nkgz6gCVgE9LvbRWv9Oa12utS7Pyxv9HmtC9KbytUrMmNnraJNhNbBYLex4euQH0c2ESdW7VWz+y2Y2/2UzB985SDKe7P2euJkqXrEZWBwWErGuVcE6qUnGk8QCMaJtUSKtEWLtMSpeqhhwUgep9Z1rfrkGZ6YTV46rY9o+oySDwxsOU/NezYCep5Si/NZyom1R4uEPl1JrUxM8HCRrchYTzp4ApBLCl7/5Mg3bGvAV+xg3axyF8wrJm5NHqCHEsn9fJhXPQoi0M6ipZ611BTBDKZWrtW762Omvk1pXeMy01q1KqT2kRikHSpEaVRRizKt8tRKbt+8pSVeui/2v70f/Z8+VtUNtz8t7WPfgOqJt0U7rJu1eO+W3ljPjihndxuIr8nX8C82cmEnD1s4j+2biwxFGw2Kk/kUnwXAY7H5+Nw6vg7NvP3tA79Nf5SfSEumyplAphWExqFpdRclZJQN6/5MvmEzirgTrHlhHsDWY6v2Y1BSfUcy5d52LxZ4aOax6t4rWylZ8xZ2XDSil8OR78Ff7qVxRyaxrZg3o9YUQYjgdU5lkN0kiQ1lAopQqILXm8IkB3lcKnAP8fahiEWI0xYIxDGvfEwCG1ehIsEai2fT2/9vOml+uwZXr6rJuMh6Os+reVURbo5x808ld7s2dmUvWpCza69rxFnpp299GPBxPrVHUqWTraNNtVGqUDgV2r52syVns/MdOpl02jfw5+f2Ot9c1hUenwQdh+ienM+WiKdRtqSMRTpA5MbPLbjIVL1VgcfT8fIfPwe7nd0uiKIRIK4Odeh5ySqm/K6W+r5S6Wil1gVLqK6SqlxMc6aGolDpPKZVQSt38kfvuU0r9Qil1/ZH7bgHeBkzgntF4L0IMNXeem2S096lcSBW12Dy2fiWVx6rtYBtr/3stngIPNnfX0U6by4Z3vJf3f/8+zXuau5xXSnH2bWcTj8RJRBKMLx+P1WXFjJskE0m0eSShs6iOpNHqspI9ORuLzYJhMdj5950DijmjJIPMCZlEWztP8WpTY5omE8+fOLD/CB9hsVsoOq2I0oWl3W45GAvEOkYXu2PYDGKB7tc6CiHEaEmbRBFYA1wD/Bl4EfgPUoniPK317iPXKFKtcz4a9zZgIanK5+Wk1ku+A5yptd41IpELMcxmXj2zo5ijN+GmMGVXl43ItPOu53ehUL0mPxabBWUodj7bfUJXeEohi+9fjGExiLREGDdzHDaPDZ1MjR4aVgPDYmB1W7G6rPgKfWSWppIwR4ZjwIU7SinO+fY5JBNJgrVB4qE44ZYw/mo/0z85nfyT+j86OVC5M3KJt/fcEjYWiJE7I7fH80IIMRrSpkOv1vqnwE/7uOYNPrbcXWv9R+CPwxeZEKNvwoIJuHJchFvC3TZrhtRUr7IoZl49c9ji0FqnRjZVam2iM7vvTliuHBd7X93LgtsXdHu+6LQiPv3Mp6leV03jzkYSsQQfPPIBzXubUUqhSI0kZk7MxFfk60iCj442DlTB3AKu/sPVbHtqG4c3HCajJIOya8uYcuGUYU2wZ1w5gx1P7yAZT3aZ4jaTJslYkllLZdpZCJFe0iZRFEL0zOqwcvHPLmbZ15YRPBxM7QRi/XAv5HBzmEQ0wQU/uoCMkqHfAi4WjLF3+V62/GULwcNBAJr3NJM7IzfVYsboOcEybAbxULzXresMq8GEBROYsCBVIezKcvHeg++leiUquh21DNYEUUrx/Feex5PvYeZVMyk6rajXWI7KmpTFOd86pz9vfcjkTM3h1C+dyvu/fx+Hz4EjM9UKJxaIEWmNMPtTsymclx47zgghxFHHlCgqpSYAE+imwbbWeuWxPFsI0Vnu9FyuevgqNv5xI/tW7usYW9dJzfj54zn1S6dSMLdgyF+3vb6dZV9fhv+QH2emk4ySDLTWNFU0Ub+lnsDhAIWnFPa4LjIZS+LwObpNEpPxJI07Gom1x/Dkeciemo1SiqmLp7Lh9xswzW5aAmmo31ZPW1UbFrsFM2HSXNHM/tf3U7qwlAt+dEGv0+Gj6ZTPn0LmpEw2/XkTLXtbgNS6yTP//UymLp46JhqlCyFOLErrgXeQUUpNIVWJfMbRQ0e+6iN/11rr9Pyk7kZ5eblev379aIchRL+FW8K07m9FmxrfeF+XiuOhYiZNnvun5/Af9OMp8HQ611LZQsu+FhQKd56bgpO7T1IDhwOc9OmTOP2rH7Y11Vqz89mdbPzDRqKBKMpIFaxkTc7i7G+eTeG8Qva/sZ/Xv/86Foelo+eh1prm3c0072km/+T8TkUj2tT4q/3M+/w8Tvvyad2Fkja01sQCMbTWODK6T6KFEKInSqn3tdblI/Fagx1RfBgoJbUry05ASvWEGEGubFePaxWHUs36Glr3tXabiPqKfLQdaENZFaGGEPH2ODZP59G/RCSBUoqZV3VeN7nxDxvZ+MhG3OPc+Mannq21pr22nZe/8TKX3HcJk86fxGUPXsbGP27k8MbDHclkpC1CwSkFXabYlZHqR7j9b9s5+aaT03orPKWU7MIihBgTBpsong58QWv99FAGI4RIL7ue29Vpr+KPsjqtjJs9jvqt9eiExn/YT+60VNWu1ppoW5RYMMai7y3qlNT5D/nZ9OgmfON9naarlVI4s51E/VHe/n9vc93frqPg5AIu/eWlhBpDRFojmEmT5//l+R5HUK0OK5HWCP4qv1QQCyHEEBhsongIGUUUoldaaxKRBFaHtV8FFukoUBPodWTOW+DFYrNQv7WeUH0Iu9vesSdS1uQszv/h+ZSc2Xmnk90vprpd9bSm0ZHhIFAToPaDWopOKwLAPc6Ne5ybqD+aasbdS2EMZs/PFkIIMTCDTRTvAb6tlFqptW4fyoCEGOuaKprY+cxOKl6uwEyYGBaDKRdPYfbS2YwrGzfa4Q2I1WXtc09lV46LnBk5FJ9ezLTFqd02MyZkkDsjt9tkrmlXU5/TwtrUBKoD8LGlhnafndyyXNoOtnU79R5rj+HIcpA5sWvDayGEEAM32L2eH1NKlQH7lVJrgJaul+jPH3N0QowxO/6+gzW/WIMyFK5cFxZbqiq3cnkle1/ZS/mt5cz97NzRDhNIFarUba4j3BzGW+glb3Zel8RuyiemsPZXa3Fm9tEv0YRZ185iwtkT+nxdm9vWZ/KpjO4beSulmP/P83n1tlexuW1YHR9+hCXjScLNYRZ+e2Fqb2ghhBDHbFCJolLqC8B3gCQwn67T0AMvpRZijDv4zkFW/3w1nnxPpwTGsBp4C70k40nW/fc6PHkepnxiyihGCk27m1h510qCdcEPp4onZXHRPRd1Wk845RNTWP+b9cRD8W636QOItEVw5bgoPr24X689+aLJHHjrQI/njyaR4+eP7/Z8yZklnPPtc1jzizWE4qFUkYupMQyD0/7lNGZcOaNfcQghhOjbYNvjHADWA1/SWrcOeVQjTNrjiGOltebZzz9Le307zqyeR9+i/ijxUJy8OXm017Xj8DmYfvl0pnxiSq/3DaVIa4Snb3gaM2HiynF1xB9uDOPIcrD0L0s7Jbr739zP6997HZvHhiPT0WlnlHBTGIBLf3Up+XP6t/1dIprg6RueJuaP4crtPH2stSZQE2DqxVM57wfn9f4+2iIcePMAgZoA7jw3k86blGrQLYQQx7mRbI8z2PmZXOA3x0OSKMRQaK5opnV/a8duG91JRBLUb63n8MbD1G6oJRlPEqwLsvaBtfztM3+jbnPdiMS655U9xIKxjiQRUlO67jw37fXtHHi782jfpPMmccn9l+Ae5yZ4OEigJtDxJ2daDpf/5vJ+J4mQqkxefP9ibB4b/mo/kbYI8XCcUFOIQHWA8aeO73G7v49yZjqZedVMym8pZ/bS2ZIkCiHEMBhsMcsqYBawYghjEWLMCtYFUYbqsRJXm5rajbUkoqkqaIvDkirocKWqfKP+KK/8xytc86drBrUFXyKaoGp1FdVrqwEoPqOYCedM6DQyeFTtxlqszu7/6RsWg4atDUz9xNROx4tPL2bJY0to3NmIv8oPCnKm5ZA9OXvAsQJkTcxiyWNL2LdiH7ue30XUHyV7SjazPzWbkjNLpGpZCCHSxGATxa8DTymlWoCX6VrMgta699XqQhxH+iqeCDWGiIVi2Fy2jibUH3W0Jcz2v23nrG+cNaDXbqls4dXbX6W9oR2L1QIKKl6qwJXj4pKfX0LOtJzOr5XpIBlLdvssbeoeR0WVUuTNyiNvVt6A4uuJw+eg7Joyyq4pG5LnCSGEGHqD/bV9BzAXeBSoB+If+yM9FsUJJXdGLmh6rOb1V/kxLAZaa9Bgz7B3ucad52b387tJxrtP4roTC8Z4+RsvEw1EySjOwFPgwZPvwVfkIx6Kp875o53umXbpNMyEiTY7r082EyZaayadP6nfrz8atNYkookB/XcSQggxOIMdUfwRUtks0oyZNDm46iDb/ndbx3rBsqvLmH7Z9GHfLs09zk3puaUcXHUQb6G3y/lEJIFhMUhGkzhznN32EbTYLCQTSeKhOJbM/m2VXrmikkhrpNudSlw5LvzVfipXVDJrySy0qWne24zFbmHKxVPY++peHD4HNreNWDBGvD3OyTedPOjp5OGmTc3e5XvZ/NhmWve3opSi5OwSTr7pZArmdr/PtBBCiGMz2D6Kdw9xHEIcEzNh8sbdb7D/jf3Y3DbsXjuxQIx1D65j21PbuOzByzr2FB4u5beUU/N+DeGmcJdqXsNqEA1GMSwGudO731rOTJqg6XH9YHf2rdyH1dXz9XaPncrlleTNyuP1H7xOe107qFQj7RlXzqBlT0tHUcpJnz2J0oWl/X7tkaRNzaqfrmL387txZjnxFftAQ837NRxac4hF31/E1Iun9v0gIYQQAzLYEUUh0sqOZ3awb+U+MkoyOrbLs9gt2L122uvaeePuN7jit1f0vO3bEMgoyeDyBy/ntTtfI1ATwLAaWOwWkrEkFqcFAqnegHZv12lngHBTuMcClJ4kY8letwdUFkU0EOWVb75CMpHsSCoNi8Gel/Zw+UMDq1geLVWrq6h4oaLTzxcFnjwP8XCcVfeuovj04hFrMSSEECeKwTbc/jZQorX+WjfnHgCqtNY/O9bghOgPM2my+YnNuMe5u02a3PluGrY30FzRnFpLOAiR1ggH3j5AqDGEe5ybiedO7DYpyZ6SzdInl1Kzvoa9r+wl1BTCle1iwoIJrP756h6fn4wlScaTA961pWh+EQ3bGnrcOSUWiJFRnEFdTR2hhhBmIrWG0rAYuAvcbP+/7WROyERr3efuK1prQg0hDJvR7fZ5w2nLk1uweWzd/nxtLhuRlgiVKyqZvXT2iMYlhBDHu8GOKH4RuK+Hcx8AtwOSKIoREWmJpNbp9TC1rFSqbU1LZcuAE0WtNZse3cQHj3yAmTRRKDSad+9/l/n/NJ+5n5vbZZTSsBiUnFlCyZklQKp1TSwY46J7LuoYbXTnuVPb+yVNwk1hkvEkZ339LApOHthau+lXTGfzXzYTD8exOqwE64MEDgVIRlOjh44MB1a3lbYDbakt745Ma5sJE/8BP5se28S+lftAQ8EpBZzz7XPInNB1n+RD6w6x5hdrUvsvAwUnF7Dg9gVkTcoaULyD1bKnBbuv+5FYAMNm0LizcURiEUKIE8lgE8VSoKKHc5XAxEE+V4gBM6wG6FRS19vUsrIMfNp521Pb2PC7DXgKPVhsHxaYJONJ3vvte1hcFuZ8ak6398baY2x4eAO7n9+NGTexuqxMWzwNFOxZtodkIgkaSheWctJnThpwkgjgG+9j0V2LePNHb9K6v5VYewzDYmAmTaKBKBa7hYZtDWitO/UmVBZFLBhDK03RaUUANGxvYNnXlrH0L0s7bddXt6WO5bcvx+ax4R3vBQ2NOxp58asvsuTRJSPS6NrqsmImzE4/g4/SCd1rIimEEGJwBpsohoCeNnYtAaI9nBNiyDkyHeRMzyFQHeh2OthMmqCgcF7hgJ6biCTY+IeNuPPdXRIUi82CJ8/Dxj9sZOaVM7usKzQTJq/e/ir1W+vx5KeSzEQ0wY5ndzD5wsncsOwGEuEEVqd1QGsSuzPlE1Pw1/h54/tvYHVYUUrhLfSSOSETi8NC1btVqdePJDpGFBOhBFprHF5Hx3SuJ9+Dv9rPwXcOdioM2fiHjVjslg+nplWqlU+gOsDuF3Yz7wvzjin+/ph+2XQ2P76522pxrTVaayafP3nY4xBCiBPNYPsovg3coZTq1HPkyPe3HTkvxIhQSnHqP51KLBjr0kham5rg4SDTLpmGJ88zoOfWbqpNTeH2kMhZnVYSoUS3W+8dWnuI+q31+Ip8HUmm1WEloziDA28eoG1/G85M5zEniUdVr60mtyyXiYsmUnpuKXmz87D77KmCHrcdZ5YTZ7aTRDiRavhtUVidVpzZH0usNQQPBzsdqttS120CbvPYqHm/Zkji70vZNWXYPXbCzeFOx7XWBGuCFJxSQP7c9C/KEUKIsWaw/5e6G1gN7FZKPQ5Ukxph/BypfaC/MBTBieOD1qlkLRaM4cn3DEtlauk5pZz1zbNY9+t1aFOnehLGP5zaPfu2swf8zHh7HN1Hu1CNJh6Kdz6mNZXLKwFQdJ7uVoZCm5rq9dWDLqzpTqgh1GNbHXeem3BTmJypOVjnWNGmJtQUon5zPRklGZgJk/b6dqL+KPFQHJun86idM8tJIprAbu08tZuMJvGMG1jyPViefA+XPnBpxxpPbaYalyuLoviMYs67+7xhrWgXQogT1WD7KG5SSl0A/Bz4NqmRSZPUHtBLtdabhi5EMZbVflDL2l+vpbmiObVG0IRJF0zijH87Y8jXts1eOpuJiyZSubyS5r3NuHJcTL5wMuPKxg0qifAWentd+3h0lxVvgbfj+32v72PTnzdx4K0DRFoitO5vJWtiFr5iX6dnDHVSkzszl6pVVT028j79X0/nwNsHCDeG0WhcuS6mXjqVpp1N+Gv86KTGTJrY3Dbe/cW7NO9uZsEdCzCsBnOum8PaB9Zic31YdWwmTJLxJDOumjGk76PX9zg9l+ueuo6a9TU07W7CYrNQdHoROVNz+r5ZCCHEoAx63ktrvQ5YpJRyAdlAi9Y63Mdt4gRyaN0hlt+xHKvTine8F6UUZsJk3+v7qN9az5W/uxJXztC2WfHkeZh+2XSi/ijuce5ORRkDNW7WODJKMjpa3HxcpCVC1qQscmemRgY3PLyBTX/ahD3DTvaUbOo212EmTBp3NhJpjZA3Jy81CqYUxaf3tMS3d9rU1G2pI3g4iDPbyfj547HYLMy5bg4H3jiQ6tlo/3A9ZaQtgt1rZ/4/z+eMr51B675WtNZkT8mmdV8rT171JBarBeVU+Ap9ZE7MRBmKXS/swp3vZv6X5jNr6SxqP6jl4DsHU1XfWqMMxbwvzhvwus9j9fGKciGEEMPrmBdIHUkOJUEUnZhJk3d+8g52j73T9nmG1cA33oe/2s+WJ7dwxr+eMWSv6T/kZ+0Dazm05hDKkmqJM+OKGZz2ldOwewZeEauU4ty7zmXZ15bR3tCOO9fdMXUcagqhUCy8cyFKKRp3NbL5sc14x3sxrAba1LiyXYRbwqm2NbVBHJkO0Km9lrOnDnybvLaDbbz2ndfwV/mPBAgOn4MLfnwB408dz1nfOIu1D6xFmxplScVp99i5+GcXdzT5zpn24ejbnlf24B7n7rba2pPvYdv/bmPuDXOxuWxcdO9F1G+tp+a9Gix2C6ULS0esNY4QQojR0+9EUSn1HHC31npjP693Al8FQlrr3w4yPjFGNWxroL2xHW+Bt9upW0+eh13P7qL8lnIMy2Brqj4UrA3ywi0vEAvG8BZ6UYYiGU+y45kdNO1u4pO//mSPrVV6kz8nn8sfupz1D63n8IbDqQQsmWopU35recc6w53P7kQZqqMFjTIUBScX0LKvhUB1ADNp0lbVxif+6xOc9NmTBjz1HA/FWfb1ZcQCsU77OkcDUV69/VWW/HkJs5bOYsI5E9i3ch/tje3kTM1h0vmTekySq96pSiWv3bA6rERaI7TuayVvdh5KKQrmFsieykIIcYIZyIjiQWCNUuoD4AlS6xE3a60TRy9QShUBZwBXAteSKnL5p6ELV6SjcEuYva/uZd+KfcTDcRw+B22H2qjfVE+jrRHDZpBRkkFGcUZHwYXFnmoXkwgnetzSbiC2/GULUX+0UxJlsVnwFfuo31pP1TtVTDp/0qCePW7mOC795aW0N7QTaY3gzHJ2qaBu2N7Q5X0Y1tS+zjlTc0jEEkRaI5x808mDimH/W/sJNYXIKMrodNzhcxAPxtn57E7O+Lcz8BZ6mXtD/3Z3UYai11odTa/bAwohhDj+9TtR1Fp/TSn1S+AbpKqeMwGtlPKT6puYDdgABaw7ct1jWmtzqIM+XmitadjewI6nd3Dw7YPEI3Fc2S5mXjWTGVfMSBVTpLmqNVW8/r3XScaS2L12AocDtFa2YiZNzKSJJ8ODaZq07m/FX+Wn8NRCnJmpKlqrw9qx9/CxqnixAmVRBKoDGHajY5pYKYXVZaXipYoeE8VIayS1pjHP3W0xyFGePE+PLXZsbhvt9e3dnjsah805+PWStRtqsVi7HxG1++xUr6se8DMnXziZDx75oNtEPR6OY3PZBjVFLoQQ4vgxoP9La633Al9TSt0GnA2cCRQBTqAJ2Am8pbU+MNSBHm+S8STv/PQd9r6yF2VRuHJdqT530QSbHt3E5sc3s+COBcy4fOSqSgeqaXcTK+5cgd1nxz3OTagxhL/Kj91nRxmKYF2QcGsYV7YLw2WQjCep/aCWkjNLaN3fyvjy8exZtoei04s6KocHo35bPbUf1IL6sJrYsBnkn5SPK8eFYTWItce63Nfe0M67979L1TtVKIvCsBjM+tQs5n9p/oCnqacunsqa+9b0uF9ypDlC2TVlA39zR9i9dnSy++E/M2EOag3mjCtnsO2pbURaIp36KZoJk1BDiDO/ceagpuuFEEIcPwbbHicGvHnkjxggrTWrf76aimUVZBRndJres7ls2Fw2EtEEq+5dhc1lY/KF6bnjxKbHNoECu8eO1pqWypaOYg80qWKOpnDH6JTFZiEWjLFvxT4sTgsWh4WmnU0oQ1F2dRlnfv3MTtvM9UVrTc17NTz3xecwE6ndV+zeVJJqJsyOpDTeHu/Ypu6oWDDGS//6EsHaIDavDR1PFYBsfmwzoYYQ533/vH7F0LS7iYoXK6jfUU/rgVYadzVicVpwZbrImJCBI8NBNBDl/2fvvcPjKu/0/fs9Zapm1CXLlty7MdW0GAglBQIkEEIKm7LJZlN2k2/KbnrZ1E22/LIk2exmU8imwbIkpCw1YCCUYAOuuDe5qNcZTT/t/f3xzhxJlmzLsk0gOfd1+TLWlPPOjLj06FOeRxiCZTctO6H3dyzzrpzHtru2qUWVMd8vUkqsjMWi6xed8HPGG+Nc/c0x3oSuVGJbF5z9rrNZcfPk0YQBAQEBAX8+nJq+X8AJMbxvmL337Z0gEsdihFVqxtPfeJrZl85+0VV2SpkSh544RKxReSGOdI4osVFZXJGghTS/tewUHCSSUraEYRrMuWQOeli9Js/12H73dvSwzgUfmNoWtF2wefxLj7P9l9vJ9mYRmsoutgs24aowZtxU1jS7B0i0JFh07Xghtf/h/aQOpCgMFiiOFP0zh6vD7Ll3D2e/42yqZ1dTypTYc98eDjx6AM3UWPSaRcy7ch56SGfTjzax8UcbkZ4k1Z6ilCnhllw0U6OULpE+nCbeHKd2bi2v+pdXUT27etrvd+OKRua/Yj77HtxHtD5KKB7CKTnk+/PqtivnT+t5G5Y08Ma73kjns52MHB7BjJu0XjYCW3UAACAASURBVNRKrP705zcHBAQEBLz4CYTiH4Edd+9Q9i3HWRQIxUNkujJ0PtPJ7NWzp/TcTtFhcM8gruUSb4yflDg5FqV0CYTytUsfStO/vR/pyXEVQc/2lE1MXZQZZ80gfSjtp6ZURCKo50i0JNj+y+2c+dYzj5vcIqVkzafWcPjpw7iuixlT1UqhCeysTXGkiFNycG2XYqpIKBbi0c8/yvnvP9/3/du/Zj+pgynf11B6Ki+4MFSgmC7Stb6LUCLEvX9zr99Ol56kZ0MPe+/by+zLZ/P4Vx9H0zUlEIsu0ZqoX+GL1kdBh9JIiVAyxPN3PE9huMDsS2ZPK7ZPCMFln72MxuWNPH/784x0jhCKhzjz7Wdy5l+cedRUlqmgGRptF7epYZKAgICAgIAxvKiFohDiAeDVwFellJ89zn0jwJdRMYI1wCbgE1LKx0/7QU+Qw08fnnKMndAEXc92HVco2gWbzT/ZzI67d+CWXBAgXUnjskbOe995tJzTctTHSimRrvS9B6eCGTORrsTKWgztGVK5xwVnnBVOZau2MFhA6IJiuohmaJO2lzVDAw96t/Qy57I5Rz3n/of38+x3nuXAowfQQzpW3kLTNbSkRigewogYo1F0MZNITYS6xXWk2lPc///u55X/+kpaL2gl05PBzttIRyI9lUoiPTUDKIRg/8P7GTk8wkjHCMnW0U3jSE2EfWv28fwdz6t2t1TVVVB/G1EDI2yo53alP5dZTBXpXNtJsi3J1bdeTbxp8qUYz/Xo3tBN+5p2nKJD68WtzLlsDmbURNNVSsryNyz3jbWD2LqAgICAgNPJi1YoCiHeApx1Ag/5IXAt8DFgP/C3wINCiIullJtO1bmsnEXnM50Uh4sYEYPmM5vHCYmp4BZdjNjU3nqhCeyCfcz72AWbBz/6IL1beok3xf0UESklw+3DPPD/HuDyL13OvCvGzzqOdIyw4+4d7L5nty+slrxuCUtvWEpy1vjXZOUsDj5+kL6tfURrVTRe08om2h9uV5VFU0MYwp9zG4tmamS7s0qQOR41cyY3apao2ye9TUrWfXMd2+/aTjFTRDNVW1tKSTFdxHM8tbiia0hXKvFoaFS3ViOEIFoXpZgusu7Wdcz6+Sw8y8MpOGiGhud6o1YwUqWfbP7JZppXNhNvHhV0haECg3sGGTk8oiqjIX3c65VS4louTtFR0X4zqtBDOm7J9Vu5ma4Maz61hut/cP0EkedaLms+s4aOtR1KUGsa7Wva2diykWu+fY2/8COEmFZVMiAgICAg4ER5Uf60EULUAP8GfAS4fQr3Pwu4BXiXlPJH5a/9HtgGfAl47cmeybVc1v9gPTvv3olruUogaOqH9sxVM7n47y6eIK6ORqwpRr4/P6W5Q8/xjmuTs/knm+l7vo9ka3JCnnC0Lqrm+b78OM1nNvuCpeOZDtZ8ag3SUbm/0booTslh253b2Pmrnbzyn19Jy7mqCjm8f5gHPvwAheECuqHjOR6bf7yZ1ktaVUKJEFgZS70vjhJOmqEhhEAzNfSwTn4gjxk3KaaK47wOraxFYagAGtgZm+o5k7fKO57uYPtd26maWYW91/bb9mbUxCk6OEWH/JBKS6kINc/2MBOjljThZJiRTlUlLAwW1NJLuZJYmVGUqMqq53gM7xv2z5rtydK3rQ/P8XxB6VruaPW03L4G/GWeSlW30maXUmLGTLrWd7Hnvj0ses2icZ/X1ju2cvipwyTbxn+Oud4cT3zlCa759jXH/D4ICAgICAg41Zx8JMbp4Z+BbVLKO6Z4/9cCNnBn5QtlI/D/AV4thJg8fmKKuLbLmk+v4fmfP084GSYxM0GyTRlIV82oomdjD/e89x5GOkam9HxLb1yKlZlo13IkFeFxtK1nKSWdz3by9P/3NLn+HKn21KTVRzNq4tou+x7cB0CmO8Mjn3oEM2pS1VLlZwMbYYPEzARGxOChTzxEri+H53o89PGHcAoOyVlJ4s1xErMSxJvjHH5cJXsUU0XsvK1a14bwRZrruISTYaSU5Ify2DlVtcz157CyFgceO8C+3+2j85lOOtd2ku5Mc+/771XbvXK8FczzdzyPETPQdA0zbo7eLlQ7WGgCt+DiWq4SaZqqZPZu7PU/FyFUaz0/kMfO24STYd/jsPJcmq6pa0RN7JJNfiCP53oM7hr05yCRqNfpfxCMGldLwFN/53qVQXe4OoxdsOl6rouuZ7sYbh/md3/3O37zzt+QPpT2P8utd24l1hibUGmMNcbo2dxD+nD6uN8zAQEBAQEBp5IXnVAUQlwCvB0V/zdVVgDtUsr8EV/fBoSAhSdzpp2/3snhPxwm2Zr0RZV/Xk0Qb47jFBwe++JjEwTOZMy/aj5G1MDKHlss5vpyzDhnxqStWjtv87u//x33vPcecn1KkAy3D9PxdAepg6kJ5wgnw+xfsx+AXb/dhWu7R01ECVWFcEsuu+/ZTfeGbnJ9ObWcMQbN0DAiBoWhAkZMGWcLlOjSQ2pZRWiCwlCBwqCKAo/PiBNritG/tZ+9D+wl159TVUddU++rpyqMa29dy+afbPavJT1Jz8Yev6Ueb4qPq+BZWUs9T0gjUhNRbemwQTgRVhY8uwaVmC3YmHGTcHWYUCJE1YwqNF21zLWQ5ldBjaiB0AXhRBjN1Ei1p/Acz29ro4FuqPnAI99nzx7TOpdKAKYPpTn85GGsjKVsgUI64ZowI4dHuO+D92FlLZyiQylTmtTwW2gCoSuBGxAQEBAQ8EIyLaEohLhICPEFIcQDQogtQog9QoinhRD/LYR4pxBiWnEOQggT+C/gX6WUu07goXXA8CRfHxpz+7TwXI+tt28lWh895uJAtCHK4K5BBncPHvc5w4kwV37lSkqZEsXh4gSxIT1JtidLtC7KZZ+9bNLneOqfn6LzmU6i9VH0kI4RNtANHafo0LOhhwOPHmBozxB2XlUYNV3DKai0xT337iFaF530eStEaiLs/r/dZLuzfpvVzttqnq+M67jgqTi+SDJCrDFGvDmu/jTFidRG8BwPM2Yye/VsaufW0ri0kdpFtardixJo4Zqweh1hnZGOEeLNcTbdtskXRmOrh6CuV7ewDqfo4NkeTtFR1UyUeDSjJnpYH/UcFDDcPkx+IM+57zqXeKMSmvVL6qlbVIdAgKf8A0OJEKGEEsqzL5nNdf95HfEZcYrpItmeLE7JQQihKpeael8rbWb/b039rYU0Yg0qIaY0UvKFqEAQrY0Sa4xRHC7S/kg7RsQgUh2ZtCIsPbVsdLRUmICAgICAgNPFCQlFIcQ7hBDPA39ARfTFgD3AOpRQuxD4AdBZFo0n6hT9CSAKfPUEH1f5MT3Z1yd/gBDvEUI8J4R4rr+//6hPPLx/mMJw4bjJF5Xq0uGnDk/pwLMumMU137qGWGOMTFeGkY4RMt3q72xPllkXzuL6710/qTjIdGVof6SdREsCM6LasMVUkcJQQQkYVJUtdShFx9oO0gfTWDnLn/8rZUpo5rE/ej2kq21eDYYPDHPoiUN0rO3g0BOHGNo3hPSUlYwRMUi2JpXAkYwT005eCdNkaxIzNlopK/QXVCXPVBZBRthQFUFDwy7YaLqGJz32PaRa5Zqu0bC0gVKq5D9H9exqGlc04truuK3lUFWI1pe10rCkwReRUkpyvTlWvXcVS1+/lHAizLwr51EYKNC4rJHGFY2EkiFC8ZC/PGTGTS79zKX0b+9X84xCIFGCTXpS/bejLHWMiEHVjCoiNRGqZlSpSqSh+S3xSpXRKahZylAi5Ce46CGdrue6EEKw4k0ryPfnJ/zikOvLMePsGSe8NBUQEBAQEHCyTHmZRQixGWgCfoJqDW+Sk/RZhRDVwHXAXwDbhBDvlFLeeeT9JnncbOAzwLuB8BFzheHygktGSulO8vAhYDL/mNoxt49DSvk94HsAq1atOmq/2M7bU7Yg0QyNYqo4pfsCzDhrBjf+9Eb6t/fTvb4bO28Ta4jRtrqNREviqI/r2dTjb+macRPpSJyi49vOVOxu9JBq/1ZMp5fdqJJBonVRZQwdO7pYdIoO4eowm364CTtvo4d0JeTyNv3b+hk5NIIW1ojUR2hY3IARNkgfTPtCVXpqA9iMmeOWVyq3VeL23JLrV/4qHotCE+imzvD+0SLxyltW8sjnHiFcHfYXWRItCZDQt7UPiSRaG2XmeTPJdGfI9mSRUvk6xpvjGBGD5Tcv9z/Lc/7qHDrWdpDry1G3sI5oQ5T0gbQSiYbJ2X95NjVzarj/A/eT6czgOZ4ScAJ/lpEYuAWX2sW1RJIRBnYM4LkeuqmTWJwg35vHE6O2O07RIZaI0Xxms/+6PMfDrFIi+ow3n0H/1n4O/eGQEtK6EplVLVVHrSwHBAQEBAScTk5k6/lHwHellMdUQlLKNPBz4OflbeQZU3z++ajM6J9Nctvfl/+cg/JHPJJtwI1CiNgRc4rLAQvYO8UzTCCcCON53jh/wKPhOd6EWb7jIYSgaUUTTSuapvwYX7SghKxkdGu3IsDk2AKrVPY2zWcrgbL8puU8+91nx1X5jqSYLjLr/Fl0PtvJrPNn0bW+S20nl582P5An2hClWCriWA41c2uobqumkCrgOR6e7TGwcwChCyLJ8Z6R4ZqwSlOhXKWTakvYLbnUzKvBczxcyyWUGK3iznn5HOa/Yj77H95POBHGKZU3mz0Pz/UIJ8I0rWiid0sv+YG82rYO6XiuR/pgmmh9dJx/Y3JWkmv/81qe+qenOPj7g2S6Mri2q2YRDY0N39/A0L4hBvcOUugvqPcVMXpWzyWWjBFqDqEJlcTi2R7x+ji182qJ1ESw5llkOjPk+/PkSjliTTFaL2hF6Or7qFIJXfDKBQDops5VX7uK7o3dtD+ifBRnXTiLuZfNPSlD7YCAgICAgOky5Z8+UspbT/TJpZSbgc3HvaNiE3DFJF9/FCUef8jRBd9vgS8CNwM/BhBCGMCbgN9JKUtHedxxqZlXQ2JGglK2RDhx9OXpipA8mln0qaR2Qa2yXpGSbHcW3dDRq3VKIyXwQAp1FtdWG8CRWtUSHdg+wIyzZ7Dw6oVs+dkWCsMFf0FkLIWhApHqCMWRIqF4iHAyjG7qvnefEGrur25RHcN7h+l6ThmCC1349jt2wca1XWrbaie0uRMzE4wcHlHtag8194fwZxTTB1RlsnF5I8P7h6mdX4uma6z+xGoGdw+y73f7fBseUIsltQtrKaaL5AfyahmlLOp1Tce1lY3Ngd8fYMErFvjnqJlTw7LXL6PzmU5ijTFyfTk/5cW1XLb/73ZlXj4ZGtTMryFSHaH5rGYu/ujF/PKWX6plGk1VXo2IQf3iemoX1tL1bBfhRJhStkSoKoSdtykOF1nw6gV+WgyoKvHM82ZOyKYOCAgICAj4YzCtMoUQ4sPABmCjlDJzKg4ipUwBj01yLYCDUsrHyv+eA+wDviSl/FL5sZuEEHcCt5YXYtqB9wPzUC3waSOEYOVbV/Lk15/0Z9gmI9ebY8ZZM6idN609nhOicXkjdQvqlNjKK09BPaz7SSVO3sGMm8TqYyRbk0Rro2R7s/5ySLQuytW3Xs2DH32QTFeGUCLkG0NbGYtIbYRXf+PVrPvmOpWsMlTAs71xaTJ2wUbTNGZdNIuOP3QwtG+IWF0MPaxj52yckkPtvNpJM4OjNVElUKVamkm0Jhg5NIJdVC1uKSSRmgiDuwf57V//lqtvvZrmlc089fWnSB9KM/fyub5XotAFfVv66Fzb6fs3aobmG19LV2LGTeLNcXb8Ysc4oWgXbR77wmNYOYtsT5ZQVcivOhq6QX7wiC3jykdftsAppoqYEZO6+XUkWhKc++5zWfPJNX5ajBCCqpYqzJjJstcvY94r5rHtjm2kD6dJtCQ47z3nsfi6xUG6SkBAQEDAi5bp9rO+DpiAFELsA9ajhON6YEO5/Xy6EIDOxEWcd6KWYL6CivDbDFwtpdxwshdcfO1iejb1sPeBvcQaYuMsTDzHI9eXI9YY47LPvTBzZEIIrvjyFdz3t/dh5S1cx0UzNdU21TRq5tbQfGbzOFErhBiXr1y/uJ6bbr+J/Wv2s/PXOykOFYk1xjj3Pecy/6r5hBNh5r9qPt0bu3FKzoRWdmVz14gY1C+pZ/H1i0kfSFNMFWlY2sCym5YhdMGDH35Qzf2NtX0pVyN7NvUQa4xRSpewcpZqFVtqS3rGOTMwIgbFdJHH/uExrvr6VRx49ADJWUl/htG1Xbqf66aUK6GHdLUsYjvYOdu3lKnMPA7uGhyXZpI+lOa37/4tvc/3qnnKohKWmq6+rTzXU36IYzlikjXbk6WquYpF1y7CKTns+90+wjVqVMGzPTzPI3UgRevFrVz1j1dhhA0WX7P4pD//gICAgICAF4rpCsXlwEOoJZG9qEWS61Fb0LK8+PI94PtSyskz2aaIlFIc8e8DTLLNLKUsAB8t/zmlCE1w6acvpX5RPVt+tsVflAAlwBa8egGr3rdq0uoZqDnCTFcGzdBItiV9MXIyVLdVc8N/38Dab67l2e8869vQJGcnqWqqGicSXdtFaGJcixOUt+KyG5f5Sy792/vZ8csdbPrRJgSClvNaiDXEGNgx4Isk6UqckkP17GqMqPr20XSN2atnM/vjE/eJLv/i5Tzx1Sf8yEMpJW7JxYgY3PyLmyllStz3/vvQwzqhWIhkW5JYY8x/jyLVETJdGbb8bAvAuNeVak9h5S1CsRCOrixynLyDFKotHauP+RVCO2eTOpDCcz08x+OBjzygtrbDBq7l4gqVtuM4Drqp+wsoPpPs1Xu2x/I3Lqd6djXtj7YzvH+YphVNSE+q2VFPbUTn+nNkOjPUzj/91eaAgICAgIBTyXSF4neBX0kp/77yBSFEHfB5VKv3D6jq3huEEK+RUh47rPglgKZrnPHmM1h20zJ6t/RSTBXRQzrNK5vHtWTHUsqU2PSjTez67S48Vy1oCE2w8NULOfOtZ1Izd/LM46kSrYvy8s+/nIEdAxSGC5MKVSklqQMpEi0JHvv8YyTbkix6zSLql9SPa3lu/ulm1n9vPZquEamNgIT2R9rVrOCKRtofaVdzd2GDhiUNvlWLa7loujZBhFaYf9V8Zp43k/0P71c2MJpg1oWzmHflPMKJMIWhgpoDLW95SylVNU56o1vcniTXk/OXQCpkujKjc5OawAgpE3PN0PzM5spzIpSReOWzy/fliTXESB1Mqbxnx/OTWybLm/bP4qrn1MIaVS1VtF3cBqiIwUoko9DEeDPz8mZ2IBQDAgICAl5qTFcoXoJqP/tIKYeADwshoqhK4yqUYPx74Gsnc8gXE7qpT2nRoJQpcf8H7mdo3xDhZJjMwQz5vjye59G7uZcNP9jAotcs4oIPXkDjssZpn0fTNV7x9Vdw3wfuI9OVIdYY8wWLlbfo29KHU3Qwo6ay4dnYzY5f7WDZjcu46CMXIYSgY10Hz333OapmVI3Ln66aUYVrueT6c6x44wp6t/SSmJnw02mcokO+P8957z/vmD6TkZoIy9+wnMXXLfZ9B1PtKTb/92YGdg0wcmgE3VSzjelDaX8JJ1ofpW5BHUIT1MytYWDngP+clY3hylmkI4nURrCyqhWPVEsyle3o6jnVhJIh8gN5+rf1q7Z0WMezPay85aeoTIqOvyFeEatWzlJm2WuUn6UeGV+FlJ6kMKxmOyvxhgEBAQEBAS81pisU+1BWNY9MctudwA+llP8ghPg34B38CQnFqbL1jq0M7RsiWhel67kuPMdTFiflbWUrZ9H1XBf3/s29vPobr6blnJZpX6t2fi2v/cFr2fKzLey9f6+aJ5SoNBBTo/XcVozQ6EftuR7bf7Gd2gW1LH3dUjb/ZDOhWGicSKygh8qRc8kwZ771THbcvcMXREbY4IIPXcCKm1dMeJyUksFdg+QGcqTb0xx4/IASeuVKX2mkRKQ2QigWwnVcDj1xSFnp1EZ8K5jCcIGOZzuomVPD2X95NgcfP4iVtQhVqaUiM2bi2Z6/hZ1sTap2sjAoDhexczaaoaGHdXJ9OaSUxBpiGFEDp+QwtH5IicPjDUd4yoYnnAhTHC6qimN5EWf3PbvZ+8Be5r96PsMHhhneP4zruLgFd1zu9abbNtGwpIG6hdMOCQoICAgICHjBEVPJJp7wICG+BHwQuFJKufGI294G/IeUMiGEuBK4V0p5YuaCLzCrVq2Szz333Cl7PtdyueO1d2BEDXq39GJn7XGLJKCqXdG6qGpHSnjT3W86JV55VtZSosiTPPDhB3xBNeF+OQvd1LnhJzfw82t+TqIlcdSNbukpG553PPYO7LxN7/O9dD7TqRZJMiXq5tex9KaleI7H7t/uZnj/MKkDKYQuKKVL5PpyhOIhms9qJjeQY2D7gGrPJkL+XGEprVJgIskIZtz0r2tlLeoX1/Pude+me0M3D33sIaSrqoe5/hx9W/vQdDX7Wd1WTe+WXrI9WWXV0xDz2+uVDehb7r2FcDLMHa+9w0/cyQ3kcAuT2OBo+CJSGMJP3xFCEK2L0npRK0bEYGjfEEN7hgglQhRTRdyi65tyGzGDugV1RKojaIbGDT++gXhTEMUXEBAQEDB9hBDrpZSrXohrTVeZfBk4E1hbtqW5D+gBlgD/AFRUVxh4yc8nniiFoYKfP2yNWP7Sx1h0Q8casQgnwmS6Mhx88uA465bpEqoKEaoKMdIxgpW1qJpRNfn94iGy3VlyvTkE4qgiEfCroNJTf9Z/dz3D+4eVv2JYp2dzD1vv3IoRNqiaWcXgrkGkK/E8tTkcrlEG2fsf2a/8D8u/m9g5GzNhqrk/oeb/SiMl3zQcoaqlekSn85lOEjMTXPvda9l9z252/99u8gN5hCZwig6p9hSp9pQvDEOxkPKURPrLPPWL6ln/vfW0XdKmcpuLDlbWmnRxRQ/rfqazdMvRfVL6wq92QS1GWM1Epg+k0XSN6rZqPMejaCtPeokkFA/5s4mZrgw7fr2DVe95Qf7fBlQE5dDeIcyYSct5LeO3zwMCAgICAo7DtIRieTnlBiHEe4CPAW/FzwVhG/De8l0vQnke/lmhmRrSk+QH8+NtZcYgpfQ3e/WQzoFHRs2g04fT9GzsQXqS+sX1NCxrOHGvPXGMmbvy9aWUhBIhYg0xrJw1bs5QSpXlbGUtPNujZm4Nuqnz1L8/RepAalzu8EhmBMdy1LJOjwBNCbXCcAG36GLaJnbGRtoTz2NnxvweUT5z9exqQomQHzXYu7mXe953j29mLQyBnbPJ9+VBghEx0MM6ybYkmY4MQhdUz65WPohSbYgnW5NoYY19v9tH37Y+lSozVMQu2kq8jn3rdIFA+DnSmqFhRA2/CqsZmj8bmenKKGFrCAqpAtKRxBrHVDLL+c5GxCBSE2H/Q/tfEKFoZS0e+8JjdK7r9GMHjbDBpZ+9lLmXzT3t1w8ICAgI+NPgpHqdlbxkIcQ8YCbQD+wZkwF9P/D7kzviS49oXZTaBSqNQ0x08gGUZU0lA1nTNZyCg12wefJrT9L+aLtfdXMtl+q2alZ/YjUzz585ZcGYaEkQb4r7M31HYmUskm1J4o1xznjLGay9dS1mzEQIgVNw6N7UjZN38KSHZ3nYRZuNP9rI/of3j2udSk8y0jGCGTFxbZdMV4ZofVRtL1seaEq0OCVnUouZcUhVVXQtl0RLglK6ROf6TjzLo3lmM+FEmIGdAwzvH0aP6AhdYEZMZbljubhFl0iNstMxIgZtL2sb9/S5/hylTImZM2ciDEGuOze+mlg+n9BGo/pAideKwbcUkvShNLqpUzOvhlK6pAy+bVfZ6hz5AoV6/fn+PIO7B7FyFv+x8j9Ycv0SVr1vFdWzq6f0eZ4oT/zjE3Su66RqZpX/PWPnbR793KO89vuvpX5x/Wm5bkBAQEDAnxYnb+gHSCnbpZRPSSl3jxGJSCnXSiknW3j5k0YIwdl/ebbfqj2SijF2YpYSinbRpnp2NY9/+XHaH1FbtEbUINOZYXj/MPvX7Of2627nF2/5Be2PtU/tDJrgnHedo6p69vj5O9d2KaaLnPNX5yCEYMnrltBybguZjgx20VYiseighTQEgsTMBPUL61n37XVqQWZMZrJbUq3Zigm253qjLpflFrJruaPt5CkwtHeIwlCBgV0DSEcSrY8SqgohXakSVBIhSill0p3vz1MYKOAUHDJdGcy4EqwDuwYYbh/Gyln+86YOpjCjJp7n0f98v3pfxr41ErSQpuYLy1XhyuuQrhKOVtrCtVxSB5UnozCE+rv8PplRc1yFUkrJwI4BOp/tpDBcQGiCbFeWZ7/zLLe/7na6N3ZP6T05EUY6Rzj05CGqWqrGveeVXwS2/e+2U37NgICAgIA/TaYsFIUQvxFCnHMC948IIT4qhHjf9I720mbOZXO48EMX4rkedsHGczxc28UpOEhP0nRmE2bUxMpb5HpzDO4ZZMfdO4jWR8n2ZOnZ1KNsbWIm4WRY+SG2p3jkM4+w9X+2TukMC69eyHnvOY98f55MZ4ZsT5ZMZ4bCQIELPnAB866YB6iW5Kv+9VWc/Zdnk+vJUUqV/Ji62vm1NK1swogYhKvCZLuz41raY0Wj9KQSi+VNZN3U/aqcpmlqEeRYs5Bl3JLLgd8fUDnWIZ3GZY2q0llS753QhO9LiQZoZfGbKjK8dxjP8rCyFsP7h+lc28nArgHSHWlyPTmsgsW+B/b5fouV6MOKuNU0DelI7KytqotCIB3pXxdUhbCYKtL+aDsjh0bI9+cp5dTSTrwljpWzyPXnyPZm1TJPf049t64RToQx4yZmlcnIoREe/uTDE4T8yZI+mFbJNJO81+FkmL6tfaf0egEBAQEBf7qcSOv5EGp5ZRPwc+BJYIuU0qncQQgxE7gAldLyeqATeNepO+5LByEEq967imxvlk23bfJFUrw5rnz3TJ3+7f2kDqYIJ8K0P9ZOpitDfiCPU3KIJCPjvPeEJiiNlGhcD7EThgAAIABJREFU1siz//EsrRe3UjPn2IbdQgjOfsfZLLx6Ie2PtJPtyZJoSTDvqnnEG8dv3hoRg3P/+lxESPDst5+laobKKB4rNmJNMQb3DFJMFVVWM6ryFq2LUhgqIKUkOTupFmR0tW1tF22MiIFneQhZ3hyepMp6JJ7l4eGNtoHBt+9xS2Vh5anWvN/tlag4Q0NTj3Mlju3Qu6VX3efIZJWyybZmaioS0HFHW+SVpxxTVaxco3IGz1Ur0cIQeJbHwPYBPDw0oanlF0/6dkBCF4TrwmimEtaarqFpGumDabo3dNN6Yetx35OpEkqEjjqf6pSccfOlAQEBAQEBx2LKQlFK+UEhxK3Ah4EvANWouL4RoATUovKfBfBM+X4/PdkIv5c6l33mMoyQwe57d6Mbuko9EdC9sZtcb454c5wZZ81gpGOEfCiv5u2KLrZhE0qMzhZWBJYeUtu4u367iws/eOGUzlDVXMXKt6yc0n1jdcpncLK5Rs/2aFjcgFtyKQwXiNREEEJQM6+GTFcGz/XI9+fxbI98Ng8aqhUrlTm2GTdVVbIvO2GB5EgqHoSu5XL4qcMseNUCJUrro6QOpEa9D4/47rJzNpqpEY6HsQu2n6Qy6Wxk2WTbF57+xRk/TymYUJ2r2A5FqlWKTXGkiJVVxt0iLIjVxwglQ4wcGsF1XCVIhTbhOk7RoThcPOZ7caI0Lm8k3hinmCqOSw2q2A0tff3SU3q9gICAgIA/XU5omUVKuQ/4oBDi74CLgQtRSywRYBDYCTwupTx4qg/6UsG1Xbqe6yLbo9qmM86awepPrGbhNQvZ8csddD3XpRZX8jYzV830N2RDiRAI8EoeQhfYBRszPlrR81zPr+KFkiE6n+k87llKIyU61nZgZS3izXFmnT/L39Y9Gm0Xt6HpGq7lTrhvYbDAOe86h5bzWnj6G0+TOpBC0zWsjKXa455aKjGiBpqpqnqxxhgLXrWA1MEUh35/iGK6eFyRiABBOTnFUckm+YE88aY4oaqQ8ik8BmbM9EXYcc20J+PIx5SrkUdW6fwtcaEyqd2ii9AFRsRg1sWz0A2d4lCRYrqI0NRnOtZPU0qJHtan5atoF2xGOkYwIgbJ1uS4WURN17j8i5fz4IcfJNOVUe+Z5eIUHea/Yr4/chAQEBAQEHA8pmuPY6G2mf/sNpqPxd4H97LuW+uwMta4lmXrBa1c8qlLuOJLVwCw5edbWP/d9eMEQrQ2qmYWc5ZfPazMKFaWX6paRj0Rj9W+lVKy+aeb2fyjzXiup5ZNDEEoHuKyz13m5xNPRqwhxqr3r2Ldt9YRiocIV4dxLZfCYIGauTUsf8NywskwN/z3DaQPpSkMF7j3ffeqfGbXQzd1qlqqlJ1OSGekY4S6+XVc881rsHIWv3jLL9j/u/0q3USgvA7HvBahCSQST3pE4iqSz7M8ZXUjGJ2vO8YGdSldKr8RR/+sjolQFjnjllKOeL+lVP6MY2c0PU8ttbiOizViEa2PUjWzSplwW676HE0NI2L4mdJ1C+toPqt5ykeTnmTLz7ew5adblMejJ6mZV8Mln7iExuWjUZBNK5q48ac3svv/dtO1votwdZgl1y+h9eJW35YpICAgICDgeEwrmeVPjVORzLLr/3bx5NefJFYfG5eEIj2plhya4lz3X9cRrY2y9ta17Pz1zglm2FbO4tDjh9SmrlCVMSNkIDRB48pG4g1KWGa7syx57RIu/ruLJz3L1ju2su7f103IbrayFqVMiWu/cy1NZzQd8/V0rO1g808207+jn3AizNIblvoisYLnetzzvnvYduc2zLiJpmvYeRu35BJOhmm9WM3dlUZK3HLPLWiGxg8v/iGpAyl/sUfoZb9Cd8z3YdmRU+hKMAshiLfElZ2NO8Xv1+NZ8Zzs48tb3KFEyBf1nu35G9ORZIRInRK6+YG8sgoSo8s/ekindkEtN//vzTQsaZjysTbctoGNP9hIvDmOETaQUlIcLiI9yetue91ps9sJCAgICHjx8KJPZhFCHMvyxgPSwHpU5nPvdK7xUqI0UmLtv60l3qR+eI9FaIKqGVWMdIzw/M+f54IPXEC8KT5B8LiWy0jHCMIQfv6wU3SIN8RpXNHoi0/P8ZCeZMnrlkx6Frug/A7jTfEJ2c2hqhBO0WHDDzZw9a1XH/M1tV7USutFx16w6FjbQcfTHX6KSa4vpyqFKM++vffvpfnsZoywQWGoQLQuqmYPNdW2lZ6aDxRC+PnUPnLUkkYiyXZklWXNVIXiyXK8y5Rb0X71cgzhRBg9oqqpAqHmBdNFv6UvNEHDkgbe+uBbiTXEpnwkK2ux9edbqWoZ/QWgEieY7c6y9c6trP7Y6hN5lQEBAQEBAcdkuj0ogYrruxyYg5pRnFP+9zJgHvA5YKsQYvlJn/JFzv5HVCv1SJE4lnhTnJ2/2YldsJl7xVw1j1jemnVKDl3PdSnj6qhJtCaqrFSSYYrpIsP7h5FSYudtMt0Zznz7mdQtrJv0Or1benFL7lHPEq2L0r2hW0XlnSS7frPLb6MWh4v+FnHFmsW1XAZ2DJDrzWHGTPSwTuPSRqK1URzLIVwdJlofxYybSvgIRr8jKyN3Y7SuZ5/AwGElJ+iFpFwFdW1XtZptD6fkkB/Mq5b8jCraXtbGomsXYcZNNUN5AgztG8LzvAm/AABEaiMc/sPhU/VKAgICAgICgOkLxW8AReA8KeUCKeXLpJQLgPPLX/8isAiV1PLVU3LSFzG9m3uPuySih9RiRrZbWdQsfd1Sf1N4eO8wTsHBjJp+xnHN3BrMiFrKSB9OM7hrEIBLPnEJ57773KNexyk6R40NBHwRd6IiZTLyg3miDWrBxik5/tkr1bZKSzY/lKdvex/F4SLn/PU5hGvCmDETp6DOYMZNZe4thKpHl1u04WR42vN0WkjF7nHsj+WUIQyBMAThZJhITURVTMvvgR7SMaMmxeEiPZt6GNo9hNAFxfT4bef+Hf386h2/4jvLv8P3L/o+z/z7M+ME/VhfyiPxHA8jclJBSwEBAQEBAROY7k+WrwBfkFJuHPtFKeV6IcQXga9IKVcKIf4F+NeTPeSLHSHECc/DXfihCxGaYNtd20gdSqGHdOyCjUBQM6eG2oW1CASlTIn8YJ5kW5Kb77z5uMIp2ZpUCyJSTpqE4hQdjKihbHpQFcit/7OVrme7QEDby9o4481n0LD0+HNzjcsaGd43TLItSb4/P9o+HvNeuCWX/ECe37zzN1TNqGLxtYu59NOX8sy3nyHTmSHXn8PKWgiUyLLylh976HneMfOqj4Vne36E4AtBJZ3GtVyMiEF1WzWp9hQA1oiFU3CI1EQwoyapwylqtBoSLQn/8e2PtPOLN/0Cp+RghA2yPVke+ewj7Ll/D2+44w2Ek2Hql9Sr92iSWMbCcIEz3nzGC/NiAwICAgL+bJiuUFwMDBzltn5gYfm/9wEn7v3xEqPl3Bb2PbTvmPdxSg5GyPBj+zRD46KPXETTmU3c///ux4yaGBGDeGN8nIVKOBkmVBUi05WZUgRe7fxaGpY1MLxvmFjj+Pk3KSW5/hznvOscdFNn213bWPetdeimTrgmTH4gz/rvrWftrWtpXN7IqvevYukNSwknwpNea+kNS9n1211UzahCC2t4pYmtYc1UkXj5gTx1C+vY+ZudLCgs4JZ7bqFvax+5vhy//9LvScxMUBwu0rmuU81hotJRNFM7ZoX0SCqVPelKf9YTDb9SeVILLseiPFNpZ22yXVnl9TgGz1Yek6GkEniR6gjROlWN9VyP+z5wH57rjfM9dG2XzrWdbL1zK8tuXIYe0ln9ydWs+eQa7LxNtC6Ka6uN9Np5tUedWw0ICAgICJgu0623HADefZTb3lO+HaAB5a/4J83cK+ZiRAzsgn3U++T78iy7admE2cFYQ4x4U5y6hXUkW5PjROJY/LbucRBCcNlnL1NZ0V0ZZaEiJVbOYqRjhOaVzay8ZSVDe4d45tvPEG+ME2uMkT6QZmD7gNoyNgQ9m3pY88k13PHaO9hw2wbuetNd3Hbpbdx+/e1svG0jxVSRuoV1rP74auysTawuNv67SZRFmybQdNVWzvZkSc5K0v5wO7neHDPPm4mmaxTTRTqe7qDv+T40Qy18eJYHUiW0nAiVmUkzYqLruqrAStTZJAjz9A8uWjnrqOe2RlRWdLx59PengZ0DZDozo76MZXRTjSs8+vlHufOmO7n9utvZftd2Lvv8Zcw8fybFVBEknP3Os7n2P649qqAPCAgICAiYLtOtKH4J+JkQYgvwS6APaAJuAs4Abinf7xXAupM95IudUDzEJZ+8hMe+8BhewiOUCPnVP8/xyPXlqF1QyxlvmdgarFtQp1qWtjvpkgJAYahAy6qWKVUUAapnV/O6217Htru2ses3u7DzNvGmOBd96CIWX7+YbHeWBz7yAAM7BwjFQxhxg0xnBj2k+0spUkryg3myT2bpXNfJzPNnkmhJ4JQcNt62kb337+Xa/7yWxdctZsbZM9j4o4088dUn0Aw1G1gcViLGc1SVTDOUMXfFJ7Fvax+xxhiPffExNbMnBFLIUe/C6VT/NNXqNiKGst6pbJCPaYdLW45WGE8Xxzm3a7kcfPwgI50jJGclR5NhxMT72XkboavNeST0buplaM8QN/7kRr8iGRAQEBAQcLqYruH2HUKIAdTSyqdR0X028BzwKinlw+W7fhQ4dozGnwjzr5qPETVYd+s6st3ZcT/0579iPhd9+KJJKz6hqhBLrlvC9ru3k5iZmCAGPdfDKTonPH8Wb4pzwd9ewPl/cz7Sk/5s4+57dvPUvzzFwI4BPNcj25dVSyVjxI3QVCZzJUfZxWVg54BvCm62mmS7szz9b09z1VevItma5OWfezn7H95P+lCaTGdG2dho+MktWkjzrWAqm9FPfu1Jejb1gKa2hYUUuKL87SJV27riTXhc0VhZokHlKiNUu3dSO50/dqikpyyV7rr5Li755CX0bO7BtV2yvVlC8ZCfsV3KlJBIqtuq/YpyvDlOpjPD7nt2c9bbz/ojv5CAgICAgD91pr0mKaV8CHhICKGhWswDR+Y6SylPbYjti5zZL5tN28Vt9G3t8yP8mlc2H7fyc+57zqV/ez992/t8w24pJcVUETtrc8YtZzDrglnTOpMQQgknYGjvEE/981NE66Popk4xVfS3oMcKKimlL3Qrt3mOx0jnCHULlC1PrCnGoScPkR/I+wLwrLeexSOfe4RwTRinoAyohSb8/OpwTVglxUhJ5zOd/OFf/4BrKx9Fv1Ury7nWUqr5RE9OzTux3F7WDR2hqfg/O3v0UYA/Np7j0bO5h1+97VdqBlOAdCSlkRJ2wSZcFcYtuZhVJtVzx5tom1UmHWs7AqEYEBAQEHDaOWk/jbI47DsFZ/mTQAhB88pmmldOPZYtFA9x9beuZuevd7L1jq1kujJIKWlY0sBZbz+L2ZfOnnLb+VjsuHsHCGXVY5dsJcq0SWYfK61agb9IIj1JtjvrC0VN19A0jUx3xheKcy6fo6LuPPWa8kN5cFVuszAEVsYi42WI1cfY++BeFWmnq0xodHAsR800lucU3ZKLZmq4nnvciqJmaISqQmhhDSfv+NY7k9+5LEZfKPPuSXCLrmqVCzWvaOdtVcEVquVcypXQDA2BoHt9N7Xzav1FKM8av/RyovRv72fH3TsY3j9M3cI6lr1+2ZS23AMCAgIC/vyYtlAUQrQAfwe8HKhDLa08BnxDStlzSk73Z4QZNVn5lpWseOMKNcunC0JVoVMiECt0b+gmUh1Rzy/FaEReeW5wUuRoVXFs3rGUEs/zxi1g6KZO/eJ6SpkSQ3uGfJseUNWyYqrIuX91Ltvu2kZiZgI9pKukknJbVTfU8oZru/4W8TiReIwWtJSSWFOMZFuS7ue6jy0CyzeZcRM790esOkolwPN9eb/dr5maGuSQUL2gmnxvHs/z6N/Rj+d6JGapOdHF1y+e1iX33LeHJ7/2pJoljRkM7x9m7wN7ueyzl7HgVQtO7esLCAgICHjJM90Iv8XAE0At8BSwF5gBfAh4uxDiUinlnlN2yj8jNF07qWrRsdBNHcuzVGyeLojURiilSuMEoE85y7gy8yelHJfzbGUsEjMT1Myr8b+WmJXAiBoM7RsiVBXyo/2kKyllS8qIOhlSyzvlpRM7Z49LdJlwlrH/LG8uTzZjKJGk9qcYOTzixwgelbIhuGZoCFOoBZcXmjGi13M8ZTiul9v8noemaVQ1VuEWXKyMyv7u39GPlbOYf9V8Ws5tOaHLOSWHA48dYM2n1hBvihOtV+MQ4UQYp+jw1D89Rdvqtgmb1wEBAQEBf95Mt6L4T8AIcKGU8kDli0KIOcDvyre//qRPF3BKmf+q+Wz43gbMKpUbrZs60YYoTtGhMFxQa0faqIF4uDqMlbNU1VEXVM9Ws3JW1qKUKXHZ5y4bV/HUTZ0ZZ82gc13naDVUqlZqrC5GtC7Kvgf2+ZGFEjWHWNmy9jeeJ0OApmnjZw91/KUXTdOQUqptZ30qPkIoc28p0MLK67FyjtO+flURiWPFYjmeUBhqqceIGOT6c7Sc10JhsMDQviFlau5J9j20j613biXWGKPpjCaWvX4ZC1654Kj2OP3b+3no4w+RPpgmdShFpivjP1Zo6lqF4QLd67uZc9mc0/ziAwICAgJeSkzXR/EK4HNjRSKAlPIg8IXy7QEvMha9ZhGhRAjPVbnUlUUSM2oSiofQDM33PDSrTIyogR7W0U2deH0c13LJ9mTRTI2rvnYVbRe3TbhGfiBPw5IGNF3DKTg4JYeqmVXMOHcG8cY46UNpcj05XMslFAsRb44TToYnijtxxB9ZTlupbEELMMIGuqGjaeVv44qzjnZ8oSiEQNOUlU+0Ljoaj3e6RGJlOUgX6NGyDdIRvpOV6wtdjQX4M5y6wM7ZynYoVWRw9yDZniz9W/vZ/dvdrPnkGn75ll8ytHcIUNVSp+T4G/MPffwhZVNUF0EP6RhRg1xfzk+OUQ9Sgj4gICAgIGAs060ohoDMUW7LlG8PeJERq49x9a1X89DHHyJSHyHVnsLRHDRdI9GSIN4cp29LnzIOl2DnbJrPaObl//ByaubWkO/PE06GaVzReNQoQddyic+IUzu/Ftd2VXu3LNwqsYJ6WEdm1Daz0FV0n5TSnxeUrvTnFqVX9kHUAZdRz0HAyTtKMGoGkvIMpRitzh2Lyv3tgq1SThqi1C6spXdj74m9qWXRWmkbH9V6p+LjKCXSKv9jjC4TCLSQhud5eI6n/Cw9j9JIiVR7ym+nZ7uzqsIrVJveKlgMtw+T7c7y46t+TNvqNpCQak/5OdHFdJHG5Y1KkKPGCIyIwUjHCLULavFc9dxNZzT5Z9z+i+1s/9/t5AZz1M2v48y3ncmcy+ac0pnZgICAgIAXP2I6WbpCiD+gWs+vGWuJI9RPkXuBainl6lN2ytPMqlWr5HPPPffHPsYLhmu7dD7TyZ7793Dg0QN4locZN5GepOW8Fla8cQWR6giJWQki1Sc2L7num+uUJ+SYHOMKxXSRSG2E9KE0QghS+1P+4opdUBWzUrqEZ3vKGueIBZpjUZnxcwsnVhUL1yjx1Hx2M/n+PKmDqROz1ZmqMXhZUIaqQtg5W4nKsY+t6K9yS7oyH2pGTTRTIxQLkR/MK8FYnrEcd90xdkahKuXF6JQc3JKLYzmEYiGSs5MAZLuy6GG1SNR6USuFwQLL37Cciz96MYN7BvnNu35D7+Zef5M8OSsJGlzwgQtY+ZaVE15a+nCaPffvIduZpXFFIwteteC0zdkGBAQEBIAQYr2UctULcq1pCsWrgXtQWc53At2oZZabgUXAtVLK353Cc55W/tyE4liklKQPpillSsQb4yoB5CRIH0rz63f8GrPKHLcY4dou2e4sV3z5Cp7592fwHA8zZuIWXYQmOPyHw2imRmG4gFN00EM60pNTj/DT1QzjVKqJY9EMjcSsBLMunEWqPUXv870ndt2pCkVdLSrFZ8TJ9+ZxLddv9XuuN+7cmqmp2dGCg5WxCFWF1PykJ3FL7kSROPY4pqpsaoZGvCmOU3AoDBXUjKeuEa4OE6mNUBxWHpqzLpjFmX9xJstvXk62J8vdt9xNz+YejJiBnbfV+yCgaWUToViIN//6zeOWmtofbef3X/w90pP+FnsoEeI1334NtfNrp/YeBgQEBAScEC+kUJxuMssDQojrgK8An2H0x+V64LqXkkj8c0cIQc3cmuPfcYpUz67myq9dyaOfe5RSuqTMtMtbvOe99zzmXTmPwmCBtd9cixlVc5AAoUQIK2OpLOxy9N8JiT4XPM9Ts46aEo1j29RHw3M8CukC2Z4s1XOq6d3cq7awj9zAPpognOLvWQLlD5ntyaqlncrM5SQb2lJKrIzlizShCZySM9qGP5bzjzPqe2llLOy87Z+z0t4vDhcxYgaXfvpSVn9stT9GsP2u7cr7Uqps8rGvv3djL9Vzq3nyn570fTEblzXy7H8+S6Qmghk1/fvm+/M88Y9PcP33rw9a1QEBAQEvcU4mmeUB4AEhRAxlkzMspcyfspMFvGRpu6iNN939Jg4+fpDUgRSxhhhzXz7Xr1Yue/0yep/v5cCjB9DDOkbEIJwMk+vLUbewDs3Q6N3Se0KZzMJQG9a6qeNJbzRxZgqta93Q6d+ufAq1kKY8JcfMFB4pzowqAyd7DEPvSQ84JlJw7JGOOF7lddh5Gz2ko6E2snVTP7aJ+BHPJ13pzyhWvi4dieOo57CzNk985Ql6NvRw0x03oekaHes60HSNUrbkJ+RU3gPXdhnaO8S2/92m5idtTwlzBE0rm/yNeIBoQ1Qt3HRnScycOIIQEBAQEPDS4VQks+SB0yIQhRAPAK8Gviql/Oxx7ns0RXCOlHLTKT9cwDEJJ8IsvnZyU2jN0Ljii1fQeV0nu36zi1xfjtmrZ2PEDHb/djdOSVncCE8cs806dpM41hgj35cnUhsh159TNj9TEYoVo2/XY3DHIPHGOGbcJNuVxXVcrBFr/OuqDauq3SQWN8dCOhIjboCtBGPF7PxIn8hKBRZPVQWNkKEqeFPY5B5/wfLL0ydWRisRiY7lsOfePWy8bSPn/fV5RKojFFIF8Ji4hV4W7IXBghL35Q1su2jTv72fYqqIdCXh6jDJWUlVBS2OF7alTIn0wTTR+uikM6wBAQEBAS8+piwUhRAeU260IaWUJyVChRBvAU40zPa/gf864mu7T+YcAacHoQlaL2yl9cJWAKycxd4H9jLrwllq6zdVpG5+HVbe4tBTh/BKk7psg4BoTRTpSiI1EaK1UfID+VGrlyOF3CTCznVcpKs2gePNcarbqtFCGn2bJyZTOgXHF23CEMf2fjwCu2CrLXAxiUhEiWuhC0ojJWVwXhVW29lSThBdx6X8Oo9MqKlskwuUPZBjOaz/3nrO++vzWHrjUrb+z1bfnxKOWJqpbKGjbJCkp7wvS+kSpZESekhZDPVs6SE5M4ljOf5zbPj+Bp75zjNYGQvN0Fh83WKu/MqVmDGTgICAgIAXLyci5r7E1IXiSSGEqAH+DfgIcPsJPLRTSrn29Jwq4HRhF2zu+8B9DO4exIyY2EWb0kgJ13aJN8ZZ8KoFdDzdgVNw1MydxK++mXHTF2w1c2u45tvXsPX2rWz/5XZV5fKkEo1HfueWZ/+SbUmi9VE8y2PFG1ew98G9AKqSWIkvlNJvg7sltYSCNlGEHYtITYRSRm10h5NhtZFsuaOtdV0ZmSPw29PFdFHN/okTuxZwzKhDPEZb4VKS7coCMP+q+VQ1V2EXVFrOhLa/p/K4K6J1XJVTjrEu0sqf6d/cx/l/ez4dT3fw3HefU8KxXCld9611dG/q5m0Pvk15WI4h051h56920retj+o51Sy7cRn1i+pP7PUHBAQEBJwSpiwUpZRfOI3nOJJ/BrZJKe8QQpyIUAx4CXLwiYMM7Rmium10zq2YKpI6kKJxeSOhWIjZq2eTOphiYMeAv9WrhTT0kI5u6ESboyy9cSltF7fRdnEb13z7GrK9WfIDeXq39vLIZx4h15NTAsgATWpE6iJUt6pr1p9Vz4UfvhA7b3PgsQPk+nP+rKIQannDs73R/GqpWugVu5qjIiBaG8WMm3h4WCmVahOpieB5nu+pqAltfPVOAz2sYyZM8v0nMdlxZAW10pIuVxbxyhXVcqTh6k+s5uFPP+znT6ONLsggymJQghExJhev5ZY/HnRv7Ob+D95PYaigHjdWV7qSg48d5O633c2NP75RLTEBA7sGuP+D9+MUHcy4Sf+2fvbeu5eXf+HlzLti3vTfh4CAgICAaXHSM4qnGiHEJcDbOfG2M8D7hRAfQ1kZrwX+QUr5xKk8X8CpJ9WemjCDF2+MU9VSpcSdp8RYOBFm9SdWk+3L0v98P05RtYH1sM6MM2dw0YcuGvccVc1VVDVX0bSiieWvX07nM530bOjBiBrMumAWme4MI4dHqFtYR9vL2tBDOpd/8XJ237Obe957D7Zm+xGBQggwylW4MTN/mq4dM1s6WhfFjJmj0YDlvOpiqgge/rxfZQvaKTkkWhJ4noedtf9/9t47Ps7qyv9/33mmaoqa1azqKtw7NrZjbFNDLyGEkJBNNgmbskmWJJv+S8KmbdpuNpu2KYRvIARSIEDAxmDAgDHGxnLFRZYtS1ax6hRNf+b+/rijsSRLtmSEm+779ZqXrWfu8zx3ztyRzpx7zucQ6YiMaHv7BIaKLPZ5Dc4cJ92Hu8mdkMvUG6ay/qvrlSObSBGPxFVhkFWoYh/DQiqewoynZXronwfZ6zz3HOsBg0wv70HnIqFuXR37n9rP9JunkzJTPPvFZwm2BHGPc2P32HFmO0mEE7zy/VcoX1qecSh7CbeH8Tf48RR5TrtwJhFWc7R77bpKW6PRaAZwTjmKQggbKsfwh1LKfSM8/QGUtmMTUAl8HlgvhLiUq64SAAAgAElEQVRCSvnCqE5UM6oUTC9Q+W7pzi1SSlKJFHP/aS7ll5TTvK2ZVDJFwfQCvCVeZErSsr2FxlcbkUjKl5RTPLf4pAUfhs2gYlkFFcsqMseKZhcNOm7azdNo3d7Kpv/ahET1j5YiHV00BM58J5gQ88ewZlmJ98T7dVkxXAZmxEQYIpODl4yqLVvDZuAt8Sopnp2txINxJapttZCMJlVbRUNgGAYpe0ptR7+NCJsgGojy2AcfI9IRwZXvwpmrnLOelh71mq0CZ66TZCSJ4TCUvI7DijPHSbgjnIkyAv31J02U3U5CPBRn22+2MeXqKaz59BoOrz+MsAp6mnswHAYl80uwZdmINkXZ9fAu1UVovNK93PrLrez9+14lhZQ0VeW1hEhXhIqlFcz/6Hw8RYPrgqaSKY6+fpQdD+ygdUcrZswkpyqHld9cScG0gtEyr0aj0Zz3nJbg9tuFEOKrwIeAGVLKSPqYZBhVz4NcywvsAhqklMsHef6jwEcBKioqFtTX17/V6WtOk1RSRZIaNzVmjhXPKebKH195QgTpTOFv8PPwLQ8TbAqSjCRVtxi7BXeRm3f/+d0YdoO/vOcvRDojJEIJYiFVgGJYDdWVRChH0rAbWKwWov4oMimxuW2ULCghKz+LIy8foaetB4fXgd1jJx6Kk4wmyRqXhZSSaGf0uA7iaCNUJNFMmKoTjUUVuGCATKicTFuWDTNmqhZ/KcACVpfacvaV+civzqfumbqT612mc0GHyrG02CxkFWRRvrScunV1JCIJbG4l1p6MKrtLJMmeJN5SL74yVVGdSqYwE8q5sxgWAo0BWre3kl2RTX51vnJ6x7m45Q+3YDgMug91Y7FZyK7IpmNfB8987hkaXmkg1hNDSIE1y5r5kjLrzllMvHwiBdMKyJ2Ye9bWoEaj0QzFOd+Z5e1ACFEB7AM+jGoD2EsX8EPg20BQSjnsHm1CiJ8D/yyldJxs3FjuzHKukDJTHN18lM4DneRMyKFsSdkJRQ5nmsbNjWy4d4PaJhbgzHbyjq+8g/JLygE4tvsYm3+6mWO7jhEPxbHYLWTlZ1Eyr4TZ75vNvif38cZv3iDRkyAeiGdEqns1B2P+GIdfPJypcLZ77aTMVKayOhFJnCDP85ZJ98xGgMPnIOaPneqMExA2gafQQyKsxLtHC0eOQzmHyRRWlxUzavZ3QgW4C91Urqjk0PpDuPJclMwvIeqP0rSlKVPNXbWyCmERBI4GmPHuGRx4+oDqIy4hZ0IOgcYAx3YdI9wR7t9v26LaJgpDYFjV2nN4Hcy4Ywar/2M1Du/Qv0YSkQRNrzeRjCaxe+3Urqmlq66L4rnFzHrvLC0HpNFoRpWx6iiuBJ4/xbARaSIKIX4BfFBKedLGs9pR1AxFKpmifV87APlT8wd1XpPRpCqusVpOeC7cHqZ1ZysHnznI4RcOk1N5vAuOTEm667uVdmIyha/cRyqeomlLE8mYiix21XWNjtZAr+akOK5NafPYRtbXOn0dYaguM55CD4GmANKUJ40ajnieg12mT1ZBwcwCwsfCmAkTZ46TqD9KrCum8iSFKrRx5buIBWMkehIIIbA6rRTMKCCVTNGxrwMzYWJGT/GdM/1aDZvB5HdO5raHbxv0Pe440MHaf1ubuV9nrSrMyq7KJuaPYffauem+m3AXuvudF24Ps+2326hbXweoqvMF/7IAp290+2THgjEaNzVixk3GLxiP1Wmlq64Ld6EbX5lvVO+l0WjODGPVUcwB5g7y1POo/MPfAluklKFhXs+H2no+JKW89GRjtaOoebuJ+qM8efeTBBoDOLIdqnNKMMakKyax8OML2f3Qbg48fQAzYVK2uIzSxaVYDAsbvrWB1l2tJMPJQSV+LHaL0pgUqpBEmjLjCPYWmVgMi8oBTclMHqck7dyNtFAmvZWMBHexW1U0n8rhGuH10xMckl69y1BrCFuWjVQydfLIa7rPtUxJrC6rii4Ot0c3KkfT4XNw51N3ZnQ/ewk2B3nktkeIdETIqcyhdWcr0a4oFquFimUVWOwWWmpa8BR5mHXnLOa8fw6uPBfJaJJHP/AonbWdBBoDqtONmSKnMofrf339Cfc5XY5uOcpjdz1Gz7EeLFYLVqcVu9eu0htMSdWqKqpWVhFoCJA7KZeyxWWDOsMajebcYkw6ikMxMEdRCFEJHATulVLemz72OaAa5VT2FrP0HrvsVJXP2lHUnAmi/ih7H93LoecPYcuyUX1jNZOumJTptTwY/iN+1n9tPU2vN+E/6icRUPmEhs3AmevEmeuk80CncgjT1dlmwkQIgTNb5SD2FsT0rdC2GKpV4ek4ir3YPXbMuDmsntqjjc1tU/mMTpvqgz3caOYIHMReenUzF/7LQiZdOYmeYz0Em4LY3DY2/3Qz/nq/Gig53v4xrcGZESkXqv+4Z7yHu9bdRaAxwPNff56uui5kSmLYDKSpWiUWzy3mxt/fSOOmRiIdEcYvHE9WQRYt21qwZdkoW1KG3W0/5bzNpMnPLvoZ4Y4w9iw7yViSSFcEw24wfsF4XONcNL7aiCvXRda4LJBQNLeIy793OYdfOEz9i/V4ijxcdMtF5E3KG5nRNBrN24p2FPswiKNYBRwCvtmr7SiEuB74IsoxzAYCwCvAt6SUm091D+0oas5lpJT46/2E28Ns/OFGOg924j/iV1GopCqycXgdBI8GQaoilcnvnMzkaybTvqedmt/XEGoJYTGU9qRAbXVnFWcRaYsQD44gD1KQidBZbCpCFe2Kjsz5Opmzln6uX4vDQcbbvar4Z2ALxOH0Bs/oX45kvoCnxEMyksRiWMirzqNle4sSYE9rYJ60qIf0azIlzlwnM++Yyf4n9xPpiuDwOjJfFhKRBLlVuRh2I5MzGemIkIwmcRe7iXZFiffEqbq0inn/PI+qS6tOkPQx40qz8siGIzx8y8PYfUr2p+dYT8apt1gtGE4Di2HBke3A6XMSbAmSiqfIrszGlmXD4XMQ7Y4S88eY9b5ZzLlrTr/UCY1Gc/bQjuIZRjuKmvOFqD/Kk//yJHXr6rBl2fCO9+IucpPoSXDzAzcT6YjgzHb2yz2rXVvLk3c/qbZE7ZZMZMuZ7cSWZePQ+kP9ooKZbjRD5AraPDaKZhbRtrcNmZRKHijVf8ypRMiHunavELiv1Ec0ECUWVFXJkuMdZYQhMjmRp5MXafPakClVST1sLGB1WEmZKexuO+5CN92Hu1UkNy1tdEpnuU9/cMNukJIpMJXT5sxxIoTSBE0lU3jHe/GWegk0Bmjb06ZE5QtdxLpiKr80y4avwsfizyymeHYxx3YdIxFO0LK9hSMbjhDpiIAFAo0BXLkuUmaKSHvkuN6lTTnLFqsFu8eeKQSKR+KkEinyp+RjdVrpru9WGpNuO/lT87nse5dRvqR8WCZre7ON3Q/vpqethwmrJlB9Q7Vq86jRaN4y2lE8w2hHUXM+sfbf1nJszzFcua7MsWBzkFsfvHXI4gR/g5+n//Vpug91Y/fasXvtxPwxHD4HNrcN/2E/8VAcm8dGMpIk2BTEjJnYPDbMuInFoqKRvlIfcz84l0lXTeKxDzxGvCdOx74OUomUct7S26iGwzgh4mfzKE1Jh8dBdkU2KTNF9+FuzKSJGTEzWppSShw+BxNWTcjk8ElTkowlsdgtmFFTiYALkenTPZKIpjAEVpdV6WMO0nP7xBNUBNOMqypsYRO4fC4leZSSGA5j+EVBvcG/tBxR7/0tNgvCJrBarSRjSQpnFRI8GiQRTpAIJzIOqbvQnen9Pa56HG2725CoaGavCLrVYcXqtCrB9B4lmG51WVXkOP1aDaeBmTDBVO9LKq6i032F2LGCYTGw2JUz2atpedsjtxFsCtK8tVlthV9y4lZ4685Wnv7Xp1VxkcNKpCuCK1dpdHoKPcz94FyK5xZnxve+JqvTOqjoeTKW5OAzB2nY2EB2RTbTb51+QnGQRjOWOJOOohYI02jOM0oXl9L4WmMmChULxnBmO/EUDy4uDZBdns3tj96Ov96PGTfpONDBy997WWk+AnlT8gg2B5l89WSCTUFyJ+ZSfVM1qZiqxra77ZgJE4thyRTELP7XxWz51RYKZxRmKoltbtWFxua24S5yEw1EERaBGVUOXd7kPN7913fjLnRjsVlY97l1HH3tKMloku4j3cQCMVy5LvIvyifUEqJieQWBxgCR7ghdB7qIdkeVxmI6L9GwGTgLnES7oyQjw4wQpvtcGw4DV66LYGsQBjm1d+vX6rKqnuHpoh1pSmJGDHehm+DRYGard9jOqlT5ihbDgs1tI9IdUZJAdqsSO48kOLbzGFaHFZvblhF8TyVVwUvvNVp2tZAIJjCsBikzlemUk4wksbpU0Yppmshkn37nlnTOZbo1JTaOa2UO3OZPgnSo123JPp7esP+J/bz641fV9r2ArPwsrv35tf0642y/fzsWQ2lkSinpquuibXcbBdMLCDWFWPPpNVz7i2spmF5A7dpaNv5gI8lYkuyKbC779mXkVB3f4o73xHn0rkc5tP5QJoK8+X83c+sfb6V4jnI2Q60htt+/nVBLiKLZRcy8Y2ZG/zIWiFG7ppbu+m5KLy6lYnnFSfOCNRpNf3REER1R1JxfJCIJnvvSczS/0axaGNoNLv/PyymZVzLsa9Q9V8cL33gBX6mKQKZSKRpfbcRd4Mbhc2DGTSZeMZGV31h50rZ2waYg7fvaM9Edf4Mfh8eBI9uBr8xHy/YWdj20i1ggRtWqKuZ+YG4/Aet4T5zt92/n0HOHCDQFSEQSFFykOqPIlCTUHOI9f38P3Ye76WnrYeOPNnJ001GERZCMJHFkOzLdY+xZdsKdYeKh+JBFOsImyC7NZsHHFtD8RjOBIwGCzUH8R/zH+2Cnq7o9pR6cPieh1pDayoXjTpSAvMl5pMxUf2dxYJriAAfSmqUimRarchIdXgfxnjjxYJxJV0/C5rLR09pDw8YGpRs5TlWWy5QklUrh8DoQFoHFZiHRk1BtLIXqyd3bT1ug5ICceU6S0WSmk03ds3WEj4VV3/F09LBXkzIRSiCMdG/vxPEJC5vAIiz4yn34KnwkI0nMuImnxIPD48isgQmXTWDl11dmzvvLHX8hHopjd6tc0rr1dZhxE7vbjtVpJbssm+qbqqm+oZqnPvkUrjwXVqeVSHsEu8/OLQ/cgt1tp2N/B2s+s4ZDzx/CYrXgynEhkSR6Eky6chLv+tO7CHeE+fuH/q46BrmsxENxKpZVsOQzS+jY38Er33+FWCCGxWbBjJtMeecUVnx1xZBrupdYMEYyksSV79KOpeacQ0cUNRrNkNhcNq76r6vo2NdBLBCjYEbBsKpg+1J6cSneEq+q3s2y0XOsB2lKcifnYrEoKZnDzx+m+59UD+ih8I739oskFc4o7Pf8hJUTmLBywpDn2912Fn18EYs+vohN/7OJN//65gljDLtq5QdQ8Y4KHn3fo8qxs0DgSICUmWL+h+Yz8/aZrL1nLY2vNpKUAyqh07mN46rH4SnyMP9D87F+wkrt07XsengXSJT2oj+mdCGtglv+cAvte9t59svPqksIgRRqa1emJL4KH3c+dSc199Xw4r0vkgyrSF4imlCOl0WQOyk3s/WaSqawZdkINAQyskWJaIJUIoXNY8Oepd5DT7EHX5mPQGMALEq/M5VK4T/sR1gE7iI3PW09x3Mae9smpp1EKaXqZhNN4i5w4/Q5WXrPUlZ+fSVP3P0EzVubcfgclF9SzlU/voq1n1vL3r/tzRT4SIvqzCOEyBQWJSIJWt5owVPiIdQUItoVpWR+iXLe8ly0bGvp955VvKOCXQ/uwpZlU2kMEeXE9oqodx3qItoVpfHVRoQQ2FwqLSGrIItQa4juw904s5089Ymn6DzYqSrCTZOoP6oi6YagfV87UkoOrjuoHEyvnXBHGGER7H9iP4fWHyJ4NEhPew/OHCcl80pwF7g5uPYgs98/G0+Rh2BTELvH3m8bW0rJ1l9vZecDOwHIrsjmiu9fcdq9xDWa8x3tKGo05yFCCMZdNO60z3d4HVz7i2up+V0N/iN+ShaWcGTDESwWFTkRFoEwhCrSOENMvnIye/+2l0hXBKvTSrgtzMQrJqpiizT2LDs3/f4m9v59L42vNjLt5mnMuG0GeZOVfMudT99J05YmGl5toLuum+ZtzQSbg0Q6IriL3ORPzWfFV1YoORhg+rumM/X6qaz793W0vNGCzWlDWATVN1ZTuriU0sWltOxs4fWfvo7hMLBn2VW/a3+UwhmFWO1W5n94PvFQnC2/2KIcFQSFMwu55LOXANBZ20nelDwqllfQuqOVbb/bRseBDhX1TKl8QcNmKLmftMPkLnRj2A3cBe5MD/RlP19G+SXlNGxsoHlbM/Ub6okFYviP+NW1LGpLu3eXqLcf9+y7ZpNfnY8QgjufvJNodxQzbpJVkIUQguVfWE7brjY6D3YqBzGqcj97q6JTyRSpRIriecVk5WcR6YwQ9UcJHA2QU5lDzB9j/MXj+72Xc943h5atLXTUdtBZ1wkcF3uXpiQejlO2pIxEJNHPoZdSZvJcGzY2kAgn8JZ6CTWHSKVSaps8qfIpi+cUI4RQLTSDMdr3t0MKTNMk0ZPAV64KooQQxLpjNG1pomxJGcIqaN7WzPb7txPtVnmms+6YxYK7FyCEYN/j+9j4g43YXDZsHhtHXjnC/avvZ/mXljPrvbPOescojeZMo7ee0VvPGk0sGONvd/6NRE8CZ66TSGcEZ66TWx64JeO8nAma32jm9Z+/TtQfpWJ5BQvvXojVefrfZ82ESag5pAppUuDKc2VyLPuSMlM0v9FMsClI3uQ8CqYXZLbc4+E4v5r7K0ItIVWtnFRbwHeuubNfBDXSGaHrUBd2j52cqhza32xnzWfWZHILY/4YjmwHhs3AjJl4y7z4Sn3Mfv9sEj0JXrz3xYzT5B3vZfV3VtOxr4NIV4SSeSX9vhiYCZNXf/Qq+/+xX22Nt0Wwe+zkTs5l5ntmUjS7CDNmkjc5b1jdV0ItIbb+ZitHXzsKqGIUw27gcDvwlHo4suEIE6+YiGEz8B/xc2z3MbLys/CV+rBmWbn2Z9dmnPW+Nj303CEevvVhEuFEJtopLAK7x85nmz9LpDPC4x96HDNhYnVZifljlMwr4ar/vorNP93MS99+CYTqfpSMqiixzW0jb1Ie73rkXeRPyadpaxMPXPkAqWSKZCyp8jWTKrfU5rER98eRSGxOG75KH+5xblz5LqJdUbLGZdHT1kNXXRcz3zOTRR9bxEM3PkR3neoNHgvEsDqsWKwW8qvzmXrd1GFtW2s0bze66vkMox3FC4NAY4DXf/46LTUt5E7KZdHHF1EwreBsT+u8oftwNxu+vYGuui7yp+Sz4qsrTulkmAlzTERYgk1Bnv3yszRtbiKnKoflX1lO5bLKk57zxN1P0H2oG1eei1BriOatzeRPzSdvch6hlhAl80u46sdXZcZHu6O07mjF5rZRPKd4WB1SIp0R4qE4vjIfKVPJ3Zwsp3Q4NL7WyLp/X5fpTy1TkvoN9eRPycdb6iVlpuis7aRiWQVlS8qYeMVEPEWDF1Lt/NNOnv38s4Tbw6RSx6uq5//zfK775XUAdB3qYuuvttJzrIeiOUXM//B8pCn5041/onVHK8IQmW3xgpkFLPr4Iqqvq+4Xaf7Z9J/ReaAzs2VuJkwEAk+xh3goTiKcwHAYeMd7ueq/r+Ll77yMt9RLz7Eeju08hpkw8ZZ4ySrIwl/vJ+aPYSZMlQNqEVizrBhWVSm++rurWfLJJW/JxhrNW0XnKGo0IyQRTvDUJ58i2h3Fleei/c12nv7U09zyh1tOWg2sOU5OVQ43/PqGYY1t39vO+q+tJ9QcImdCDqu/tfqCFmP2jvdy8+9vHtE58WAcw6Gc6N62gb2OkjPXSWdtZ7/xzhwnlStO7nwOxJXnwpWnZJIMy+g47IUzC/vlr8aCSnA71BxSeZMS5v3TPC7+1MWndEotFov6/FlU9bE0Jc4cJ5f/5+WZMbkTclnymSUceuGQ2kruSRBqCSGRlC4ppWN/B2bMRBiC2/96O+6CE2VxqlZV4W/0Zwps4qE4yVhS6XBaBJ4SD+5CN9f/3/WUzC9h22+3EfPHMpFDAG+pV0VwOyOYMTPzXklTkggmSFpVGsb6L6/HnmVn/ofmj9i24fYwr/74VTr2dzBu2jgu+bdLMu/fyWje1kzH/g7chW4qV1Tq4hrNGUU7ipoLgqOvHyXSGckknGeNyyJ4NEjdc3XMvnP2WZ7dhUU8FGftPWtJmSm8pcqheOazz3DrQ7eOiejicJl0xSS2/nrrcUkhScYpiHZGKb249CzPcHDsbjvX/Owatv12G4HGACXzSph15yyERRBqUf21h+PcAFReWknN72swHCoaZ8ZNVn1zFc5sZ2ZMsCnI4x95nGiXyifc+cBOLv36pcikxJZlY/yC8SQiCRI9iSHve8k9l1D7VC2xYAzDbpA7KRdXvhrbWtOKTEmqb6ymeJ6S01l17yqeuecZEpEEqWQKX6kPp89J1K/6dJvxE9tSCotQ69sC2367bcSOYspM8cznnqGrrgtnrpP6F+sJNYW47v+uw2JYCDYF2f3wbuLhOFPeOSVTwLXnz3vY9JNNgMrhnHj5qdUINJrRRDuKmguCwX6xS+RZ6UN8vtNbPDEUgaNKxqZ3u9Fd4CbUEiLcHs5sV2pg9l2zMZMm+x/fj6/MR+GsQtr3thNqCeEt87L080vP9hSHJCs/i2X/vuyE48PJd+yLp8jD9b++nj2P7CEWjDHhsglULKvoN+bAUweI+WNkV2QDqr95+952Ztwxg10P7coUWK342oohI2l5k/K4/tfX89J3XkKmJI5sB1NvmErNb2uoWlUFEg6uPcikKydRtriMwhmF3PrQrbzx2zfY9dAu9cWyOYhhMyiYUUDDKw39+pojVWTRlCYyJgdtAZmMJdnyyy0qPWFiDks+vSRTNAUqD7TrUBfuAjcdBzpUQVBjAP8RPw6vgyc++oSS8TEsHFx7kCt/eCUlC0rY/PPNmeKmlJmi9ulaKt9RSeWllfqLmeaMoB1FzQXB+IXjM9WozmynSp4XYsRbeecjUX+UunV1xENxxi8af4JEzXCJ98R55fuvUP9CPXavnUvuuYQJq0+UtnHlulR1aTo/MRlTWn4On+OtvpQLCothYcFHFrDgIwsA5YCHWkIko0l8Zb4x80feV+pjyb8NndMnzf5fTIRQHWsWfWwRVSuq6GnrIXdCbj8R7sGYePlEyi4pI+aPkTUuS0VzbZbjzqVUecwsVj+68lws/dxSimYVUfdsHTaPjaxxqqrb5raRjCq9S8NuEA/EM9cAMGzGCV+oNv1kE/se24cr30X9i/UEGgLc8NsbMve3ZamCqpaaFqKBKBbDQk93Dw9c9QB5k/IItYbIn5oPQLgjzJ6/7KFoTlGm1aKUktaaVgJHAzz5sSepurSKq39yNTaXjXC70sccqUyWRjMctKOouSBw5bq44vtX8NK3X1LaaF47K7624oRKzAuNqD/KEx95gkBjAGERbPvdNlZ9axVVK6pGfK1Xf/Qqdc/W4R3vJRlN8sI3XsBT4jmhIMhd6Gb+3fPZ+sutCIsgZaZY9LFFZ7Q6+nxECKEjroMw6apJ7PnrHoJNQZCqXeKE1RMQQskMjQS7255xlsZdNI5UMoWZSOcbCsid2F8TVAjB5KsnM/nqyXTWdiITkkMvHFIdf9I905PxJIEjASXunoLsCdmYUZO6Z+to3NiIt9TLrPfOov6FejzFHgy7gd1tz2hF9kYVXbkuZrxnBs99+TkshoVwVxiZknTXd2PYDAJNAXKqcjDsBtKUWGwWrA4rVZdWcej5Q8SDcbrqukCowrMdDTuwuqxEu6J01nZidVpZ+u9LmXrN1NF5YzSaNLrqGV31fCEhU5JYIIbdax8TCd+7HtnFaz95jexytW0XD6kCitv/evuIr/XAVQ9g89gyka5AY4DFn17MjNtmAEq65vCLh3F4HVx080VEu6K8+eib7H10LxarhezKbK78wZW6eEgzYroPd7P/H/sRQjD1uqmZbei3gpSSN377Bjsf3InFsLDw7oVMv236oGODTUEe+6fHSMaSx6OGDiVj5CtX0d+uui5c+S4iHREcPgeRrgg2p41ENEHpolKigSiBIwGyxmWpHt09Cd77j/f2k3cykya/W/Y7OvZ3KI3SlJpnb4ce1zgXORU5WF1WrvnpNRRMLyARSbD111vZ+ION9LQqYXwpZaYlo9VpxZaloqHuQje3PXyb/gyOAXTVs0ZzmgiLyPQvvtBo2trExh9sJOqPMmHVBBZ/anGmA0gvhsMgEUqc1vWdeapnspGtttWklJnt5MMvHmb9V9dnBJgPPH2AlV9fyf4n9+MudGN1Wgk0BNjwrQ1c87/XjMrr1YwdcqpyuPgTF4/qNYUQLPjwAub/8/zMz0PRUtNCIpLItLQMNYe49GuXUjy3GLvXTrQryis/eIW2XW2ULy1XouUJE1euCyklzVubufp/rub5/+95Qq0hhBBc+v9deoIGqGE1uOw7l/HILY9kZH+EVRANRLF77Bh2g7wpeaz8xspMRySby8aSTy2heWszux7alYmOApkUEKfDqbafnTbCHWE8xR6klOx+ZDf7/r6PrHFZLPm3JSftsqTRDIV2FDWa8wD/ET/rPr8Ow2Fgc9nY+/e9CEMw6cpJ1NxXk4kkhlpCVN9QfVr3WPq5paz73DoCISWBUjSriKqVVQDU3FeDw+vIOI6BxgC1a2sRQmT+GGaNy6LtzbZReb0azWgxnOpgZ45TFaykZObhyndl1rsrz8Xl3z0u6fPyd1+mbU8b5EIyksRwGBTOKORdf3oXoeYQWeOyhvzCOvGyiUy7dRpNW5uIdkUJNgUxHAYF0wtw5blIJVKDOnRLPrOE2qdrCbeF+/UPlwlJ+LoQfJEAACAASURBVFhYyfxYyOwu1D1bx2v/8xquXBehlhCP/dNjVK2swl3kZvZ7Z1+wX6g1o492FDWa84D2ve2kkqlMT1p3gZsjG46w9LNLWfkfK9n8k83Eg3Gqr69m8acWn9Y9xi8Yz03338SxXcewuqyULy3H6lC/IlLJ1AlVoM4cJzIlMeMmht0g0hkhd5KOWGjOP8qWlDH1uqkceOoAADPvnEnB9KHF+ud/ZD6tu1oJNAYwrAaXfv1SDLuhIoLDyIu+6sdXsem/N9G+r532ve3YXDZ8ZT5CTSFK5pUMek7JvBJuuv8mHr71YdU7uw+pRAokLLx7Yca5balpwbAbCIug+3A3gcYAPa09uAvcNG1u4obf3DAsUXeNRjuKGs15gN1rV5GOdKVlIpzIRA4mrJzAhJUnViefDtkV2YPmh824fQYvf+9lUvEUZtzEkePgopsuwpnj5PX/fR2EklS59GuXjso8NJozibAIln1hGQs+umBY6StZ47K46fc30dPagyPbgcM7sop/Z46Tld9YCajo/HNffo6uui6KZhex5DP9K8QPbzjMvsf2YXVZaXi1AcNqgFNV1fcKuQuLUGknkeNpJ/lT8tkb3Uvz/mbV01pKetp6yK7MputQl5JpKvXSuKmRaHeU8QvGZ76IajR90Y6iRnMeUHpxKZUrKql/qR6LxYLhMLjks5ecsftPvW4qNpeNumfrcGY7mf3+2bgL3My4bQYTL59ILBDDU+zJRCA1mvMNIcSwhcRBSeSMVFdyMHxlPm7+fzerFowDCvBad7Sy/ivrsblsHNt9DGlK9RmzgEyq/WeLYcGZ5yQZSSL6hP2nXj+V1p2tvPqjV7G6rFhsqr1juD2MK8eFI9vBa//9GjX/r4ZUIoWnxJPpZGXGTbWz4LQybtq4EYt7dx/uZuv/bcWMmcz90NzTluzSnBvo3+oazXmAxbCw6j9W0VLTQjwUp2BawRn99i+EYOLlE5l4+cQTnnPlupS2okajOW0GU2nY/4/9hFpD5E3Kw2JVkjqG08CMmjjznGQVqAprUA7n5Gsm97ve8i8up6WmhUhnhMDRAD0tPZCC0iWl7H18Ly988wWS0aQqRjsaYN8T+5hz1xzWfHoNbXvaMJMmlcsrufiTFw+7Ej0ZTfL0p58mHogjDMHaz6zl1odu7Sc+rjm/0I6iRnOeYDEsjF8w/mxPQ3OeIaWkZVsLPW09FM0u0lqO5wm1a2vZ88geelp6iLRHMBwGqWQKq7BisVuYdvM0ln1+Ga//4nXiwTiz3z+b3Im5HFx3EDNuUvmOShw+B1f/19Vs+q9N+Mp8eEu9bL9vO6/99DWSkSQyJbEYFpLRJMIq6DrYRUtNC22723CNc9G0pYmtv9pKw8YGVn5j5aBfFAcSbg8T88cyEj09rT0EGgPaUTyP0Y6iRqPRXMDseGAHW3+lxNFtLhvX/eq6U3Y50Zx9au6rIdodxTveq7rNFGSRVZCFK8eVqdDOqcrhiv+8AlAyOX+68U/Uv1SPEIKS+SW857H3kF2RzZU/vpL1X13Ptt9so6e9B7vHjkB1wEmRgiTYHfZMAZtEEm4Lk+hJYDgM7F47W3+9lQmrJ3DklSMkwgnKl5YPmpuZVZClWiI2BREWgdVlJbty6Ghk9+Fudv5xJzaPjbl3zdXV2Ocg2lHUaDSaC5gdf9hBVkEWVoeVQGOAg+sOZtoKas5N2ve207SliUhXBAS4clws/MRC9jy8J9PXvvqGaqV3mo4KtmxroeHVBuxuO1JKju08Rsv2FirfUUnTlia2/HKLEvmWql2nxbAgjHTuoQWsDis7H9qJM9tJ0awi6jfUkzJTFMwoIJVI4fA6eO0nr7H9we0kQglyJ+Zy+99uP0Er0uqw8s6fvpPt92/HjJvMeu+sIVNTEuEET33yKeKhOKlkio69HVz782vfVttqRo52FDUajeYCxuFzKJ1Nu4FMSd2T+zygu74bT7EHu9dOuC1MdmU2Cz+ykMlXTObYrmNkV2Tjynfx1zv+SvBokGm3TVPRQLsVM2GqR9Sk5nc1jKsex/5/7EeaEme2k55Yj9KJLHCRlZ+FxWrBW+KlYVMDh9YfIh6IYzgNVnxtBeFjYeo31BPpiFAwvYA37nuDQEMAaUqCzUF2PrSTeR+cR7wnzovffJGWbS1MuGwCSz+/lOVfXH7K1xnuCBMLxvCWeEmZKaVN+RY5uvkox3Ydo2xJ2UkljjTDR4soaTQazQXMpV+/FITqNlKyoITq609PkF1z5iicWYgty4bNZSOnModFn1iEsAhyJ+ZSfUM1xXOLef1nrxNqCeEudrPnz3uw2q3M+cAchEWQDCfJnZhLZ20nr/znK2TlZeEr82W6La3+zmruabyHlV9fid1rp6etB1COWzKWJOaP8frPXuey717G9HdPx3AaHNt1jM4DncT8MRI9CZLhJC01LQDs+csejrx8BLvPzr4n9lG/oZ5EOEGoJcTJ2gR7ij3kTsjF3+An2BQcVg7kyWja2sTaz65l2++28dQnn8J/xP+WrqdR6IiiRqPRXMAUzSrijr/fQTwUx5nrHLHUiebM4yv1cf2vr+fo5qN4x3spX1p+wphUMoWwiMz7KaXk8u9dzsz3zOTpTz2Np8RDPBQn2BJkxddW0PZmG22726haWcXSe5ZiMSxMe9c0cifnEmoOsemnm6hbU6cuLiB8LIwQqsDF6XPiyHEcv1da0zXUGgJUpbOwiIyAd1ddFxt/sJF4KE7lpZWs+uaqfq1GezFsBu/86Tup31CP1Wml6tKqYdknZaaIdkdx5br6XbezthNpSrIrsgk2B+k+3D0qfcPHOtpR1Gg0mgscw26MSCNQc/bJqcwhp3LooqOFH1vI2nvWEmwOMuXqKRTNKiLUEsJ/1I+7yE3gSADDbnDJv12CM8fJdb+4LiPY34sQQnWCmaeie7/b8DuSkSQAEsmBpw9QfX01G761gVgwdvzmFnB4jqcwTLt5GoeeO0TwaJCC6QX4D/tJhBN4ij0cfuEwm36yie66biZfM5kp75wCqBaDB9cdpPyScqpvrB72F5hYMMbT//o0nQc7KZlXwhU/uCKj31p6cSl2j51QcwiHz0F+df6Q19nxxx3U/L4Gu9vOoo8tYuIVE/WXqCHQjqJGo9FoNOcZeZPyuP1vt5OMJrG77bTubOXxjzxOx94OrC4rhTMKuebn1zBu6riMRFLUH6VsSRk2l+2E6xXNKWLm7TPZ8eAOkODKd7H30b1c/3/Xk12RTf3L9dT8toauui6igShWt5XiucVIKXEXupl26zQ2/WQTgaMBsstVRK99bzvCEOz64y5ceS5atreQNykPYRG8eO+LGA6Dhlca8JZ6KV1UOqzXffS1o3TWduIt9dJS08KxXccysmG5E3K5+Q83U3N/Dfv+vo/H//lxrv7vq09oqxhqCbHlF1voqusiHojTeaCTa352zbAjmmMN7ShqNBqNRnMeYjEs2N12AGrX1BLrjmGxWpCmJB6MkwgpMe59f9/Hxh9tBAmFswsZN20cRzcdpfySchbcvQDDZmAxLKy8dyV7H9tLKpki0hHJbOuOu2gcnmIP+x7bBwICDQGsLisHnjyAw+Ng4ccWsuXnW/AUeQi3h+k42KE0H11WZEoSC8bIrswmHo7T09ZDze9qaHuzjeI5ytGMdkeHfI2xQIxQa4jciblYDAuOHAcxf0w5yF77CY0HPEUeDq8/jLvATaQrwp6/7mH5F/oX1lidVixW1QLRYrcgLIJgc3AU35kLC+0oajQajUZznlM4oxDDYWDGTeVQZTsyepn1L9Vjc9mw++zs+MMOkuEkwhAcff0o2ZXZxwucUpA/NZ+UmSIeijP+4uMC/84cJzf85gaatjTx+s9fRxgCYRHUratj0ScW4cxz0vJGC5GuCOG2MA6fA0eOg/bd7fS09NB1qIvyZeXs+fMedjy0A5mU1G+oZ/b7Zg+agwkq8vf4hx8n6o9SvrScy793OW/++U2klEQ6Iyz42IJMz/u+5E/Jp2lLEwgVeR2IM8fJ/A/PZ8O9G4hH4hTNKWLS5ZNOaWMpJTF/DEe2Y0xtU2tHUaPRaDSa85zJ75yM4TRofLURT7GHyVdPzuSlTrxsIk2bmwh3hEnFUwhDIOVxUe1efOU+Zr13Frsf2U3B9AIuuvGifvfwFHuYet1Ugs1Btt+/HYDpt01HCME7vvwOHr75YXzlPlJminBbmK66LhBg89gQQtDT0sPO3TtJxVJY7BasLitLP780ExUdSOuO1ozo+JGXjxAPxdX29eQ8Qi0h6l+ox4KFme+d2W87ffW3V3PwmYNqng9sp2FjA6vuXYXdo+6TiCSoua8Gb6kXM2Gy8hsrT9k5JmWmWPf5dTS+1siEVRNY9R+rxoyzqB1FjUaj0WjOc4QQTFw9kYmrT5SYmXLNFHKqcvA3+PnHx/9B8GiQVCJF/tR8plw7pd81Fn9qMYs+vkhFDIdwhOZ/eD4l80qQKcn4hSrq6PA5SCVTBBoCCIugYFoBFoeFtt1tmDFTiXtblLOZCCcwoyYTLptA7iQlj2P32LF77Bg2I3OfghkF2L12Qi0hxi8cj91jZ96H57H5fzYTC8Ro291Gx/4OUqlUPxF5h8/B1Oumct+K+zBjJqHmEPUb6plyzZTM6wQyxT2ncvhkStJ9uJujm4/iK/Vx6PlDLPUvHTNdZLSjqNFoNBrNBU7B9AIKphfwvjXv47kvPUfrzlay8rOIdEROaMXXK3MzFEKIjIPYy65HdmGxWcidlEsiksBiU7mS7/jSO4iH4xg2g4plFbzy/VdweBxMe9c0Fn9qMTsf3MlrP30Nf72fnAk5XP7dy5mwegIA3vFervnfa0iEE4y7aBxCCGa8awZT3jmFjT/cyME1B5FSYsbME+ZYc38NgcYAsUAMX7nqc92L1Wnlyh9eyd7H9lK+tJzciblDvtZERHWPad/XjsPjINgUpGxJGY7ssSNcrx1FjUaj0WjGCPlT8jHjJuOqxxFuV51XhtP720yYNGxswJnjpHhOcb/nuuu72f/EfuKhOImeBJd89hLmvH8OKTNF7oT+TljpxaUkIgmc2Soat/fve+ms7SQeiGN1Wtnx4A4mrJ6AlJLnvvQc9S/VU7Gsgsv/83IADj5zkNo1tUy6ahJCCAybwZy75pww31Qiha/MRywQY84H5pww5+K5xRTP7X9MSsmOB3cQOBJg/kfm4y5w032om459HTi8DrLys7jxdzfiLnKPmW1nOIccRSHESuD5QZ7ySylPuoqFEE7gP4D3ATlADfAFKeWG0Z6nRqPRaDTnM+XLy9n/xH4Mu0HhrMJTjg82B/nD5X+g+0g32ZXZ3HTfTf3kbFLJFDaHjcIZhUgpWfb5ZRh2Y9BrGXaj33PZldmqB7UFot1R8qcq7cN4ME79S/UqP/GVI8T8MYRFsOHbGzBsBi01Lbx/3fuxGINHP+d8QDmPhsNgzvtPdCQHo3V7K1t+sQVpqi3p5V9aTu6kXIrnFNO6s5WZ/zoT73jvqS90gXHOOIp9+BTwep+fk8M457fAtcDngTrgE8BaIcQlUsqa0Z+iRqPRaDTnJ0s/u5RJV0zClesaVjTxz+/+c6YPc1dtF/4j/n6OYt6kPJZ9YRkNGxuYcfuMIZ3EgXTWdtK0uSlTzOLIdnDgHweYcs0UCmcWMunKSRx85iATL5uII9uBGTdx5broaeshd2LuoN1eenF4HVz8yYuHNY9eXPkubC4biUgCb7lyCK0OK9f87BpkSp70fhcy56Kj+KaUctNwBwsh5gDvBT4kpbwvfexFYDdwL3DD2zJLjUaj0WjOQyyGRXVkGSZmzERYBDIlceQ4Bu3JPPW6qUy9buqI5hELxpBSkjc5D7vXTjwQx0yYRDoiBI8G6WntYdKVk1j+peUIIbA6rFz/f9dzbPcxSuaVjPr2b3Z5Njf9/ibCHWGKZhf1e26sOolwbjqKI+UGIAE83HtASpkUQvwJ+KIQwiGljA15tkaj0Wg0miG56f6bePpTT2N327n2l9dm8gvfKsVzi1nymSX46/1U31jN3kf34sxzUrG8gs0/20zzG81YDAvV11dTMl85tu5CN5V5laqK+m3AV+bDU+Khp7VnzOUiDsW56Cg+KIQYB3QDa4EvSimPnGT8DOCQlDI84PhuwA5MTv9fo9FoNBrNCCmYVsBd6+4a9esKIZhx24zMz8v+fVnm/6WLS9n76F5cef23x2vX1LLh2xuoXFHJ6m+tPi51k5IcefkIzlwnRbP6RwNHyvqvrqf+xXrmfWge8z88/y1d60LgXHIU/cCPgBeBADAP+DLwqhBinpTy2BDn5QFdgxzv7PO8RqPRaDSaU2AmTJ794rMc23GMVd9eRdnFZWdlHuVLyrnjiTsw7AZWx3FX5dD6Q6SSKepfrMeMm5nnDq0/xPqvrcfqsHLLA7fgK/Od9r079neQjCbpONDxll/HhcA54yhKKbcB2/ocelEIsQHYjCpw+eoQpwpADnF8SIQQHwU+ClBRUTHi+Wo0Go1Gc6ERagnR+GojCKh9qvakjmLL9hbe+PUbXHTTRYPmLb5VBuo7Asz/yHykKalcUdnPgbS5bRhW44Sq6tPhiv+8goaNDUy+evJbus6FgpByMB/r3EEIsQdokFJeNcTzDwNzpZTVA46/G5W3OFNKedKt54ULF8otW7aM1pQ1Go1GozkvkSnJKz94hdYdraz4ygoKphcMOfaJu5+gtaYVZ66T96153xmc5eB07O/A7rXjLbnwJWyEEFullAvPxL3OmYjiSRgqYtjLbuBmIUTWgDzF6UAcqH07J6fRaDQazYWCsAiWf2H5sMZWX19N54FOqm+sPvXgM0CvBqNmdDmnHUUhxEJgKvDISYY9DnwTuA24P32eFbgdeEZXPGs0Go1GM/qcjiSO5vzjnHEUhRAPAoeAN1AVz/OALwFHgZ+mx1QCB4F7pZT3Akgpa9Lbz/8thLClr/ExYAJw55l+HRqNRqPRaDQXCifv/H1m2YXSRLwPJYvzGeBvwGIpZXt6jAAMTpz3B9PnfQv4B1AOXC2lfOMMzFuj0Wg0Gs0YoX5DPc98/hmCTcGzPZUzwjkTUZRSfhf47inGHGaQamYpZQS4J/3QaDQajUajeVvY89c91D5VS9WlVWOi9/M54yhqNBqNRqPRvB3EgjFad7RSuqj0LcvnLP3sUqpWVjHhsgmjNLtzm3Np61mj0Wg0Go1m1Nn6y6088ZEn2Pv43rd8reyKbKbdPA2byzYKMzv30Y6iRqPRaDSaC5qypWWULi6laOZba+/3VpBSEu2OnrX7ny7aUdRoNBqNRnNBkkqmiHRFqFhWwU333cS4i8adtbkc+McBHnzngxx55chZm8PpoB1FjUaj0Wg0FyQvf+9l/njNH/nTTX9i66+3jvr1uw9389gHHqNhU8Mpx2YVZuEr9+HKdY36PN5OtKOo0Wg0Go3mgsRX5sOV7yLUGqL7cPeoXz/cHqZjfwfdh0597bKLy7jtkdtO2hbxXOSc7/V8JtC9njUajUajuXCJdkexuW0YtrdW8TwY4fYwrnwXQpyg3ve2cSZ7PeuIokaj0Wg0mgsaZ45z1J3EWCBG/Uv1OHOdZ9RJPNNoR1Gj0Wg0Go1mhOz+827WfHoNLTUtZ3sqbytacFuj0Wg0Go1mhEy9dirOHCeFMwrP9lTeVrSjqNFoNBqNRjNCPMUept86/WxP421Hbz1rNBqNRqPRaAZFO4oajUaj0Wg0mkHRjqJGo9FoNBqNZlC0o6jRaDQajUZzjiKl5GxqXmtHUaPRaDQajeYc5eXvvczae9aetfvrqmeNRqPRaDSac5TCWYVEO6Nn7f7aUdRoNBqNRqM5R6m+rvqs3l9vPWs0Go1Go9FoBkU7ihqNRqPRaDSaQRFns5LmXEEI0QbUv4VLjAPaR2k6YwVts9ND223kaJudHtpuI0fb7PTQdhs51VJK75m4kc5RBKSUBW/lfCHEFinlwtGaz1hA2+z00HYbOdpmp4e228jRNjs9tN1GjhBiy5m6l9561mg0Go1Go9EMinYUNRqNRqPRaDSDoh3F0eH/zvYEzkO0zU4PbbeRo212emi7jRxts9ND223knDGb6WIWjUaj0Wg0Gs2g6IiiRqPRaDQajWZQxryjKIS4RgixQQgREkIEhBBbhBCr089VCSHkEI+cAddxCiF+IIRoFkJEhBCvCiFWDHI/ixDiS0KIw0KIqBBiuxDi1jP1ekeDk9ks/XyuEOI3Qoh2IUSPEOJZIcSsQa4zJmwmhFg5xBrq7jNGr7U+DMdm6XF6rZ0EIcSatN2+1eeYXmunYDC7pY/r9ZZGCHGVEGK9EKJFCBETQjQKIR4RQkzvM2ZYn+P02FG17bnIcGyWHlcuhPiLEMIv1N/YvwkhKga53pmxmZRyzD6Au4EE8F/AFcBVwBeA69LPVwES+A6wZMDDGHCtB4Fu4CPAZcDfgAgwd8C4bwMx4HPAKuBXQAq45mzbY5RsJoCXgEbgDuBq4EWURlbZGLXZyvQ6+tcBa2hhnzF6rY3cZnqtndyGdwDNaTt+S6+1t2w3vd5OtNMPgHcBlwLvB3YDAaAyPeaUn+O3y7bn4mOYNssCDgC7gJuAG4GdwEHAfTZsdtYNdxbfsKq0oT5zijES+PAprjUnPe6DfY5ZgX3A432OFaZ/KXxzwPnPATvOtk1GyWY3pm2xqs+xbKAT+J+xZrP0XHt/WV6u19qo2kyvtaFtkwO0pP+ADOUo6rU2Mrvp9XZq+1WnX/tn0z+f8nP8dtj2fHoMYrNPAyYwuc+YCUASuOds2Gwsbz1/CPXN7ZejcK0bUFG2h3sPSCmTwJ+Aq4QQjvThqwA78MCA8x8AZgkhJozCXN5OhmOzG4AmKeXzvQeklH7gCdTC7jtuLNhstNF2O45ea0PzfWC3lPKht3ANbbf+6PV2ajrS/yZGeN5o2/Z8YqDNbgA2SSlrewdIKQ8Br3CiLc6Izcayo7gc2Au8RwhxUAiRFELUCiE+McjY76af9wshHh8kB2AGcEhKGR5wfDfqF8HkPuNiQO0g4wCmc24zHJvNQIXMB7IbqBBCePqMGws268uDQghTCNEhhPjjYDkn6LU2kJPZTK+1QRBCLAfuAj5+iqF6rfVhGHbT620QhBCGEMIuhJiC2jpvQTkhfTnV777Rtu05zSlsdjJb9F0TZ8xmY7mF3/j04wfAl1H7/7cB/yuEsEopf4L6EP8KeAZoAy5Kj90ohLhYSvlm+lp5QNcg9+js83zvv90yHfs9ybhzleHYLA84PMi5va8xFwgxdmwG4Ad+hMofCQDzUPZ7VQgxT0p5DL3WBjIcm+m1NgAhhA21jn4opdw3xDC91gYwTLvp9TY4rwEL0v+vBVanP58wvM8xjL5tz3VOZrOTvcbcPj+fMZuNZUfRAniBf5JS/i19bL0Qogr4khDif6SUzcC/9DnnJSHEGpQn/hXgfenjApUDMBAxyM/DGXeuckqbMfq2ON9thpRyG7Ctz6EXhRAbgM3Ap4Cv6rXWn+HYDL3WBuMLgAtVJDEoeq0Nyinthl5vQ/F+wAdMRBXlrBNCLJdSHh7m5xi0zTI2Sz9/Tq2zsbz13JsXsG7A8WeAIqBksJOklA3Ay8CiPoc7Gdwrz+3zfO+/uUKIgW/QwHHnKsOx2als0fvNZqzYbFCklG8A++m/jgaOGctr7QQGsZlea31Ib+d9Bfga4BBC5Ijjcje9PxuDnTuW19oI7KbX2yBIKd+UUr6Wzuu8DPAAXzzJ+MF+9422bc9pTmGzLoZ+jX0jg2fMZmPZUdw9xPHeD23qJOcO9NB3AxOEEFkDxk0H4hzPQdkNOIBJg4wD2HOyCZ8DDMdmu1E5EQOZDhyRUob6XGss2OxkDPVN72Rjxrrd+tpDr7X+TAScqGKIrj4PUFGLLuAEjbU+jNW1Nly76fV2CqSU3ajXd6q8t8HW2mja9rxhEJudzBZ918QZs9lYdhQfTf971YDjVwGNUsqWwU5Kf/tchsox6OVxwIbK1+sdZwVuB56RUsbSh9eg3pg7B1z2fcCudGXTucxwbPY4UCqEuLT3SSGED7g+/VwvY8VmgyKEWAhMpf86GjhmLK+1ExjEZnqt9acGpcU38AHKCVrFEH8UxvhaG67d9Ho7BUKIIlTO68GTjBnsd99o2/a8YRCbPQ4sEUJM7DOmCvX5HGiLM2Oz4WjoXIgP1Dea9ajt1H8BrkQ12ZaoHDxQSbj/Bbwb9cviX4B6lHBl9YDr/Qn1zfPDqFDyX4AoMH/AuO+lj9+D0pj6BSoSd/3Ztsko2cwCbAQagPegnMgXUOHt8rFms/T8HwS+BdwCrAY+ixJFPQKM02vttG2m19rwbDlQD1CvtdOzm15v/ef+KGq7/sb0OrobpYrRDUxNjznl5/jtsu25+BimzdyoLyY70+NuALYDdYDnbNjsrBvuLL9pPuBnQCvqm90O4L19nv8Q8HrawElUCfsfGfDLND3WBfw4PSaK+ra0cpBxBiqBtx5VfbgDeNfZtsVo2Sw9Jg/4XXrBhlECsnPGsM2+lJ6zH6Vn1YBysEv0Wjt9m+m1NmxbDnR49Fo7Dbvp9XbC3L8AbEU5OWGUgPOvgKo+Y4b1OX47bHsuPoZjs/S4CuCvqErxIPDYwDFn0mYifRGNRqPRaDQajaYfYzlHUaPRaDQajUZzErSjqNFoNBqNRqMZFO0oajQajUaj0WgGRTuKGo1Go9FoNJpB0Y6iRqPRaDT/f3t3EGJVGYZx/P9gqbUIDYmINmKU5EKJrEmIaBENSgUt2hTkKqg2Cm2kRdQiDYIEyYQCo8ACoQSz9kYSJpVSVhDUwkIoM4RQh/Rtcc7EbTpzx8vcq4v7/8Fw57vnPXPeu7k8fGe+70jqZFCUJElSJ4OiJEmSOhkUJUmS1MmgKGnsJFmZpC7h58AIe9iRZH/PeGN7zVtm1K1N8keSr5IsS7I5ybEkfn9LIFGeYwAAAw1JREFUGrmrrnQDknQFnATu6RnfCewAnqd5nnlv3dAlWUHznNd1c9StAz6heR7sZFWdTrKL5lFgTwK7R9GfJE0zKEoaO1X1J/D59DjJ2vbXfVV1/DK0sAk4WlVHZitIch/wEXAUWF9VZwCq6mySd4DnMChKGjFvXUgSrAHOAT+M+kJJFgFPAHv61DxAM5P4BfDgdEjs8T5wezvjKEkjY1CUJFgNfFNVFy7DtSaAJcCnsxzfAOwHDgIbquqvjpqvgTPA5Eg6lKSWQVHSWEuyAFhFc4v3cpgACjg2y/HtwAngkao621VQVRfb8ydG0qEktQyKksbdSmAxzSzdJUvycJLv2tXIa2aO+5x6E3CmqqZmOX4AWAFsmaOF39q/JUkj42IWSeNuOtQNOqP4NPBSVb0HkGRr77iPxcD5Psc306y2fiHJuaraNkvdWeCaAXuWpIEYFCWNu9Xt6/+CYrsa+hXgOmAB8HJV7U3yOnAvsDLJJuBI77iq7u5zvVPA0j7HC3gKWARsbcPi9o6664Hf+380SZofg6KkcbcG+GnmyuIkS4A3abam+TXJMuDLJIeq6tkkq4DtVbWvrf/PuI/vgauT3FxVJ7oKqupiko3AQuC1NizumlG2HDg84GeVpIEYFCWNu9XAZx3vr6MJYx8n6X3/NuCXeVzvYPt6F82ilU5VdSHJ4zQzizuTnK+q3fBviL0VeHUefUjSnAyKksZWkhuBG+j+/8QA31bVUPcqrKqfkxwGHgI+mKP27ySPAfuAt9qwuIdmC50p4MNh9iZJM7nqWdLYqqqTVZWqerHj8CFgebv5NQDt6uaFQ7j0G8CjSa7t6eXttpcfZ/Q4VVXrq2pBGxKh2bB7b1WdGkIvkjQrg6Ikdaiq0zQzd1uSHE1yHNjGcL4336W5ff3MoCe2W+/cD3SFW0kaqlTVle5BksZOkgngjqraOeB5k8DSS9iGR5LmzaAoSZKkTt56liRJUieDoiRJkjoZFCVJktTJoChJkqROBkVJkiR1MihKkiSpk0FRkiRJnQyKkiRJ6vQPM+9o7oZ6+UsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "color = specmatch['fe_c'] #np.exp(2*specmatch['fe_c'])\n",
    "#color = 'purple'\n",
    "\n",
    "stars = ax.scatter(specmatch['teff_c'], specmatch['logg_c'], \n",
    "                   alpha=0.66, s=50*specmatch['mass_c']**2,\n",
    "                   c='purple', label='$\\propto \\mathrm{Mass}$')\n",
    "\n",
    "ax.set_xlabel(r'$T_\\mathrm{eff}$ (K)')\n",
    "ax.set_ylabel('log($g$) (cm s$^{-2}$)')\n",
    "ax.set_ylim([5.3, 2.35])\n",
    "ax.set_xlim([6600, 3000])\n",
    "#cbar = fig.colorbar(stars)\n",
    "#cbar.ax.set_ylabel('[Fe/H]')\n",
    "ax.legend()\n",
    "\n",
    "fig.savefig('plots/main_sequence_gravity.png', dpi=1000, bbox_inches='tight')\n",
    "fig.savefig('plots/main_sequence_gravity.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "color = specmatch['fe_c'] #np.exp(2*specmatch['fe_c'])\n",
    "#color = 'purple'\n",
    "\n",
    "stars = ax.scatter(specmatch['teff_c'], specmatch['logg_c'], \n",
    "                   alpha=0.66, s=50*specmatch['mass_c']**2,\n",
    "                   c=color, label='$\\propto \\mathrm{Mass}$')\n",
    "\n",
    "ax.set_xlabel(r'$T_\\mathrm{eff}$ (K)')\n",
    "ax.set_ylabel('log($g$) (cm s$^{-2}$)')\n",
    "ax.set_ylim([5.3, 2.35])\n",
    "ax.set_xlim([6600, 3000])\n",
    "cbar = fig.colorbar(stars)\n",
    "cbar.ax.set_ylabel('[Fe/H]')\n",
    "ax.legend()\n",
    "\n",
    "fig.savefig('plots/main_sequence_metallicity.png', dpi=1000, bbox_inches='tight')\n",
    "fig.savefig('plots/main_sequence_metallicity.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(specmatch['mass_c'], \n",
    "        bins=25, density=False, histtype='step', range = (0.1, 2), \n",
    "        lw=3, color='black', alpha=1, label='_nolegend_')\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "ax.set_xlim([0.1, 2])\n",
    "ax.set_xlabel(r'$M$ (M$_{\\odot}$)')\n",
    "ax.set_ylabel(r'$N$')\n",
    "\n",
    "masses  = np.asarray(specmatch['mass_c'])\n",
    "umasses = np.asarray(specmatch['mass_err_c'])\n",
    "\n",
    "for i in np.arange(500):\n",
    "    ax.hist(np.random.normal(masses, umasses), \n",
    "            bins=25, density=False, histtype='step', range = (0.1, 2), \n",
    "            lw=3, color='green', label='_nolegend_', alpha=0.02)\n",
    "\n",
    "fig.savefig('plots/stellar_histogram_mass.png', dpi=1000, bbox_inches='tight')\n",
    "fig.savefig('plots/stellar_histogram_mass.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(specmatch['fe_c'], \n",
    "        bins=25, density=False, histtype='step', range=(-1.9, 0.6), \n",
    "        lw=3, color='black', alpha=1)\n",
    "\n",
    "ax.set_xlim([-1.9, 0.6])\n",
    "ax.set_xlabel(r'[Fe/H]')\n",
    "ax.set_ylabel(r'$N$')\n",
    "\n",
    "metals  = np.asarray(specmatch['fe_c'])\n",
    "umetals = np.asarray(specmatch['fe_err_c'])\n",
    "\n",
    "for i in np.arange(500):\n",
    "    ax.hist(np.random.normal(metals, umetals), \n",
    "            bins=25, density=False, histtype='step', range=(-1.9, 0.6), \n",
    "            lw=3, color='purple', label='_nolegend_', alpha=0.02)\n",
    "\n",
    "fig.savefig('plots/stellar_histogram_metal.png', dpi=1000, bbox_inches='tight')\n",
    "fig.savefig('plots/stellar_histogram_metal.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(specmatch['teff_c'], \n",
    "        bins=25, density=False, histtype='step', range=(3000, 6700), \n",
    "        lw=3, color='black', alpha=1)\n",
    "\n",
    "ax.set_xlim([6700, 3000])\n",
    "ax.set_xlabel(r'$T_\\mathrm{eff}$ (K)')\n",
    "ax.set_ylabel(r'$N$')\n",
    "\n",
    "teffs  = np.asarray(specmatch['teff_c'])\n",
    "uteffs = np.asarray(specmatch['teff_err_c'])\n",
    "\n",
    "for i in np.arange(500):\n",
    "    ax.hist(np.random.normal(teffs, uteffs), \n",
    "            bins=25, density=False, histtype='step', range=(3000, 6700), \n",
    "            lw=3, color='orange', label='_nolegend_', alpha=0.02)\n",
    "\n",
    "fig.savefig('plots/stellar_histogram_teff.png', dpi=1000, bbox_inches='tight')\n",
    "fig.savefig('plots/stellar_histogram_teff.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(specmatch['mv'], \n",
    "        bins=25, density=False, histtype='step',# range=(3000, 6700), \n",
    "        lw=3, color='black', alpha=1)\n",
    "\n",
    "ax.set_xlim([1, 17])\n",
    "ax.set_xlabel(r'$V$')\n",
    "ax.set_ylabel(r'$N$')\n",
    "\n",
    "fig.savefig('plots/stellar_histogram_V.png', dpi=1000, bbox_inches='tight')\n",
    "fig.savefig('plots/stellar_histogram_V.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(specmatch['bvcolor'], \n",
    "        bins=25, density=False, histtype='step',# range=(3000, 6700), \n",
    "        lw=3, color='black', alpha=1)\n",
    "\n",
    "#ax.set_xlim([1, 17])\n",
    "ax.set_xlabel(r'$B - V$')\n",
    "ax.set_ylabel(r'$N$')\n",
    "\n",
    "fig.savefig('plots/stellar_histogram_BV.png', dpi=1000, bbox_inches='tight')\n",
    "fig.savefig('plots/stellar_histogram_BV.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(np.log10(1/specmatch.query('iso_parallax > 0 and iso_parallax < 1')['iso_parallax']), \n",
    "        bins=25, density=False, histtype='step', range=(0, 2.7), \n",
    "        lw=3, color='black', alpha=1)\n",
    "\n",
    "def format_fn(tick_val, tick_pos):\n",
    "    #return str(10**tick_val)\n",
    "    return str(int(10**tick_val))  \n",
    "ax.xaxis.set_major_formatter(FuncFormatter(format_fn))\n",
    "\n",
    "ax.set_xlim([0, 2.8])\n",
    "ax.set_xlabel(r'$d$ (pc)')\n",
    "ax.set_ylabel(r'$N$')\n",
    "#ax.set_xlabel(r'log$_{10}$($d$ / 10 pc)')\n",
    "\n",
    "np.amax(specmatch.query('iso_parallax_emp > 0')['iso_parallax_emp'])\n",
    "fig.savefig('plots/stellar_histogram_distance.png', dpi=1000, bbox_inches='tight')\n",
    "fig.savefig('plots/stellar_histogram_distance.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pull Specmatch metallicities into planet properties. In 'Masses_good_with_specmatch'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "merge_columns = list(planets.columns)\n",
    "merge_columns.append('fe')\n",
    "m_good_spec = pd.DataFrame(columns=merge_columns)\n",
    "\n",
    "planets['name'] = planets['hostname']\n",
    "m_good_spec = pd.merge(planets, specmatch, on=['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_good_spec['sep'] = m_good_spec['axis']*m_good_spec['iso_parallax_emp']\n",
    "m_good_spec['sep_med'] = m_good_spec['axis_med']*m_good_spec['iso_parallax_emp']\n",
    "m_good_spec['sep_minus'] = m_good_spec['axis_minus']*m_good_spec['iso_parallax_emp']\n",
    "m_good_spec['sep_plus'] = m_good_spec['axis_plus']*m_good_spec['iso_parallax_emp']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter out planet candidates beyond 2 AU and above 0.1 M_Jup, and stars with measured metallicity < -0.6 (bad measurement)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "179 161\n"
     ]
    }
   ],
   "source": [
    "good_spec_filter = m_good_spec.query('fe > -0.6 and (status == \"K\" or status == \"C\")')#axis < 5 and mass > 0.1 \n",
    "print(len(m_good_spec), len(good_spec_filter))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overplot total sample histogram in metallicity, and eccentricity/metallicity scatterplot for planets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "#plt.title('a < 5 AU, M > 0.05 MJup')\n",
    "plt.title('Eccentricity and Metallicity')\n",
    "#matplotlib.rcParams.update({'font.size': 14})\n",
    "ln1 = ax.scatter(good_spec_filter.fe, good_spec_filter.e, s=list(50*good_spec_filter.mass), \n",
    "                alpha=0.75, c='red', label=r'Size $\\propto$ Msini')\n",
    "\n",
    "#Accurately calculate median for planet hosts, don't duplicate metallicity for each planet.\n",
    "unique_fe = np.unique(good_spec_filter.fe)\n",
    "median_fe = np.median(unique_fe)\n",
    "mad_fe = astrostats.mad_std(unique_fe)\n",
    "#matplotlib.rcParams.update({'font.size': 14})\n",
    "ln2 = ax.axvline(x=median_fe, c='red', ls='--', label='Planet host med.')\n",
    "#matplotlib.rcParams.update({'font.size': 14})\n",
    "ax.set_xlabel('[Fe/H]')\n",
    "ax.set_ylabel('Eccentricity')\n",
    "ax.set_xlim([-0.75, 0.5])\n",
    "ax.set_ylim([0, 1])\n",
    "#matplotlib.rcParams.update({'font.size': 14})\n",
    "matplotlib.rcParams.update({'font.size': 10})\n",
    "ax2 = ax.twinx()\n",
    "ln3 = ax2.hist(fe_select, bins=40, histtype='step', color='purple', density=True, label='CLS Sample')\n",
    "median_sample_fe = np.median(fe_select)\n",
    "#mad_sample_fe = astrostats.median_absolute_deviation(specmatch.fe)\n",
    "mad_sample_fe = astrostats.mad_std(fe_select)\n",
    "ln4 = ax2.axvline(x=median_sample_fe, c='purple', ls='--', label='Sample med.')\n",
    "ax.axvline(x=median_sample_fe+mad_sample_fe, c='purple', ls=':', label=r'Sample $\\sigma$')\n",
    "ax.axvline(x=median_sample_fe-mad_sample_fe, c='purple', ls=':')\n",
    "ax2.set_ylabel('Number density')\n",
    "#ax2.set_ylim([0, 1])\n",
    "\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "matplotlib.rcParams.update({'font.size': 10})\n",
    "ax2.legend(lines2 + lines, labels2 + labels, loc=2)\n",
    "\n",
    "fig.savefig('plots/fe_eccentricity_hist.png', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "#plt.title('a < 5 AU, M > 0.05 MJup')\n",
    "\n",
    "# Accurately calculate median for planet hosts, don't duplicate metallicity for each planet.\n",
    "unique_fe = np.unique(good_spec_filter.fe)\n",
    "median_fe = np.median(unique_fe)\n",
    "mad_fe = astrostats.mad_std(unique_fe)\n",
    "\n",
    "ln1 = ax.hist(unique_fe, bins=20, histtype='stepfilled', color='green', \n",
    "              alpha=1, density=False, label='Host stars')\n",
    "ln2 = ax.axvline(x=median_fe, c='green', ls='--', label='Planet host median')\n",
    "ax.set_xlabel('[Fe/H]')\n",
    "ax.set_ylabel('Number')\n",
    "\n",
    "ln3 = ax.hist(fe_select, bins=50, histtype='stepfilled', color='blue', \n",
    "               alpha=0.5, density=False, label='Sample')\n",
    "median_sample_fe = np.median(specmatch.fe)\n",
    "mad_sample_fe = astrostats.mad_std(specmatch.fe)\n",
    "ln4 = ax.axvline(x=median_sample_fe, c='purple', ls='--', label='Sample median')\n",
    "\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend()\n",
    "\n",
    "fig.savefig('plots/fe_two_hist_num.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'good_spec_filter' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-362a23a7b108>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'a < 5 AU, M > 0.1 MJup'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m im = ax.scatter(good_spec_filter.axis, good_spec_filter.e, s=list(50*good_spec_filter.mass), \n\u001b[0m\u001b[1;32m      8\u001b[0m                 c=good_spec_filter.fe, alpha=0.75, label=r'Size $\\propto$ Msini')\n\u001b[1;32m      9\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me_max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr'$e_{max}$ (0.03 AU)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'good_spec_filter' is not defined"
     ]
    },
    {
     "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": [
    "# Overplot peri-distance limit 0.03 AU.\n",
    "axes = np.logspace(np.log10(0.03), np.log10(6))\n",
    "e_max = 1 - 0.03/axes\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.title('a < 5 AU, M > 0.1 MJup')\n",
    "im = ax.scatter(good_spec_filter.axis, good_spec_filter.e, s=list(50*good_spec_filter.mass), \n",
    "                c=good_spec_filter.fe, alpha=0.75, label=r'Size $\\propto$ Msini')\n",
    "ax.plot(axes, e_max, label=r'$e_{max}$ (0.03 AU)')\n",
    "ax.set_xlabel('Semi-major axis (AU)')\n",
    "ax.set_ylabel('Eccentricity')\n",
    "ax.set_xlim([0.03,6])\n",
    "ax.set_ylim([-0.025,1])\n",
    "ax.set_xscale('log')\n",
    "ax.legend(loc=2)\n",
    "cbar = fig.colorbar(im)\n",
    "cbar.ax.set_ylabel('[Fe/H]')#, rotation=270)\n",
    "fig.savefig('plots/axis_eccentricity_log_lim.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot eccentricity with confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "matplotlib.rcParams.update({'font.size': 12})\n",
    "#plt.title('Axis and Eccentricity')\n",
    "matplotlib.rcParams.update({'font.size': 12})\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_xticks((0.1, 1, 10))\n",
    "ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax.get_xaxis().set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
    "\n",
    "ax.scatter(planets.axis, planets.e_med, #.axis_med, #.e_mode,\n",
    "           s=list(50*planets.mass_med), \n",
    "           c='blue', edgecolors='black', \n",
    "           alpha=0.75, linewidth=2,\n",
    "           label=r'$\\propto$ $M$sin$i$')\n",
    "ax.legend(loc=2)\n",
    "\n",
    "# Differentiate between circular-consistent and eccentric-consistent.\n",
    "ax.vlines(planets.axis, planets.e_minus,\n",
    "          planets.e_plus, alpha=0.5, color='black')\n",
    "\n",
    "ax.set_xlabel(r'$a$ (AU)')\n",
    "ax.set_ylabel('Eccentricity')\n",
    "ax.set_ylim([0, 1])\n",
    "ax.set_xlim([2.5*10**-2, 30])\n",
    "fig.savefig('plots/axis_eccentricity_ci.png', dpi=1000, bbox_inches='tight')\n",
    "fig.savefig('plots/axis_eccentricity_ci.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>hostname</th>\n",
       "      <th>e_minus</th>\n",
       "      <th>e_mode</th>\n",
       "      <th>e_plus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>120066</td>\n",
       "      <td>0.829928</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.934481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>145675</td>\n",
       "      <td>0.303072</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.619581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>213472</td>\n",
       "      <td>0.444000</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.649000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>26161</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.881000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>50499</td>\n",
       "      <td>0.166044</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.330395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>66428</td>\n",
       "      <td>0.167421</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.556626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>68988</td>\n",
       "      <td>0.372318</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.587606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>95128</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.547707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hostname   e_minus  e_mode    e_plus\n",
       "16    120066  0.829928   0.940  0.934481\n",
       "37    145675  0.303072   0.270  0.619581\n",
       "77    213472  0.444000   0.529  0.649000\n",
       "92     26161  0.770000   0.820  0.881000\n",
       "117    50499  0.166044   0.240  0.330395\n",
       "121    66428  0.167421   0.375  0.556626\n",
       "123    68988  0.372318   0.400  0.587606\n",
       "148    95128  0.229270   0.360  0.547707"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.query('axis_med > 10')[['hostname', 'e_minus', 'e_mode', 'e_plus']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>hostname</th>\n",
       "      <th>mass_minus</th>\n",
       "      <th>mass_med</th>\n",
       "      <th>mass_plus</th>\n",
       "      <th>axis_med</th>\n",
       "      <th>e_minus</th>\n",
       "      <th>e_med</th>\n",
       "      <th>e_plus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>107148</td>\n",
       "      <td>0.052812</td>\n",
       "      <td>0.062632</td>\n",
       "      <td>0.072330</td>\n",
       "      <td>0.140635</td>\n",
       "      <td>0.180533</td>\n",
       "      <td>0.335719</td>\n",
       "      <td>0.470649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>136925</td>\n",
       "      <td>0.885964</td>\n",
       "      <td>1.076818</td>\n",
       "      <td>1.281420</td>\n",
       "      <td>4.680605</td>\n",
       "      <td>0.367470</td>\n",
       "      <td>0.680105</td>\n",
       "      <td>0.750475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>141004</td>\n",
       "      <td>0.038372</td>\n",
       "      <td>0.042840</td>\n",
       "      <td>0.047503</td>\n",
       "      <td>0.123790</td>\n",
       "      <td>0.059317</td>\n",
       "      <td>0.163534</td>\n",
       "      <td>0.273011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>145675</td>\n",
       "      <td>4.811323</td>\n",
       "      <td>5.841370</td>\n",
       "      <td>7.203498</td>\n",
       "      <td>16.426313</td>\n",
       "      <td>0.303072</td>\n",
       "      <td>0.452164</td>\n",
       "      <td>0.619581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>164922</td>\n",
       "      <td>0.012793</td>\n",
       "      <td>0.014872</td>\n",
       "      <td>0.016945</td>\n",
       "      <td>0.102329</td>\n",
       "      <td>0.055199</td>\n",
       "      <td>0.180095</td>\n",
       "      <td>0.349462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>170469</td>\n",
       "      <td>0.482555</td>\n",
       "      <td>0.554716</td>\n",
       "      <td>0.629851</td>\n",
       "      <td>2.212173</td>\n",
       "      <td>0.048397</td>\n",
       "      <td>0.154503</td>\n",
       "      <td>0.312677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>177830</td>\n",
       "      <td>0.089411</td>\n",
       "      <td>0.104277</td>\n",
       "      <td>0.120063</td>\n",
       "      <td>0.496229</td>\n",
       "      <td>0.389725</td>\n",
       "      <td>0.539209</td>\n",
       "      <td>0.660149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>192310</td>\n",
       "      <td>0.039094</td>\n",
       "      <td>0.045073</td>\n",
       "      <td>0.051469</td>\n",
       "      <td>0.326237</td>\n",
       "      <td>0.047769</td>\n",
       "      <td>0.141149</td>\n",
       "      <td>0.262648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>213472</td>\n",
       "      <td>2.885000</td>\n",
       "      <td>3.479000</td>\n",
       "      <td>4.553000</td>\n",
       "      <td>12.990000</td>\n",
       "      <td>0.444000</td>\n",
       "      <td>0.529000</td>\n",
       "      <td>0.649000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>216520</td>\n",
       "      <td>0.028835</td>\n",
       "      <td>0.032621</td>\n",
       "      <td>0.036213</td>\n",
       "      <td>0.195377</td>\n",
       "      <td>0.067118</td>\n",
       "      <td>0.187106</td>\n",
       "      <td>0.314167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>219134</td>\n",
       "      <td>0.009821</td>\n",
       "      <td>0.011232</td>\n",
       "      <td>0.012608</td>\n",
       "      <td>0.064659</td>\n",
       "      <td>0.051075</td>\n",
       "      <td>0.159784</td>\n",
       "      <td>0.283953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>24040</td>\n",
       "      <td>0.174145</td>\n",
       "      <td>0.201119</td>\n",
       "      <td>0.228269</td>\n",
       "      <td>1.300008</td>\n",
       "      <td>0.032111</td>\n",
       "      <td>0.111044</td>\n",
       "      <td>0.233157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>285968</td>\n",
       "      <td>0.024187</td>\n",
       "      <td>0.028531</td>\n",
       "      <td>0.032841</td>\n",
       "      <td>0.066490</td>\n",
       "      <td>0.053657</td>\n",
       "      <td>0.163448</td>\n",
       "      <td>0.299084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>31253</td>\n",
       "      <td>0.383296</td>\n",
       "      <td>0.445807</td>\n",
       "      <td>0.508966</td>\n",
       "      <td>1.295621</td>\n",
       "      <td>0.275216</td>\n",
       "      <td>0.440430</td>\n",
       "      <td>0.559104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>34445</td>\n",
       "      <td>0.144788</td>\n",
       "      <td>0.166367</td>\n",
       "      <td>0.188502</td>\n",
       "      <td>0.726717</td>\n",
       "      <td>0.073101</td>\n",
       "      <td>0.174406</td>\n",
       "      <td>0.280003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>34445</td>\n",
       "      <td>0.100368</td>\n",
       "      <td>0.114473</td>\n",
       "      <td>0.129095</td>\n",
       "      <td>0.486722</td>\n",
       "      <td>0.148526</td>\n",
       "      <td>0.257892</td>\n",
       "      <td>0.379580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>34445</td>\n",
       "      <td>0.055254</td>\n",
       "      <td>0.064727</td>\n",
       "      <td>0.075415</td>\n",
       "      <td>0.272246</td>\n",
       "      <td>0.179638</td>\n",
       "      <td>0.354213</td>\n",
       "      <td>0.544104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>4203</td>\n",
       "      <td>2.441611</td>\n",
       "      <td>2.681533</td>\n",
       "      <td>3.667077</td>\n",
       "      <td>7.784331</td>\n",
       "      <td>0.100875</td>\n",
       "      <td>0.190164</td>\n",
       "      <td>0.480939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>45184</td>\n",
       "      <td>0.033650</td>\n",
       "      <td>0.037449</td>\n",
       "      <td>0.041464</td>\n",
       "      <td>0.064122</td>\n",
       "      <td>0.083647</td>\n",
       "      <td>0.184753</td>\n",
       "      <td>0.284891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>45184</td>\n",
       "      <td>0.028612</td>\n",
       "      <td>0.034463</td>\n",
       "      <td>0.040153</td>\n",
       "      <td>0.109533</td>\n",
       "      <td>0.246258</td>\n",
       "      <td>0.484646</td>\n",
       "      <td>0.664791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>66428</td>\n",
       "      <td>9.784151</td>\n",
       "      <td>27.244769</td>\n",
       "      <td>48.999393</td>\n",
       "      <td>23.023208</td>\n",
       "      <td>0.167421</td>\n",
       "      <td>0.324164</td>\n",
       "      <td>0.556626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>68988</td>\n",
       "      <td>13.433975</td>\n",
       "      <td>14.966295</td>\n",
       "      <td>17.815117</td>\n",
       "      <td>13.151161</td>\n",
       "      <td>0.372318</td>\n",
       "      <td>0.453138</td>\n",
       "      <td>0.587606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>7924</td>\n",
       "      <td>0.017148</td>\n",
       "      <td>0.019180</td>\n",
       "      <td>0.021307</td>\n",
       "      <td>0.153170</td>\n",
       "      <td>0.066189</td>\n",
       "      <td>0.168675</td>\n",
       "      <td>0.271601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>86728</td>\n",
       "      <td>0.024338</td>\n",
       "      <td>0.028879</td>\n",
       "      <td>0.033507</td>\n",
       "      <td>0.198213</td>\n",
       "      <td>0.038885</td>\n",
       "      <td>0.132257</td>\n",
       "      <td>0.288696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>90156</td>\n",
       "      <td>0.031036</td>\n",
       "      <td>0.037047</td>\n",
       "      <td>0.043210</td>\n",
       "      <td>0.250846</td>\n",
       "      <td>0.046611</td>\n",
       "      <td>0.155711</td>\n",
       "      <td>0.331646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>95128</td>\n",
       "      <td>1.339077</td>\n",
       "      <td>1.514869</td>\n",
       "      <td>1.738369</td>\n",
       "      <td>13.782922</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.383690</td>\n",
       "      <td>0.547707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>95735</td>\n",
       "      <td>0.048441</td>\n",
       "      <td>0.056769</td>\n",
       "      <td>0.065833</td>\n",
       "      <td>3.104151</td>\n",
       "      <td>0.042478</td>\n",
       "      <td>0.137213</td>\n",
       "      <td>0.296278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>97101</td>\n",
       "      <td>0.028384</td>\n",
       "      <td>0.032219</td>\n",
       "      <td>0.036473</td>\n",
       "      <td>0.240303</td>\n",
       "      <td>0.146085</td>\n",
       "      <td>0.276135</td>\n",
       "      <td>0.403782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>hip57050</td>\n",
       "      <td>0.230524</td>\n",
       "      <td>0.259932</td>\n",
       "      <td>0.289674</td>\n",
       "      <td>0.930202</td>\n",
       "      <td>0.100038</td>\n",
       "      <td>0.266747</td>\n",
       "      <td>0.404906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>hip74995</td>\n",
       "      <td>0.013737</td>\n",
       "      <td>0.015934</td>\n",
       "      <td>0.018093</td>\n",
       "      <td>0.073581</td>\n",
       "      <td>0.037190</td>\n",
       "      <td>0.120737</td>\n",
       "      <td>0.238811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>hip83043</td>\n",
       "      <td>0.253872</td>\n",
       "      <td>0.275242</td>\n",
       "      <td>0.297103</td>\n",
       "      <td>1.132498</td>\n",
       "      <td>0.071290</td>\n",
       "      <td>0.174831</td>\n",
       "      <td>0.275399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>37124</td>\n",
       "      <td>0.622838</td>\n",
       "      <td>0.658914</td>\n",
       "      <td>0.692695</td>\n",
       "      <td>2.670433</td>\n",
       "      <td>0.054732</td>\n",
       "      <td>0.164653</td>\n",
       "      <td>0.284938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     hostname  mass_minus   mass_med  mass_plus   axis_med   e_minus  \\\n",
       "3      107148    0.052812   0.062632   0.072330   0.140635  0.180533   \n",
       "27     136925    0.885964   1.076818   1.281420   4.680605  0.367470   \n",
       "29     141004    0.038372   0.042840   0.047503   0.123790  0.059317   \n",
       "37     145675    4.811323   5.841370   7.203498  16.426313  0.303072   \n",
       "51     164922    0.012793   0.014872   0.016945   0.102329  0.055199   \n",
       "59     170469    0.482555   0.554716   0.629851   2.212173  0.048397   \n",
       "61     177830    0.089411   0.104277   0.120063   0.496229  0.389725   \n",
       "73     192310    0.039094   0.045073   0.051469   0.326237  0.047769   \n",
       "77     213472    2.885000   3.479000   4.553000  12.990000  0.444000   \n",
       "78     216520    0.028835   0.032621   0.036213   0.195377  0.067118   \n",
       "87     219134    0.009821   0.011232   0.012608   0.064659  0.051075   \n",
       "91      24040    0.174145   0.201119   0.228269   1.300008  0.032111   \n",
       "94     285968    0.024187   0.028531   0.032841   0.066490  0.053657   \n",
       "95      31253    0.383296   0.445807   0.508966   1.295621  0.275216   \n",
       "99      34445    0.144788   0.166367   0.188502   0.726717  0.073101   \n",
       "100     34445    0.100368   0.114473   0.129095   0.486722  0.148526   \n",
       "101     34445    0.055254   0.064727   0.075415   0.272246  0.179638   \n",
       "108      4203    2.441611   2.681533   3.667077   7.784331  0.100875   \n",
       "111     45184    0.033650   0.037449   0.041464   0.064122  0.083647   \n",
       "112     45184    0.028612   0.034463   0.040153   0.109533  0.246258   \n",
       "121     66428    9.784151  27.244769  48.999393  23.023208  0.167421   \n",
       "123     68988   13.433975  14.966295  17.815117  13.151161  0.372318   \n",
       "137      7924    0.017148   0.019180   0.021307   0.153170  0.066189   \n",
       "142     86728    0.024338   0.028879   0.033507   0.198213  0.038885   \n",
       "144     90156    0.031036   0.037047   0.043210   0.250846  0.046611   \n",
       "149     95128    1.339077   1.514869   1.738369  13.782922  0.229270   \n",
       "150     95735    0.048441   0.056769   0.065833   3.104151  0.042478   \n",
       "153     97101    0.028384   0.032219   0.036473   0.240303  0.146085   \n",
       "166  hip57050    0.230524   0.259932   0.289674   0.930202  0.100038   \n",
       "169  hip74995    0.013737   0.015934   0.018093   0.073581  0.037190   \n",
       "170  hip83043    0.253872   0.275242   0.297103   1.132498  0.071290   \n",
       "176     37124    0.622838   0.658914   0.692695   2.670433  0.054732   \n",
       "\n",
       "        e_med    e_plus  \n",
       "3    0.335719  0.470649  \n",
       "27   0.680105  0.750475  \n",
       "29   0.163534  0.273011  \n",
       "37   0.452164  0.619581  \n",
       "51   0.180095  0.349462  \n",
       "59   0.154503  0.312677  \n",
       "61   0.539209  0.660149  \n",
       "73   0.141149  0.262648  \n",
       "77   0.529000  0.649000  \n",
       "78   0.187106  0.314167  \n",
       "87   0.159784  0.283953  \n",
       "91   0.111044  0.233157  \n",
       "94   0.163448  0.299084  \n",
       "95   0.440430  0.559104  \n",
       "99   0.174406  0.280003  \n",
       "100  0.257892  0.379580  \n",
       "101  0.354213  0.544104  \n",
       "108  0.190164  0.480939  \n",
       "111  0.184753  0.284891  \n",
       "112  0.484646  0.664791  \n",
       "121  0.324164  0.556626  \n",
       "123  0.453138  0.587606  \n",
       "137  0.168675  0.271601  \n",
       "142  0.132257  0.288696  \n",
       "144  0.155711  0.331646  \n",
       "149  0.383690  0.547707  \n",
       "150  0.137213  0.296278  \n",
       "153  0.276135  0.403782  \n",
       "166  0.266747  0.404906  \n",
       "169  0.120737  0.238811  \n",
       "170  0.174831  0.275399  \n",
       "176  0.164653  0.284938  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#planets.query('e > 0.6')[['hostname', 'mass_med', 'e_minus', 'e_med', 'e_plus']]\n",
    "#planets.query('axis_med > 10')[['hostname', 'mass_minus', 'mass_med', 'mass_plus', \n",
    "#                                'axis_med', 'e_minus', 'e_med', 'e_plus']]\n",
    "planets.query('e_plus - e_minus > 0.2')[['hostname', 'mass_minus', 'mass_med', 'mass_plus', \n",
    "                                         'axis_med', 'e_minus', 'e_med', 'e_plus']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make observational stats histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Nobs', 'Nobs_CORAVEL', 'Nobs_apf', 'Nobs_j', 'Nobs_k',\n",
       "       'Nobs_lick', 'Nobs_mcdonald', 'baseline', 'baseline_CORAVEL',\n",
       "       'baseline_apf', 'baseline_j', 'baseline_k', 'baseline_lick',\n",
       "       'baseline_mcdonald', 'med_err_CORAVEL', 'med_err_apf', 'med_err_j',\n",
       "       'med_err_k', 'med_err_lick', 'med_err_mcdonald', 'name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_stats = pd.read_csv('legacy_tables/observation_stats.csv')\n",
    "obs_stats = obs_stats.query('baseline > 2922 and Nobs > 17')\n",
    "obs_stats.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59210.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(obs_stats.Nobs_apf) + np.sum(obs_stats.Nobs_j) + \\\n",
    "    np.sum(obs_stats.Nobs_k) + np.sum(obs_stats.Nobs_lick)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 44.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "logobs   = np.log10(obs_stats.Nobs)\n",
    "baseyear = obs_stats.baseline/365.25\n",
    "#extent=(8.5, 35, 16, 630)\n",
    "minobs  = np.amin(logobs)\n",
    "maxobs  = np.amax(logobs)\n",
    "minbase = np.amin(baseyear)\n",
    "maxbase = np.amax(baseyear)\n",
    "hard_extent = (minbase-2, maxbase+2, minobs-0.07, maxobs+0.06)\n",
    "\n",
    "hb = ax.hexbin(baseyear, logobs, gridsize=15, bins='log', \n",
    "               extent=hard_extent, vmin=1, vmax=44,\n",
    "               cmap='inferno', mincnt=1)\n",
    "print(np.amin(hb.get_array()), np.amax(hb.get_array()))\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 12})\n",
    "ax.set_title(\"All stars\")\n",
    "ax.set_xlabel('Baseline (yr)')\n",
    "ax.set_ylabel(r'N$_\\mathrm{obs}$')\n",
    "\n",
    "median_base = np.median(baseyear)\n",
    "median_obs  = np.log10(np.median(obs_stats.Nobs))\n",
    "ax.scatter([median_base], [median_obs], color='white', \n",
    "           s=500, alpha='0.6')\n",
    "\n",
    "# Manually set Nobs logarithmic tick marks.\n",
    "\n",
    "def format_fn(tick_val, tick_pos):\n",
    "    return str(int(10**tick_val))\n",
    "\n",
    "#ax.locator_params(axis='y', nbins=1)\n",
    "ax.yaxis.set_major_formatter(FuncFormatter(format_fn))\n",
    "ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "\n",
    "formatter = LogFormatter(10, labelOnlyBase=False) \n",
    "cb = fig.colorbar(hb, ax=ax, format=formatter)\n",
    "cb.set_label('Number of stars')\n",
    "fig.savefig('legacy_tables/hist_base_nobs_2D.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make same plot for stars with planets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "plnames = np.unique(objects_good.hostname)\n",
    "obs_known = obs_stats.loc[obs_stats['name'].isin(plnames)]\n",
    "obs_none = obs_stats.loc[~obs_stats['name'].isin(plnames)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "156"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(obs_known)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "hb = ax.hexbin(obs_known.baseline/365.25, np.log10(obs_known.Nobs), \n",
    "               extent=hard_extent, vmin=1, vmax=44,\n",
    "               gridsize=15, bins='log', \n",
    "               cmap='inferno', mincnt=1)\n",
    "ax.set_title(\"Stars with detections\")\n",
    "ax.set_xlabel('Baseline (yr)')\n",
    "ax.set_ylabel(r'N$_\\mathrm{obs}$')\n",
    "\n",
    "median_base = np.median(obs_known.baseline/365.25)\n",
    "median_obs  = np.log10(np.median(obs_known.Nobs))\n",
    "ax.scatter([median_base], [median_obs], color='white', \n",
    "           s=500, alpha='0.6')\n",
    "\n",
    "# Manually set Nobs logarithmic tick marks.\n",
    "def format_fn(tick_val, tick_pos):\n",
    "    return str(int(10**tick_val))\n",
    "ax.yaxis.set_major_formatter(FuncFormatter(format_fn))\n",
    "ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "\n",
    "# Make a colorbar.\n",
    "formatter = LogFormatter(10, labelOnlyBase=True, minor_thresholds=(0, 0))#, style='plain') \n",
    "cb        = fig.colorbar(hb, ax=ax, format=formatter)#ticks=[1, 10], \n",
    "#cb.ax.set_yticklabels(['1', '10'])\n",
    "#cb.set_ticks([1,2,3,4,6])\n",
    "#cb.set_ticklabels(['1', '2', '3', '4', '6'])\n",
    "cb.set_label('Number of stars')\n",
    "\n",
    "fig.savefig('legacy_tables/hist_base_nobs_2D_planets.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47.0 76.5 40.0\n"
     ]
    }
   ],
   "source": [
    "print(np.median(obs_stats.Nobs), np.median(obs_known.Nobs), np.median(obs_none.Nobs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "hb = ax.hexbin(obs_none.baseline/365.25, np.log10(obs_none.Nobs), \n",
    "               extent=hard_extent, vmin=1, vmax=44,\n",
    "               gridsize=15, bins='log', \n",
    "               cmap='inferno', mincnt=1)\n",
    "ax.set_title(\"Stars without detections\")\n",
    "ax.set_xlabel('Baseline (yr)')\n",
    "ax.set_ylabel(r'N$_\\mathrm{obs}$')\n",
    "\n",
    "median_base = np.median(obs_none.baseline/365.25)\n",
    "median_obs  = np.log10(np.median(obs_none.Nobs))\n",
    "ax.scatter([median_base], [median_obs], color='white', \n",
    "           s=500, alpha='0.6')\n",
    "\n",
    "# Manually set Nobs logarithmic tick marks.\n",
    "def format_fn(tick_val, tick_pos):\n",
    "    return str(int(10**tick_val))\n",
    "ax.yaxis.set_major_formatter(FuncFormatter(format_fn))\n",
    "ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "\n",
    "formatter = LogFormatter(10, labelOnlyBase=False) \n",
    "cb = fig.colorbar(hb, ax=ax, format=formatter)\n",
    "cb.set_label('Number of stars')\n",
    "fig.savefig('legacy_tables/hist_base_nobs_2D_none.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine mass and period distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(np.log10(masses_planet.query('mass < 15')['mass']), \n",
    "        bins=10, density=False, histtype='step',# range = (0, 1), \n",
    "        lw=3, color='black', alpha=1, label='All Planets')\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "ax.set(xlabel=r'$M\\mathrm{sin}i/M_J$', \n",
    "          ylabel='Number density')\n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "#ax.set_title(r'$M$sin$i$ > 0.1 $M_J$, a > 0.1 AU')\n",
    "        \n",
    "def format_fn(tick_val, tick_pos):\n",
    "    return str(10**tick_val)\n",
    "    #return str(int(10**tick_val))\n",
    "ax.xaxis.set_major_formatter(FuncFormatter(format_fn))\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))   \n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "ax.legend(loc=2)\n",
    "\n",
    "fig.savefig('planet_candidates/mass_histogram_all.png', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "masses_close = masses_planet.query('axis < 1 and mass < 15')\n",
    "masses_mid   = masses_planet.query('axis > 1 and axis < 3 and mass < 15')\n",
    "masses_far   = masses_planet.query('axis > 3 and mass < 15')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "mclose = masses_close.mass\n",
    "#thresh_close = np.log10(0.075) #50% completeness threshold\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(np.log10(mclose), bins=10, density=True, histtype='step',\n",
    "        lw=3, color='black', alpha=1, label='a < 1 AU')\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "ax.set(xlabel=r'$M\\mathrm{sin}i\\ (M_J)$', \n",
    "          ylabel='Number density')\n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "#ax.set_title(r'$M$sin$i$ > 0.1 $M_J$, a > 0.1 AU')\n",
    "        \n",
    "def format_fn(tick_val, tick_pos):\n",
    "    return str(10**tick_val)\n",
    "    #return str(int(10**tick_val))\n",
    "ax.xaxis.set_major_formatter(FuncFormatter(format_fn))\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))   \n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "ax.legend(loc=1)\n",
    "\n",
    "fig.savefig('planet_candidates/close_histogram.png', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "mmid = masses_mid.mass\n",
    "thresh_mid = np.log10(0.15) #50% completeness threshold\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(np.log10(mmid), bins=10, density=True, histtype='step',\n",
    "        lw=3, color='black', alpha=1, label='1 AU < a < 3 AU')\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "ax.set(xlabel=r'$M\\mathrm{sin}i\\ (M_J)$', \n",
    "          ylabel='Number density')\n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "        \n",
    "def format_fn(tick_val, tick_pos):\n",
    "    return str(10**tick_val)\n",
    "    #return str(int(10**tick_val))\n",
    "ax.xaxis.set_major_formatter(FuncFormatter(format_fn))\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))   \n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "ax.legend(loc=2)\n",
    "\n",
    "fig.savefig('planet_candidates/mid_histogram.png', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "mfar = masses_far.mass\n",
    "thresh_far = np.log10(1.2) #50% completeness threshold\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(np.log10(mfar), bins=10, density=True, histtype='step',\n",
    "        lw=3, color='black', alpha=1, label='a > 3 AU')\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "ax.set(xlabel=r'$M\\mathrm{sin}i\\ (M_J)$', \n",
    "          ylabel='Number density')\n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "        \n",
    "def format_fn(tick_val, tick_pos):\n",
    "    return str(10**tick_val)\n",
    "    #return str(int(10**tick_val))\n",
    "ax.xaxis.set_major_formatter(FuncFormatter(format_fn))\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))   \n",
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "ax.legend(loc=2)\n",
    "\n",
    "fig.savefig('planet_candidates/far_histogram.png', dpi=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make distributions of periods and period ratios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(np.log10(masses_planet.per), bins=10, density=True, histtype='step',\n",
    "        lw=3, color='black', alpha=1)#, label='1 AU < a < 3 AU')\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 18})\n",
    "ax.set(xlabel=r'$P$ (days)', \n",
    "          ylabel='Number density')\n",
    "matplotlib.rcParams.update({'font.size': 18})\n",
    "        \n",
    "def format_fn(tick_val, tick_pos):\n",
    "    #return str(10**tick_val)\n",
    "    return str(int(10**tick_val))\n",
    "ax.xaxis.set_major_formatter(FuncFormatter(format_fn))\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))   \n",
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "#ax.legend(loc=2)\n",
    "\n",
    "fig.savefig('planet_candidates/per_histogram.png', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def period_ratios(pers):\n",
    "    ratios = []\n",
    "    nplanets = len(pers)\n",
    "    sorted = np.sort(pers)\n",
    "    for i in np.arange(nplanets):\n",
    "        for j in np.arange(i):\n",
    "            ratios.append(sorted[i]/sorted[j])\n",
    "    return ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "systems = masses_planet.hostname.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_ratios = []\n",
    "for system in systems:\n",
    "    sys_db = masses_planet.query('hostname == \"{}\"'.format(system))#    .loc[masses_planet.hostname == system]\n",
    "    if len(sys_db) > 1:\n",
    "        sys_ratios = period_ratios(sys_db.per)\n",
    "        all_ratios.append(sys_ratios)\n",
    "all_ratios = np.concatenate(all_ratios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Only use ratios below 20.\n",
    "filtered_ratios = np.extract(all_ratios < 20, all_ratios)\n",
    "axper = sns.distplot(filtered_ratios, rug=True)\n",
    "axper.set_xlim([0,25])\n",
    "axper.set(xlabel='Period ratio', ylabel='Number density')\n",
    "axper.set_title('System period ratios (< 20)')\n",
    "\n",
    "figper = axper.get_figure()\n",
    "figper.savefig('planet_candidates/period_ratio_histogram.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do some preliminary work on giant multis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "planets_big_all   = masses_planet.query('mass > 0.1')\n",
    "planets_small_all = masses_planet.query('mass <= 0.1')\n",
    "\n",
    "names_big_all = np.unique(planets_big_all.hostname)\n",
    "nplanets_big_all = np.array(planets_big_all.groupby('hostname').count()['status'])\n",
    "db_nplanets_big_all = pd.DataFrame({'hostname':names_big_all, 'nplanets':nplanets_big_all})\n",
    "\n",
    "names_small_all = np.unique(planets_small_all.hostname)\n",
    "nplanets_small_all = np.array(planets_small_all.groupby('hostname').count()['status'])\n",
    "db_nplanets_small_all = pd.DataFrame({'hostname':names_small_all, 'nplanets':nplanets_small_all})\n",
    "\n",
    "# Do same nplanets thingy for all planets, no mass cuts.\n",
    "names_all = np.unique(masses_planet.hostname)\n",
    "nplanets_all = np.array(masses_planet.groupby('hostname').count()['status'])\n",
    "db_nplanets_all = pd.DataFrame({'hostname':names_all, 'nplanets':nplanets_all})\n",
    "\n",
    "planets_all       = pd.merge(masses_planet, db_nplanets_all, on='hostname').reset_index()\n",
    "planets_big_all   = pd.merge(planets_big_all, db_nplanets_big_all, on='hostname').reset_index()\n",
    "planets_small_all = pd.merge(planets_small_all, db_nplanets_small_all, on='hostname').reset_index()\n",
    "\n",
    "db_nplanets_all       = None\n",
    "db_nplanets_big_all   = None\n",
    "db_nplanets_small_all = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "planets_big_multi_all = planets_big_all.query('nplanets > 1')\n",
    "planets_multi_all     = planets_all.query('nplanets > 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>hostname</th>\n",
       "      <th>status</th>\n",
       "      <th>mass</th>\n",
       "      <th>mass_med</th>\n",
       "      <th>mass_minus</th>\n",
       "      <th>mass_plus</th>\n",
       "      <th>axis</th>\n",
       "      <th>axis_med</th>\n",
       "      <th>...</th>\n",
       "      <th>per_plus</th>\n",
       "      <th>k</th>\n",
       "      <th>k_med</th>\n",
       "      <th>k_minus</th>\n",
       "      <th>k_plus</th>\n",
       "      <th>e</th>\n",
       "      <th>e_med</th>\n",
       "      <th>e_minus</th>\n",
       "      <th>e_plus</th>\n",
       "      <th>nplanets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>105</td>\n",
       "      <td>187123</td>\n",
       "      <td>K</td>\n",
       "      <td>0.506373</td>\n",
       "      <td>0.506559</td>\n",
       "      <td>0.486685</td>\n",
       "      <td>0.526043</td>\n",
       "      <td>0.042075</td>\n",
       "      <td>0.042076</td>\n",
       "      <td>...</td>\n",
       "      <td>3.096599</td>\n",
       "      <td>68.949742</td>\n",
       "      <td>68.975676</td>\n",
       "      <td>68.706070</td>\n",
       "      <td>69.243043</td>\n",
       "      <td>0.004495</td>\n",
       "      <td>0.003601</td>\n",
       "      <td>0.001081</td>\n",
       "      <td>0.007340</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>140</td>\n",
       "      <td>217107</td>\n",
       "      <td>K</td>\n",
       "      <td>1.431095</td>\n",
       "      <td>1.431618</td>\n",
       "      <td>1.364445</td>\n",
       "      <td>1.496929</td>\n",
       "      <td>0.075125</td>\n",
       "      <td>0.075134</td>\n",
       "      <td>...</td>\n",
       "      <td>7.126882</td>\n",
       "      <td>141.846736</td>\n",
       "      <td>141.870364</td>\n",
       "      <td>141.604159</td>\n",
       "      <td>142.138018</td>\n",
       "      <td>0.128338</td>\n",
       "      <td>0.128082</td>\n",
       "      <td>0.126377</td>\n",
       "      <td>0.129803</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>113</td>\n",
       "      <td>249</td>\n",
       "      <td>9826</td>\n",
       "      <td>K</td>\n",
       "      <td>0.661731</td>\n",
       "      <td>0.661570</td>\n",
       "      <td>0.626295</td>\n",
       "      <td>0.696077</td>\n",
       "      <td>0.058421</td>\n",
       "      <td>0.058415</td>\n",
       "      <td>...</td>\n",
       "      <td>4.617128</td>\n",
       "      <td>69.685089</td>\n",
       "      <td>69.687528</td>\n",
       "      <td>68.988786</td>\n",
       "      <td>70.382609</td>\n",
       "      <td>0.006143</td>\n",
       "      <td>0.007490</td>\n",
       "      <td>0.002219</td>\n",
       "      <td>0.015544</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>131</td>\n",
       "      <td>300</td>\n",
       "      <td>68988</td>\n",
       "      <td>K</td>\n",
       "      <td>1.969198</td>\n",
       "      <td>1.970428</td>\n",
       "      <td>1.885562</td>\n",
       "      <td>2.053510</td>\n",
       "      <td>0.071213</td>\n",
       "      <td>0.071212</td>\n",
       "      <td>...</td>\n",
       "      <td>6.276428</td>\n",
       "      <td>192.104859</td>\n",
       "      <td>192.214938</td>\n",
       "      <td>191.571474</td>\n",
       "      <td>192.855609</td>\n",
       "      <td>0.157166</td>\n",
       "      <td>0.156755</td>\n",
       "      <td>0.153332</td>\n",
       "      <td>0.160005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  Unnamed: 0 hostname status      mass  mass_med  mass_minus  \\\n",
       "53      53         105   187123      K  0.506373  0.506559    0.486685   \n",
       "65      65         140   217107      K  1.431095  1.431618    1.364445   \n",
       "113    113         249     9826      K  0.661731  0.661570    0.626295   \n",
       "131    131         300    68988      K  1.969198  1.970428    1.885562   \n",
       "\n",
       "     mass_plus      axis  axis_med  ...  per_plus           k       k_med  \\\n",
       "53    0.526043  0.042075  0.042076  ...  3.096599   68.949742   68.975676   \n",
       "65    1.496929  0.075125  0.075134  ...  7.126882  141.846736  141.870364   \n",
       "113   0.696077  0.058421  0.058415  ...  4.617128   69.685089   69.687528   \n",
       "131   2.053510  0.071213  0.071212  ...  6.276428  192.104859  192.214938   \n",
       "\n",
       "        k_minus      k_plus         e     e_med   e_minus    e_plus  nplanets  \n",
       "53    68.706070   69.243043  0.004495  0.003601  0.001081  0.007340         2  \n",
       "65   141.604159  142.138018  0.128338  0.128082  0.126377  0.129803         2  \n",
       "113   68.988786   70.382609  0.006143  0.007490  0.002219  0.015544         3  \n",
       "131  191.571474  192.855609  0.157166  0.156755  0.153332  0.160005         2  \n",
       "\n",
       "[4 rows x 25 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets_big_multi_all.query('axis < 0.1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>hostname</th>\n",
       "      <th>status</th>\n",
       "      <th>mass</th>\n",
       "      <th>mass_med</th>\n",
       "      <th>mass_minus</th>\n",
       "      <th>mass_plus</th>\n",
       "      <th>axis</th>\n",
       "      <th>axis_med</th>\n",
       "      <th>...</th>\n",
       "      <th>per_plus</th>\n",
       "      <th>k</th>\n",
       "      <th>k_med</th>\n",
       "      <th>k_minus</th>\n",
       "      <th>k_plus</th>\n",
       "      <th>e</th>\n",
       "      <th>e_med</th>\n",
       "      <th>e_minus</th>\n",
       "      <th>e_plus</th>\n",
       "      <th>nplanets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>132</td>\n",
       "      <td>301</td>\n",
       "      <td>68988</td>\n",
       "      <td>K</td>\n",
       "      <td>16.389258</td>\n",
       "      <td>17.617583</td>\n",
       "      <td>15.124601</td>\n",
       "      <td>19.992019</td>\n",
       "      <td>14.353306</td>\n",
       "      <td>16.508762</td>\n",
       "      <td>...</td>\n",
       "      <td>32532.222445</td>\n",
       "      <td>126.453866</td>\n",
       "      <td>132.159067</td>\n",
       "      <td>120.845171</td>\n",
       "      <td>141.813569</td>\n",
       "      <td>0.475855</td>\n",
       "      <td>0.533665</td>\n",
       "      <td>0.424231</td>\n",
       "      <td>0.623832</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  Unnamed: 0 hostname status       mass   mass_med  mass_minus  \\\n",
       "132    132         301    68988      K  16.389258  17.617583   15.124601   \n",
       "\n",
       "     mass_plus       axis   axis_med  ...      per_plus           k  \\\n",
       "132  19.992019  14.353306  16.508762  ...  32532.222445  126.453866   \n",
       "\n",
       "          k_med     k_minus      k_plus         e     e_med   e_minus  \\\n",
       "132  132.159067  120.845171  141.813569  0.475855  0.533665  0.424231   \n",
       "\n",
       "       e_plus  nplanets  \n",
       "132  0.623832         2  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets_big_multi_all.query('hostname in [\"187123\", \"217107\", \"9826\", \"68988\"] and axis > 10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P(HJ | Outer > 10 AU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      145675\n",
       "89      50499\n",
       "94      66428\n",
       "132     68988\n",
       "Name: hostname, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets_outer = planets_big_multi_all.query('axis > 10')\n",
    "planets_outer.hostname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25 +\\- 0.25\n"
     ]
    }
   ],
   "source": [
    "prob_outer = len(planets_big_multi_all.query('hostname in @planets_outer.hostname \\\n",
    "                 and axis < 0.1'))/float(len(planets_outer))\n",
    "sig_outer = np.sqrt(float(len(planets_big_multi_all.query('hostname in @planets_outer.hostname \\\n",
    "                    and axis < 0.1')))) / len(planets_outer)\n",
    "print(prob_outer, '+\\-', sig_outer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P(HJ | 10 AU > Outer > 4.64 AU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15        114783\n",
       "26        134987\n",
       "37        156279\n",
       "51        183263\n",
       "66        217107\n",
       "71         24040\n",
       "83          4203\n",
       "99         75732\n",
       "107        92788\n",
       "121        gl317\n",
       "123    hip109388\n",
       "Name: hostname, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets_med = planets_big_multi_all.query('axis < 10 and axis > 4.64')\n",
    "planets_med.hostname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09090909090909091 +\\- 0.09090909090909091\n"
     ]
    }
   ],
   "source": [
    "prob_med = len(planets_big_multi_all.query('hostname in @planets_med.hostname \\\n",
    "               and axis < 0.1'))/float(len(planets_med))\n",
    "sig_med = np.sqrt(len(planets_big_multi_all.query('hostname in @planets_med.hostname \\\n",
    "                  and axis < 0.1')))/float(len(planets_med))\n",
    "print(prob_med, '+\\-', sig_med)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P(HJ | 4.64 AU > Outer > 2.15 AU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         145675\n",
       "10        108874\n",
       "20         12661\n",
       "32        141399\n",
       "43        168443\n",
       "46        169830\n",
       "54        187123\n",
       "79         38529\n",
       "88         50499\n",
       "92         52265\n",
       "93         66428\n",
       "96         74156\n",
       "111        95128\n",
       "115         9826\n",
       "122    hip109388\n",
       "130        37124\n",
       "Name: hostname, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets_inner = planets_big_multi_all.query('axis < 4.64 and axis > 2.15')\n",
    "planets_inner.hostname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.18181818181818182 +\\- 0.128564869306645\n"
     ]
    }
   ],
   "source": [
    "prob_inner = len(planets_big_multi_all.query('hostname in @planets_inner.hostname \\\n",
    "                 and axis < 0.1'))/float(len(planets_med))\n",
    "sig_inner = np.sqrt(len(planets_big_multi_all.query('hostname in @planets_inner.hostname \\\n",
    "                 and axis < 0.1')))/float(len(planets_med))\n",
    "print(prob_inner, '+\\-', sig_inner)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hot Jupiter Occurrence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 0.019498607242339833 +/- 0.00520397411234206\n"
     ]
    }
   ],
   "source": [
    "hot_jupiters = planets.query('per < 10 and mass > 0.1 and hostname != \"102195\"')\n",
    "nhot = len(hot_jupiters)\n",
    "nstars = len(specmatch)\n",
    "print(nhot, nhot/(nstars - 1), '+/-', np.sqrt(nhot)/nstars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nhot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wright et al. finds 0.0120 +/- 0.0038. We find more Hot Jupiters, but our results are statistically consistent within ~1.5 sigma."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot completeness contours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumming_20 = pd.read_csv('completeness_contours/cumming_20.csv', names=['logP', 'logM'])\n",
    "cumming_80 = pd.read_csv('completeness_contours/cumming_80.csv', names=['logP', 'logM'])\n",
    "mayor_20   = pd.read_csv('completeness_contours/mayor_20.csv', names=['logP', 'logM'])\n",
    "mayor_80   = pd.read_csv('completeness_contours/mayor_80.csv', names=['logP', 'logM'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>logP</th>\n",
       "      <th>logM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.068881</td>\n",
       "      <td>-1.452504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.142681</td>\n",
       "      <td>-1.435233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.211562</td>\n",
       "      <td>-1.400691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.275523</td>\n",
       "      <td>-1.369603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.344403</td>\n",
       "      <td>-1.359240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>3.842558</td>\n",
       "      <td>0.257340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>3.891759</td>\n",
       "      <td>0.326425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>3.916359</td>\n",
       "      <td>0.392055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>3.950800</td>\n",
       "      <td>0.454231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>3.985240</td>\n",
       "      <td>0.530225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        logP      logM\n",
       "0   0.068881 -1.452504\n",
       "1   0.142681 -1.435233\n",
       "2   0.211562 -1.400691\n",
       "3   0.275523 -1.369603\n",
       "4   0.344403 -1.359240\n",
       "..       ...       ...\n",
       "69  3.842558  0.257340\n",
       "70  3.891759  0.326425\n",
       "71  3.916359  0.392055\n",
       "72  3.950800  0.454231\n",
       "73  3.985240  0.530225\n",
       "\n",
       "[74 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumming_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(10**cumming_20.logP, 10**cumming_20.logM*317.8)\n",
    "ax.plot(10**mayor_20.logP, 10**mayor_20.logM)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}