gistfile1.txt

{
 "metadata": {
  "name": "Manuscript_Code"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext autoreload\n",
      "%autoreload 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab\n",
      "pylab.rcParams['xtick.major.pad']='8'\n",
      "pylab.rcParams['ytick.major.pad']='8'\n",
      "#import matplotlib.gridspec as gridspec\n",
      "from matplotlib import rc\n",
      "rc('text', usetex=False)\n",
      "rc('font', family='serif')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from os import listdir\n",
      "files = listdir('.')\n",
      "if 'blackouts.txt' not in files:\n",
      "    import urllib\n",
      "    urllib.urlretrieve('https://raw.github.com/jeffalstott/powerlaw/master/manuscript/blackouts.txt', 'blackouts.txt')\n",
      "if 'words.txt' not in files:\n",
      "    import urllib\n",
      "    urllib.urlretrieve('https://raw.github.com/jeffalstott/powerlaw/master/manuscript/words.txt', 'words.txt')\n",
      "if 'worm.txt' not in files:\n",
      "    import urllib\n",
      "    urllib.urlretrieve('https://raw.github.com/jeffalstott/powerlaw/master/manuscript/worm.txt', 'worm.txt')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import genfromtxt\n",
      "blackouts = genfromtxt('blackouts.txt')#/10**3\n",
      "words = genfromtxt('words.txt')\n",
      "worm = genfromtxt('worm.txt')\n",
      "worm = worm[worm>0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_basics(data, data_inst, fig, units):\n",
      "    from powerlaw import plot_pdf, Fit, pdf\n",
      "    annotate_coord = (-.4, .95)\n",
      "    ax1 = fig.add_subplot(n_graphs,n_data,data_inst)\n",
      "    plot_pdf(data[data>0], ax=ax1, linear_bins=True, color='r', linewidth=.5)\n",
      "    x, y = pdf(data, linear_bins=True)\n",
      "    ind = y>0\n",
      "    y = y[ind]\n",
      "    x = x[:-1]\n",
      "    x = x[ind]\n",
      "    ax1.scatter(x, y, color='r', s=.5)\n",
      "    plot_pdf(data[data>0], ax=ax1, color='b', linewidth=2)\n",
      "    from pylab import setp\n",
      "    setp( ax1.get_xticklabels(), visible=False)\n",
      "    #ax1.set_xticks(ax1.get_xticks()[::2])\n",
      "    ax1.set_yticks(ax1.get_yticks()[::2])\n",
      "    locs,labels = yticks()\n",
      "    #yticks(locs, map(lambda x: \"%.0f\" % x, log10(locs)))\n",
      "    if data_inst==1:\n",
      "        ax1.annotate(\"A\", annotate_coord, xycoords=\"axes fraction\", fontsize=14)\n",
      "\n",
      "    \n",
      "    from mpl_toolkits.axes_grid.inset_locator import inset_axes\n",
      "    ax1in = inset_axes(ax1, width = \"30%\", height = \"30%\", loc=3)\n",
      "    ax1in.hist(data, normed=True, color='b')\n",
      "    ax1in.set_xticks([])\n",
      "    ax1in.set_yticks([])\n",
      "\n",
      "    \n",
      "    ax2 = fig.add_subplot(n_graphs,n_data,n_data+data_inst, sharex=ax1)\n",
      "    plot_pdf(data, ax=ax2, color='b', linewidth=2)\n",
      "    fit = Fit(data, xmin=1, discrete=True)\n",
      "    fit.power_law.plot_pdf(ax=ax2, linestyle=':', color='g')\n",
      "    p = fit.power_law.pdf()\n",
      "    #ax2.set_ylim(min(p), max(p))\n",
      "    ax2.set_xlim(ax1.get_xlim())\n",
      "    \n",
      "    fit = Fit(data, discrete=True)\n",
      "    fit.power_law.plot_pdf(ax=ax2, linestyle='--', color='g')\n",
      "    from pylab import setp\n",
      "    setp( ax2.get_xticklabels(), visible=False)\n",
      "    #ax2.set_xticks(ax2.get_xticks()[::2])\n",
      "    if ax2.get_ylim()[1] >1:\n",
      "        ax2.set_ylim(ax2.get_ylim()[0], 1)\n",
      "    \n",
      "    ax2.set_yticks(ax2.get_yticks()[::2])\n",
      "    #locs,labels = yticks()\n",
      "    #yticks(locs, map(lambda x: \"%.0f\" % x, log10(locs)))\n",
      "    if data_inst==1:\n",
      "       ax2.annotate(\"B\", annotate_coord, xycoords=\"axes fraction\", fontsize=14)        \n",
      "       ax2.set_ylabel(r\"$p(X)$\")# (10^n)\")\n",
      "        \n",
      "    ax3 = fig.add_subplot(n_graphs,n_data,n_data*2+data_inst)#, sharex=ax1)#, sharey=ax2)\n",
      "    fit.power_law.plot_pdf(ax=ax3, linestyle='--', color='g')\n",
      "    fit.exponential.plot_pdf(ax=ax3, linestyle='--', color='r')\n",
      "    fit.plot_pdf(ax=ax3, color='b', linewidth=2)\n",
      "    \n",
      "    #p = fit.power_law.pdf()\n",
      "    ax3.set_ylim(ax2.get_ylim())\n",
      "    ax3.set_yticks(ax3.get_yticks()[::2])\n",
      "    ax3.set_xlim(ax1.get_xlim())\n",
      "    \n",
      "    #locs,labels = yticks()\n",
      "    #yticks(locs, map(lambda x: \"%.0f\" % x, log10(locs)))\n",
      "    if data_inst==1:\n",
      "        ax3.annotate(\"C\", annotate_coord, xycoords=\"axes fraction\", fontsize=14)\n",
      "\n",
      "    #if ax2.get_xlim()!=ax3.get_xlim():\n",
      "    #    zoom_effect01(ax2, ax3, ax3.get_xlim()[0], ax3.get_xlim()[1])\n",
      "    ax3.set_xlabel(units)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_data = 3\n",
      "n_graphs = 4\n",
      "f = figure(figsize=(8,11))\n",
      "\n",
      "data = words\n",
      "data_inst = 1\n",
      "units = 'Word Frequency'\n",
      "plot_basics(data, data_inst, f, units)\n",
      "\n",
      "data_inst = 2\n",
      "#data = city\n",
      "#units = 'City Population'\n",
      "data = worm\n",
      "units = 'Neuron Connections'\n",
      "plot_basics(data, data_inst, f, units)\n",
      "\n",
      "data = blackouts\n",
      "data_inst = 3\n",
      "units = 'Population Affected\\nby Blackouts'\n",
      "plot_basics(data, data_inst, f, units)\n",
      "\n",
      "f.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.3, hspace=.2)\n",
      "f.savefig('FigWorkflow.eps', bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n",
        "Calculating best minimal value for power law fit"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Calculating best minimal value for power law fit"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAggAAAINCAYAAABbMYfQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYTUQR0EQgghhNRBHQRCCCGE1PH/vfdFJSSYxhMAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "blackouts = blackouts/10**3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "####\n",
      "import powerlaw\n",
      "fit = powerlaw.Fit(data)\n",
      "fit.power_law.alpha\n",
      "fit.power_law.sigma\n",
      "fit.distribution_compare('power_law', 'exponential')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "(12.754562675882063, 0.1522925560442657)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = words\n",
      "####\n",
      "figPDF = powerlaw.plot_pdf(data, color='b')\n",
      "powerlaw.plot_pdf(data, linear_bins=True, color='r', ax=figPDF)\n",
      "####\n",
      "figPDF.set_ylabel(r\"$p(X)$\")\n",
      "figPDF.set_xlabel(r\"Word Frequency\")\n",
      "savefig('FigPDF.eps', bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEaCAYAAAAsQ0GGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = words\n",
      "fit = powerlaw.Fit(data, discrete=True)\n",
      "####\n",
      "figCCDF = fit.plot_pdf(color='b', linewidth=2)\n",
      "fit.power_law.plot_pdf(color='b', linestyle='--', ax=figCCDF)\n",
      "fit.plot_ccdf(color='r', linewidth=2, ax=figCCDF)\n",
      "fit.power_law.plot_ccdf(color='r', linestyle='--', ax=figCCDF)\n",
      "####\n",
      "figCCDF.set_ylabel(r\"$p(X)$,  $p(X\\geq x)$\")\n",
      "figCCDF.set_xlabel(r\"Word Frequency\")\n",
      "savefig('FigCCDF.eps', bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEaCAYAAAAsQ0GGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "fit = powerlaw.Fit(data)\n",
      "###\n",
      "x, y = fit.cdf()\n",
      "bin_edges, probability = fit.pdf()\n",
      "y = fit.lognormal.cdf(data=[300,350])\n",
      "y = fit.lognormal.pdf()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "####\n",
      "import powerlaw\n",
      "fit = powerlaw.Fit(data)\n",
      "fit.xmin\n",
      "fit.fixed_xmin\n",
      "fit.alpha\n",
      "fit.D\n",
      "fit = powerlaw.Fit(data, xmin=1.0)\n",
      "fit.xmin\n",
      "fit.fixed_xmin\n",
      "fit.alpha\n",
      "fit.D"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "0.37601504850371759"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "####\n",
      "fit = powerlaw.Fit(data, xmin=(250.0, 300.0))\n",
      "fit.fixed_xmin\n",
      "fit.given_xmin\n",
      "fit.xmin"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "272.0"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "fit = powerlaw.Fit(data)\n",
      "####\n",
      "fit = powerlaw.Fit(data, xmax=10000.0)\n",
      "fit.xmax\n",
      "fit.fixed_xmax"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n",
        "Calculating best minimal value for power law fit"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = words\n",
      "#FigCCDFmax = powerlaw.plot_ccdf(data, linewidth=3)\n",
      "fit = powerlaw.Fit(data, discrete=True, xmax=None)\n",
      "FigCCDFmax = fit.plot_ccdf(color='b', label=r\"Empirical, no $x_{max}$\")\n",
      "fit.power_law.plot_ccdf(color='b', linestyle='--', ax=FigCCDFmax, label=r\"Fit, no $x_{max}$\")\n",
      "fit = powerlaw.Fit(data, discrete=True, xmax=1000)\n",
      "fit.plot_ccdf(color='r', label=r\"Empirical, $x_{max}=1000$\")\n",
      "fit.power_law.plot_ccdf(color='r', linestyle='--', ax=FigCCDFmax, label=r\"Fit, $x_{max}=1000$\")\n",
      "#x, y = powerlaw.ccdf(data, xmax=max(data))\n",
      "#fig1.plot(x,y)\n",
      "####\n",
      "FigCCDFmax.set_ylabel(r\"$p(X\\geq x)$\")\n",
      "FigCCDFmax.set_xlabel(r\"Word Frequency\")\n",
      "handles, labels = FigCCDFmax.get_legend_handles_labels()\n",
      "leg = FigCCDFmax.legend(handles, labels, loc=3)\n",
      "leg.draw_frame(False)\n",
      "savefig('FigCCDFmax.eps', bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n",
        "Calculating best minimal value for power law fit"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/alstottjd/Code/powerlaw/powerlaw.py:1031: RuntimeWarning: divide by zero encountered in double_scalars\n",
        "  C = 1.0/C\n",
        "/home/alstottjd/Enthought/lib/python2.7/site-packages/scipy/optimize/optimize.py:301: RuntimeWarning: invalid value encountered in subtract\n",
        "  and max(abs(fsim[0]-fsim[1:])) <= ftol):\n",
        "/home/alstottjd/Code/powerlaw/powerlaw.py:1011: RuntimeWarning: invalid value encountered in zeta\n",
        "  CDF = 1 - zeta(self.alpha, x)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/alstottjd/Code/powerlaw/powerlaw.py:734: RuntimeWarning: invalid value encountered in multiply\n",
        "  likelihoods = f*C\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAEaCAYAAAAlqOH8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAmKBwAgAkKBwBggsIBAJi4AXsVKeebKgxrAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "fit = powerlaw.Fit(data)\n",
      "####\n",
      "fit = powerlaw.Fit(data, xmin=230.0)\n",
      "fit.discrete\n",
      "fit = powerlaw.Fit(data, xmin=230.0, discrete=True)\n",
      "fit.discrete "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "fit = powerlaw.Fit(data)\n",
      "####\n",
      "fit.power_law\n",
      "fit.power_law.alpha\n",
      "fit.power_law.parameter1\n",
      "fit.power_law.parameter1_name\n",
      "fit.lognormal.mu\n",
      "fit.lognormal.parameter1_name\n",
      "fit.lognormal.parameter2_name\n",
      "fit.lognormal.parameter3_name == None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "####\n",
      "fit = powerlaw.Fit(data)\n",
      "R, p = fit.distribution_compare('power_law', 'exponential', normalized_ratio=True)\n",
      "print R, p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n",
        "1.43148048496"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.152292556044\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = worm\n",
      "fit = powerlaw.Fit(data, discrete=True)\n",
      "####\n",
      "fit.distribution_compare('power_law', 'exponential')\n",
      "fit.distribution_compare('power_law', 'truncated_power_law')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n",
        "Assuming nested distributions"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "(-0.081336372762826459, 0.68670761175575712)"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = words\n",
      "fit = powerlaw.Fit(data, discrete=True)\n",
      "####\n",
      "fit.distribution_compare('power_law', 'lognormal')\n",
      "fig = fit.plot_ccdf(linewidth=3, label='Empirical Data')\n",
      "fit.power_law.plot_ccdf(ax=fig, color='r', linestyle='--', label='Power law fit')\n",
      "fit.lognormal.plot_ccdf(ax=fig, color='g', linestyle='--', label='Lognormal fit')\n",
      "####\n",
      "fig.set_ylabel(r\"$p(X\\geq x)$\")\n",
      "fig.set_xlabel(r\"Word Frequency\")\n",
      "handles, labels = fig.get_legend_handles_labels()\n",
      "fig.legend(handles, labels, loc=3)\n",
      "savefig('FigLognormal.eps', bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEaCAYAAAAsQ0GGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n"
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "fit = powerlaw.Fit(data)\n",
      "####\n",
      "fit.loglikelihood_ratio('power_law', 'truncated_power_law')\n",
      "fit.loglikelihood_ratio('exponential', 'stretched_exponential')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n",
        "Assuming nested distributions"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Assuming nested distributions"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/alstottjd/Code/powerlaw/powerlaw.py:1126: RuntimeWarning: invalid value encountered in double_scalars\n",
        "  CDF = 1 - exp((-self.Lambda*x)**self.beta)\n",
        "/home/alstottjd/Code/powerlaw/powerlaw.py:1126: RuntimeWarning: invalid value encountered in power\n",
        "  CDF = 1 - exp((-self.Lambda*x)**self.beta)\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "(-13.024005037666845, 3.3303191937505972e-07)"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "####\n",
      "fit = powerlaw.Fit(data, discrete=True, estimate_discrete=True)\n",
      "fit.power_law.alpha\n",
      "fit = powerlaw.Fit(data, discrete=True, estimate_discrete=False)\n",
      "fit.power_law.alpha"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n",
        "Calculating best minimal value for power law fit"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "2.2691417084814285"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "####\n",
      "fit = powerlaw.Fit(data, discrete=True, xmin=230.0, xmax=9000, discrete_approximation='xmax')\n",
      "fit.lognormal.mu\n",
      "fit = powerlaw.Fit(data, discrete_approximation=100000, xmin=230.0, discrete=True)\n",
      "fit.lognormal.mu\n",
      "fit = powerlaw.Fit(data, discrete_approximation='round', xmin=230.0, discrete=True)\n",
      "fit.lognormal.mu"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "0.39905257607693184"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "####\n",
      "fit = powerlaw.Fit(data)\n",
      "fit.power_law.alpha, fit.power_law.sigma, fit.xmin\n",
      "\n",
      "fit = powerlaw.Fit(data, sigma_threshold=.1)\n",
      "fit.power_law.alpha, fit.power_law.sigma, fit.xmin\n",
      "\n",
      "parameter_range = {'alpha': [2.3, None], 'sigma': [None, .2]}\n",
      "fit = powerlaw.Fit(data, parameter_range=parameter_range)\n",
      "fit.power_law.alpha, fit.power_law.sigma, fit.xmin\n",
      "\n",
      "parameter_range = lambda(self): self.sigma/self.alpha < .05\n",
      "fit = powerlaw.Fit(data, parameter_range=parameter_range)\n",
      "fit.power_law.alpha, fit.power_law.sigma, fit.xmin"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n",
        "Calculating best minimal value for power law fit"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Calculating best minimal value for power law fit"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Calculating best minimal value for power law fit"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "(1.8833765811180314, 0.094168259953067143, 124.0)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "fit = powerlaw.Fit(data, sigma_threshold=.1)\n",
      "print fit.xmin, fit.D, fit.alpha\n",
      "fit = powerlaw.Fit(data)\n",
      "print fit.xmin, fit.D, fit.alpha\n",
      "####\n",
      "from matplotlib.pylab import plot\n",
      "plot(fit.xmins, fit.Ds, label=r'$D$')\n",
      "plot(fit.xmins, fit.sigmas, label=r'$\\sigma$', linestyle='--')\n",
      "plot(fit.xmins, fit.sigmas/fit.alphas, label=r'$\\sigma /\\alpha$', linestyle='--')\n",
      "####\n",
      "ylim(0, .4)\n",
      "legend(loc=4)\n",
      "xlabel(r'$x_{min}$')\n",
      "ylabel(r'$D,\\sigma,\\alpha$')\n",
      "savefig('FigD.eps', bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n",
        "50.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.0998297854528 1.78313986533\n",
        "Calculating best minimal value for power law fit\n",
        "230.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.0606737962944 2.27263721983\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEQCAYAAABiGgneAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbEFYbtaRG/gAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = blackouts\n",
      "####\n",
      "fit = powerlaw.Fit(data, sigma_threshold=.001)\n",
      "fit.power_law.alpha, fit.power_law.sigma, fit.xmin, fit.noise_flag\n",
      "\n",
      "fit.lognormal.mu, fit.lognormal.sigma\n",
      "range_dict = {'mu': [10.5, None]}\n",
      "fit.lognormal.parameter_range(range_dict)\n",
      "fit.lognormal.mu, fit.lognormal.sigma, fit.lognormal.noise_flag\n",
      "initial_parameters = (12, .7)\n",
      "fit.lognormal.parameter_range(range_dict, initial_parameters)\n",
      "fit.lognormal.mu, fit.lognormal.sigma, fit.lognormal.noise_flag"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating best minimal value for power law fit\n",
        "No valid fits found."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "No valid fits found."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "(10.500000000422041, 5.1423189016918585, False)"
       ]
      }
     ],
     "prompt_number": 26
    }
   ],
   "metadata": {}
  }
 ]
}