dft_tools/different_moves.ipynb

{
 "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": [
        "[<matplotlib.lines.Line2D at 0x3bbb7d0>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD9CAYAAAC7iRw+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nItVJhFBPil9cl3PnYPx4yKtN
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}