MVA_thesis_plots.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_sgc =pd.read_fwf('mva_csg150_50pc_injscale.txt',index_col=0)\n",
    "mva_sgc = mva_sgc.drop('170817')\n",
    "mva_sgc = mva_sgc.drop('E264930')\n",
    "mva_adi =pd.read_fwf('mva_adi-a_50pc_injscale.txt',index_col=0)\n",
    "mva_adi = mva_adi.drop('170817')\n",
    "mva_adi = mva_adi.drop('E264930')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_sgc =pd.read_fwf('x_csg150_50pc_injscale.txt',index_col=0)\n",
    "x_sgc = x_sgc.drop('E264930')\n",
    "x_adi =pd.read_fwf('x_adi-a_50pc_injscale.txt',index_col=0)\n",
    "x_adi = x_adi.drop('E264930')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_df = pd.concat([x_adi, mva_adi],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fc4a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plot_df.plot(kind='bar')\n",
    "plot.set_ylabel(\"Injection 50% UL for ADI-a\")\n",
    "plot.legend(bbox_to_anchor=(1.1, 1.1))\n",
    "fig = plot.get_figure()\n",
    "\n",
    "#fig.savefig(fd+\"/\"+args.page_name[0]+\".png\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_df = pd.concat([x_sgc, mva_sgc],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fa91198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plot_df.plot(kind='bar')\n",
    "plot.set_ylabel(\"Injection 50% UL for CSG\")\n",
    "plot.legend(bbox_to_anchor=(1.1, 1.1))\n",
    "fig = plot.get_figure()\n",
    "\n",
    "#fig.savefig(fd+\"/\"+args.page_name[0]+\".png\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X-pipeline    0.945129\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_sgc.median(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MVA    0.723649\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mva_sgc.median(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X-pipeline    0.308376\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_adi.median(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MVA    0.238171\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mva_adi.median(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgchrss =pd.read_fwf('csg_hrss90.txt',index_col=0)\n",
    "sgchrss = sgchrss.drop('170817')\n",
    "sgchrss = sgchrss.drop('E264930')\n",
    "adia90 =pd.read_fwf('adi_a_injscale90.txt',index_col=0)\n",
    "adia90 = adia90.drop('170817')\n",
    "adia90 = adia90.drop('E264930')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.constants as const\n",
    "from astropy.constants import M_sun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "f0 = 150\n",
    "c=const.c\n",
    "pi=const.pi\n",
    "E = 0.01*M_sun*c**2\n",
    "G=const.G\n",
    "A=5/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = np.sqrt(A*E*G/(pi**2*c**3*f0**2*sgchrss**2))/3.086e+22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = np.sort([dist[i][0].value for i in range(len(dist))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "csg_dist = dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "adi_dist = np.array(20/adia90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "adi_dist = np.sort([adi_dist[i][0] for i in range(len(adi_dist))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([32.59393981, 38.05522574, 45.90346043, 47.58041685, 48.70683357,\n",
       "       56.23727632, 65.62799428, 65.99244387, 66.65933414, 76.63481213,\n",
       "       85.76145451, 95.57305604, 97.02708996])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adi_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 47.59303495,  52.33835832,  52.75246687,  56.84335099,\n",
       "        73.52027991,  74.77974944,  76.98814457,  77.19361081,\n",
       "        82.14114943, 108.29630939, 109.16804297, 112.83887208,\n",
       "       117.79339311])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csg_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([32.59393981, 38.05522574, 45.90346043, 47.58041685, 48.70683357,\n",
       "       56.23727632, 65.62799428, 65.99244387, 66.65933414, 76.63481213,\n",
       "       85.76145451, 95.57305604, 97.02708996])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adi_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "adi_dist = np.append(adi_dist,max(csg_dist)*5)\n",
    "csg_dist = np.append(csg_dist,max(csg_dist)*5)\n",
    "#sorted_nsbh_align_ex.append(max(sorted_nsbh_align_ex)*5)\n",
    "hist_xlim=(adi_dist[0]/2.5, csg_dist[-2]*2.5)\n",
    "#hist_xlim=(adi_dist[0]/2.5, sorted_nsbh_align_ex[-2]*2.5)\n",
    "\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "matplotlib.rcParams['font.family'] = 'serif'\n",
    "matplotlib.rcParams['font.serif'] = 'Computer Modern Roman'\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\usepackage{amsmath}']\n",
    "matplotlib.rcParams.update({'font.size': 26})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120554ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Make plot\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.gca()\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlim(hist_xlim)\n",
    "ax.set_ylim([0,len(csg_dist)-1])\n",
    "ax.hist(adi_dist,bins=len(adi_dist)*10,label='ADI-a',linewidth=5.,cumulative=True,histtype='step')\n",
    "ax.hist(csg_dist,bins=len(csg_dist)*10,label='CSG 150 Hz',linewidth=5.,cumulative=True,histtype='step')\n",
    "\n",
    "# Make legend lines not box\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "new_handles = [matplotlib.lines.Line2D([], [], c=h.get_edgecolor(),\n",
    "                                       linewidth=5.) for h in handles]\n",
    "ax.legend(handles=new_handles, labels=labels, loc='upper left', fontsize=22)\n",
    "\n",
    "#plt.legend(loc='best')\n",
    "ax.set_xlabel('Exclusion Distance (Mpc)')\n",
    "ax.set_ylabel('Number of GRBs')\n",
    "ax.grid()\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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