doc/rtd/nb_fissionYields.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading CGMF Fission Yields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "### initializations and import libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "%matplotlib inline\n",
    "%pylab inline\n",
    "\n",
    "from CGMFtk import yields as yld"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CGMF can be used to generate pre-neutron emission fission fragment yields in charge, mass, kinetic energy, excitation energy, spin, and parity. Those yields $Y(Z,A,KE,U,J,\\pi)$ set the initial conditions for the decay of the fragments by emission of prompt neutrons and $\\gamma$ rays.\n",
    "\n",
    "To create an output file containing such yields, CGMF can be used with a negative number of events using the '-n' option:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "./cgmf.x -i 98252 -e 0.0 -n -500000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It creates an output file that can easily be read:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "yields = yld.Yields('98252-yields.cgmf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of events in the file and number of fission fragments can be printed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of events:  500000\n",
      "Number of fission fragments:  1000000\n"
     ]
    }
   ],
   "source": [
    "print ('Number of events: ',yields.numberEvents)\n",
    "print ('Number of fission fragments: ',yields.numberFragments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting the data by mass, charge, etc, is trivial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z  = yields.getZ()\n",
    "A  = yields.getZ()\n",
    "KE = yields.getKE()\n",
    "U  = yields.getU()\n",
    "J  = yields.getJ()\n",
    "p  = yields.getP()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total kinetic energy and total excitation energy of each event is also easily available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "TKE = yields.getTKE()\n",
    "TXE = yields.getTXE()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Manipulating and plotting those initial conditions can be done very simply with the help of numpy routines. For instance, finding the min, max, and mean values of the initial excitation energy in the fragments can be done by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0231779 62.9756 16.027517501660697\n"
     ]
    }
   ],
   "source": [
    "print (np.min(U), np.max(U), np.mean(U))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and plotting the distribution of the excitation energy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=figure(figsize(8,6))\n",
    "h,b = np.histogram(U,bins=100,density=True)\n",
    "plt.step(b[:-1],h)\n",
    "plt.xlim(0,50)\n",
    "plt.xlabel(\"Initial Excitation Energy (MeV)\",fontsize=18)\n",
    "plt.ylim(0,0.1)\n",
    "plt.ylabel(\"Probability\",fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separating the distributions for the light and heavy fragments can be done by recognizing that "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ul = yields.getULF()  # light fragments\n",
    "Uh = yields.getUHF() # heavy fragments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=figure(figsize(8,6))\n",
    "h,b = np.histogram(U,bins=100,density=True)\n",
    "hl,bl = np.histogram(Ul,bins=100,density=True)\n",
    "hh,bh = np.histogram(Uh,bins=100,density=True)\n",
    "plt.step(b[:-1],h,label=\"All Fragments\")\n",
    "plt.step(bl[:-1],hl,label=\"Light Fragments\")\n",
    "plt.step(bh[:-1],hh,label=\"Heavy Fragments\")\n",
    "plt.xlim(0,50)\n",
    "plt.xlabel(\"Initial Excitation Energy (MeV)\",fontsize=18)\n",
    "plt.ylim(0,0.12)\n",
    "plt.ylabel(\"Probability\",fontsize=18)\n",
    "lg=plt.legend(fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar plots and statistical analyses can be carried out for any initial quantity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "A summary table can also be easily printed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width=60%><tr><td></td><td>All Fragments</td><td>Light Fragments</td><td>Heavy Fragments</td></tr><tr><td>A</td><td>126.00</td><td>108.62</td><td>143.38</td></tr><tr><td>Z</td><td>49.00</td><td>42.63</td><td>55.37</td></tr><tr><td>TXE / U (MeV)</td><td>32.06</td><td>18.52</td><td>13.54</td></tr><tr><td>TKE / KE (MeV)</td><td>185.78</td><td>105.57</td><td>80.21</td></tr><tr><td>J ($\\hbar$)</td><td> 9.34</td><td> 8.85</td><td> 9.83</td></tr><tr><td>parity</td><td> 0.00</td><td> 0.00</td><td> 0.00</td></tr></center>"
      ],
      "text/plain": [
       "[['', 'All Fragments', 'Light Fragments', 'Heavy Fragments'],\n",
       " ['A', '126.00', '108.62', '143.38'],\n",
       " ['Z', '49.00', '42.63', '55.37'],\n",
       " ['TXE / U (MeV)', '32.06', '18.52', '13.54'],\n",
       " ['TKE / KE (MeV)', '185.78', '105.57', '80.21'],\n",
       " ['J ($\\\\hbar$)', ' 9.34', ' 8.85', ' 9.83'],\n",
       " ['parity', ' 0.00', ' 0.00', ' 0.00']]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yields.summaryTable()"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}