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scopatz/eds

Created November 14, 2012 18:10
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EAF Data Source
Raw
eds
{
"metadata": {
"name": "eds"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Sources\n",
"\n",
"Below are examples of how to grab cross sections from the cinder \n",
"and simple data sources and re-discretize them."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"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)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EAF Data Source"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"eds = EAFDataSource()\n",
"rx = eds.reaction('U235', 'a')\n",
"rx"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 2,
"text": [
"array([ 3.12743000e-03, 1.79947000e-03, 1.45970000e-03,\n",
" 1.05536000e-03, 5.73392000e-04, 4.58749000e-04,\n",
" 3.70669000e-04, 2.99866000e-04, 2.44442000e-04,\n",
" 1.94097000e-04, 1.32066000e-04, 1.01302000e-04,\n",
" 7.55698000e-05, 5.11123000e-05, 3.51971000e-05,\n",
" 2.46585000e-05, 1.74354000e-05, 1.24045000e-05,\n",
" 8.83855000e-06, 6.56467000e-06, 4.94574000e-06,\n",
" 3.78109000e-06, 2.93520000e-06, 2.28974000e-06,\n",
" 1.96626000e-06, 1.73785000e-06, 1.45776000e-06,\n",
" 1.17165000e-06, 9.48172000e-07, 6.08618000e-07,\n",
" 5.21465000e-07, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00])"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"eds.dst_group_struct = dst_e_g\n",
"rxc = eds.discretize('U235', 'a')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"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]')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<matplotlib.text.Text at 0x376abd0>"
]
},
{
"output_type": "display_data",
"png": 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++++rbdu2Wrp0qfr27auvv/5aP//8s2bNmqWPP/7YWCjTOEMFAN+wY6FeELDF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\n",
"text": [
"<matplotlib.figure.Figure at 0x3760450>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
}
],
"metadata": {}
}
]
}
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