{ "metadata": { "name": "different_moves.ipynb" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from pytriqs.archive import HDFArchive\n", "R = HDFArchive('histo.h5', 'r') # Opens the file myfile.h5 in readonly mode \n", "H = R['H'] \n", "P = HDFArchive('params.h5', 'r') # Opens the file myfile.h5 in readonly mode \n", "pl = P['pl'].real[0]\n", "pr = P['pr'].real[0]\n", "xmax = P['xmax'].real[0]\n", "N_Cycles = P['N_Cycles'].real[0]\n", "Length_Cycle = P['Length_Cycle'].real[0]\n", "\n", "temp=(pr+pl)/2\n", "pr/=temp\n", "pl/=temp\n", "sigma=sqrt(Length_Cycle*min(pr,pl))\n", "\n", "plot(H, 'b')\n", "x=[i for i in range(100)]\n", "y=[ 1/ (sqrt(2*pi)*sigma) * exp( - (x[i]-xmax)**2 / (2*sigma**2) ) for i in range(100)]\n", "plot(x, y, 'g')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 8, "text": [ "[]" ] }, { "output_type": "display_data", "png": 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Lc+bMISkpiezsbFavXk1OTk69MYmJieTl5ZGbm8uyZcuYPXs2AK1atSI1NZW9e/eyf/9+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\nAABJRU5ErkJggg==\n" } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }