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Created May 31, 2017 22:07
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"yt : [INFO ] 2017-05-31 17:07:01,488 Calculating time from 1.000e+00 to be 4.250e+17 seconds\n",
"yt : [INFO ] 2017-05-31 17:07:01,489 Assuming length units are in kpc/h (comoving)\n",
"yt : [INFO ] 2017-05-31 17:07:01,522 Parameters: current_time = 4.2498606916019334e+17 s\n",
"yt : [INFO ] 2017-05-31 17:07:01,524 Parameters: domain_dimensions = [2 2 2]\n",
"yt : [INFO ] 2017-05-31 17:07:01,525 Parameters: domain_left_edge = [ 0. 0. 0.]\n",
"yt : [INFO ] 2017-05-31 17:07:01,527 Parameters: domain_right_edge = [ 16000. 16000. 16000.]\n",
"yt : [INFO ] 2017-05-31 17:07:01,528 Parameters: cosmological_simulation = 1\n",
"yt : [INFO ] 2017-05-31 17:07:01,530 Parameters: current_redshift = 4.4408920985e-16\n",
"yt : [INFO ] 2017-05-31 17:07:01,531 Parameters: omega_lambda = 0.7\n",
"yt : [INFO ] 2017-05-31 17:07:01,533 Parameters: omega_matter = 0.3\n",
"yt : [INFO ] 2017-05-31 17:07:01,534 Parameters: hubble_constant = 0.7\n",
"yt : [INFO ] 2017-05-31 17:07:01,543 Allocating for 4.195e+06 particles (index particle type 'all')\n",
"yt : [INFO ] 2017-05-31 17:07:02,140 Identified 2.887e+05 octs\n"
]
}
],
"source": [
"import yt\n",
"\n",
"ds = yt.load('gizmo_cosmology_plus/snap_N128L16_151.hdf5')\n",
"\n",
"ds.field_list\n",
"\n",
"agrid = ds.arbitrary_grid(ds.domain_left_edge, ds.domain_right_edge, [64, 64, 64])\n",
"\n",
"dm_dens = agrid['deposit', 'PartType1_cic']"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x125b0da90>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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1iLzEMX6pE9cMrJBFlQQ25nKaPq1TyK7NiuMx+eJ29N2tYAeHDg8NXdgZxvvO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\nfAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11776aa20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.colors import LogNorm\n",
"\n",
"plt.imshow(dm_dens[32, :, :].d, norm=LogNorm())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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