opotowsky/gamma (2).ipynb

## gamma (2).ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:1066cd0c2299a8ae03643f05ac1cec1959ea77d049784469bbefc3f0601b60fa"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import cymetric as cym\n",
      "from pyne import data\n",
      "from pyne import nucname\n",
      "from pyne.material import Material\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "snf = Material({'U238': 0.94, 'U235': 0.01, 'Pu239': 0.015, 'Cs137': 0.035}, 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 175
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tbl = []\n",
      "for nuc, frac in snf.comp.iteritems():\n",
      "    gammy = data.gamma_energy(nuc)\n",
      "    intensity = data.gamma_photon_intensity(nuc)\n",
      "    for g, i in zip(gammy, intensity):\n",
      "        if np.isnan(i[0]) == False:\n",
      "            row = g[0], g[1], i[0], i[1]\n",
      "            tbl.append(row)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 176
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "fig.suptitle(r'Fake', fontsize=20)\n",
      "plt.yscale('log', nonposy='clip')\n",
      "for g, _, i, _ in tbl:\n",
      "    plt.plot((g, g), (0, i), 'k-', color='#FF8C00')\n",
      "plt.xlabel(r'$Energy [eV]$', fontsize=18); plt.ylabel(r'$\\phi \\left[\\frac{1}{cm^2 s}\\right]$', fontsize=18)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZeC/wMyJwfLiD860gglDabGJ9vwVEcsUZRPD7GvB64Arixl6BvQRJuTJTYLq0C+esuoPo\nMV1D9KLatYZYQSJtPrG1+1bgN8Aq4CTgl8DZwP8CVnbwmZKkLplp5Yf/CVzYg3pk/TnpuSSIIbwj\nmn1zOvWxMgaVrGolZcntKNRH3djuoqofSxL1Qke/rZNy8pcu6bAqKqWyBoXqsG4Z26ZM1e5Zt2RJ\ndtfKmQLTD4HbMvu0xu7J+Hw7mEhyIPl6e4PXTuFGgZLUnG70nGYKTK/J9NN6Zx2xs+4wsUDsaUQC\nRFOyvotZbSprr0Qqkeof8Vn2mMqQMr0SWAvMI+aVzgKeAM4HbgHuBVYDG/pVQWlgmfGpNmQxx/QU\n4obZpxIBYiyDc7aiUU/opuTRMofylAv2GFUA/RjKm8m+xP1AzwV2A/YgUr8/0uF5+8qhPElqTh6H\n8i4C/hZ4JrAP8HLgxcAnOjyvpF6ZbrjNoTj1QSs9piXArsB/Ad8llhT6BXBz6jXfA15NrKxwFPDt\nbKopSRoUrfSYFhNLB12RvO984FhghFj6Z8/kdePAe+jdVhZS8XSjJ2LvRiXRzhzTI8C3kseexFDe\nS4m17/ZOnl9D9KwKqTTJD16oJHVZHpMf9gL2A0aTB0RwOooIVh8DHgfuJob/Hujw83rC5AdJak4e\nkx8+TdxHtH+q7BHgBmI1hw8BHyVWYijqzbqSpB7qNDBtIrad+D6x59GLiW0m/h54KHnN40RSxGc6\n/Cyp2BxalZqSxcoP1wBvA84khuvuSs7byVYWKpqyXXQbtads7ZRyKKvVxW9JHk8HHgN+ldF5JUkD\nJuttLx6a+SWSJDVWhkVcMzcyMtK1DbCkjjiUqJwZHR3NPJO5rBsFdsR0cUlqTh7TxYvmOcDngC/3\nuyKSpPoGLTD9mMgglIrBoTsNoEELTJLywICraRQ1MC0nNiRcX1O+ANhI3Pi7KCk7E7gMOKhntZMG\nRbMBJqtAVHseA1wpFTUwrSCCUNpsYFlSfjixs+1hxA3A7yXW6dsfuBJ4IROBS+AveNGU/f+r7O3T\ntIqalbcGGK4pmw9sBrYmx6uAk4ANqdf8HDivy3WTJHWgqIGpnrnAttTxduCIdk6UThevjEGlk1pJ\nUgl1Y7uLqjIFpvGsTjTpPqal2eXmZ27pLLggs2YrTwZlKMuf4cKq3bPO+5jq2wEMpY6HiF5Ty1z5\nQWqgmYA5KEFVQHdWfihTYFoHHErMPc0BTgOua+dE1R1spYFW1gBT1nb1SaVSMTAlVgJrgXnEvNJZ\nwBPA+cQq5/cCq5mc+CCpCAwcA6+oc0xnNCi/KXl0pNpjstckSdPrRhJEUXtMXeVQniZp9y94//LX\nAHAoT/nkBXgwDPr/86C3v4cMTFK7vFB110zf30H7/g9Qew1MkqRcMTDV0dR9TGX/66Ve+1ppcy+/\nP0X+v+hn3bvx2UX+v1BbvI+pR0qd/OCFQ4PIn/uuMflBkvJi6SwDXpcYmCR1rpfDvHkNBul65bWO\nBWFgkqRuMki1zMAkKb+8qA8kA5MkdYuBtS0Gpjrc9kIDr9cXVC/ghWW6eI+UOl1cKqqskwv6FQxL\nFoS7kS5e1NXFO3EScCLwVOCfgFv7Wx1JUtog9pi+DrwdOI/YTFBSHpSsJ6H2DWJgqroIWNbvSkjK\nCZfRyo0iB6blwBiwvqZ8AbAR2AQsSsrOBC4DDgJmAZcQGwre3ZOaSiomA0hfFDkwrSCCUNpsohe0\nADic2On2MOAa4L3AA8A7gWOBU4Fze1XZTPhLIhWHv69tK3LywxpguKZsPrAZ2JocryKSHTakXvOp\n5NFQOsOkMgaVTmrZKn+YVTZFzqBTQ93YUr2qyIGpnrnAttTxduCIVk8yKfVx6ZJO6yRJpVOpVCbd\nVrNkSXbXyiIP5dUz3u8KSIViT2Qqvyd9V7bAtAMYSh0PEb2mlrjyw4DygiS1zJUfZrYOOJSYe5pD\n3Kd0XasnGeiVH5wP0CBwi4rMuFHgZCuBtcA8Yl7pLOAJ4HzgFuBeYDWTEx8kSTlX5OSHMxqU35Q8\n2lbtMQ1sr0mSmtSN7LwiB6auybpbKkllVf0j3qw8DQ7H/6WBY2CSJOWKgakO08VLzl6YlJlupIs7\nx1SHc0yS1BznmCRJpWdgkiTlioFJkpQrBiZJUq4YmCRJuWJgkiTlioGpDu9jkqTmeB9Tj3gfkyQ1\nx/uYOvdc4NPAtcBb+1wXSVIdgxaYNgLvAE4Hju9zXSRJdQxaYAL4M+AGYFW/KyJJmqqogWk5MAas\nrylfQPSKNgGLkrIzgcuAg5Lj64ETgIXdr6YkqVVFTX5YAVwOfD5VNhtYBrwK2AHcCVwHXJM8AI4G\nTgF2B27rVWUlSc0ramBaAwzXlM0HNgNbk+NVwEnAhtRrbk8e00pn5VXGoNJuLSWppLqxpXpVUQNT\nPXOBbanj7cAR7ZxoUrr40uxSICWpLKpp4lWmi9c33u8KSJI6V6bAtAMYSh0PEb2mlrnygyQ1pxsr\nP5QpMK0DDiXmnuYApxHJDy0bGRmZ1EWVJNVXqVQMTImVwFpgHjGvdBbwBHA+cAtwL7CayYkPkqQC\nKGrywxkNym9KHh2p9pjsNUnS9LqRnVfUwNRVLuIqSc1xEVdJUukZmCRJuWJgqsN0cXVk6ax+10Dq\nGTcK7BHnmCSpOc4xSZJKz8AkScoVA5MkKVcMTJKkXDEwSZJyxcAkScoVA1Md3sckSc1x24ts7Anc\nCZzY6AVueyFJzXHbi2xcSGyJIUnKoUELTMcRezU92O+K9Mvo5n7XoLtsX7GVeQi9zG3LWlED03Jg\nDFhfU74A2AhsAhYlZWcClwEHAUcDLwHeAJwDDNyiZqNb+l2D7rJ9xVbmi3eZ25a1oq6VtwK4HPh8\nqmw2sAx4FbCDmEe6DrgmeQBclPy7kOg1jfeispKk5hU1MK0BhmvK5gObga3J8SrgJOpvr351tyom\nSepMkYeyhoHrgeclx6cCxxNDdABvAo4A3tnieTcDh2RQP0kaJFuA38/iREXtMdWT1bBcJt9YSVJ7\nipr8UM8OYCh1PARs71NdJEkDaJjJWXm7EF3JYWAOcDdwWM9rJUkaSCuBB4BfA9uAs5LyE4AfEfNE\nH2rjvPXSzYtkCLgN+CFwD/CupHx/4Fbg/wLfAPZNvedDRHs3Av+jZzXtzGzgLmKOEcrVvn2BrxBJ\nO/cS86Rlat+HiJ/P9cCXgN0odvvq3brSTnv+NDnHJuCTXaxvK+q17e+In83vA18D9kk9V6S2FcZs\nIqANA7tSzB7XgcALk6/3IoL0YcClxIoXEAH34uTrw4l27kq0ezPFGN59H/BF4nYAKFf7rgbOTr7e\nhfjFL0v7hoH7iGAEsQLLQordvlcAf8zki3cr7akmoH2PyCwGuJH4I7nf6rXtOCb+Dy6muG0rjCOB\nm1PHH0weRfYvxH1dG4EDkrIDk2OIv3DSPcObiRuQ8+zZwL8CxzDRYypL+/YhLty1ytK+/Yk/lvYj\ngu71xIWu6O0bZvLFu9X2PIvJt7WcDlzZjYq2YZipCxlUnQx8Ifk607bl7a+PfppLDAtWbU/KimqY\n+Gvnu8QvyVhSPsbEL81BTE4QKUKbLwM+AOxMlZWlfc8hbvxeAfw78Fli0eGytO/nwFLgJ8RQ/C+I\nIa+ytK+q1fbUlu+gGO08m+gBQcZtMzBNKNMqEHsBXwXeDTxS89w407c1z9+H1wA/I+aXGt2DV+T2\n7QL8CXBF8u9jTO21F7l9hwDvIf5oOoj4OX1TzWuK3L56ZmpPUf0l8DgxT5g5A9OEsqSb70oEpWuI\noTyIv9oOTL5+FnFxh6ltfnZSllcvBV4L/JhIgHkl0c6ytG978rgzOf4KEaD+g3K070XAWuA/gSeI\nyfMjKU/7qlr5edyelD+7pjzP7XwL8GrgjamysrQtd8qQbj6LWD/wspryS5kY//0gUycs5xDDSFso\nzmogRzMxx1Sm9n0bmJd8PUK0rSztewGRLboHUc+rgb+g+O0bZmryQ6vt+S6RgTmLfCUIDDO5bQuI\nrMqn17yuiG0rjE7Tzfvt5cTcy93EcNddxA/B/kTCQL301Q8T7d1ILOlUFEczkZVXpva9gOgxpdNx\ny9S+C5lIF7+a6OEXuX3VW1ceZ+LWlXbaU02p3gx8quu1bk5t284mUr7vZ+L6ckXq9UVqmyRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkpQfeVzeQ1L33Az8iljV+4oZXtuKdxFrF+5GrKAiScrI0cC1wC+JHZJX\nEQvFXpN8/X+A3xJbUxTRbW2857PEwps7iaWSPlrz/K7E0js7ga3EGn+SpIxtJ1Zpr+cdwN/0sC5Z\naicwAbydCDwvbPD8XGI1+++0eX7pSW57IU31XGK/oFsaPH8rsZDlINma/HtIg+f/EngzseCn1BED\nkzTVK5N/v5EqW8DEUv+ziRWyB0l1y/fn1HnuTGILkv/Xu+qozHbpdwWkHHolsUT/1uR4X+CdwInJ\n8Y+Sf98F/CHRS1gMvI0Y7ppPDAOurjnv7sS28PsTm8cNEdur/BfwfuKiP0TsCvpyYruAtcAFqXMc\nCZwP3AvsTczvPEjs+XMCsRnfy4hN3P4zec/rgfOA4xq0d7p6Vd1PzK39Xs17n5XU8z0Nzi1J6tAs\n4CFgA5HgcG1yfG3N6w4jtgX/IyIYfZzISAM4iYmgULU3sI4IQFWnAK9LzrGQCAg7gc8lz98KfCH1\n+uOI3VGHk+N5RLB4I/A04KKkfENy3qrVqfPUzjFNV69aPyay+tKuZPJ+Q+3OYUmSGngBERz+PFV2\nKpHwkLYQ2I8ITg8RG/pVnU30YtI+w0RPC6KncRWxBfcriADxOuBRYM869dqb2IL83TXn2An8LnAa\ncDDwfGLb8oNSr/spcG7ydW3gaFSvA+rU4VtET7LqTcRW92kGJnXMoTxpsur80rdTZTuJi3LV+cCy\n5OtjiJ5NetjrGGA0dbwPkRgwSuzguivwCPA+4OdEwKm+7w7gsTr1egvwTCYPD1aINO6fJA+ATwDf\nJHYehUjkOABYU+ecM9Wr1n3EEOMsIqC+hPheSJK66HpiS+xG5gNvSB3fx0RvBGK+5mGil1X1YiK4\nHTPDZ/8AWNTgua8R80pp/wh8saZsO3BO6vhcokdXle7RNFuvqg8nrx8melr713mNPSZ1zKw8acJs\n4Cjg9mmefz/w5eT4YOIinb4Yvw74DfB14oL/UqIXAjGkVusPkn+fQSRSNLqw78bUwHQ0k3tC+xND\neN9LlVWIXlg9jzZRr7RqZt5HiFT6er0qqWMGJmnCi4m5nHrDXr9DTPRvIgIPxLDfT5ncw3op8JXk\nNa8lVorYSPSGnpd63e7ECgp7JccVYghvXYO6fZPJSQbvIpIf0kHn8VTdAA4lMvXqtQciSWKmeqVV\nA9NeNL75WOqYc0xSrGbwISLVepxIbKimVu9CzO28CNiDyTeY/h5Th9K+lJzrUiKrb2dSfgpwCXA4\nkZwwG/gHJuaChoklj3ZS3zIigHwM+AWRpPAwcE/qNY8SKzR8ELg7qetTaRyYmqlX2mZiqPAvpjmf\nJGlALQOum+E1S4j08vTISLfngJxjUsccypPy7wCmJii8iqn3FP0NEzcBPwU4HfgkjXthkiS15Vom\nD629lRjC2yNV9gxijunNyfEHiASF2TXnssek3HOOScq/rxNJDRcSmXdziJtyf5V6zYPE0kUHEvNb\njxK9p9/2tKaSJLXoVuAmJq8gkYX3Jef9xkwvlCRJkiRJkiRJkiRJkiRJkiRJkiRJkiSl/H+Cs7dZ\n5OBhNgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb57eac6850>"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "totes = []\n",
      "for nuc, frac in snf.comp.iteritems():\n",
      "    gammy = data.gamma_energy(nuc)\n",
      "    intensity = data.gamma_photon_intensity(nuc)\n",
      "    chirren = data.decay_children(nuc)\n",
      "    for n in chirren:\n",
      "        branch, br_err = data.decay_photon_branch_ratio(nuc, n)\n",
      "        for g, i in zip(gammy, intensity):\n",
      "            if np.isnan(i[0]) == False:\n",
      "                row = g[0], g[1], frac*branch*i[0], i[1]\n",
      "                totes.append(row)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 171
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "fig.suptitle(r'Test_I', fontsize=20)\n",
      "plt.yscale('log', nonposy='clip')\n",
      "for g, _, i, _ in totes:\n",
      "    plt.plot((g, g), (0, i), 'k-', color='#FF8C00')\n",
      "#plt.axis([0, 1200, 0, 10**5])\n",
      "plt.xlabel(r'$Energy [eV]$', fontsize=18); plt.ylabel(r'$\\phi \\left[\\frac{1}{cm^2 s}\\right]$', fontsize=18)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fb581eb7090>"
       ]
      }
     ],
     "prompt_number": 172
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Test w gaussian using FWHM as 2.35*sigma"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}