mirror of
https://github.com/triqs/dft_tools
synced 2024-12-26 14:23:38 +01:00
63 lines
17 KiB
Plaintext
63 lines
17 KiB
Plaintext
{
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"metadata": {
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"name": "different_moves.ipynb"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"from pytriqs.archive import HDFArchive\n",
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"R = HDFArchive('histo.h5', 'r') # Opens the file myfile.h5 in readonly mode \n",
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"H = R['H'] \n",
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"P = HDFArchive('params.h5', 'r') # Opens the file myfile.h5 in readonly mode \n",
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"pl = P['pl'].real[0]\n",
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"pr = P['pr'].real[0]\n",
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"xmax = P['xmax'].real[0]\n",
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"N_Cycles = P['N_Cycles'].real[0]\n",
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"Length_Cycle = P['Length_Cycle'].real[0]\n",
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"\n",
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"temp=(pr+pl)/2\n",
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"pr/=temp\n",
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"pl/=temp\n",
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"sigma=sqrt(Length_Cycle*min(pr,pl))\n",
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"\n",
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"plot(H, 'b')\n",
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"x=[i for i in range(100)]\n",
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"y=[ 1/ (sqrt(2*pi)*sigma) * exp( - (x[i]-xmax)**2 / (2*sigma**2) ) for i in range(100)]\n",
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"plot(x, y, 'g')"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 8,
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"text": [
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"[<matplotlib.lines.Line2D at 0x3bbb7d0>]"
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]
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},
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{
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"output_type": "display_data",
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"png": 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Rj9Xit1gszJkzh6SkJLKzs1m9ejU5OTn1xiQmJpKXl0dubi7Lli1j9uzZdb+b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\nAABJRU5ErkJggg==\n"
|
|
}
|
|
],
|
|
"prompt_number": 8
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": []
|
|
}
|
|
],
|
|
"metadata": {}
|
|
}
|
|
]
|
|
} |