GalacticDNSMass

ResultsPlotting.ipynb (244220B)

---

 {
  "cells": [
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Samples found and loaded.\n"
      ]
     }
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import corner\n",
     "\n",
     "import pymultinest\n",
     "\n",
     "#Imports\n",
     "import numpy as np\n",
     "from scipy import stats\n",
     "from scipy.special import erf\n",
     "import json\n",
     "import os\n",
     "import sys\n",
     "import random\n",
     "\n",
     "from math import log10, floor\n",
     "\n",
     "\n",
     "### Top directory for the location of project files.\n",
     "topDirectory = '../'\n",
     "\n",
     "if not os.path.exists(topDirectory + \"Samples/pulsarSamples.npy\"):\n",
     "    print(\"Samples NOT found\")\n",
     "    sys.exit()\n",
     "else:\n",
     "    print(\"Samples found and loaded.\")\n",
     "\n",
     "### Load samples from files\n",
     "pulsarMassSamples = np.load(topDirectory + 'Samples/pulsarSamples.npy')\n",
     "companionMassSamples = np.load(topDirectory + 'Samples/companionSamples.npy')\n",
     "totalMassSamples = np.load(topDirectory + 'Samples/totalSamples.npy')\n",
     "bothMassSamples = np.load(topDirectory + 'Samples/bothSamples.npy')\n",
     "ratioSamples = np.load(topDirectory + 'Samples/ratioSamples.npy')\n",
     "chirpSamples = np.load(topDirectory + 'Samples/chirpSamples.npy')\n",
     "\n",
     "\n",
     "bothCorrSamples = np.load(topDirectory + 'Samples/bothCorrSamples.npy')\n",
     "\n",
     "### PDF functions\n",
     "# Single Gaussian Functions\n",
     "def evalSingleGaussian(theta, x):\n",
     "    mu, sig = theta[0], theta[1]\n",
     "    return stats.norm(mu, sig).pdf(x)\n",
     "\n",
     "# Two Gaussian Functions\n",
     "def evalTwoGaussian(theta, x):\n",
     "    mu1, mu2, sig1, sig2, alpha = theta\n",
     "    return alpha * stats.norm(mu1, sig1).pdf(x) + (1-alpha) * stats.norm(mu2, sig2).pdf(x)\n",
     "\n",
     "\n",
     "# Uniform Functions\n",
     "def evalUniform(theta, x):\n",
     "    mMin, mMax = theta[0], theta[1]\n",
     "    return stats.uniform(mMin, mMax-mMin).pdf(x)\n",
     "\n",
     "def evalUniformLowerOnly(theta, x):\n",
     "    mMin = theta[0]\n",
     "    return stats.uniform(mMin, 1-mMin).pdf(x)\n",
     "\n",
     "\n",
     "# Half Gaussian Functions\n",
     "def evalHalfGaussian(theta, x):\n",
     "    sigma = theta[0]\n",
     "    \n",
     "    #Calls erf from scipy\n",
     "    normaliser = 1/(erf(0.3889/sigma))\n",
     "    \n",
     "    return np.piecewise(x, [(0.45 <= x) & (x <= 1)], [lambda x:normaliser * ((2**(1.0/2))/(sigma*(np.pi**(1.0/2))))*np.exp(-((x-1)/(sigma*2**(1.0/2)))**2), 0])\n",
     "  \n",
     "    \n",
     "def evalTwoHalfGaussian(theta, x):\n",
     "    sigma1, mu2, sigma2, alpha = theta[0], theta[1], theta[2], theta[3]\n",
     "    \n",
     "    normaliser1 = 1/(erf(0.3889/sigma1))\n",
     "    normaliser2numerator = 2/(sigma2*(np.pi*2)**(1.0/2))\n",
     "    normaliser2denominator = erf((1-mu2)/(sigma2*2**(1.0/2))) + erf((mu2-0.45)/(sigma2*2**(1.0/2)))\n",
     "    \n",
     "    #lambda x: alpha * normaliser1 * ((2**(1.0/2))/(sigma*(np.pi**(1.0/2))))*np.exp(-((x-1)/(sigma*2**(1.0/2)))**2) + (1-alpha)*(normaliser2numerator/normaliser2denominator)*np.exp(-((x-mu2)/(sigma2*2**(1.0/2)))**2)\n",
     "    \n",
     "    #Calls erf from scipy\n",
     "    return np.piecewise(x, [(0.45 <= x) & (x <= 1)], [lambda x: alpha * normaliser1 * ((2**(1.0/2))/(sigma1*(np.pi**(1.0/2))))*np.exp(-((x-1)/(sigma1*2**(1.0/2)))**2) + (1-alpha)*(normaliser2numerator/normaliser2denominator)*np.exp(-((x-mu2)/(sigma2*2**(1.0/2)))**2), 0])\n",
     "        "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "def loadData(filePath):\n",
     "    #Loads text file into an array, excluding the name column.\n",
     "    stringArray = np.genfromtxt(filePath, dtype='str')[:,1:]\n",
     "    #Converts strings to floats\n",
     "    floatArray = stringArray.astype(np.float)\n",
     "    return floatArray\n",
     "\n",
     "### Load core data (See Table 1 of paper).\n",
     "coreData = loadData('PSR_BNSmass.txt')\n",
     "\n",
     "### Load total mass data\n",
     "totalData = loadData('BNSmtot.txt')\n",
     "\n",
     "pulsarMass, pulsarUncertainty, companionMass, companionUncertainty = np.transpose(coreData[:,:4])\n",
     "totalMass, totalUncertainty = np.transpose(totalData)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7f2e13396b00>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "######################### NEW FIG 2 ##########################\n",
     "\n",
     "pulsarPDFarray = np.transpose(np.loadtxt(topDirectory + 'Samples/pdfmr_0820_5p.txt'))\n",
     "companionPDFarray = np.transpose(np.loadtxt(topDirectory + 'Samples/pdfms_0820_5p.txt'))\n",
     "extraStarNames = [r\"J1946+2052\", r\"J1411+2551\", r\"J1811$-$1736\", r\"J1829+2456\", r\"J1930$-$1852\", ]\n",
     "\n",
     "starOrder = [2, 0, 9, 4, 3, 6, 11, 5, 1, 8, 10, 7]\n",
     "\n",
     "starnames = np.genfromtxt('PSR_BNSmass.txt', dtype='str')[:,0]\n",
     "starNameList = [r'{}'.format(starname.replace('-','$-$')) for starname in starnames]\n",
     "\n",
     "#plotRange = [1, 1.8]\n",
     "plotRange = [0.8, 2]\n",
     "\n",
     "starNameListOrdered = [starNameList[i] for i in starOrder]\n",
     "pulsarMassOrdered = [pulsarMass[i] for i in starOrder]\n",
     "pulsarUncertaintyOrdered = [pulsarUncertainty[i] for i in starOrder]\n",
     "companionMassOrdered = [companionMass[i] for i in starOrder]\n",
     "companionUncertaintyOrdered = [companionUncertainty[i] for i in starOrder]\n",
     "\n",
     "\n",
     "plt.figure(num=None, figsize=(20, 20), facecolor='w', edgecolor='k')\n",
     "\n",
     "masterOrder = [12, 2, 0, 9, 4, 3, 6, 11, 5, 1, 8, 10, 7, 15, 13, 16, 14]\n",
     "weirdPDForder = [12, 15, 13, 16, 14]\n",
     "\n",
     "\n",
     "##### NEW #####\n",
     "for index, (pPDF, cPDF) in enumerate(zip(pulsarPDFarray, companionPDFarray)):\n",
     "    starNumber = weirdPDForder[index]\n",
     "    \n",
     "    \n",
     "    subplotIndex = masterOrder.index(starNumber)\n",
     "    plt.subplot(6, 3, subplotIndex+1)\n",
     "\n",
     "    \n",
     "    xValues = np.linspace(0.8, 2, 1201).flatten()\n",
     "    plt.fill_between(xValues, pPDF, color='green', alpha=0.5)\n",
     "    plt.fill_between(xValues, cPDF, color='cyan', alpha=0.5)\n",
     "    \n",
     "    plt.xlabel(r\"$m$ ($M_\\odot$)\", fontsize=18)\n",
     "    #plt.ylabel(r\"P($m_r,m_s$)\", fontsize=18)\n",
     "    plt.xlim(plotRange)\n",
     "    plt.ylim(ymin=0)\n",
     "    plt.tick_params(axis='x', labelsize=18)\n",
     "    plt.title(extraStarNames[index], fontsize=22)\n",
     "    plt.rc('ytick', labelsize=0)     \n",
     "    \n",
     "    frame1 = plt.gca()\n",
     "    frame1.axes.yaxis.set_ticklabels([])\n",
     "    \n",
     "#plt.show()\n",
     "\n",
     "############################ OLD ################\n",
     "\n",
     "for index, ((mass1, unc1), (mass2, unc2)) in enumerate(zip(zip(pulsarMassOrdered, pulsarUncertaintyOrdered), zip(companionMassOrdered, companionUncertaintyOrdered))):\n",
     "    \n",
     "    starNumber = starOrder[index]\n",
     "    \n",
     "    subplotIndex = masterOrder.index(starNumber)\n",
     "    plt.subplot(6, 3, subplotIndex+1)\n",
     "    \n",
     "    xValues1 = np.linspace(mass1-5.5*unc1, mass1+4*unc1, 5000)\n",
     "    xValues2 = np.linspace(mass2-5.5*unc2, mass2+4*unc2, 5000)\n",
     "    \n",
     "    yValues1 = evalSingleGaussian([mass1, unc1], xValues1)\n",
     "    yValues2 = evalSingleGaussian([mass2, unc2], xValues2)\n",
     "    \n",
     "#     plt.plot(xValues1, yValues1, linewidth=4, color='green', alpha=0.5)\n",
     "#     plt.plot(xValues2, yValues2, linewidth=4, color='cyan', alpha=0.5)\n",
     "    \n",
     "    #plt.plot(xValues1, yValues1, color='green', linewidth=5)\n",
     "    #plt.plot(xValues2, yValues2, color='cyan', linewidth=5)\n",
     "    \n",
     "    \n",
     "    if index in [1, 3, 5, 7, 9]:\n",
     "        plt.plot(xValues1, yValues1, color='#84BF7D', linewidth=2)\n",
     "        plt.plot(xValues2, yValues2, color='#8FFFFE', linewidth=2)\n",
     "    \n",
     "    \n",
     "    plt.fill_between(xValues1, yValues1, color='green', alpha=0.5)\n",
     "    plt.fill_between(xValues2, yValues2, color='cyan', alpha=0.5)\n",
     "    \n",
     "#     plt.fill_between(xValues1, yValues1, color='green')\n",
     "#     plt.fill_between(xValues2, yValues2, color='cyan')\n",
     "    \n",
     "    #plt.hist(pulsarSample, bins=binz, color='green', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "    #plt.hist(companionSample, bins=binz, color='cyan', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "    \n",
     "    \n",
     "    plt.xlabel(r\"$m$ ($M_\\odot$)\", fontsize=18)\n",
     "    #plt.ylabel(r\"P($m_r,m_s$)\", fontsize=18)\n",
     "    plt.xlim(plotRange)\n",
     "    plt.ylim(ymin=0)\n",
     "    plt.tick_params(axis='x', labelsize=18)\n",
     "    plt.rc('ytick', labelsize=0) \n",
     "    \n",
     "    \n",
     "    frame1 = plt.gca()\n",
     "    frame1.axes.yaxis.set_ticklabels([])\n",
     "    \n",
     "    plt.title(starNameListOrdered[index], fontsize=22)\n",
     "#plt.figlegend([\"Pulsar Samples\", \"Companion Samples\"], loc='upper center', bbox_to_anchor=(0.5, 0.325), prop={'size': 20})\n",
     "\n",
     "plt.tight_layout()\n",
     "\n",
     "#plt.suptitle('Pulsar Star Masses',fontsize=30)\n",
     "plt.savefig('fig_pcSamples', dpi=200)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "def createPredPostDist(dataName, modelName, evalFunction, plotrange, ndims, modelNum, drawNumber=1000, massSamples=[], showSampleLines=False, save=False):\n",
     "\n",
     "    prefix = 'out/' + dataName + '/' + modelName + '/'\n",
     "    \n",
     "    nDims = ndims\n",
     "    \n",
     "    a = pymultinest.analyse.Analyzer(nDims, outputfiles_basename=prefix)\n",
     "    \n",
     "    paramList=[]\n",
     "    for params in a.get_equal_weighted_posterior():\n",
     "            paramList.append(params)\n",
     "    \n",
     "    paramArray = np.asarray(paramList)\n",
     "    #print(np.shape(paramArray))\n",
     "    \n",
     "    paramsWithoutEvidence = paramArray[:,:-1].tolist()\n",
     "    #print(np.shape(paramsWithoutEvidence))    \n",
     "    \n",
     "\n",
     "    drawnParams = [random.choice(paramsWithoutEvidence) for i in range(drawNumber)]\n",
     "    \n",
     "    xValues = np.linspace(plotrange[0], plotrange[1], 5000)    \n",
     "    plt.xlim(plotrange[0], plotrange[1])\n",
     "    \n",
     "    \n",
     "    yValueList = []\n",
     "    for params in drawnParams:\n",
     "        #print(params)\n",
     "        yValues = evalFunction(params, xValues)\n",
     "        yValueList.append(yValues)\n",
     "\n",
     "        \n",
     "    print(np.shape(np.asarray(yValueList)))\n",
     "    \n",
     "    yValueArray = np.asarray(yValueList)\n",
     "    \n",
     "    meanyValues = np.mean(yValueArray, axis=0)\n",
     "    \n",
     "#     if len(massSamples != 0):\n",
     "#         weights = np.ones_like(massSamples.flatten())/float(len(massSamples.flatten()))\n",
     "#         #plt.hist(massSamples.flatten(), bins=12, color='#3fcca6', histtype=\"stepfilled\", alpha=0.3, weights=weights)\n",
     "        \n",
     "#         plt.hist(massSamples.flatten(), bins=40, color='#3fcca6', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "        \n",
     "    if showSampleLines == True:\n",
     "        for yValues in yValueList:\n",
     "            plt.plot(xValues, yValues, alpha=0.1)\n",
     "\n",
     "    \n",
     "    if modelNum == 0:\n",
     "        plt.plot(xValues, meanyValues, color='#3fcca6', linewidth=3)\n",
     "    else:\n",
     "        plt.plot(xValues, meanyValues, color='#3fcca6', alpha=0.3)\n",
     "        #plt.plot(xValues, meanyValues, color='#3fcca6', alpha=0.3, scaley=False)\n",
     "    \n",
     "    if save == True:\n",
     "        saveName = \"plotOutput/\" + dataName + 'Fit.png'\n",
     "        plt.savefig(saveName, dpi=200)\n",
     "    \n",
     "    #plt.show()\n",
     "    return meanyValues\n",
     "    "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "modelList = [['singleGaussian', evalSingleGaussian, 2], ['twoGaussian', evalTwoGaussian, 5], ['uniform', evalUniform, 2], ['uniformRatio', evalUniformLowerOnly, 1], ['half', evalHalfGaussian, 1]]\n",
     "\n",
     "def createMultiPredPostdataName(dataName, modelList, plotrange, plotLabels=[\"M\",\"P(M)\"], binz=40, drawNumber=1000, massSamples=[], showSampleLines=False, save=False):\n",
     "    plt.gcf().clear()\n",
     "    #plt.gca()\n",
     "    plt.rcParams.update({'font.size': 15})\n",
     "    \n",
     "    plt.gcf().subplots_adjust(bottom=0.15)\n",
     "    \n",
     "    plt.xlabel(plotLabels[0]); plt.ylabel(plotLabels[1])\n",
     "    \n",
     "    if len(massSamples != 0):\n",
     "        plt.hist(massSamples.flatten(), bins=binz, color='#3fcca6', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "        \n",
     "    \n",
     "    for index, (modelName, evalFunction, modelDims) in enumerate(modelList):\n",
     "        createPredPostDist(dataName, modelName, evalFunction, plotrange, modelDims, index, drawNumber, [], showSampleLines=False, save=False)\n",
     "        \n",
     "        \n",
     "    if save == True:\n",
     "        saveName = \"plotOutput/\" + dataName + 'Fit.png'\n",
     "        plt.savefig(saveName, dpi=200)\n",
     "    return plt"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "pulsarModels = [modelList[i] for i in [1, 0, 2]]\n",
     "createMultiPredPostdataName('pulsar', pulsarModels, [1.1, 1.8], [r'$m_r$ ($M_\\odot$)',r'P($m_r$)'], 15, 2000, pulsarMassSamples, save=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "companionModels = [modelList[i] for i in [2, 0, 1]]\n",
     "createMultiPredPostdataName('companion', companionModels, [1.0, 1.7], [r'$m_s$ ($M_\\odot$)',r'P($m_s$)'], 15, 2000, companionMassSamples, save=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "totalModels = [modelList[i] for i in [2, 0, 1]]\n",
     "createMultiPredPostdataName('total', totalModels, [2.3, 3], [r'$M_T$ ($M_\\odot$)',r'P($M_T$)'], 12, 2000, totalMassSamples, save=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "bothModels = [modelList[i] for i in [0, 1, 2]]\n",
     "createMultiPredPostdataName('both', bothModels, [1, 1.7], [r'$m$ ($M_\\odot$)',r'P($m$)'], 15, 5000, bothMassSamples, save=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "chirpModels = [modelList[i] for i in [2, 0, 1]]\n",
     "createMultiPredPostdataName('chirp', chirpModels, [1.05, 1.3], [r'$\\mathcal{M}_c$ ($M_\\odot$)',r'P($\\mathcal{M}_c$)'], 8, 2000, chirpSamples, save=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "ratioModels = [modelList[i] for i in [4, 3]]\n",
     "createMultiPredPostdataName('ratio', ratioModels, [0.45, 1], [r'$q$',r'P($q$)'], 15, 5000, ratioSamples, save=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "plt.figure(num=None, figsize=(20, 20), facecolor='w', edgecolor='k')\n",
     "\n",
     "i = 0\n",
     "plt.subplot(5, 5, i + 1)\n",
     "pulsarModels = [modelList[i] for i in [1, 0, 2]]\n",
     "createMultiPredPostdataName('pulsar', pulsarModels, [1.1, 1.8], [r'$m_r$ ($M_\\odot$)',r'P($m_r$)'], 15, 1000, pulsarMassSamples, save=False)\n",
     "\n",
     "i += 1\n",
     "plt.subplot(5, 5, i + 1)\n",
     "companionModels = [modelList[i] for i in [2, 0, 1]]\n",
     "createMultiPredPostdataName('companion', companionModels, [1.0, 1.7], [r'$m_s$ ($M_\\odot$)',r'P($m_s$)'], 15, 1000, companionMassSamples, save=False)\n",
     "\n",
     "i += 1\n",
     "plt.subplot(5, 5, i + 1)\n",
     "totalModels = [modelList[i] for i in [2, 0, 1]]\n",
     "createMultiPredPostdataName('total', totalModels, [2.3, 3], [r'$M_T$ ($M_\\odot$)',r'P($M_T$)'], 12, 1000, totalMassSamples, save=False)\n",
     "\n",
     "i += 1\n",
     "plt.subplot(5, 5, i + 1)\n",
     "chirpModels = [modelList[i] for i in [2, 0, 1]]\n",
     "createMultiPredPostdataName('chirp', chirpModels, [1.05, 1.3], [r'$\\mathcal{M}_c$ ($M_\\odot$)',r'P($\\mathcal{M}_c$)'], 8, 1000, chirpSamples, save=False)\n",
     "\n",
     "i += 1\n",
     "plt.subplot(5, 5, i + 1)\n",
     "ratioModels = [modelList[i] for i in [4, 3]]\n",
     "createMultiPredPostdataName('ratio', ratioModels, [0.45, 1], [r'$q$',r'P($q$)'], 15, 1000, ratioSamples, save=False)\n",
     "\n",
     "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "#createPredPostDist('pulsar', 'twoGaussian', evalTwoGaussian, [1.2, 1.8], 5, 5000, pulsarMassSamples, save=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "#createPredPostDist('companion', 'uniform', evalUniform, [1.1, 1.5], 2, 5000, companionMassSamples, save=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "#createPredPostDist('total', 'uniform', evalUniform, [2.35, 3], 2, 5000, totalMassSamples, save=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "#createPredPostDist('chirp', 'uniform', evalUniform, [1.05, 1.3], 2, 5000, chirpSamples, save=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "#createPredPostDist('ratio', 'half', evalHalfGaussian, [0.45, 1], 1, 5000, ratioSamples, save=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "from scipy import integrate\n",
     "from scipy import optimize\n",
     "\n",
     "# import warnings\n",
     "# warnings.filterwarnings('error')\n",
     "\n",
     "\n",
     "prefix = 'out/' + 'ratio' + '/' + 'half' + '/'\n",
     "\n",
     "nDims = 1\n",
     "    \n",
     "a = pymultinest.analyse.Analyzer(nDims, outputfiles_basename=prefix)\n",
     "    \n",
     "paramList=[]\n",
     "for params in a.get_equal_weighted_posterior():\n",
     "    paramList.append(params)\n",
     "    \n",
     "paramArray = np.asarray(paramList)\n",
     "#print(np.shape(paramArray))\n",
     "    \n",
     "paramsWithoutEvidence = paramArray[:,:-1].tolist()\n",
     "\n",
     "drawnParams = [random.choice(paramsWithoutEvidence) for i in range(5000)]\n",
     "#random.sample(paramsWithoutEvidence, 1000)\n",
     "#print(len(paramsWithoutEvidence))\n",
     "\n",
     "\n",
     "### Function which calculates integral of halfgaussian from 0.45 to upperLimit, returns area-percentile\n",
     "#Turning this into a root finding problem, solving for which upperLimit the function returns zero.\n",
     "def integralHalfPercRoot(upperLimit, sigma, percentile=0.01):\n",
     "    #Integrate from 0.45 to upperLimit\n",
     "    result = integrate.quad(lambda x: evalHalfGaussian(sigma, x), 0.45, upperLimit)\n",
     "    \n",
     "    #Returns the probability - percentile.\n",
     "    return result[0] - percentile\n",
     "\n",
     "#Create empty list of lower bounds\n",
     "lowerBounds = []\n",
     "lowerBounds2 = []\n",
     "\n",
     "#For each sigma in the posterior\n",
     "for index, sigma in enumerate(drawnParams):\n",
     "    \n",
     "    print(index, sigma)\n",
     "    \n",
     "    #Find the root of the integralHalfPercRoot function with a starting guess of 0.45\n",
     "    lowerBound2 = optimize.fsolve(integralHalfPercRoot, 0.45, args=sigma)\n",
     "    lowerBound = optimize.brentq(integralHalfPercRoot, 0.45, 1, args=sigma)\n",
     "    \n",
     "    #Add the lowerbound to the lower bound list.\n",
     "    lowerBounds.append(lowerBound)\n",
     "    lowerBounds2.append(lowerBound2)\n",
     "    \n",
     "#     if lowerBound > 0.8:\n",
     "#         print(index, sigma, lowerBound)\n",
     "    \n",
     "    \n",
     "    if index % 1000 == 0:\n",
     "        print(\"{} done.\".format(index))\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "print(integralHalfPercRoot(0.58049468, [0.06580650180578232], percentile=0.005))\n",
     "print(integralHalfPercRoot(0.815275, [0.06580650180578232], percentile=0.005))\n",
     "\n",
     "optimize.brentq(integralHalfPercRoot, 0.45, 1, args=[0.06580650180578232])\n",
     "\n",
     "#optimize.brentq(integralHalfPercRoot, 0.6, args=[0.06580650180578232])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
     "print(len(paramsWithoutEvidence))\n",
     "sigmaList = np.asarray(paramsWithoutEvidence)\n",
     "plt.hist(sigmaList.flatten(), bins=1000, color='#3fcca6', histtype=\"stepfilled\", alpha=1, normed=True)\n",
     "plt.xlim(0.04,0.1)\n",
     "print(min(sigmaList))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "lowerBoundArray = np.asarray(lowerBounds)\n",
     "plt.hist(lowerBoundArray.flatten(), bins=100, color='#3fcca6', histtype=\"stepfilled\", alpha=1, normed=True)\n",
     "#plt.xlim(0.75, 0.9)\n",
     "plt.xlabel(r'$q_{\\mathrm{0.01}}$')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "lowerBoundArray = np.asarray(lowerBounds2)\n",
     "plt.hist(lowerBoundArray.flatten(), bins=100, color='#3fcca6', histtype=\"stepfilled\", alpha=1, normed=True)\n",
     "#plt.xlim(0.75, 0.9)\n",
     "plt.xlabel(r'$q_{\\mathrm{0.01}}$')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "lowerBoundArray = np.asarray(lowerBounds)\n",
     "plt.hist(lowerBoundArray.flatten(), bins=50, color='#3fcca6', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "plt.xlabel(r'$q_{\\mathrm{min}}$')\n",
     "plt.show()\n",
     "plt.hist(lowerBoundArray.flatten(), bins=100, color='#3fcca6', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "plt.xlabel(r'$q_{\\mathrm{min}}$')\n",
     "plt.savefig('ratio_lowerbound', dpi=200)\n",
     "plt.show()\n",
     "plt.hist(lowerBoundArray.flatten(), bins=200, color='#3fcca6', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "plt.xlabel(r'$q_{\\mathrm{min}}$')\n",
     "plt.show()\n",
     "plt.hist(lowerBoundArray.flatten(), bins=400, color='#3fcca6', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "plt.xlabel(r'$q_{\\mathrm{min}}$')\n",
     "plt.show()\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "print(lowerBounds[0])\n",
     "\n",
     "integrate.quad(lambda x: evalHalfGaussian(lowerBounds[0][1], x), 0.45, 0.72066778)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "starOrder = [12, 2, 0, 9, 4, 3, 6, 11, 5, 1, 8, 10, 7, 15, 13, 16, 14]\n",
     "\n",
     "plotRange = [2.2, 3]\n",
     "\n",
     "starnames = np.genfromtxt('BNSmtot.txt', dtype='str')[:,0]\n",
     "starNameList = [r'{}'.format(starname.replace('-','$-$')) for starname in starnames]\n",
     "#print(starNameList)\n",
     "\n",
     "starNamesListOrdered = [starNameList[i] for i in starOrder]\n",
     "\n",
     "\n",
     "binz = np.arange(2.2, 3, 0.01)\n",
     "\n",
     "plt.figure(num=None, figsize=(20, 20), facecolor='w', edgecolor='k')\n",
     "for index, (mass, unc) in enumerate(zip(totalMass, totalUncertainty)):\n",
     "    plt.subplot(6, 3, index + 1)\n",
     "    \n",
     "    xValues = np.linspace(plotRange[0], plotRange[1], 5000)\n",
     "    yValues = evalSingleGaussian([mass, unc], xValues)\n",
     "    \n",
     "    #plt.plot(xValues, yValues, color='#3fcca6')\n",
     "    \n",
     "    plt.fill_between(xValues, yValues, color='#3fcca6', alpha=0.5)\n",
     "    \n",
     "    #plt.hist(sample, bins=binz, color='#3fcca6', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "\n",
     "    \n",
     "    plt.xlabel(r\"$M_T$ ($M_\\odot$)\", fontsize=18)\n",
     "    plt.ylabel(r\"P($M_T$)\", fontsize=18)\n",
     "    plt.xlim(plotRange)\n",
     "    plt.ylim(ymin=0)\n",
     "    plt.tick_params(axis='both', labelsize=18)\n",
     "\n",
     "    plt.title(starNameList[index], fontsize=22)\n",
     "#plt.figlegend([\"Total Mass Samples\"], loc='lower right', bbox_to_anchor=(0.95, 0.08), prop={'size': 20})\n",
     "\n",
     "#plt.figlegend([\"Total Mass Samples\", \"Companion Mass Samples\"], loc='lower center', prop={'size': 20})\n",
     "\n",
     "plt.tight_layout()   \n",
     "#plt.suptitle('Pulsar Star Masses',fontsize=30)\n",
     "plt.savefig('fig_tSamples', dpi=200)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "starnames = np.genfromtxt('PSR_BNSmass.txt', dtype='str')[:,0]\n",
     "starNameList = [r'{}'.format(starname.replace('-','$-$')) for starname in starnames]\n",
     "\n",
     "plotRange = [1.1, 1.75]\n",
     "\n",
     "#print(binz)\n",
     "\n",
     "#pulsPair = pulsarMass, pulsarUnc\n",
     "#compPair = companionMass, companionUnc\n",
     "\n",
     "\n",
     "\n",
     "plt.figure(num=None, figsize=(20, 20), facecolor='w', edgecolor='k')\n",
     "for index, ((mass1, unc1), (mass2, unc2)) in enumerate(zip(zip(pulsarMass, pulsarUncertainty), zip(companionMass, companionUncertainty))):\n",
     "    plt.subplot(6, 3, index + 1)\n",
     "    \n",
     "    xValues = np.linspace(plotRange[0], plotRange[1], 5000)\n",
     "    yValues1 = evalSingleGaussian([mass1, unc1], xValues)\n",
     "    yValues2 = evalSingleGaussian([mass2, unc2], xValues)\n",
     "    \n",
     "    plt.fill_between(xValues, yValues1, color='green', alpha=0.5)\n",
     "    plt.fill_between(xValues, yValues2, color='cyan', alpha=0.5)\n",
     "    \n",
     "    #plt.hist(pulsarSample, bins=binz, color='green', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "    #plt.hist(companionSample, bins=binz, color='cyan', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "    \n",
     "    \n",
     "    plt.xlabel(r\"$m$ ($M_\\odot$)\", fontsize=18)\n",
     "    plt.ylabel(r\"P($m_r,m_s$)\", fontsize=18)\n",
     "    plt.xlim(plotRange)\n",
     "    plt.ylim(ymin=0)\n",
     "    plt.tick_params(axis='both', labelsize=18)\n",
     "\n",
     "    plt.title(starNameList[index], fontsize=22)\n",
     "#plt.figlegend([\"Pulsar Samples\", \"Companion Samples\"], loc='upper center', bbox_to_anchor=(0.5, 0.325), prop={'size': 20})\n",
     "\n",
     "plt.tight_layout()   \n",
     "\n",
     "#plt.suptitle('Pulsar Star Masses',fontsize=30)\n",
     "plt.savefig('fig_pcSamples', dpi=200)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "starOrder = [12, 2, 0, 9, 4, 3, 6, 11, 5, 1, 8, 10, 7, 15, 13, 16, 14]\n",
     "\n",
     "plotRange = [2.2, 3]\n",
     "\n",
     "starnames = np.genfromtxt('BNSmtot.txt', dtype='str')[:,0]\n",
     "starNameList = [r'{}'.format(starname.replace('-','$-$')) for starname in starnames]\n",
     "#print(starNameList)\n",
     "\n",
     "starNamesListOrdered = [starNameList[i] for i in starOrder]\n",
     "totalMassOrdered = [totalMass[i] for i in starOrder]\n",
     "totalUncertaintyOrdered = [totalUncertainty[i] for i in starOrder]\n",
     "\n",
     "binz = np.arange(2.2, 3, 0.01)\n",
     "\n",
     "plt.figure(num=None, figsize=(20, 20), facecolor='w', edgecolor='k')\n",
     "for index, (mass, unc) in enumerate(zip(totalMassOrdered, totalUncertaintyOrdered)):\n",
     "    plt.subplot(6, 3, index + 1)\n",
     "    \n",
     "    xValues = np.linspace(mass-5.5*unc, mass+4*unc, 5000)\n",
     "    yValues = evalSingleGaussian([mass, unc], xValues)\n",
     "    \n",
     "    if True:\n",
     "    #if index in [1, 2, 3, 4, 5, 6, 8, 10, 11]:\n",
     "        plt.plot(xValues, yValues, color='#3fcca6', linewidth=2)\n",
     "    \n",
     "    plt.fill_between(xValues, yValues, color='#3fcca6', alpha=1)\n",
     "    plt.fill_between(xValues, yValues, color='#3fcca6', alpha=0.5)\n",
     "    \n",
     "    #plt.hist(sample, bins=binz, color='#3fcca6', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "\n",
     "    \n",
     "    plt.xlabel(r\"$M_T$ ($M_\\odot$)\", fontsize=18)\n",
     "    plt.ylabel(r\"P($M_T$)\", fontsize=18)\n",
     "    plt.xlim(plotRange)\n",
     "    plt.ylim(ymin=0)\n",
     "    plt.tick_params(axis='both', labelsize=18)\n",
     "\n",
     "    plt.title(starNamesListOrdered[index], fontsize=22)\n",
     "#plt.figlegend([\"Total Mass Samples\"], loc='lower right', bbox_to_anchor=(0.95, 0.08), prop={'size': 20})\n",
     "\n",
     "#plt.figlegend([\"Total Mass Samples\", \"Companion Mass Samples\"], loc='lower center', prop={'size': 20})\n",
     "\n",
     "plt.tight_layout()   \n",
     "#plt.suptitle('Pulsar Star Masses',fontsize=30)\n",
     "plt.savefig(\"plotOutput/\" + 'fig_tSamples', dpi=200)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "scrolled": false
    },
    "outputs": [],
    "source": [
     "starOrder = [2, 0, 9, 4, 3, 6, 11, 5, 1, 8, 10, 7]\n",
     "\n",
     "starnames = np.genfromtxt('PSR_BNSmass.txt', dtype='str')[:,0]\n",
     "starNameList = [r'{}'.format(starname.replace('-','$-$')) for starname in starnames]\n",
     "\n",
     "plotRange = [1, 1.8]\n",
     "\n",
     "\n",
     "starNameListOrdered = [starNameList[i] for i in starOrder]\n",
     "pulsarMassOrdered = [pulsarMass[i] for i in starOrder]\n",
     "pulsarUncertaintyOrdered = [pulsarUncertainty[i] for i in starOrder]\n",
     "companionMassOrdered = [companionMass[i] for i in starOrder]\n",
     "companionUncertaintyOrdered = [companionUncertainty[i] for i in starOrder]\n",
     "\n",
     "\n",
     "plt.figure(num=None, figsize=(20, 20), facecolor='w', edgecolor='k')\n",
     "for index, ((mass1, unc1), (mass2, unc2)) in enumerate(zip(zip(pulsarMassOrdered, pulsarUncertaintyOrdered), zip(companionMassOrdered, companionUncertaintyOrdered))):\n",
     "    plt.subplot(6, 3, index + 1)\n",
     "    \n",
     "    xValues1 = np.linspace(mass1-5.5*unc1, mass1+4*unc1, 5000)\n",
     "    xValues2 = np.linspace(mass2-5.5*unc2, mass2+4*unc2, 5000)\n",
     "    \n",
     "    yValues1 = evalSingleGaussian([mass1, unc1], xValues1)\n",
     "    yValues2 = evalSingleGaussian([mass2, unc2], xValues2)\n",
     "    \n",
     "#     plt.plot(xValues1, yValues1, linewidth=4, color='green', alpha=0.5)\n",
     "#     plt.plot(xValues2, yValues2, linewidth=4, color='cyan', alpha=0.5)\n",
     "    \n",
     "    #plt.plot(xValues1, yValues1, color='green', linewidth=5)\n",
     "    #plt.plot(xValues2, yValues2, color='cyan', linewidth=5)\n",
     "    \n",
     "    \n",
     "    if index in [1, 3, 5, 7, 9]:\n",
     "        plt.plot(xValues1, yValues1, color='#84BF7D', linewidth=2)\n",
     "        plt.plot(xValues2, yValues2, color='#8FFFFE', linewidth=2)\n",
     "    \n",
     "    \n",
     "    plt.fill_between(xValues1, yValues1, color='green', alpha=0.5)\n",
     "    plt.fill_between(xValues2, yValues2, color='cyan', alpha=0.5)\n",
     "    \n",
     "#     plt.fill_between(xValues1, yValues1, color='green')\n",
     "#     plt.fill_between(xValues2, yValues2, color='cyan')\n",
     "    \n",
     "    #plt.hist(pulsarSample, bins=binz, color='green', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "    #plt.hist(companionSample, bins=binz, color='cyan', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "    \n",
     "    \n",
     "    plt.xlabel(r\"$m$ ($M_\\odot$)\", fontsize=18)\n",
     "    plt.ylabel(r\"P($m_r,m_s$)\", fontsize=18)\n",
     "    plt.xlim(plotRange)\n",
     "    plt.ylim(ymin=0)\n",
     "    plt.tick_params(axis='both', labelsize=18)\n",
     "\n",
     "    plt.title(starNameListOrdered[index], fontsize=22)\n",
     "#plt.figlegend([\"Pulsar Samples\", \"Companion Samples\"], loc='upper center', bbox_to_anchor=(0.5, 0.325), prop={'size': 20})\n",
     "\n",
     "plt.tight_layout()\n",
     "\n",
     "#plt.suptitle('Pulsar Star Masses',fontsize=30)\n",
     "plt.savefig(\"plotOutput/\" + 'fig_pcSamples', dpi=200)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "dateSets = [['pulsar', 'singleGaussian'],\n",
     " ['pulsar', 'twoGaussian'],\n",
     " ['pulsar', 'uniform'],\n",
     " ['companion', 'singleGaussian'],\n",
     " ['companion', 'twoGaussian'],\n",
     " ['companion', 'uniform'],\n",
     " ['total', 'singleGaussian'],\n",
     " ['total', 'twoGaussian'],\n",
     " ['total', 'uniform'],\n",
     " ['both', 'singleGaussian'],\n",
     " ['both', 'twoGaussian'],\n",
     " ['both', 'uniform'],\n",
     " ['chirp', 'singleGaussian'],\n",
     " ['chirp', 'twoGaussian'],\n",
     " ['chirp', 'uniform'],\n",
     " ['ratio', 'uniform'],\n",
     " ['ratio', 'half'],\n",
     " ['ratio', 'uniformRatio']]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "xValues1 = np.linspace(-5, 5, 5000)\n",
     "xValues2 = np.linspace(-5, 5, 5000)\n",
     "    \n",
     "yValues1 = -xValues1**2 + 10\n",
     "yValues2 = -xValues2**2 + 18\n",
     "    \n",
     "plt.ylim(0,20)\n",
     "    \n",
     "plt.plot(xValues1, yValues1, color='green', linewidth=5, alpha=0.5)\n",
     "plt.plot(xValues2, yValues2, color='cyan', linewidth=5, alpha=0.5)\n",
     "    \n",
     "    \n",
     "plt.fill_between(xValues1, yValues1, color='green', alpha=0.5)\n",
     "plt.fill_between(xValues2, yValues2, color='cyan', alpha=0.5)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "newList = []\n",
     "for i in range(6):\n",
     "    newList.append([dateSets[3*i + j][1] for j in range(3)])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "print(newList)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "color='#3fcca6'\n",
     "\n",
     "def plotPDF2(massSamples, resFuncList, plotRange, binz=8, labelz=[\"M\",\"P(M)\"]):\n",
     "    plt.rcParams.update({'font.size': 15})\n",
     "    \n",
     "    plt.gcf().subplots_adjust(bottom=0.15)\n",
     "    \n",
     "    plt.hist(massSamples.flatten(), bins=binz, color='#3fcca6', histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
     "    \n",
     "    \n",
     "    for index, (params, plotFunction) in enumerate(resFuncList):\n",
     "        M_fit = np.linspace(plotRange[0], plotRange[1], 5000)\n",
     "        \n",
     "        if index == 0:\n",
     "            plt.plot(M_fit, plotFunction(params, M_fit), '-k', linewidth=3)\n",
     "        else:\n",
     "            plt.plot(M_fit, plotFunction(params, M_fit), '-k', alpha=0.3, scaley=False)\n",
     "            \n",
     "    plt.xlabel(labelz[0]); plt.ylabel(labelz[1])\n",
     "    #plt.title(str(plotFunction) + \" Parameters = {}\".format(resultsArray))\n",
     "\n",
     "    return plt"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "scrolled": false
    },
    "outputs": [],
    "source": [
     "resFuncList = [[[0.251394979847697009E+01, 0.287915671720423338E+01], evalUniform], [[0.266440616414464682E+01, 0.104527740335558672E+00], evalSingleGaussian], [[0.257402721037644788E+01, 0.272932163717520648E+01, 0.906463133388197249E-02, 0.921064253192622473E-01, 0.454904404939819818E+00],evalTwoGaussian]]\n",
     "plot = plotPDF2(totalMass, resFuncList, (2.45, 2.95),8, [r'$M_T$ ($M_\\odot$)',r'P($M_T$)'])"
    ]
   }
  ],
  "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"
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