Fit-Covid-CH.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy\n",
    "import os\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import pymultinest\n",
    "import json\n",
    "import corner\n",
    "import warnings\n",
    "from glob import glob\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "class simple_multinest():\n",
    "    \n",
    "    def __init__(self, x, y, model, parameters):\n",
    "        \n",
    "        self._x = x\n",
    "        self._y = y\n",
    "        self._model = model\n",
    "        self._parameters = parameters\n",
    "        \n",
    "        #parameters is a dictionar with { 'par_name': {'min': val, 'max': val}}\n",
    "    \n",
    "    def create_uniform_prior(self, par_name):\n",
    "        \n",
    "        def uniform_transform(x):\n",
    "            return self._parameters[par_name]['min']+ x * (self._parameters[par_name]['max']- self._parameters[par_name]['min'])\n",
    "        \n",
    "        self._parameters[par_name].update(transform = uniform_transform)\n",
    "        self._parameters[par_name].update(aftertransform=lambda x: x)\n",
    "        \n",
    "    def make_all_uniform(self):\n",
    "        \n",
    "        for kk in self._parameters.keys():\n",
    "            self.create_uniform_prior(kk)\n",
    "    \n",
    "    \n",
    "    def test_statistics(self, par):\n",
    "        \n",
    "        #print(par)\n",
    "        s = self._model(self._x, par)\n",
    "    \n",
    "        res = s - self._y + self._y * numpy.log( self._y / s)\n",
    "    \n",
    "        \n",
    "        \n",
    "        return 2 * numpy.sum(res)\n",
    "    \n",
    "    def log_likelihood(self, cube, ndim, nparams):\n",
    "        \n",
    "        par = {}\n",
    "        for i, k in enumerate(self._parameters.keys()):\n",
    "            t=self._parameters[k]\n",
    "            par.update({k : t['aftertransform'](cube[i])})\n",
    "        \n",
    "        #print(par)\n",
    "        return -0.5 * self.test_statistics(par)\n",
    "    \n",
    "    def create_prior_function(self):\n",
    "        \"\"\"\n",
    "        Creates a single prior transformation function from parameters\n",
    "        \"\"\"\n",
    "\n",
    "        def prior(cube, ndim, nparams):\n",
    "                try:\n",
    "                        for i, k in enumerate(self._parameters.keys()):\n",
    "                                t=self._parameters[k]\n",
    "                                transform = t['transform']\n",
    "                                cube[i] = transform(cube[i])\n",
    "                except Exception as e:\n",
    "                        print('ERROR: Exception in prior function. Faulty transformations specified!')\n",
    "                        print('ERROR:', e)\n",
    "                        raise e\n",
    "\n",
    "        return prior\n",
    "    \n",
    "    def nested_run(self, prior_function = None, sampling_efficiency = 0.3,\n",
    "        n_live_points = 400, evidence_tolerance = 0.5,\n",
    "        outputfiles_basename = 'nested-', verbose=True, clean=False, **kwargs):\n",
    "        \n",
    "        n_params = len(self._parameters)\n",
    "        \n",
    "        prior_function=self.create_prior_function()\n",
    "        \n",
    "        if clean:\n",
    "            to_be_cleaned=glob(outputfiles_basename+'*')\n",
    "            for ff in to_be_cleaned:\n",
    "                os.remove(ff)\n",
    "        \n",
    "        pymultinest.run(self.log_likelihood, prior_function, n_params,\n",
    "                sampling_efficiency = sampling_efficiency, n_live_points = n_live_points,\n",
    "                outputfiles_basename = outputfiles_basename,\n",
    "                verbose=verbose, **kwargs)\n",
    "        \n",
    "        paramnames = [str(k) for k in self._parameters.keys()]\n",
    "        json.dump(paramnames, open('%s-params.json' % outputfiles_basename, 'w'), indent=4)\n",
    "        \n",
    "        \n",
    "        self._analyzer = pymultinest.Analyzer(n_params = len(self._parameters),\n",
    "                                                outputfiles_basename = outputfiles_basename)\n",
    "        \n",
    "        return outputfiles_basename\n",
    "\n",
    "    def marginal_plots(self,minweight=1e-4,  title='Title', **kwargs):\n",
    "        \"\"\"\n",
    "        Create marginal plots\n",
    "        \n",
    "        * analyzer: A instance of pymultinest.Analyzer\n",
    "        * d: if more than 20 dimensions, by default only 1d marginal distributions\n",
    "           are plotted. set d=2 if you want to force a 2d matrix plot\n",
    "        \n",
    "        \"\"\"\n",
    "        prefix = self._analyzer.outputfiles_basename\n",
    "        parameters = json.load(open(prefix + '-params.json'))\n",
    "\n",
    "        data = analyzer.get_data()[:,2:]\n",
    "        weights = analyzer.get_data()[:,0]\n",
    "\n",
    "        mask = weights > minweight\n",
    "        \n",
    "        \n",
    "        with warnings.catch_warnings():\n",
    "                warnings.simplefilter(\"ignore\")\n",
    "                corner.corner(data[mask,:], weights=weights[mask],\n",
    "                        labels=parameters, show_titles=True, **kwargs)\n",
    "                \n",
    "        axes=plt.gcf().get_axes()\n",
    "        my_ax=axes[0]\n",
    "        for ax in axes[1:]:\n",
    "            pos=ax.get_position()\n",
    "\n",
    "\n",
    "            if pos.x0 > my_ax.get_position().x0:\n",
    "                my_ax=ax\n",
    "            if pos.y0 > my_ax.get_position().y0:\n",
    "                my_ax=ax\n",
    "\n",
    "        my_ax.text(0.5,0.5,title,  horizontalalignment='center',\n",
    "           verticalalignment='center', transform=my_ax.transAxes, fontsize=14)\n",
    "        \n",
    "        plt.savefig(prefix + 'corner.pdf')\n",
    "        \n",
    "    def model_plot(self, nsample=100, xlabel='Days', ylabel='Poissonian variable', yscale='log', title='Title' ):\n",
    "        \n",
    "        posterior = self._analyzer.get_equal_weighted_posterior()\n",
    "        print(len(posterior))\n",
    "        if nsample > len(posterior):\n",
    "            raise RuntimeError('Cannot plot more than sample')\n",
    "        \n",
    "        chosen = numpy.random.choice(numpy.arange(len(posterior)), replace=False, size=nsample)\n",
    "        posterior = posterior[chosen,:]\n",
    "        \n",
    "        \n",
    "        plt.figure()\n",
    "        plt.errorbar(self._x, self._y, yerr=numpy.sqrt(self._y), marker='o', linestyle='')\n",
    "        plt.xlabel(xlabel)\n",
    "        plt.ylabel(ylabel)\n",
    "        plt.yscale(yscale)\n",
    "        plt.title(title)\n",
    "        \n",
    "        xx=numpy.linspace(self._x.min(), self._x.max()+1, nsample)\n",
    "        \n",
    "        for k, row in enumerate(posterior):\n",
    "            par = {}\n",
    "            for i, k in enumerate(self._parameters.keys()):\n",
    "                t=self._parameters[k]\n",
    "                par.update({k : t['aftertransform'](row[i])})\n",
    "            yy=self._model(xx, par)\n",
    "            \n",
    "            plt.plot(xx,yy, 'r-', alpha=0.5, linewidth=3)\n",
    "        plt.savefig(self._analyzer.outputfiles_basename+'plt.pdf')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def pl(x,par):\n",
    "    t1  = par['t1']\n",
    "    b1  = par['b1']\n",
    "    \n",
    "    return numpy.exp(b1*(x-t1))\n",
    "    \n",
    "\n",
    "\n",
    "def broken_pl(x, par):\n",
    "    \n",
    "    #print(par)\n",
    "    \n",
    "    break_value = par['break']\n",
    "    t1  = par['t1']\n",
    "    t2  = par['t2']\n",
    "    b1  = par['b1']\n",
    "    b2  = par['b2']\n",
    "    \n",
    "    ind = x > break_value\n",
    "    \n",
    "    \n",
    "    \n",
    "    y = numpy.exp(b1*(x-t1))\n",
    "    \n",
    "    if numpy.sum(ind) >0:\n",
    "        y[ind]=numpy.exp(b2*(x[ind]-t2))\n",
    "    \n",
    "    return y\n",
    "\n",
    "def multiple_pl(x, par):\n",
    "    \n",
    "    #print(par)\n",
    "    \n",
    "    break_value1 = par['break1']\n",
    "    break_value2 = par['break2']\n",
    "    t1  = par['t1']\n",
    "    t2  = par['t2']\n",
    "    t3  = par['t3']\n",
    "    b1  = par['b1']\n",
    "    b2  = par['b2']\n",
    "    b3  = par['b3']\n",
    "    \n",
    "    tb_1=break_value1\n",
    "    tb_2=break_value2\n",
    "    \n",
    "    if tb_2 < tb_1:\n",
    "        tb_1=break_value2\n",
    "        tb_2=break_value1\n",
    "    \n",
    "    \n",
    "    ind1 = x > tb_1\n",
    "    ind2 = x > tb_2\n",
    "        \n",
    "    y = numpy.exp(b1*(x-t1))\n",
    "    \n",
    "    if numpy.sum(ind1) >0:\n",
    "        y[ind1]=numpy.exp(b2*(x[ind1]-t2))\n",
    "    if numpy.sum(ind2) >0:\n",
    "        y[ind2]=numpy.exp(b3*(x[ind2]-t3))\n",
    "    \n",
    "    \n",
    "    return y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Date   AG  AI   AR   BE   BL   BS   FR  GE  GL  ...   SO  SZ  TG  \\\n",
      "0   2020-03-06  NaN NaN  NaN  0.0  NaN  NaN  NaN   0 NaN  ...  NaN NaN NaN   \n",
      "1   2020-03-07  NaN NaN  NaN  0.0  NaN  NaN  NaN   0 NaN  ...  NaN NaN NaN   \n",
      "2   2020-03-08  NaN NaN  NaN  0.0  NaN  NaN  NaN   0 NaN  ...  NaN NaN NaN   \n",
      "3   2020-03-09  NaN NaN  NaN  0.0  NaN  NaN  NaN   1 NaN  ...  NaN NaN NaN   \n",
      "4   2020-03-10  NaN NaN  NaN  0.0  NaN  NaN  NaN   2 NaN  ...  NaN NaN NaN   \n",
      "5   2020-03-11  NaN NaN  NaN  0.0  NaN  NaN  NaN   2 NaN  ...  NaN NaN NaN   \n",
      "6   2020-03-12  NaN NaN  NaN  0.0  NaN  NaN  NaN   2 NaN  ...  NaN NaN NaN   \n",
      "7   2020-03-13  NaN NaN  NaN  0.0  NaN  NaN  NaN   2 NaN  ...  NaN NaN NaN   \n",
      "8   2020-03-14  NaN NaN  NaN  0.0  NaN  NaN  NaN   2 NaN  ...  NaN NaN NaN   \n",
      "9   2020-03-15  NaN NaN  NaN  0.0  NaN  2.0  NaN   4 NaN  ...  NaN NaN NaN   \n",
      "10  2020-03-16  NaN NaN  NaN  1.0  NaN  4.0  NaN   4 NaN  ...  NaN NaN NaN   \n",
      "11  2020-03-17  NaN NaN  NaN  NaN  NaN  4.0  NaN   4 NaN  ...  NaN NaN NaN   \n",
      "12  2020-03-18  NaN NaN  NaN  NaN  NaN  4.0  NaN   5 NaN  ...  NaN NaN NaN   \n",
      "13  2020-03-19  NaN NaN  NaN  NaN  NaN  4.0  1.0   5 NaN  ...  NaN NaN NaN   \n",
      "14  2020-03-20  NaN NaN  NaN  2.0  2.0  4.0  1.0   8 NaN  ...  NaN NaN NaN   \n",
      "15  2020-03-21  1.0 NaN  1.0  3.0  3.0  5.0  1.0   9 NaN  ...  NaN NaN NaN   \n",
      "16  2020-03-22  1.0 NaN  1.0  3.0  3.0  5.0  1.0   9 NaN  ...  NaN NaN NaN   \n",
      "17  2020-03-23  1.0 NaN  1.0  5.0  3.0  5.0  1.0   9 NaN  ...  1.0 NaN NaN   \n",
      "18  2020-03-24  2.0 NaN  2.0  6.0  3.0  5.0  1.0  12 NaN  ...  1.0 NaN NaN   \n",
      "\n",
      "      TI  UR    VD    VS  ZG   ZH     CH  \n",
      "0    NaN NaN   NaN   NaN NaN  0.0    NaN  \n",
      "1    NaN NaN   NaN   NaN NaN  0.0    NaN  \n",
      "2    NaN NaN   NaN   NaN NaN  0.0    NaN  \n",
      "3    NaN NaN   NaN   NaN NaN  0.0    NaN  \n",
      "4    NaN NaN   NaN   NaN NaN  0.0    NaN  \n",
      "5    NaN NaN   NaN   NaN NaN  0.0    NaN  \n",
      "6    NaN NaN   NaN   NaN NaN  0.0    NaN  \n",
      "7    NaN NaN   2.0   1.0 NaN  0.0    NaN  \n",
      "8    5.0 NaN   3.0   1.0 NaN  NaN    NaN  \n",
      "9    6.0 NaN   4.0   1.0 NaN  NaN    NaN  \n",
      "10   8.0 NaN   5.0   1.0 NaN  1.0    NaN  \n",
      "11   NaN NaN   5.0   3.0 NaN  NaN    NaN  \n",
      "12  14.0 NaN   5.0   3.0 NaN  NaN    NaN  \n",
      "13  15.0 NaN   7.0   4.0 NaN  3.0    NaN  \n",
      "14   NaN NaN  12.0   4.0 NaN  3.0    NaN  \n",
      "15  28.0 NaN  15.0   9.0 NaN  3.0   83.0  \n",
      "16  37.0 NaN  15.0  10.0 NaN  3.0   95.0  \n",
      "17  48.0 NaN  17.0  12.0 NaN  5.0  116.0  \n",
      "18  53.0 NaN  16.0  12.0 NaN  5.0  127.0  \n",
      "\n",
      "[19 rows x 28 columns]\n"
     ]
    }
   ],
   "source": [
    "#data_type='cases'\n",
    "data_type='fatalities'\n",
    "canton='VD'\n",
    "model_name='pl'\n",
    "\n",
    "\n",
    "if data_type=='cases':\n",
    "    data = pd.read_csv(\"https://raw.githubusercontent.com/daenuprobst/covid19-cases-switzerland/master/covid19_cases_switzerland.csv\")\n",
    "if data_type=='fatalities':\n",
    "    data = pd.read_csv(\"https://raw.githubusercontent.com/daenuprobst/covid19-cases-switzerland/master/covid19_fatalities_switzerland.csv\")\n",
    "\n",
    "print(data)\n",
    "data = data.filter(items=['Date', canton])  # only keep data for one canton (or whole CH) in dataframe\n",
    "data.rename(columns={canton: \"TotalPosTests1\"}, inplace=True)\n",
    "data = data.dropna()  # remove dates without available data (na)\n",
    "newdata = data\n",
    "startrow = 0\n",
    "for x in data['TotalPosTests1']:\n",
    "    if x == 0:\n",
    "        startrow+=1\n",
    "newdata = newdata.iloc[startrow:]\n",
    "inhabitantsCH = 8570000.0\n",
    "datearray = newdata['Date'].to_numpy()\n",
    "for x in datearray:\n",
    "    x = datetime.strptime(x, '%Y-%m-%d')\n",
    "datetimearray = [datetime.strptime(x, '%Y-%m-%d').date() for x in datearray]\n",
    "try:\n",
    "    firstdate = datetimearray[1]\n",
    "except IndexError:\n",
    "    sys.exit('No cases in database for {}.'.format(canton))\n",
    "\n",
    "datesincezero = [float((x - firstdate).days) + 1.0 for x in datetimearray]\n",
    "poscasearray = newdata['TotalPosTests1'].to_numpy().tolist()\n",
    "x = numpy.array(datesincezero)\n",
    "y = numpy.array(poscasearray)\n",
    "\n",
    "#data.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analysing data from 2020-03-24-pl-fatalities-VD-.txt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2020-03-24-pl-fatalities-VD-'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "if model_name == 'broken_pl':\n",
    "    parameters = {\n",
    "        'break' : {'min': x.min()+1, 'max' : x.max()-1} , \n",
    "        't1' : {'min': -40, 'max' : 0.} , \n",
    "        't2' : {'min': -200, 'max' : 0.} ,  \n",
    "        'b1'  : {'min': 0, 'max' : 2.} , \n",
    "        'b2' : {'min': 0, 'max' : 2.} \n",
    "    }\n",
    "    model=brokel_pl\n",
    "    \n",
    "\n",
    "if model_name == 'multiple_pl':\n",
    "    parameters = {\n",
    "        'break1' : {'min': x.min()+1, 'max' : 11.} , \n",
    "        'break2' : {'min': 11, 'max' : x.max()-1} , \n",
    "        't1' : {'min': -40, 'max' : 0.} , \n",
    "        't2' : {'min': -200, 'max' : 0.} ,  \n",
    "        't3' : {'min': -200, 'max' : 0.} ,  \n",
    "        'b1'  : {'min': 0, 'max' : 2.} , \n",
    "        'b2' : {'min': 0, 'max' : 2.} ,\n",
    "        'b3' : {'min': 0, 'max' : 2.} \n",
    "    }\n",
    "    model=multiple_pl\n",
    "\n",
    "if model_name == 'pl':\n",
    "    parameters = {\n",
    "        't1' : {'min': -40, 'max' : 0.} , \n",
    "        'b1'  : {'min': 0, 'max' : 2.} \n",
    "    }\n",
    "    model=pl\n",
    "\n",
    "\n",
    "today=datetime.today().strftime('%Y-%m-%d')\n",
    "\n",
    "outputfiles_basename = today + '-' + model_name + '-'+ data_type+ '-' + canton+'-'\n",
    "\n",
    "my_multinest=simple_multinest(x, y, model, parameters)\n",
    "\n",
    "my_multinest.make_all_uniform()\n",
    "\n",
    "my_multinest.nested_run(outputfiles_basename = outputfiles_basename, clean=True)\n",
    "\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analysing results...\n",
      "  analysing data from 2020-03-24-pl-fatalities-VD-.txt\n",
      "\n",
      "\n",
      "Parameter estimation summary\n",
      "****************************\n",
      "\n",
      "       parameter name: median, 10%, q90% quantile\n",
      "  --------------------\n",
      "                   t1: -5.477  -9.210 -3.042 \n",
      "                   b1: 0.180  0.141 0.219 \n",
      "\n",
      "\n",
      "Model evidence: ln(Z) = -8.19 +- 0.03\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# analyse results\n",
    "print('analysing results...')\n",
    "analyzer = pymultinest.Analyzer(n_params = len(parameters),\n",
    "        outputfiles_basename = outputfiles_basename)\n",
    "s = analyzer.get_stats()\n",
    "# store information in readable, hierarchical format\n",
    "json.dump(s, open(outputfiles_basename + 'stats.json', 'w'), indent=4)\n",
    "\n",
    "#   print out summary\n",
    "print()\n",
    "print()\n",
    "print('Parameter estimation summary')\n",
    "print('****************************')\n",
    "print()\n",
    "print(' %20s: median, 10%%, q90%% quantile' % ('parameter name'))\n",
    "print(' ', '-'*20)\n",
    "for t, m in zip(parameters.keys(), s['marginals']):\n",
    "        print(' %20s: %.3f  %.3f %.3f ' % (t, m['median'], m['q10%'], m['q90%']))\n",
    "print()\n",
    "print()\n",
    "print('Model evidence: ln(Z) = %.2f +- %.2f' % (s['global evidence'], s['global evidence error']))\n",
    "print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1363\n"
     ]
    },
    {
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       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabel= \"Days since %s\"%firstdate.strftime('%Y-%m-%d')\n",
    "ylabel= '# of '+data_type+' (BAG)'\n",
    "yscale='log'\n",
    "my_multinest.model_plot(100, xlabel, ylabel, yscale, title=canton+ ' ' + data_type)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"550\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_multinest.marginal_plots(title=canton+ ' ' + data_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model comparison\n",
      "****************\n",
      "\n",
      "model 2020-03-24-VD-: log10(Z) =   -69.6  XXX ruled out\n",
      "model 2020-03-24-bkn2-VD-: log10(Z) =    -1.2    <-- GOOD\n",
      "model 2020-03-24-bkn-VD-: log10(Z) =     0.0    <-- GOOD\n",
      "\n",
      "The last, most likely model was used as normalization.\n",
      "Uniform model priors are assumed, with a cut of log10(30) to rule out models.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from math import log, log10\n",
    "\n",
    "prefixes=['2020-03-24-bkn2-VD-',  '2020-03-24-bkn-VD-',  '2020-03-24-VD-']\n",
    "\n",
    "models = dict([(f, json.load(open(f + \"stats.json\"))['global evidence']) for f in prefixes])\n",
    "\n",
    "best = max(models, key=models.__getitem__)\n",
    "Zbest = models[best]\n",
    "for m in models: models[m] -= Zbest\n",
    "Ztotal = log(sum(numpy.exp([Z for Z in list(models.values())])))\n",
    "limit = 30 # for example, Jeffreys scale for the Bayes factor\n",
    "\n",
    "print()\n",
    "print('Model comparison')\n",
    "print('****************')\n",
    "print()\n",
    "for m in sorted(models, key=models.__getitem__):\n",
    "        Zrel = models[m]\n",
    "        print('model %-10s: log10(Z) = %7.1f %s' % (m, Zrel / log(10),\n",
    "                ' XXX ruled out' if Zrel < Ztotal - log(limit) else '   <-- GOOD' ))\n",
    "\n",
    "print()\n",
    "print('The last, most likely model was used as normalization.')\n",
    "print('Uniform model priors are assumed, with a cut of log10(%s) to rule out models.' % limit)\n",
    "print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}