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opotowsky/gamma-Copy0.ipynb

Created July 7, 2015 15:07
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PyNE "Spectra" for Group Meeting
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gamma-Copy0.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:c2c558bd68c2502166ccb5102f07af49e05830ab8ab112b2b73f9e508ea679c4"
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"nbformat": 3,
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"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": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"snf1 = Material({'U235': 156.729, \n",
" 'U236': 102.103, \n",
" 'U238': 18280.324, \n",
" 'Np237': 13.656, \n",
" 'Pu238': 5.043, \n",
" 'Pu239': 106.343, \n",
" 'Pu240': 41.357, \n",
" 'Pu241': 36.477, \n",
" 'Pu242': 15.387, \n",
" 'Am241': 1.234, \n",
" 'Am243': 3.607, \n",
" 'Cm244': 0.431, \n",
" 'Cm245': 1.263, \n",
" 'Cs137': 2.0}, \n",
" 30000)\n",
"\n",
"snf2 = Material({'U235': 156.729, \n",
" 'U236': 102.103, \n",
" 'U238': 18280.324, \n",
" 'Np237': 13.656, \n",
" 'Pu238': 5.043, \n",
" 'Pu239': 106.343, \n",
" 'Pu240': 41.357, \n",
" 'Pu241': 36.477, \n",
" 'Pu242': 15.387, \n",
" 'Am241': 1.234, \n",
" 'Am243': 3.607, \n",
" 'Cm244': 0.431, \n",
" 'Cm245': 1.263}, \n",
" 30000)\n",
"\n",
"test = Material({'Pu238': 5.043, \n",
" 'Pu239': 106.343, \n",
" 'Pu240': 41.357, \n",
" 'Pu241': 36.477, \n",
" 'Pu242': 15.387, }, 4.5)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def function(material):\n",
" total_intensity = []\n",
" for nuc, frac in material.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",
" total_intensity.append(row)\n",
" if branch > 0:\n",
" print branch, branch*i[0]\n",
" return total_intensity"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"total_intensity = function(snf1)\n",
"\n",
"fig = plt.figure()\n",
"fig.suptitle(r'Test', fontsize=20)\n",
"plt.yscale('log', nonposy='clip')\n",
"for g, _, i, _ in total_intensity:\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'Intensity', fontsize=18)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
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},
{
"metadata": {},
"output_type": "display_data",
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s32PqhP27iIUE+YTyRC42gL8mVrR1aw/EgoPZ4sprJZ3V6H3hrDQQ53Q0DrYi\nlgi/BFhOLBJoLS9egZhLeQmwMlMvjtyY6cNbX8le60Ri9duy7PG9gBOALYiJ/vnAv9Cee5kgttlZ\nRnenE8nh48AfiQn/B4Ff5Y55hNg54CjghizWNeiddOYSV95txPDdO2Z4PUlSA50OXDbLMccTS6zz\nIxbDnnNxTkcDcXhNGr11mT7ZvwvTr5n5KO0LWJ8G7AOcSu/ekyRJ01zE1OGuQ4hhtZVzj61DzOkc\nkN1/PzHZP7/jtezpKGnO6UijdymxQOADxAq1lYgLSh/PHXMvsV3OesR80iNEr+epSiOVJCnnKuBb\nTN3ZoAzvyV73O7MdKEmSJEmSJEmSJEmSJEmSJEmSJEmSJKkh/htlPZmtjq6irwAAAABJRU5ErkJg\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f1f84b242d0>"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"total_intensity = function(snf2)\n",
"\n",
"fig = plt.figure()\n",
"fig.suptitle(r'Test', fontsize=20)\n",
"plt.yscale('log', nonposy='clip')\n",
"for g, _, i, _ in total_intensity:\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'Intensity', fontsize=18)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1.0 0.017\n",
"1.0 0.037\n",
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"0.978 0.0121272\n",
"0.978 0.018582\n"
]
},
{
"ename": "ValueError",
"evalue": "Data has no positive values, and therefore can not be log-scaled.",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-43-1d29908db6fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnonposy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'clip'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtotal_intensity\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'k-'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'#FF8C00'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;31m#plt.axis([0, 1200, 0, 10**5])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'$Energy [eV]$'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'Intensity'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/pymodules/python2.7/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2985\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2986\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2987\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2988\u001b[0m \u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2989\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 4139\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4141\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4142\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mautoscale_view\u001b[0;34m(self, tight, scalex, scaley)\u001b[0m\n\u001b[1;32m 1961\u001b[0m \u001b[0my1\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mdelta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1962\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_tight\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1963\u001b[0;31m \u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mylocator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1964\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ybound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/pymodules/python2.7/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36mview_limits\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m 1483\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mminpos\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminpos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1484\u001b[0m raise ValueError(\n\u001b[0;32m-> 1485\u001b[0;31m \u001b[0;34m\"Data has no positive values, and therefore can not be \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1486\u001b[0m \"log-scaled.\")\n\u001b[1;32m 1487\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Data has no positive values, and therefore can not be log-scaled."
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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4BvhWpUhHVN3/Exn/cBn/8NQ59l7JmwC2Eb7sW80Ebs3arwJWAa8H7gZ+G/jv7HPXERKI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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f1f78592e10>"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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