mva_inj_scale.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from collections import Counter\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mva_sgc70 =pd.read_fwf('mva_csg70_50pc_injscale.txt',index_col=0).drop('170817').drop('E264930').rename(index=str,columns={'MVA' : 'sgc70'})\n",
    "mva_sgc100 =pd.read_fwf('mva_csg100_50pc_injscale.txt',index_col=0).drop('170817').drop('E264930').rename(index=str,columns={'MVA' : 'sgc100'})\n",
    "mva_sgc150 =pd.read_fwf('mva_csg150_50pc_injscale.txt',index_col=0).drop('170817').drop('E264930').rename(index=str,columns={'MVA' : 'sgc150'})\n",
    "mva_sgc300 =pd.read_fwf('mva_csg300_50pc_injscale.txt',index_col=0).drop('170817').drop('E264930').rename(index=str,columns={'MVA' : 'sgc300'})\n",
    "mva_adia =pd.read_fwf('mva_adi-a_50pc_injscale.txt',index_col=0).drop('170817').drop('E264930').rename(index=str,columns={'MVA' : 'adi-a'})\n",
    "mva_adib =pd.read_fwf('mva_adi-b_50pc_injscale.txt',index_col=0).drop('170817').drop('E264930').rename(index=str,columns={'MVA' : 'adi-b'})\n",
    "mva_adic =pd.read_fwf('mva_adi-c_50pc_injscale.txt',index_col=0).drop('170817').drop('E264930').rename(index=str,columns={'MVA' : 'adi-c'})\n",
    "mva_adid =pd.read_fwf('mva_adi-d_50pc_injscale.txt',index_col=0).drop('170817').drop('E264930').rename(index=str,columns={'MVA' : 'adi-d'})\n",
    "mva_adie =pd.read_fwf('mva_adi-e_50pc_injscale.txt',index_col=0).drop('170817').drop('E264930').rename(index=str,columns={'MVA' : 'adi-e'})\n",
    "mva_bns =pd.read_fwf('mva_bns_50pc_injscale.txt',index_col=0).drop('170817').drop('E264930').rename(index=str,columns={'MVA' : 'BNS'})\n",
    "mva_nsbh =pd.read_fwf('mva_nsbh_50pc_injscale.txt',index_col=0).drop('170817').drop('E264930').rename(index=str,columns={'MVA' : 'NSBH'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_sgc70 =pd.read_fwf('x_sgc70q9_50pc_injscale.txt',index_col=0).drop('E264930').rename(index=str,columns={'X-pipeline' : 'sgc70'})\n",
    "x_sgc100 =pd.read_fwf('x_sgc100q9_50pc_injscale.txt',index_col=0).drop('E264930').rename(index=str,columns={'X-pipeline' : 'sgc100'})\n",
    "x_sgc150 =pd.read_fwf('x_csg150_50pc_injscale.txt',index_col=0).drop('E264930').rename(index=str,columns={'X-pipeline' : 'sgc150'})\n",
    "x_sgc300 =pd.read_fwf('x_sgc300q9_50pc_injscale.txt',index_col=0).drop('E264930').rename(index=str,columns={'X-pipeline' : 'sgc300'})\n",
    "x_adia =pd.read_fwf('x_adi-a_50pc_injscale.txt',index_col=0).drop('E264930').rename(index=str,columns={'X-pipeline' : 'adi-a'})\n",
    "x_adib =pd.read_fwf('x_adi-b_50pc_injscale.txt',index_col=0).drop('E264930').rename(index=str,columns={'X-pipeline' : 'adi-b'})\n",
    "x_adic =pd.read_fwf('x_adi-c_50pc_injscale.txt',index_col=0).drop('E264930').rename(index=str,columns={'X-pipeline' : 'adi-c'})\n",
    "x_adid =pd.read_fwf('x_adi-d_50pc_injscale.txt',index_col=0).drop('E264930').rename(index=str,columns={'X-pipeline' : 'adi-d'})\n",
    "x_adie =pd.read_fwf('x_adi-e_50pc_injscale.txt',index_col=0).drop('E264930').rename(index=str,columns={'X-pipeline' : 'adi-e'})\n",
    "x_bns =pd.read_fwf('x_bns_50pc_injscale.txt',index_col=0).drop('E264930').rename(index=str,columns={'X-pipeline' : 'BNS'})\n",
    "x_nsbh =pd.read_fwf('x_nsbh_50pc_injscale.txt',index_col=0).drop('E264930').rename(index=str,columns={'X-pipeline' : 'NSBH'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mva_df = pd.concat([mva_sgc70, mva_sgc100, mva_sgc150, mva_sgc300, mva_adia,mva_adib,mva_adic,mva_adid,mva_adie,mva_bns,mva_nsbh],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>sgc70</th>\n",
       "      <th>sgc100</th>\n",
       "      <th>sgc150</th>\n",
       "      <th>sgc300</th>\n",
       "      <th>adi-a</th>\n",
       "      <th>adi-b</th>\n",
       "      <th>adi-c</th>\n",
       "      <th>adi-d</th>\n",
       "      <th>adi-e</th>\n",
       "      <th>BNS</th>\n",
       "      <th>NSBH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRB</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>E264842</th>\n",
       "      <td>1.14</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E265282</th>\n",
       "      <td>1.36</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E266096</th>\n",
       "      <td>1.40</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E269364</th>\n",
       "      <td>1.17</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E269496</th>\n",
       "      <td>1.28</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E269667</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E272290</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E272325</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E273104</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E273131</th>\n",
       "      <td>1.12</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E277236</th>\n",
       "      <td>1.33</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E278027</th>\n",
       "      <td>1.13</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E278188</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sgc70  sgc100  sgc150  sgc300  adi-a  adi-b  adi-c  adi-d  adi-e  \\\n",
       "GRB                                                                         \n",
       "E264842   1.14    0.88    0.78    0.87   0.26   0.16   0.18   0.52   0.18   \n",
       "E265282   1.36    1.04    0.95    1.06   0.32   0.18   0.23   0.64   0.24   \n",
       "E266096   1.40    1.08    0.92    1.06   0.31   0.18   0.24   0.63   0.23   \n",
       "E269364   1.17    0.88    0.82    0.85   0.27   0.17   0.19   0.56   0.19   \n",
       "E269496   1.28    0.87    0.80    0.84   0.26   0.16   0.19   0.56   0.19   \n",
       "E269667   1.10    0.76    0.61    0.67   0.21   0.13   0.16   0.43   0.15   \n",
       "E272290   0.83    0.59    0.55    0.58   0.18   0.11   0.13   0.37   0.13   \n",
       "E272325   0.84    0.63    0.55    0.59   0.18   0.11   0.13   0.38   0.13   \n",
       "E273104   0.65    0.51    0.42    0.51   0.15   0.08   0.11   0.30   0.11   \n",
       "E273131   1.12    0.81    0.72    0.79   0.23   0.13   0.17   0.48   0.18   \n",
       "E277236   1.33    1.09    0.87    0.98   0.28   0.18   0.22   0.63   0.23   \n",
       "E278027   1.13    0.81    0.71    0.79   0.24   0.13   0.17   0.52   0.18   \n",
       "E278188   0.72    0.54    0.49    0.54   0.17   0.09   0.12   0.33   0.12   \n",
       "\n",
       "          BNS  NSBH  \n",
       "GRB                  \n",
       "E264842  0.26  0.18  \n",
       "E265282  0.32  0.23  \n",
       "E266096  0.32  0.24  \n",
       "E269364  0.27  0.20  \n",
       "E269496  0.27  0.19  \n",
       "E269667  0.22  0.16  \n",
       "E272290  0.18  0.13  \n",
       "E272325  0.18  0.14  \n",
       "E273104  0.15  0.11  \n",
       "E273131  0.24  0.17  \n",
       "E277236  0.29  0.22  \n",
       "E278027  0.24  0.18  \n",
       "E278188  0.17  0.12  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mva_df.round(decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_df = pd.concat([x_sgc70, x_sgc100, x_sgc150, x_sgc300, x_adia,x_adib,x_adic,x_adid,x_adie,x_bns,x_nsbh],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>sgc70</th>\n",
       "      <th>sgc100</th>\n",
       "      <th>sgc150</th>\n",
       "      <th>sgc300</th>\n",
       "      <th>adi-a</th>\n",
       "      <th>adi-b</th>\n",
       "      <th>adi-c</th>\n",
       "      <th>adi-d</th>\n",
       "      <th>adi-e</th>\n",
       "      <th>BNS</th>\n",
       "      <th>NSBH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRB</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>E264842</th>\n",
       "      <td>1.36</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E265282</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E266096</th>\n",
       "      <td>1.61</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E269364</th>\n",
       "      <td>1.21</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E269496</th>\n",
       "      <td>1.37</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E269667</th>\n",
       "      <td>1.11</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E272290</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E272325</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E273104</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E273131</th>\n",
       "      <td>1.09</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E277236</th>\n",
       "      <td>1.81</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E278027</th>\n",
       "      <td>1.18</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E278188</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sgc70  sgc100  sgc150  sgc300  adi-a  adi-b  adi-c  adi-d  adi-e  \\\n",
       "GRB                                                                         \n",
       "E264842   1.36    0.97    0.86    0.94   0.28   0.17   0.21   0.64   0.20   \n",
       "E265282   1.42    1.11    0.97    1.07   0.31   0.19   0.23   0.64   0.23   \n",
       "E266096   1.61    1.11    0.95    1.06   0.33   0.19   0.25   0.66   0.24   \n",
       "E269364   1.21    0.93    0.99    0.84   0.80   0.17   0.20   0.57   0.19   \n",
       "E269496   1.37    0.89    0.96    0.83   0.31   0.16   0.19   0.56   0.20   \n",
       "E269667   1.11    0.73    0.85    0.65   0.31   0.12   0.16   0.45   0.15   \n",
       "E272290   0.86    0.58    0.66    0.58   0.22   0.11   0.13   0.38   0.13   \n",
       "E272325   0.85    0.64    0.70    0.61   0.28   0.11   0.13   0.39   0.13   \n",
       "E273104   0.67    0.49    0.60    0.48   0.20   0.08   0.11   0.30   0.11   \n",
       "E273131   1.09    0.81    0.95    0.81   0.31   0.13   0.17   0.49   0.17   \n",
       "E277236   1.81    1.41    1.82    1.34   0.45   0.21   0.26   0.80   0.26   \n",
       "E278027   1.18    0.80    0.95    0.79   0.30   0.14   0.18   0.51   0.17   \n",
       "E278188   0.73    0.55    0.64    0.54   0.21   0.09   0.12   0.33   0.12   \n",
       "\n",
       "          BNS  NSBH  \n",
       "GRB                  \n",
       "E264842  0.28  0.20  \n",
       "E265282  0.33  0.24  \n",
       "E266096  0.32  0.25  \n",
       "E269364  0.27  0.19  \n",
       "E269496  0.26  0.19  \n",
       "E269667  0.20  0.15  \n",
       "E272290  0.18  0.13  \n",
       "E272325  0.18  0.14  \n",
       "E273104  0.15  0.10  \n",
       "E273131  0.25  0.18  \n",
       "E277236  0.38  0.29  \n",
       "E278027  0.24  0.18  \n",
       "E278188  0.17  0.12  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df.round(decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sgc70     1.129500\n",
       "sgc100    0.814525\n",
       "sgc150    0.723649\n",
       "sgc300    0.793544\n",
       "adi-a     0.238171\n",
       "adi-b     0.133333\n",
       "adi-c     0.172770\n",
       "adi-d     0.518350\n",
       "adi-e     0.179514\n",
       "BNS       0.243904\n",
       "NSBH      0.177015\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mva_df.median(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sgc70     1.176600\n",
       "sgc100    0.812494\n",
       "sgc150    0.945129\n",
       "sgc300    0.812687\n",
       "adi-a     0.308376\n",
       "adi-b     0.135410\n",
       "adi-c     0.175219\n",
       "adi-d     0.510932\n",
       "adi-e     0.174373\n",
       "BNS       0.245654\n",
       "NSBH      0.177597\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df.median(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118166dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = pd.concat([x_df.median(axis=0),mva_df.median(axis=0)],axis=1,keys=['X-pipeline','MVA']).plot(kind='bar')\n",
    "plot.set_ylabel('Median 50% Injection Scale Upper Limit')\n",
    "fig = plot.get_figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>X-pipeline</th>\n",
       "      <th>MVA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sgc70</th>\n",
       "      <td>1.18</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sgc100</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sgc150</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sgc300</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adi-a</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adi-b</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adi-c</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adi-d</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adi-e</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BNS</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NSBH</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        X-pipeline   MVA\n",
       "sgc70         1.18  1.13\n",
       "sgc100        0.81  0.81\n",
       "sgc150        0.95  0.72\n",
       "sgc300        0.81  0.79\n",
       "adi-a         0.31  0.24\n",
       "adi-b         0.14  0.13\n",
       "adi-c         0.18  0.17\n",
       "adi-d         0.51  0.52\n",
       "adi-e         0.17  0.18\n",
       "BNS           0.25  0.24\n",
       "NSBH          0.18  0.18"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([x_df.median(axis=0),mva_df.median(axis=0)],axis=1,keys=['X-pipeline','MVA']).round(decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}