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|
{
"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\n",
"import h5py"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f = h5py.File(\"/home/atmyers/AMReX-Codes/WarpX/Examples/Physics_applications/plasma_acceleration/lab_frame_data/snapshot00001\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'Bx',\n",
" u'By',\n",
" u'Bz',\n",
" u'Ex',\n",
" u'Ey',\n",
" u'Ez',\n",
" u'beam',\n",
" u'driver',\n",
" u'jx',\n",
" u'jy',\n",
" u'jz',\n",
" u'plasma_e',\n",
" u'plasma_p',\n",
" u'rho']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.keys()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x7f1bdf7d8310>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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PY1ZkeHm2ixsHp7B3MMVsNkExy9yTXhHpltCzNKgTG2kWG6kSK26fhUvXZL3wxxZVugn7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\nYINOCCEjgQ06IYSMhP8PDPnsHuL8AxkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1be6b46890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.pcolormesh(f['Ez'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6.83593750e-05, 6.44531250e-05, 6.05468750e-05, 5.66406250e-05,\n",
" 5.27343750e-05, 4.88281250e-05, 4.49218750e-05, 4.10156250e-05,\n",
" 3.71093750e-05, 3.32031250e-05, 2.92968750e-05, 2.53906250e-05,\n",
" 2.14843750e-05, 1.75781250e-05, 1.36718750e-05, 9.76562500e-06,\n",
" 5.85937500e-06, 1.95312500e-06, -1.95312500e-06, -5.85937500e-06,\n",
" -9.76562500e-06, -1.36718750e-05, -1.75781250e-05, -2.14843750e-05,\n",
" -2.53906250e-05, -2.92968750e-05, -3.32031250e-05, -3.71093750e-05,\n",
" -4.10156250e-05, -4.49218750e-05, -4.88281250e-05, -5.27343750e-05,\n",
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" 6.83876027e-05, 6.44813139e-05, 6.05765108e-05, 5.66733414e-05,\n",
" 5.27719786e-05, 4.88726974e-05, 4.49758894e-05, 4.10820839e-05,\n",
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" -4.03914652e-05, 4.04056198e-05, -3.72771388e-05, -2.27206907e-05,\n",
" 2.27565833e-05, 3.72944264e-05, -3.43446168e-05, -1.96046475e-05,\n",
" 3.43655171e-05, 2.47940760e-05, 1.95969324e-05, -2.47585931e-05,\n",
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" 2.96653201e-05, 3.12839786e-05, -2.95750830e-05, -3.12447920e-05,\n",
" 2.98133659e-05, 2.93830002e-05, -2.93115653e-05, -2.97589493e-05,\n",
" -2.74057545e-05, 2.73784529e-05, -1.52491705e-05, -7.01325890e-05,\n",
" 1.48208154e-05, 7.01417548e-05, 6.46954805e-05, -6.46934720e-05,\n",
" -6.08183925e-05, 6.08202138e-05, 5.71486907e-05, -5.71455313e-05,\n",
" 5.33805358e-05, -5.33765815e-05, 4.96068508e-05, -4.96021507e-05,\n",
" 4.58433705e-05, -4.58378061e-05, -4.20780772e-05, 4.20846972e-05,\n",
" -3.83192376e-05, 3.83267274e-05, -3.45189971e-05, 3.45246001e-05,\n",
" 3.02811971e-05, -3.02860657e-05, -2.55059266e-05, 2.54917241e-05,\n",
" 2.08842578e-05, -2.08741456e-05, -1.82611992e-05, 1.83114039e-05,\n",
" -1.86607858e-05, -2.15563476e-05, 2.17295129e-05, 1.87602997e-05,\n",
" 1.81046006e-05, -1.82336607e-05, -2.63248837e-06, 2.13295028e-06,\n",
" 6.90404154e-05, -6.90337876e-05, -5.33277092e-06, 5.37905295e-06,\n",
" -6.62852213e-06, 6.75322114e-06, -6.29603687e-05, -9.14861847e-06,\n",
" 6.29561096e-05, 9.10958529e-06, 5.94327700e-05, -5.94359575e-05,\n",
" 5.41091993e-06, -5.86246514e-06, 8.01605544e-06, -8.25184434e-06,\n",
" 1.20612782e-06, -5.56780989e-05, 5.56764499e-05, -1.13647407e-06,\n",
" -5.15539960e-05, 5.15522972e-05, 1.36286373e-05, -1.33311883e-05,\n",
" -1.07985780e-06, 1.28446733e-06, 4.73653447e-05, -4.73671295e-05,\n",
" 1.31694055e-05, -1.38439857e-05, 1.79913802e-05, -1.80938646e-05,\n",
" 7.67216018e-06, -7.66591238e-06, 4.31119712e-05, -4.31142018e-05,\n",
" 5.19116877e-06, -5.26783859e-06, 3.87692231e-05, -3.87716695e-05,\n",
" -3.43907719e-05, -2.50114317e-05, 2.49860945e-05, 3.43895562e-05,\n",
" -3.00286808e-05, 3.00260697e-05, 5.65256480e-06, 2.39189621e-05,\n",
" -7.01103598e-05, 7.01176702e-05, -6.56694175e-05, 6.56745667e-05,\n",
" -6.18081621e-05, 6.18134766e-05, 5.81516122e-05, -5.81451298e-05,\n",
" 5.45628593e-05, -5.45549278e-05, -5.10607201e-05, 5.10704921e-05,\n",
" 4.76995594e-05, -4.76874269e-05, -4.44607445e-05, 4.44757956e-05,\n",
" 4.14264304e-05, -4.14079029e-05, -3.85517138e-05, 3.85739892e-05,\n",
" -3.58777162e-05, 3.59033324e-05, 3.34342649e-05, -3.34045786e-05,\n",
" -6.26378993e-06, -2.48158423e-05, 5.80313093e-06, -1.87047769e-05,\n",
" 1.86318709e-05, -5.73577625e-06, 3.67553286e-05, -3.77985357e-05,\n",
" 3.41772109e-05, 5.08392922e-05, -3.43192673e-05, -5.19919206e-05,\n",
" -1.87295260e-05, 1.91867538e-05, -6.76168870e-05, 6.76075499e-05,\n",
" -6.22893808e-05, 6.22728331e-05, 6.57150301e-05, 5.94638207e-05,\n",
" -5.94759189e-05, 2.34749156e-05, -2.36513726e-05, -6.69613180e-05,\n",
" 5.55959128e-05, -5.56051385e-05, 5.13624294e-05, -5.13702079e-05,\n",
" -4.73392969e-05, 4.73342692e-05, -4.33540639e-05, 4.33511061e-05,\n",
" 6.11405021e-06, -6.23564966e-06, -3.93497071e-05, 3.93483355e-05,\n",
" -3.54041574e-05, 3.54086610e-05, -2.84968045e-05, 2.85234903e-05,\n",
" 3.18223636e-05, -3.18050094e-05, 8.12908796e-05, 5.21495080e-05,\n",
" -5.23177145e-05, -8.26201799e-05, 8.36702777e-06, 1.82127884e-05,\n",
" -9.71071152e-06, -1.77502807e-05])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f['plasma_e/x'][:]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"278"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f['plasma_e/x'].size"
]
}
],
"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",
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