data_sources_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Sources\n",
    "\n",
    "Below are examples of how to grab cross sections from the EAF \n",
    "and simple data sources and re-discretize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyne.xs.data_source import *\n",
    "from pyne.bins import stair_step\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rc('font', family='serif', size=14)\n",
    "import numpy as np\n",
    "dst_e_g = np.logspace(1, -7, 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EAF Data Source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.40601e-04, 4.13527e-04, 4.52339e-04, 5.35260e-04, 7.79656e-04,\n",
       "       9.12824e-04, 1.05027e-03, 1.20057e-03, 1.33202e-03, 1.46200e-03,\n",
       "       1.60595e-03, 1.67206e-03, 1.76457e-03, 1.90211e-03, 2.03703e-03,\n",
       "       2.12614e-03, 2.15500e-03, 2.15923e-03, 2.17015e-03, 2.19708e-03,\n",
       "       2.24992e-03, 2.34009e-03, 2.47761e-03, 2.65621e-03, 2.80624e-03,\n",
       "       2.93429e-03, 3.18937e-03, 3.54963e-03, 3.99022e-03, 4.53866e-03,\n",
       "       5.21324e-03, 6.05776e-03, 7.06523e-03, 9.26177e-03, 1.27481e-02,\n",
       "       1.75162e-02, 2.23344e-02, 2.54474e-02, 2.88533e-02, 3.21686e-02,\n",
       "       3.59024e-02, 4.03127e-02, 4.31805e-02, 4.57085e-02, 4.57084e-02,\n",
       "       4.57112e-02, 4.87787e-02, 5.18315e-02, 5.62859e-02, 6.08943e-02,\n",
       "       6.55405e-02, 7.01358e-02, 7.41851e-02, 7.78783e-02, 8.13216e-02,\n",
       "       8.57563e-02, 8.88694e-02, 9.13386e-02, 9.53971e-02, 9.82263e-02,\n",
       "       1.00158e-01, 1.04962e-01, 1.08964e-01, 1.11877e-01, 1.14201e-01,\n",
       "       1.16384e-01, 1.19622e-01, 1.23457e-01, 1.27270e-01, 1.31726e-01,\n",
       "       1.35999e-01, 1.40273e-01, 1.44433e-01, 1.49346e-01, 1.55223e-01,\n",
       "       1.60902e-01, 1.69406e-01, 1.83582e-01, 1.92772e-01, 1.99107e-01,\n",
       "       2.07917e-01, 2.18980e-01, 2.24834e-01, 2.28807e-01, 2.28834e-01,\n",
       "       2.28851e-01, 2.32272e-01, 2.47665e-01, 2.60721e-01, 2.69491e-01,\n",
       "       2.77787e-01, 2.86525e-01, 2.95251e-01, 3.03786e-01, 3.12412e-01,\n",
       "       3.20788e-01, 3.28606e-01, 3.35689e-01, 3.45583e-01, 3.55016e-01,\n",
       "       3.64635e-01, 3.74740e-01, 3.83871e-01, 3.96374e-01, 4.20446e-01,\n",
       "       4.36756e-01, 4.49877e-01, 4.66706e-01, 4.97313e-01, 5.05926e-01,\n",
       "       5.32346e-01, 5.64841e-01, 5.50892e-01, 5.98351e-01, 6.17782e-01,\n",
       "       6.72248e-01, 6.96237e-01, 6.99460e-01, 7.01140e-01, 7.19296e-01,\n",
       "       7.33180e-01, 7.24884e-01, 7.69404e-01, 7.77250e-01, 8.32799e-01,\n",
       "       9.33925e-01, 1.01020e+00, 1.07310e+00, 1.21651e+00, 1.27445e+00,\n",
       "       1.30837e+00, 1.52976e+00, 1.62957e+00, 1.69051e+00, 1.74094e+00,\n",
       "       1.83856e+00, 1.95794e+00, 1.97871e+00, 2.60955e+00, 2.70133e+00,\n",
       "       2.82330e+00, 3.85308e+00, 4.03064e+00, 3.91338e+00, 4.59505e+00,\n",
       "       4.20223e+00, 5.61362e+00, 7.39548e+00, 8.33983e+00, 1.02707e+01,\n",
       "       1.11389e+01, 1.29232e+01, 8.42540e+00, 2.31086e+01, 1.80067e+01,\n",
       "       3.46571e+01, 1.94390e+01, 4.63772e+01, 1.76541e+01, 6.54149e+01,\n",
       "       3.43397e+01, 2.86602e+01, 2.89308e+01, 1.97519e+01, 1.58082e+01,\n",
       "       3.79141e+00, 1.33372e+01, 3.45039e+00, 1.42551e+01, 1.37460e+01,\n",
       "       7.05691e+00, 8.53781e+00, 1.27605e+01, 3.89028e+01, 9.05356e+01])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eds = EAFDataSource()\n",
    "rx = eds.reaction('U235', 'gamma')\n",
    "rx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "eds.dst_group_struct = dst_e_g\n",
    "rxc = eds.discretize('U235', 'gamma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Cross Section [barns]')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb725e652b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,7))\n",
    "plt.loglog(*stair_step(eds.src_group_struct, rx), figure=fig)\n",
    "plt.loglog(*stair_step(eds.dst_group_struct, rxc), figure=fig)\n",
    "plt.xlabel('E [MeV]')\n",
    "plt.ylabel('Cross Section [barns]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Data Source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "sds = SimpleDataSource(dst_group_struct=dst_e_g)\n",
    "print(sds.exists)\n",
    "rx = sds.reaction('U233', 'absorption')\n",
    "rxc = sds.discretize('U233', 'absorption')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Cross Section [barns]')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb725f284a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,7))\n",
    "plt.loglog(sds.src_group_struct[:-1], rx, figure=fig)\n",
    "plt.loglog(sds.dst_group_struct[:-1], rxc, figure=fig)\n",
    "plt.xlabel('E [MeV]')\n",
    "plt.ylabel('Cross Section [barns]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data sources are caches, which means that subsequent requests for \n",
    "the same data are very fast! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20 ms, sys: 0 ns, total: 20 ms\n",
      "Wall time: 16.8 ms\n",
      "\n",
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 33.1 µs\n",
      "\n",
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 21.5 µs\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%time sds.reaction('U238', 'fiss')\n",
    "print()\n",
    "%time sds.reaction('U238', 'fission')\n",
    "print()\n",
    "%time sds.reaction('U238', 'fiss')\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 1
}