spectrum_sheider.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from os.path import join as pjoin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import obr\n",
    "from functionaries import *\n",
    "from time_functions import stamp_Ymd\n",
    "from os_functions import verify_folder_existence\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import rc\n",
    "rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
    "## for Palatino and other serif fonts use:\n",
    "#rc('font',**{'family':'serif','serif':['Palatino']})\n",
    "rc('text', usetex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#SOURCES_FOLDER = \"mcnp_out_medial\\\\\"\n",
    "SOURCES_FOLDER = \"mcnp_out_20150520\\\\\"\n",
    "FOLDER_TO_SAVE_IMAGES = pjoin(SOURCES_FOLDER, '__images', stamp_Ymd())\n",
    "verify_folder_existence(FOLDER_TO_SAVE_IMAGES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['mcnp_out_20150520\\\\NP0a', 'mcnp_out_20150520\\\\PP0a'],\n",
       "       ['mcnp_out_20150520\\\\NP5a', 'mcnp_out_20150520\\\\PP5a'],\n",
       "       ['mcnp_out_20150520\\\\NP10a', 'mcnp_out_20150520\\\\PP10a'],\n",
       "       ['mcnp_out_20150520\\\\NP15a', 'mcnp_out_20150520\\\\PP15a'],\n",
       "       ['mcnp_out_20150520\\\\NP20a', 'mcnp_out_20150520\\\\PP20a'],\n",
       "       ['mcnp_out_20150520\\\\NP25a', 'mcnp_out_20150520\\\\PP25a'],\n",
       "       ['mcnp_out_20150520\\\\NP30a', 'mcnp_out_20150520\\\\PP30a'],\n",
       "       ['mcnp_out_20150520\\\\NP35a', 'mcnp_out_20150520\\\\PP35a'],\n",
       "       ['mcnp_out_20150520\\\\NP40a', 'mcnp_out_20150520\\\\PP40a'],\n",
       "       ['mcnp_out_20150520\\\\NP45a', 'mcnp_out_20150520\\\\PP45a'],\n",
       "       ['mcnp_out_20150520\\\\NP50a', 'mcnp_out_20150520\\\\PP50a']], \n",
       "      dtype='|S23')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_temp = [('NP0a', 'PP0a'),\n",
    "              ('NP5a',  'PP5a' ),\n",
    "         ('NP10a', 'PP10a'),\n",
    "         ('NP15a',  'PP15a' ),\n",
    "         ('NP20a', 'PP20a'),\n",
    "         ('NP25a',  'PP25a'),\n",
    "         ('NP30a',  'PP30a' ),\n",
    "         ('NP35a',  'PP35a' ),\n",
    "         ('NP40a', 'PP40a'),\n",
    "         ('NP45a',  'PP45a' ),\n",
    "         ('NP50a',  'PP50a' )]\n",
    "#names = names_temp\n",
    "names = []\n",
    "for name in names_temp:\n",
    "    names.append((pjoin(SOURCES_FOLDER,name[0]), pjoin(SOURCES_FOLDER, name[1])))\n",
    "names = np.array(names)\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mcnp_out_20150520\\__images\\20150602\\back flow.spectrum.sample len = 5cm. mode = full.png\n"
     ]
    },
    {
     "data": {
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       "Gr5BEEIIKYQ+CEIIIYWwgSCEEFIIGwhCCCGFsIEghBBSCBsIQgghhfx/JIhRnWbR/UgAAAAASUVO\n",
       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa20f978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "left, values, width = plot_sum_en_spectrum_in_back_going_flow(names[1],[0,5,15,50], ['red','blue','green','y'],pjoin(SOURCES_FOLDER, 'PP0a'),FOLDER_TO_SAVE_IMAGES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "TRZmqaSIWJJbxtSwZdbP+oZCDzV7SDg0cSDpAe006vegdg3rpzGfSf3KgBezPvf0ANeWGEtHIqKl\n",
       "D9jKDKrq1qhcG1vu8nch6ZnJDGBBnR605dlUF+bd3jp+O6m7hpb9LEmnlBpMB3LuuZf0gLN2f39y\n",
       "6fLoho4KIx9XlWRvZmbdU5UyjpmZdZGTvZnZFOBkb2Y2BTjZm5lNAU72ZmZTgJO9mdkU4GRvZjYF\n",
       "ONmbmU0B/wPL8alfyVin7QAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb42b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(left, values)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shneider_function_coefficients = [-6.72023666e-05,\n",
    "                                      6.47731559e-04,\n",
    "                                      -1.13328468e-04,\n",
    "                                      1.38962184e-05,\n",
    "                                      -6.03541123e-07]\n",
    "def shneider_function(x_arr):\n",
    "    ans = np.zeros(len(x_arr))\n",
    "    for power, multiplier in enumerate(shneider_function_coefficients):\n",
    "        ans += np.power(x_arr, power)*multiplier\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "shn = shneider_function(left + np.array(width)/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "ZLhLUgcZ7pLUQYa7JHXQ/wMsbcnj/UvrkAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5c4ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(left + np.array(width)/2, shn)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "IOUAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb88a860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(left, shn*values, width)\n",
    "#plt.yscale('log')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}