nb_pfns.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prompt Fission Neutron Spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": false
   },
   "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",
    "#import fission.py #-- my own library\n",
    "sys.path.append('/Users/talou/git/evaluation-tools/')\n",
    "import fission as fission\n",
    "import fissionHistories as fh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotbib Version:  2.0.0\n"
     ]
    }
   ],
   "source": [
    "### rcParams are the default parameters for matplotlib\n",
    "import matplotlib as mpl\n",
    "\n",
    "print (\"Matplotbib Version: \", mpl.__version__)\n",
    "\n",
    "mpl.rcParams['font.size'] = 18\n",
    "mpl.rcParams['font.family'] = 'Helvetica', 'serif'\n",
    "#mpl.rcParams['font.color'] = 'darkred'\n",
    "mpl.rcParams['font.weight'] = 'normal'\n",
    "\n",
    "mpl.rcParams['axes.labelsize'] = 18.\n",
    "mpl.rcParams['xtick.labelsize'] = 18.\n",
    "mpl.rcParams['ytick.labelsize'] = 18.\n",
    "mpl.rcParams['lines.linewidth'] = 2.\n",
    "\n",
    "font = {'family' : 'serif',\n",
    "        'color'  : 'darkred',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 18,\n",
    "        }\n",
    "\n",
    "mpl.rcParams['xtick.major.pad']='10'\n",
    "mpl.rcParams['ytick.major.pad']='10'\n",
    "\n",
    "mpl.rcParams['image.cmap'] = 'inferno'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we read the default CGMF output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "h = fh.FissionHistories (\"cgmf-histories.dat\", fmt=\"new\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neutron energies in the center-of-mass and laboratory reference frames can be obtained as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Ecm  = h.getNeutronEcm()\n",
    "Elab = h.getNeutronElab()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting the list of neutron energies in the center-of-mass frame of the emitting fragment for the fission event number 154:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.746, 0.582, 0.593, 1.111]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ecm[154]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All neutron energies can then be binned in histograms, and analyzed and plotted that way. The `CGMF` python package **fissionHistories** come with a function to directly extract PFNS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eout,pfns =h.pfns()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which returns two arrays: (1) the outgoing energy grid (in MeV); (2) the prompt fission neutron spectrum (in n/MeV/nu-bar). Here's how to plot the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q1SbpkqQRW8/F9RU0Xb/7Fdh3peky/uMJxyRJ0mZ3bmf/4O6DSfYDHgB8uTM/Sl9J7pXk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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c24fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=figure(figsize(14,6))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.step(eout,pfns,'r-',linewidth=3)\n",
    "plt.xlim(0.1,20.0)\n",
    "plt.ylim(1e-5,1.0)\n",
    "plt.xlabel(\"Outgoing Neutron Energy (MeV)\")\n",
    "plt.ylabel(r\"PFNS (n/f/MeV/$\\overline{\\nu}$)\")\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.step(eout,pfns,'r-',linewidth=3)\n",
    "plt.xlim(0.1,20.0)\n",
    "plt.xscale('log')\n",
    "plt.xlabel(\"Outgoing Neutron Energy (MeV)\")\n",
    "plt.ylabel(r\"PFNS (n/f/MeV/$\\overline{\\nu}$)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Average neutron energies can be simply obtained as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.33338324555 2.10256863337\n"
     ]
    }
   ],
   "source": [
    "print (h.meanNeutronEcm(),h.meanNeutronElab())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or, equivalently:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.33338324555 2.10256863337\n"
     ]
    }
   ],
   "source": [
    "print (np.mean(Ecm.sum()), np.mean(Elab.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}