{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting system in REP\n",
    "\n",
    "REP has simple internal represntation of plots prepared in report. \n",
    "\n",
    "In this notebook some details are given. This may help you to use REP efficiently, but **it is not needed to use REP** at all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are different plotting backends supported:\n",
    "* __matplotlib__ (default, de-facto standard plotting library), \n",
    "* __ROOT__ (the library used by CERN employer),\n",
    "* __bokeh__ (open-source package with interactive plots) \n",
    "\n",
    "\n",
    "Typically this works like:\n",
    "```python\n",
    "plot_object = report.roc()\n",
    "plot_object.plot()\n",
    "```\n",
    "\n",
    "So, report functions return an object, which contains all the information to be plotted.\n",
    "It's internal structire is always as simple as possible and can be inspected.\n",
    "\n",
    "To draw the plot with different backends use the following functions:\n",
    "* __matplotlib__ plot()\n",
    "* __ROOT__ plot_tmva()\n",
    "* __bokeh__ plot_bokeh()\n",
    "\n",
    "Also __mpld3__ library allows you to turn matplotlib plots into interactive (see examples below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# magic function to show matplotlib plots in the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import matplotlib\n",
    "import mpld3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FunctionsPlot\n",
    "\n",
    "Contains plots of lines (with labels, axes, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rep.plotting import FunctionsPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n_points = 200\n",
    "x_array = numpy.linspace(-5, 5, n_points)\n",
    "first_func = (x_array, numpy.sin(6 * numpy.tanh(x_array)))\n",
    "second_func = (x_array, numpy.sinc(x_array))\n",
    "third_func = (numpy.sin(x_array) * numpy.linspace(0, 1, n_points), numpy.cos(x_array) * numpy.linspace(0, 1, n_points))\n",
    "\n",
    "\n",
    "function_plot = FunctionsPlot({'first': first_func, 'second': second_func})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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IGYEah5YGDh2SOgCQU5xDejocdhiUl5vgQUREnKfAQTxJOZXRz8k+XpO9hle+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c3a2f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function_plot.plot(new_plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matplotlib using mpld3 interactive plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el7576245664358561960300029\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el7576245664358561960300029\", {\"axes\": [{\"xlim\": [-5.0, 5.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"x\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 15.0, \"position\": [0.5, -0.064516129032258063], \"rotation\": -0.0, \"id\": \"el757624568962384\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"y\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 15.0, \"position\": [-0.045285359801488831, 0.5], \"rotation\": -90.0, \"id\": \"el757624570905744\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"example\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 15.0, \"position\": [0.5, 1.0128008192524323], \"rotation\": -0.0, \"id\": \"el757624571133072\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624571454992\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"second\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.92586848635235752, 0.94930875576036866], \"rotation\": -0.0, \"id\": \"el757624571400400\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"first\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.92586848635235752, 0.90399385560675871], \"rotation\": -0.0, \"id\": \"el757624571452176\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-5.0, 5.0], \"ylim\": [0.0, 1.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el757624571399888\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 15.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 15.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data01\", \"id\": \"el757624571351376\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data01\", \"id\": \"el757624571351760\"}, {\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data02\", \"id\": \"el757624571401232\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data02\", \"id\": \"el757624571453072\"}], \"markers\": [], \"id\": \"el757624568902992\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 560.0, \"width\": 1040.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data02\": [[0.8845119933829614, 0.9600614439324117, 0.9147465437788017], [0.9076716294458232, 0.9600614439324117, 0.9147465437788017]], \"data03\": [[0.8729321753515303, 0.8847926267281104], [0.9917287014061208, 0.8847926267281104], [0.9917287014061208, 0.9846390168970812], [0.8729321753515303, 0.9846390168970812]], \"data01\": [[-5.0, 3.898171832519376e-17, 0.27993853292596743], [-4.949748743718593, 0.010110166538635959, 0.2799938238860639], [-4.899497487437186, 0.020173693248607624, 0.2800549588622857], [-4.849246231155779, 0.029938853284648553, 0.28012255534302044], [-4.798994974874372, 0.03915611627106386, 0.28019729601714044], [-4.748743718592965, 0.04758430065733656, 0.2802799356486042], [-4.698492462311558, 0.05499664551456001, 0.2803713086736993], [-4.648241206030151, 0.06118665425668851, 0.2804723375963629], [-4.597989949748744, 0.06597356550982088, 0.280584042264838], [-4.547738693467337, 0.06920731256611219, 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[3.9949748743718594, -0.0012578093872043252, -0.28331611378073707], [4.045226130653267, 0.011142546054400354, -0.28294346358746164], [4.0954773869346734, 0.022964873502558478, -0.28260638756872425], [4.145728643216081, 0.03393636521507673, -0.2823014947367364], [4.1959798994974875, 0.04381114702563671, -0.2820257164182831], [4.246231155778895, 0.05237547866341721, -0.28177627578787645], [4.296482412060302, 0.0594520853736416, -0.28155066024947156], [4.3467336683417095, 0.06490353845611738, -0.2813465964062389], [4.396984924623116, 0.06863462586760262, -0.2811620273806709], [4.4472361809045236, 0.07059367848806355, -0.2809950922681522], [4.49748743718593, 0.07077284245653725, -0.28084410752647243], [4.547738693467338, 0.06920731256611215, -0.28070755012142096], [4.597989949748744, 0.06597356550982088, -0.280584042264838], [4.648241206030152, 0.06118665425668845, -0.2804723375963629], [4.698492462311558, 0.05499664551456001, -0.2803713086736993], [4.748743718592966, 0.04758430065733648, -0.2802799356486042], [4.798994974874372, 0.03915611627106386, -0.28019729601714044], [4.84924623115578, 0.02993885328464845, -0.28012255534302044], [4.899497487437186, 0.020173693248607624, -0.2800549588622857], [4.949748743718594, 0.010110166538635845, -0.2799938238860639], [5.0, 3.898171832519376e-17, -0.27993853292596743]]}, \"id\": \"el757624566435856\"});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el7576245664358561960300029\", {\"axes\": [{\"xlim\": [-5.0, 5.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"x\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 15.0, \"position\": [0.5, -0.064516129032258063], \"rotation\": -0.0, \"id\": \"el757624568962384\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"y\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 15.0, \"position\": [-0.045285359801488831, 0.5], \"rotation\": -90.0, \"id\": \"el757624570905744\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"example\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 15.0, \"position\": [0.5, 1.0128008192524323], \"rotation\": -0.0, \"id\": \"el757624571133072\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624571454992\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"second\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.92586848635235752, 0.94930875576036866], \"rotation\": -0.0, \"id\": \"el757624571400400\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"first\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.92586848635235752, 0.90399385560675871], \"rotation\": -0.0, \"id\": \"el757624571452176\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-5.0, 5.0], \"ylim\": [0.0, 1.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el757624571399888\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 15.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 15.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data01\", \"id\": \"el757624571351376\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data01\", \"id\": \"el757624571351760\"}, {\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data02\", \"id\": \"el757624571401232\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data02\", \"id\": \"el757624571453072\"}], \"markers\": [], \"id\": \"el757624568902992\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 560.0, \"width\": 1040.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data02\": [[0.8845119933829614, 0.9600614439324117, 0.9147465437788017], [0.9076716294458232, 0.9600614439324117, 0.9147465437788017]], \"data03\": [[0.8729321753515303, 0.8847926267281104], [0.9917287014061208, 0.8847926267281104], [0.9917287014061208, 0.9846390168970812], [0.8729321753515303, 0.9846390168970812]], \"data01\": [[-5.0, 3.898171832519376e-17, 0.27993853292596743], [-4.949748743718593, 0.010110166538635959, 0.2799938238860639], [-4.899497487437186, 0.020173693248607624, 0.2800549588622857], [-4.849246231155779, 0.029938853284648553, 0.28012255534302044], [-4.798994974874372, 0.03915611627106386, 0.28019729601714044], [-4.748743718592965, 0.04758430065733656, 0.2802799356486042], [-4.698492462311558, 0.05499664551456001, 0.2803713086736993], [-4.648241206030151, 0.06118665425668851, 0.2804723375963629], [-4.597989949748744, 0.06597356550982088, 0.280584042264838], [-4.547738693467337, 0.06920731256611219, 0.28070755012142096], [-4.49748743718593, 0.07077284245653725, 0.28084410752647243], [-4.447236180904523, 0.07059367848806355, 0.2809950922681522], [-4.396984924623116, 0.06863462586760262, 0.2811620273806709], [-4.346733668341709, 0.06490353845611732, 0.2813465964062389], [-4.296482412060302, 0.0594520853736416, 0.28155066024947156], [-4.2462311557788945, 0.05237547866341713, 0.28177627578787645], [-4.1959798994974875, 0.04381114702563671, 0.2820257164182831], [-4.1457286432160805, 0.03393636521507649, 0.2823014947367364], [-4.0954773869346734, 0.022964873502558478, 0.28260638756872425], [-4.045226130653266, 0.011142546054400081, 0.28294346358746164], [-3.9949748743718594, -0.0012578093872043252, 0.28331611378073707], [-3.9447236180904524, -0.01394241609789145, 0.28372808505134234], [-3.8944723618090453, -0.026603132794308586, 0.2841835172625377], [-3.8442211055276383, -0.0389245559420691, 0.28468698406846693], [-3.7939698492462313, -0.05059146347004358, 0.2852435378997443], [-3.7437185929648242, -0.0612964322305466, 0.2858587595067289], [-3.693467336683417, -0.07074745402900974, 0.2865388124970892], [-3.64321608040201, -0.07867537143187246, 0.28729050333995915], [-3.592964824120603, -0.08484095499693588, 0.2881213473460109], [-3.542713567839196, -0.08904144811529065, 0.2890396411706454], [-3.492462311557789, -0.0911164142741262, 0.29005454242561385], [-3.442211055276382, -0.09095273411784324, 0.2911761570217943], [-3.391959798994975, -0.08848861598164103, 0.2924156349013025], [-3.341708542713568, -0.0837165032897946, 0.29378527484911476], [-3.291457286432161, -0.07668478496049512, 0.2952986391004228], [-3.241206030150754, -0.06749824027613621, 0.2969706784774938], [-3.190954773869347, -0.05631717703293782, 0.2988178687947057], [-3.14070351758794, -0.04335525059345112, 0.3008583592579676], [-3.090452261306533, -0.028875981104905024, 0.30311213354831174], [-3.040201005025126, -0.013188015962621341, 0.3056011842107286], [-2.9899497487437183, 0.003360786075701366, 0.30834970085755214], [-2.9396984924623113, 0.02039034454761886, 0.3113842725268857], [-2.8894472361809043, 0.03749628977132459, 0.31473410429289295], [-2.8391959798994972, 0.054258683270642066, 0.31843124788308635], [-2.78894472361809, 0.07025135030384726, 0.32251084558877086], [-2.738693467336683, 0.08505163122409548, 0.32701138612047503], [-2.688442211055276, 0.09825034491814089, 0.33197497021246747], [-2.638190954773869, 0.1094617484068636, 0.3374475826575163], [-2.587939698492462, 0.11833327211787988, 0.34347936597643175], [-2.537688442211055, 0.12455481053135228, 0.3501248889968623], [-2.487437185929648, 0.12786735292641016, 0.35744340110599465], [-2.437185929648241, 0.12807074878295818, 0.3654990596926664], [-2.386934673366834, 0.12503041688158315, 0.37436111410586975], [-2.336683417085427, 0.11868282604786708, 0.38410402407845357], [-2.28643216080402, 0.10903959846164743, 0.3948074836872377], [-2.236180904522613, 0.09619011305786404, 0.4065563131630794], [-2.185929648241206, 0.08030251625970011, 0.41944016976507104], [-2.135678391959799, 0.061623079508270874, 0.43355301494066734], [-2.0854271356783918, 0.04047387712537239, 0.44899225746396343], [-2.0351758793969847, 0.01724879325751397, 0.46585747044651316], [-1.9849246231155777, -0.007592097742126644, 0.4842485532555528], [-1.9346733668341707, -0.03352969789243181, 0.5042631766505029], [-1.8844221105527637, -0.059994485402843384, 0.5259933101486657], [-1.8341708542713566, -0.08637640176262688, 0.5495205843080999], [-1.7839195979899496, -0.11203585046966097, 0.5749101873681699], [-1.7336683417085426, -0.13631560535792686, 0.602202936599136], [-1.6834170854271355, -0.1585534039301998, 0.6314051025074341], [-1.6331658291457285, -0.17809498293467327, 0.6624755040486577], [-1.5829145728643215, -0.194307300080977, 0.6953093444818547], [-1.5326633165829144, -0.20659167761317146, 0.7297182354652894], [-1.4824120603015074, -0.21439660067874428, 0.765405884503513], [-1.4321608040201004, -0.2172299061743873, 0.8019390317661088], [-1.3819095477386933, -0.21467010601588704, 0.8387134642043762], [-1.3316582914572863, -0.20637660245772588, 0.8749153716317728], [-1.2814070351758793, -0.19209857194879387, 0.9094790219667105], [-1.2311557788944723, -0.17168231771268888, 0.9410428164234591], [-1.1809045226130652, -0.14507691933755834, 0.9679073398937539], [-1.1306532663316582, -0.11233803960760685, 0.9880011302168206], [-1.0804020100502512, -0.07362978397665766, 0.9988625805226263], [-1.0301507537688441, -0.029224545770916554, 0.9976495730114195], [-0.9798994974874367, 0.02049919034778512, 0.9811918284093991], [-0.9296482412060296, 0.07506107372923593, 0.9461039383059732], [-0.8793969849246226, 0.1338850884417035, 0.8889785990239462], [-0.8291457286432156, 0.19630763607934368, 0.8066781628098114], [-0.7788944723618085, 0.2615873691448637, 0.6967362948576379], [-0.7286432160804015, 0.32891660755887453, 0.5578681265743329], [-0.6783919597989945, 0.39743413802380934, 0.3905651151775864], [-0.6281407035175874, 0.46623916694249773, 0.19771960136769426], [-0.5778894472361804, 0.5344061729798645, -0.014813646658115617], [-0.5276381909547734, 0.6010003856919863, -0.23784909813616267], [-0.47738693467336635, 0.6650936023603203, -0.45885129191149054], [-0.4271356783919593, 0.7257800465804133, -0.6624195659745233], [-0.3768844221105523, 0.7821919694664742, -0.8313877531625621], [-0.32663316582914526, 0.8335146976262924, -0.9485545591010515], [-0.2763819095477382, 0.8790008412913511, -0.9989180174003316], [-0.2261306532663312, 0.9179833909871621, -0.9721228749323679], [-0.17587939698492416, 0.9498874516109141, -0.8646876451656677], [-0.12562814070351713, 0.9742403883449006, -0.6815131075733587], [-0.0753768844221101, 0.9906801889658594, -0.4362317756028779], [-0.02512562814070307, 0.9989618812074498, -0.1501520389509558], [0.02512562814070396, 0.9989618812074497, 0.15015203895096108], [0.07537688442211099, 0.9906801889658592, 0.4362317756028827], [0.12562814070351802, 0.9742403883449002, 0.6815131075733625], [0.17587939698492505, 0.9498874516109136, 0.8646876451656702], [0.22613065326633208, 0.9179833909871615, 0.9721228749323692], [0.2763819095477391, 0.8790008412913504, 0.9989180174003314], [0.32663316582914614, 0.8335146976262915, 0.9485545591010498], [0.3768844221105532, 0.7821919694664732, 0.8313877531625596], [0.4271356783919602, 0.7257800465804122, 0.6624195659745203], [0.47738693467336724, 0.6650936023603193, 0.4588512919114874], [0.5276381909547743, 0.6010003856919852, 0.23784909813615923], [0.5778894472361813, 0.5344061729798633, 0.01481364665811162], [0.6281407035175883, 0.46623916694249645, -0.1977196013676973], [0.6783919597989954, 0.397434138023808, -0.3905651151775893], [0.7286432160804024, 0.3289166075588732, -0.5578681265743352], [0.7788944723618094, 0.2615873691448624, -0.6967362948576401], [0.8291457286432165, 0.19630763607934262, -0.8066781628098135], [0.8793969849246235, 0.13388508844170247, -0.8889785990239478], [0.9296482412060305, 0.07506107372923497, -0.9461039383059737], [0.9798994974874375, 0.020499190347784243, -0.9811918284093998], [1.0301507537688446, -0.029224545770916953, -0.9976495730114197], [1.0804020100502516, -0.07362978397665801, -0.9988625805226262], [1.1306532663316586, -0.11233803960760716, -0.9880011302168205], [1.1809045226130657, -0.1450769193375586, -0.9679073398937537], [1.2311557788944727, -0.17168231771268905, -0.9410428164234588], [1.2814070351758797, -0.1920985719487941, -0.90947902196671], [1.3316582914572868, -0.206376602457726, -0.8749153716317728], [1.3819095477386938, -0.2146701060158871, -0.8387134642043758], [1.4321608040201008, -0.21722990617438728, -0.8019390317661083], [1.4824120603015079, -0.21439660067874422, -0.7654058845035123], [1.5326633165829149, -0.20659167761317135, -0.7297182354652894], [1.582914572864322, -0.1943073000809769, -0.6953093444818547], [1.633165829145729, -0.17809498293467316, -0.6624755040486571], [1.683417085427136, -0.15855340393019968, -0.6314051025074333], [1.733668341708543, -0.13631560535792672, -0.602202936599136], [1.78391959798995, -0.11203585046966084, -0.5749101873681691], [1.834170854271357, -0.08637640176262673, -0.5495205843080999], [1.884422110552764, -0.05999448540284323, -0.5259933101486649], [1.9346733668341711, -0.03352969789243167, -0.5042631766505029], [1.9849246231155782, -0.007592097742126358, -0.4842485532555528], [2.035175879396985, 0.01724879325751424, -0.46585747044651316], [2.085427135678392, 0.04047387712537265, -0.44899225746396343], [2.1356783919597992, 0.0616230795082711, -0.43355301494066734], [2.1859296482412063, 0.0803025162597003, -0.41944016976507104], [2.2361809045226133, 0.09619011305786421, -0.4065563131630794], [2.2864321608040203, 0.10903959846164755, -0.3948074836872377], [2.3366834170854274, 0.11868282604786716, -0.38410402407845357], [2.3869346733668344, 0.12503041688158317, -0.37436111410586975], [2.4371859296482414, 0.1280707487829582, -0.3654990596926664], [2.4874371859296485, 0.12786735292641016, -0.35744340110599465], [2.5376884422110555, 0.12455481053135226, -0.3501248889968623], [2.5879396984924625, 0.11833327211787978, -0.34347936597643175], [2.6381909547738696, 0.10946174840686348, -0.3374475826575163], [2.6884422110552766, 0.09825034491814076, -0.33197497021246747], [2.7386934673366836, 0.08505163122409531, -0.32701138612047503], [2.7889447236180906, 0.07025135030384708, -0.32251084558877086], [2.8391959798994977, 0.05425868327064188, -0.31843124788308635], [2.8894472361809047, 0.0374962897713244, -0.31473410429289295], [2.9396984924623117, 0.02039034454761867, -0.3113842725268857], [2.9899497487437188, 0.0033607860757011764, -0.30834970085755126], [3.0402010050251267, -0.013188015962621705, -0.3056011842107286], [3.090452261306533, -0.028875981104905024, -0.30311213354831174], [3.1407035175879408, -0.043355250593451425, -0.3008583592579676], [3.190954773869347, -0.05631717703293782, -0.2988178687947057], [3.241206030150755, -0.06749824027613645, -0.296970678477493], [3.291457286432161, -0.07668478496049512, -0.2952986391004228], [3.341708542713569, -0.08371650328979473, -0.29378527484911476], [3.391959798994975, -0.08848861598164103, -0.2924156349013025], [3.442211055276383, -0.09095273411784326, -0.2911761570217943], [3.492462311557789, -0.0911164142741262, -0.29005454242561385], [3.542713567839197, -0.08904144811529056, -0.2890396411706454], [3.592964824120603, -0.08484095499693588, -0.2881213473460109], [3.643216080402011, -0.0786753714318723, -0.28729050333995915], [3.693467336683417, -0.07074745402900974, -0.2865388124970892], [3.743718592964825, -0.061296432230546365, -0.2858587595067289], [3.7939698492462313, -0.05059146347004358, -0.2852435378997443], [3.844221105527639, -0.03892455594206883, -0.28468698406846693], [3.8944723618090453, -0.026603132794308586, -0.2841835172625377], [3.9447236180904532, -0.013942416097891163, -0.28372808505134234], [3.9949748743718594, -0.0012578093872043252, -0.28331611378073707], [4.045226130653267, 0.011142546054400354, -0.28294346358746164], [4.0954773869346734, 0.022964873502558478, -0.28260638756872425], [4.145728643216081, 0.03393636521507673, -0.2823014947367364], [4.1959798994974875, 0.04381114702563671, -0.2820257164182831], [4.246231155778895, 0.05237547866341721, -0.28177627578787645], [4.296482412060302, 0.0594520853736416, -0.28155066024947156], [4.3467336683417095, 0.06490353845611738, -0.2813465964062389], [4.396984924623116, 0.06863462586760262, -0.2811620273806709], [4.4472361809045236, 0.07059367848806355, -0.2809950922681522], [4.49748743718593, 0.07077284245653725, -0.28084410752647243], [4.547738693467338, 0.06920731256611215, -0.28070755012142096], [4.597989949748744, 0.06597356550982088, -0.280584042264838], [4.648241206030152, 0.06118665425668845, -0.2804723375963629], [4.698492462311558, 0.05499664551456001, -0.2803713086736993], [4.748743718592966, 0.04758430065733648, -0.2802799356486042], [4.798994974874372, 0.03915611627106386, -0.28019729601714044], [4.84924623115578, 0.02993885328464845, -0.28012255534302044], [4.899497487437186, 0.020173693248607624, -0.2800549588622857], [4.949748743718594, 0.010110166538635845, -0.2799938238860639], [5.0, 3.898171832519376e-17, -0.27993853292596743]]}, \"id\": \"el757624566435856\"});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el7576245664358561960300029\", {\"axes\": [{\"xlim\": [-5.0, 5.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"x\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 15.0, \"position\": [0.5, -0.064516129032258063], \"rotation\": -0.0, \"id\": \"el757624568962384\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"y\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 15.0, \"position\": [-0.045285359801488831, 0.5], \"rotation\": -90.0, \"id\": \"el757624570905744\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"example\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 15.0, \"position\": [0.5, 1.0128008192524323], \"rotation\": -0.0, \"id\": \"el757624571133072\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624571454992\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"second\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.92586848635235752, 0.94930875576036866], \"rotation\": -0.0, \"id\": \"el757624571400400\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"first\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.92586848635235752, 0.90399385560675871], \"rotation\": -0.0, \"id\": \"el757624571452176\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-5.0, 5.0], \"ylim\": [0.0, 1.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el757624571399888\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 15.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 15.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data01\", \"id\": \"el757624571351376\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data01\", \"id\": \"el757624571351760\"}, {\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data02\", \"id\": \"el757624571401232\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data02\", \"id\": \"el757624571453072\"}], \"markers\": [], \"id\": \"el757624568902992\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 560.0, \"width\": 1040.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, 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       "            })\n",
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     "execution_count": 6,
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   ],
   "source": [
    "function_plot.plot(new_plot=True, xlim=(-5, 5), ylim=(0., 1.), title='example', xlabel='x', ylabel='y', fontsize=15)\n",
    "mpld3.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bokeh interactive plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-banner\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"af5e2874-51d4-473d-8694-58e90ebde3fc\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      Bokeh.$(\"#af5e2874-51d4-473d-8694-58e90ebde3fc\").text(\"BokehJS successfully loaded\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i](window.Bokeh);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
       "\n",
       "    <div class=\"plotdiv\" id=\"02cbd2e9-d571-47cb-b94a-e7fe0be2d54c\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"02cbd2e9-d571-47cb-b94a-e7fe0be2d54c\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '02cbd2e9-d571-47cb-b94a-e7fe0be2d54c' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
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       "            var render_items = [{\"docid\":\"ab7bb9c9-7629-405d-8a89-d8b2d57465eb\",\"elementid\":\"02cbd2e9-d571-47cb-b94a-e7fe0be2d54c\",\"modelid\":\"b5badfe9-11b2-41c1-8677-8fe4c5f71ed7\",\"notebook_comms_target\":\"ce55c6ed-8052-4b54-8996-fca5f33b83f6\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function_plot.plot_bokeh(figsize=(15, 10), xlabel='x', title='Example', ylim=(-0.5, 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TMVA style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pDx4h5eHDUx+kkSyZ4u7yhVuzQYp5i1nq2QpL7oRILtsJnJzH48o/Bbic1T2qSHhaWWV7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\nGQBAEJgyAIAgMGUAAEH8P+kH75/tdes3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<ROOT.TCanvas object (\"canvas54997\") at 0x1124c0690>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function_plot.plot_tmva(new_plot=True, figsize=(6, 6), title='TMVA example', \n",
    "                        xlabel='x variable', ylabel='y variable', )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## separate two lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "function_plot1 = FunctionsPlot({'first': first_func})\n",
    "function_plot2 = FunctionsPlot({'second': second_func})\n",
    "function_plot3 = FunctionsPlot({'third': third_func})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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++0tz5ki77OL6GvbYI++RVaYizjgsWeKSh3793OrOaaE5GgDSZay1eY+hbMYY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\nxpjGVdtov2fS40MxcN3o8BHj7Awe7LZ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lMwYAkEgc4CmuGx0+Ypy/tBOHUGKcZ7nSO++4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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102eba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IDC1bBj/+CNtsA61bhx1NfKhVSUSk8rSPg4hIDI0cCfvuC3vsAWPHhh1NfBQX\nQ506sHq1LWVbv37YEYmIhEv7OIiIJFzU25SiKi8POnSw8xp1EBGpGBUOEknqqUw+5bhq4lA4RDXH\nalfKnKjmWDJHOZZ0KhxERGIoDoVDVKlwEBGpHM1xEBGJoV13halT4dtvYffdw44mXl57Dfr2haOP\nhrffDjsaEZFwaY6DiEiCrV0L06dbv/6uu4YdTfxoSVYRkcoJrHBwzg1wzs1wzq12zn3lnDugnPfb\nxTm33Dm3LKjYJPrUU5l8ynHlTZ1qqwO1bWvLsUZVVHO8yy5WdH3/PaxZE3Y08RbVHEvmKMeSLpDC\nwTl3MvAAcDvQFRgODHLONd/K/WoA/wEKgohLRCQJNL+harbZBtq1s+JrypSwoxERiY+gRhyuAAZ6\n7wd67yd77y8F5gEXbuV+9wBjgdcCiktiIj8/P+wQJGDKceXFpXCIco41QTozopxjyQzlWNJlvHDY\nMGqwN/DRJt/6EOi5hfsdBfQBLsl0TCIiSRKXwiHKVDiIiFRcECMOOwLVgAWbXL8AaFraHZxzOwFP\nAqd671cFEJPEjHoqk085rry4FA5RzrEKh8yIco4lM5RjSReVVZVeAB7z3n+14XK5loQSEck1RUUw\nebKdT60OJBWXKhwmTQo3DhGROKkewGMuAoqAJptc3wSYX8Z9egMHOudu3nDZAXnOuXXAAO/9U5ve\noX///rRu3RqA+vXr07Vr11/78FLVsS7H+3JKVOLRZV2OwuWXXy5gzRpo1iyfevXCj2dLl/Pz8yMV\nT/rlbt3s8sSJBQweDIccEq344nI5dV1U4tHlYC6nRCUeXa7a5dT5mTNnUlGBbADnnBsBjPHeX5B2\n3WTgVe/9jaXcvvMmVx0HXA90B+Z675ducnttACciOendd23jsoMPhsGDw44m3nbeGebOhWnTbJUl\nEZFcFIUN4P4O9HfOneOc6+icexBoBjy+IcA7nXMfp27svZ+Q/gX8CBR77yduWjRIbtj0KIckj3Jc\nOXGZ3wDRz7HmOVRd1HMsVaccS7pACgfv/SvA5cANwGhsNaUjvfdzNtykKdAmiOcWEUmyOBUOUafC\nQUSkYgJpVQqaWpVEJFf17AlffGFtSgcfHHY08fbYY3DRRXDWWTBwYNjRiIiEIwqtSiIikmHea8Qh\nkzTiICJSMSocJJLUU5l8ynHFLVgAS5ZAvXrQtNRdcaIl6jlOLxw0iF05Uc+xVJ1yLOlUOIiIxET6\naIPTbjes/Ci0AAAgAElEQVRV1qSJFWFLl1pRJiIiW6bCQSIpfY1wSSbluOJSm5XFpU0p6jl2Tu1K\nVRX1HEvVKceSToWDiEhMaH5D5qlwEBEpPxUOEknqqUw+5bji4lY4xCHHKhyqJg45lqpRjiWdCgcR\nkZiIW+EQByocRETKT/s4iIjEwLJlNpF3m21g5UqoVi3siJJh+nRo3x522gl+/DHsaEREsk/7OIiI\nJExqYnSHDioaMql1ayvG5s611ZVERKRsKhwkktRTmXzKccXEsU0pDjmuVg123dXOT54cbixxFIcc\nS9Uox5JOhYOISAzEsXCIC81zEBEpHxUOEklaNzr5lOOKiWPhEJccq3CovLjkWCpPOZZ0KhxERGIg\njoVDXKhwEBEpHxUOEknqqUw+5bj81q611X/y8kr68eMgLjlW4VB5ccmxVJ5yLOlUOIiIRNzUqVBc\nDG3b2gpAklm77mpF2fTpVqSJiEjptI+DiEjEvfIKnHwyHHssvPlm2NEkU/v2Vjh89x3stlvY0YiI\nZI/2cRARSZAJE+y0c+dw40iyjh3tNLVfhoiIbE6Fg0SSeiqTTzkuv7gWDnHKseY5VE6cciyVoxxL\nOhUOIiIRF9fCIU5UOIiIbJ3mOIiIRNj69bDddlBYCCtWwLbbhh1RMn3xBfTsCV27wujRYUcjIpI9\nmuMgIpIQ06db8dC6tYqGIKVGHCZPthWsRERkcyocJJLUU5l8ynH5xLlNKU45rl8fmjaF1ath1qyw\no4mPOOVYKkc5lnQqHEREIizOhUPcaJ6DiMiWqXCQSMrPzw87BAmYclw+cS4c4pbj1JKsKhzKL245\nlopTjiWdCgcRkQiLc+EQN6kRB+3lICJSOhUOEknqqUw+5XjriopKPsSmPtTGSdxyrFaliotbjqXi\nlGNJp8JBRCSiZsyAtWuhRQvYfvuwo0m+9MJBK36LiGxO+ziIiETU//4Hxx0Hhx8O778fdjTJ5z3U\nqwfLl8OCBdC4cdgRiYgET/s4iIgkgOY3ZJdzalcSEdkSFQ4SSeqpTD7leOviXjjEMccqHComjjmW\nilGOJZ0KBxGRiIp74RBHWpJVRKRsmuMgIhJBxcVQp47tZLx4MTRoEHZEuSE1r+Sww+CDD8KORkQk\neJrjICISc7NmWdHQrJmKhmxSq5KISNlUOEgkqacy+ZTjLUtCm1Icc9y2LdSsCT/8YKsryZbFMcdS\nMcqxpFPhICISQUkoHOKoenXo0MHOp3IgIiJGhYNEUn5+ftghSMCU4y1LQuEQ1xx36WKnKhy2Lq45\nlvJTjiWdCgcRkQhKQuEQV6nf+fjx4cYhIhI1KhwkktRTmXzKcdm8T0bhENccp0YcVDhsXVxzLOWn\nHEs6FQ4iIhEzZw6sWAGNGsGOO4YdTe5Rq5KISOm0j4OISMR88AEccQQcdBDoYF/2FRbCdtvBunWw\nbBlsv33YEYmIBEf7OIiIxFgS2pTiTCsriYiUToWDRJJ6KpNPOS5bUgqHOOdY7UrlE+ccS/kox5JO\nhYOISMQkpXCIM02QFhHZnOY4iIhEiPfQsCEsWQLz5kHTpmFHlJveeAN+/3ubazJoUNjRiIgEJxJz\nHJxzA5xzM5xzq51zXznnDtjCbQ9yzr3pnJvrnFvpnBvrnDsrqNhERKJq/nwrGho0gCZNwo4md6lV\nSURkc4EUDs65k4EHgNuBrsBwYJBzrnkZd+kJfAv8HugC/AN40jnXL4j4JPrUU5l8ynHp0tuUXLmO\n/0RXnHPcrh3UrAmzZ8Py5WFHE11xzrGUj3Is6YIacbgCGOi9H+i9n+y9vxSYB1xY2o2993d67//s\nvf/Cez/Te/848AZWSIiI5AzNb4gGrawkIrK5jBcOzrkawN7AR5t860NsZKG86gK/ZCouiZf8/Pyw\nQ5CAKcelS1LhEPccq11p6+KeY9k65VjSVQ/gMXcEqgELNrl+AXBIeR7AOXc0cDAVKzRERGIvSYVD\n3GllJRGRjUVuOVbn3P7Ai8Al3vuvw45HwqGeyuRTjkuXpMIh7jlW4bB1cc+xbJ1yLOmCGHFYBBQB\nm64H0gSYv6U7blh56V3gRu/9k1u6bf/+/WndujUA9evXp2vXrr8Op6X+yHU5vpfHjBkTqXh0OfOX\nU6ISTxQuL1wIixYVULs27Lxz+PHk+mUr3gr45huA8OOJ4uUxY8ZEKh5dzvxlvR8n73Lq/MyZM6mo\nQPZxcM6NAMZ47y9Iu24y8Kr3/sYy7tMLeAe4yXv/4FYeX/s4iEjiFBRA796w774wYkTY0UhhIWy3\nHaxbB0uXQt26YUckIpJ5UdjH4e9Af+fcOc65js65B4FmwOMbArzTOfdx6sbOuXzgPWwZ1pecc002\nfO0YUHwiIpHz3Xd2uttu4cYhpnp16NTJzqtdSUQkoMLBe/8KcDlwAzAam+R8pPd+zoabNAXapN3l\nTKA2cBUwN+1rZBDxSfSlD6dJMinHm0sVDrvvHm4cmZKEHKdykcqNbCwJOZYtU44lXRBzHADYsBfD\n42V876xSLmunaBHJaePG2WlSCockSOUilRsRkVwWyByHoGmOg4gkjfdQr57tUrxgATRuHHZEAvDe\ne3DUUZCfD59+GnY0IiKZF4U5DiIiUgGzZlnR0LixioYoSW9V0vEqEcl1KhwkktRTmXzK8caS2KaU\nhBw3b24jQT//bCNBsrEk5Fi2TDmWdCocREQiQCsqRZNzJTnRBGkRyXUqHCSSUpuVSHIpxxtL2opK\nkJwca2WlsiUlx1I25VjSqXAQEYmAJLYqJYVWVhIRMSocJJLUU5l8ynGJ9eth0iQ737lzuLFkUlJy\nrFalsiUlx1I25VjSqXAQEQnZlClWPLRtC3XqhB2NbCo14jB+PBQXhxuLiEiYtI+DiEjIXnoJ/u//\n4Nhj4c03w45GStO8Ofz4I0ydCu3bhx2NiEjmaB8HEZEY0YpK0ad2JRERFQ4SUeqpTD7luERSJ0Yn\nKceaIF26JOVYSqccSzoVDiIiIUviUqxJoyVZRUQ0x0FEJFTLl0PdulCjBqxcaacSPd98A3vvDR07\nwsSJYUcjIpI5muMgIhITEybYaadOKhqirFMnyMuzydFr1oQdjYhIOKqHHYBIaQoKCrRbZcCKi+Gr\nr+Czz2DVKlsOFOyo6iGH2FHwICnHZuxYO01im1KScly7Nuy6q+23MWEC7LVX2BFFQ9A5njcPPvzQ\nfu/Vq1tx3aABHH645UOCl6TXsVSdCgeRHDNpEtx/P7z9tr0pl6Z6dejZEy69FE44AVy5BjClMsaM\nsdOuXcONQ7Zuzz3t9TNmjAqHIK1fD489Bs88U1JYl6ZDBzj+eLjySmjUKHvxieQyzXEQyRGLF8Mt\nt9gbcmGhXdeiBRx5JDRpYkfy1qyxEYgRI6CoyG7Tuzc8+GAyj4hHwf77w/Dh8NFHcOihYUcjW3LX\nXfCnP1lB/eCDYUeTTB98AJdfXrKTeu3akJ8P++5rBzDWr4cZM+C992DJErtN3brw5z/DJZdAzZqh\nhS4SWxWZ46DCQSQHvPYanH++FQ95eXDOOTBggB1BLW00YckSePFFezNO3eeWW+CGGzT6kEnFxVCv\nHqxYAT/9pKOmUTdoEPTpAwcdBFqhMrPWrLH/S//+t13eZRcr1Pr0gVq1Nr/9+vUwbBjccw+8/37J\nff7zH2u3FJHy0+RoiT2tG50ZxcVw003Qt68VAL172+owTz5prTFlFQH168NFF9lE0IsusutuusmK\nj9RoRVUpx3bkdMUK2GmnZBYNScvxnnva6ZgxoGNXJhM5XrwYDjvMiobttoO777Zlb084ofSiAWyE\ntHdvK+bee89Wu5o6FQ44wIoHyZykvY6lalQ4iCTU8uXW/3v77TZicP/9MHhwyYef8mjYEB55BN54\nw1oG/vlPOO44WzZUqi7Vv12RnEh4mjWzAm/pUpg9O+xokmH2bPuwP3Qo7LwzfPEFXHMNbLNN+R/j\nyCOtmDv3XBu5OOUUuO66knZLEckcFQ4SSVrBoWqWL7dVR956y1Ygef996xuubJvRscfCJ5/ADjvA\nu+/CMcfAunVVi1E5Tn7hkLQcO1eSqy1N2s0lVcnxggXQq5fti9GlixUNlZ1Ltc02NpL6yCNQrZqN\nWvzhDzbqKlWTtNexVI0KB5GEWbkSjj7a3oRbtoQvv4Tf/rbqj7vffjaJt2lT+PRTmyOhdo2q0YpK\n8ZPeriSVt3q1HZCYNQu6d7f5Ci1aVO0xnbPWyg8+sBHSgQPh4ov1f0okk1Q4SCSpp7JyUm/GQ4ZY\n3/wnn9iEwUzZdVf43/+s7/jpp+G++yr/WMpx8kcckpjjVJGnEQdTmRwXF8NZZ9lBjZYtbWno+vUz\nF9Mhh9ho6zbbwD/+Ycu1qniovCS+jqXyVDiIJERxMZx+us1jaNLEioZ27TL/PD16wAsv2PlrrrFC\nQipu8WLr765dO7PFnQRLIw5Vd+ut8PLLsP328M479v8q0w491OZm1agBDzxgKzSJSNVpOVaRhPjL\nX+wNuV49G/bfbbdgn++vf7XlWRs0gHHjbIRDyq+gwFaF6dHDjrxKPKxfD3Xq2ByfpUuD32E9aYYM\nsX0ZnLORhj59gn2+N96AE0+082++Cb/7XbDPJxJHWo5VJMe8+qoVDXl58NJLwRcNYBthHXkk/PKL\nrWaiWr5iUq0umt8QLzVq2EResCVDpfyWL4f+/e1/xfXXB180gC3pescd9pynnqqciVSVCgeJJPVU\nlt/o0XDmmXb+b3+DI47IzvM6B089ZSMOgwbZUq0Vkes5TrW6JHV+AyQ3x2pXKlGRHP/xj/D999Ct\nm+0Lky3XXWdLtK5YYSMOCxdm77mTIKmvY6kcFQ4iMbZ0qQ3Dr15tR/KuuCK7z7/TTvDYY3b+yitt\nQzMpH404xJcmSFfce+/ZwYWaNeH55+00W1IHObp3h5kz7UCLlmkVqRzNcRCJKe/h//7PJhl262bL\nr1Zk06RMOvlkeOUV69kfPLjy+0XkivQ++WXLbJKoxIfmp1TM8uW2s/PcuXDPPXD11eHEMWeOjRYt\nXgz33msjICKiOQ4iOeHpp61oqFPHTsMqGsBGHXbYwfZ3eP318OKIi0mTrGho105FQxylWpW++067\nE5fHHXdY0dCjh41MhqV5c3j2WTt/3XUwcmR4sYjElUYcZCOrVtmHmqlT7WvpUpsMWL26fTDcfXfY\nYw/Yccdg4ygoKNBulVswfrwNu69ebUujnnZa2BHB44/DhRdCq1a2E2zt2lu+fS7n+PnnrV3i97+H\n114LO5rgJDnHrVrZcrrjx0PnzmFHE56t5XjaNJtMvm6djc706JG92Mpy+eXw4IPQpo3NEatXL+yI\noi3I17H3NhL07bf2Wlq+HAoL7atJE1uqun176NDBPodIMCoy4qA0CD/+aGvxv/OOrf2/du3W79O2\nLfTtC/362dE3taZkz7p1tjpIal5DFIoGgPPOs+Jh7FibpP3nP4cdUXR9/bWd7r13uHFI5e29txUO\nX3+d24XD1lx5pf3P6t8/GkUDwN13w9Ch8M03cMklVshL9hQVwWef2Uj5f/9bvsnq9evbwh9HH21f\nKvbCoxGHHPbFF3D//bbOdWq43Tno1Ml2CG7f3kYWioqsJ3vuXDsq8N13sHJlyePstput59+3L1Sr\nFs7PkktuvNGG/tu1s1Vd6tQJO6ISQ4bAQQfZaMOkSbYrrGzugAPg88/hgw/gsMPCjkYq44477LV4\n2WW2wZhs7v33bcnm7beHKVOgadOwIyoxZYpNcl+92t4Djz8+7IiSb9UqO7h0770wb17J9Q0b2gHI\n3Xe3zobq1W1p8blzrfNh0iSb1J6y3Xa28/hll9nnFKm6iow4qHDIQcOH2+S04cPtcvXqcMwxtkzd\nkUdufRfPoiL70PPSS7Z/wKJFdn2nTrYJ2UknaQQiKF9+CT172vDukCH2ATRq+vWzI0n9+sF//hN2\nNNFTVGSbhq1aZa+dHXYIOyKpjNSH4gMOsKPXsrHCQvsgOGmSjUBedVXYEW3u4Yfh0kuhUSNrk2nU\nKOyIkmntWnj0URvp+eknu65dO3uPOPlkO/i4tc8M06bBu+9akTdkiF3nnLV73n23dUFI5VWkcMB7\nH7svC1sqauZM7/v1894+dnrfsKH3f/qT93PmVP4x163z/qmnvG/duuRxDz/cnqsqPv3006o9QAKt\nWuV9hw72O77qqrCjKdusWd5vs43FOWZM2bfL1RyPH2+/m1atwo4keEnO8U8/WR632877wsKwowlP\nWTkeONB+P+3aeb92bXZjKq+iIu9797Y4TzjB++LisCOKpqq8jkeM8L5z55LPB927e//uu1X7XX/7\nrffnnFPyPlOzpvfXXOP90qWVf8xct+Fzdbk+g2tVpRxQXGzVfufONkpQq5YNsc+aBX/9K+y8c+Uf\nu0YNOOccmDwZnnjChhw/+MAmwz36qHYTzqQbbrDfc+fOcNttYUdTtpYtbZI0aJ5DaTS/IRkaNYIW\nLaxtc8qUsKOJlnXr4JZb7Pwtt2R3z4aKyMuDZ56xVqo33tAIaSatXm3zW37zG5gwwSY5v/OOjZr3\n6VO1roTdd7d9OaZPhzPOsL+3e+6xrodBgzL3M0gZylthROkLjTiU26xZ3h9ySEm137dv1UcDtmT+\nfO9PPLHk+X7/e++XLQvu+XLFiBHeO+d9tWrejxoVdjRbN3++99tua38DX34ZdjTRctll9nu5446w\nI5GqOv54y+ULL4QdSbQ89pj9Xjp3jsdozFNPlYzCz58fdjTxN2OG99262e80L89GA1atCu75Ro70\nfr/9Sj53nHeePndUFBpxEIA337TKfPBgm+T82mu2SVerVsE9Z5MmNu/h1Vetj/v1120ljUmTgnvO\npFu3Ds491/4l/vGPsM8+YUe0dU2a2GoloFGHTX31lZ1qxCH+UjlM5VTsSPPtt9v5W26Jx4IZZ59t\nixQsXlzyf0sq58MP7T1q9Gibd/DllzYHYWvLc1dF9+4wbJiNOtSsaTuUd+1qi4dI5qlwSKDCQrj2\nWlslYtkym/Q8frxNIsqWE0+EUaOsZWnSJHthf/RR+e9fUFAQWGxxc/fdMG6cTSa7+eawoym/q6+2\nFoAPPih98mgu5rioyN5QITcKh6TnOJXDVPtZLto0x088YavhdO0KJ5wQTkwV5Rw8+aStUPfqq9a2\nJCXK+zp++GFbMnXxYmtH+uqr7B3oqlbN3nO+/tr+9mbMsDap1IZ/kjkqHBJm0SI7cnLPPfZCuvde\nG3lo3Dj7sey6K4wYYcu0rlgBRx1lcyyk/CZOLDl6989/BnvUJtN22KFkl1iNOpjJk201pZYtg99E\nUYKXKhxGj9YO0mB/23feaedvu83mEMRFq1Zw1112fsAA+/Ar5eM9XH+9rVDlva2u+Pbb0KBB9mPZ\nbTdbav7cc2HNGlu29fzzbeReMqS8PU1R+kJzHEo1caKtYAHeN23q/WefhR2RKSry/oorSvoPH3ww\n7IjioajI+/33t9/ZueeGHU3lLFnifb169jMMHx52NOF7/nn7XRx/fNiRSKa0aGE5nTAh7EjC9/DD\n9rvYZ594rlBUVOT9gQfaz3DGGWFHEw/r13t/9tn2O6tWzftnnw07ohJPP12y8lLv3t7//HPYEUUX\nmuOQewYPtmG56dNhr71siLBXr7CjMnl5cN991nIDtmlL6qiUlO0f/7D9Mpo2tXXQ46hePTt6ByX5\nz2VaUSl51K5k1q+3EW6AP/0pnnv55OXB00/byoPPP68VeramsBBOPRUGDrTR8P/9D848M+yoSpx9\ntrXJNm0Kn35qn5GmTg07qvhT4ZAAL7xgfYVLlti8hiFDqrbEahCcg2uusX8wztmw5pY+SCa9N3pr\nfvgBrrvOzj/6KNSvH248VXHZZbDNNvamMmFCyfW5mONcKxxyIce5Xjikcvzyy7bEd4cOcNxx4cZU\nFbvsUrLc9R/+YPMEc11pr+PCQjj9dFtwpW5d+Phja0eOmu7dYeRIm/cwZQrst5+1UEvlqXCIufvv\nt3WMCwttZ87XXrPt2KPqrLNKiofrrovvkfQgeW/7IKxYYZML4zLBsCxNmtiRH7C5N7kq1yZG54pc\nLxzA9gpKzQ+45pp4zW0ozRVX2GqAc+bYzyMbKyy0zx0vvVSyAEbPnmFHVbYWLWzk4eijbe7KIYdo\nNKkqnLU2xYtzzscx7kzy3jYES7X83HdfyUTUOBg40DaOA3jssZINw8Q2ITrlFGvzmTABdtop7Iiq\nbsYMO5KXl2ftdC1bhh1R9k2caJv3tWxpR2YlGX76yYrjOnVg6dL4f2iujHfegWOOsZHu6dNthDHu\nxo+Hbt2sBeuTT6B377Ajigbv7b37mWfsb/7DD60FKA4KC+G882ylperV7fTUU8OOKhqcc3jvy9Vg\nGNi/OOfcAOfcDOfcaufcV865A7Zy+92ccwXOuVXOuR+cczcFFVvcFRbaEOqdd9rKSc89F6+iAewI\n9BNP2PmLLtLydymLFtnKFGD9wkkoGsDW8z7pJPvbve++sKMJh/ZvSKbGje2I5ooVubtfTWq04cor\nk1E0gC0lftOGTyHnnms7hIst9f7MMzanYdCg+BQNYMXCwIE2ilRYCKedZl0bUjGBFA7OuZOBB4Db\nga7AcGCQc655GbffHvgImAfsDVwGXO2cuyKI+OJszRr7APbUUzaB6803bcgwjv7wB+sl9d6OsKev\n9Z8LvdGlufxyKx569y4ZkUmKa6+106eegl9+yb0cp/pq99033DiyKVdynMrpl1+GG0cYHnusgM8/\nt6U3zzsv7Ggy67rrYM89bcT0xhvDjiY8qdfxPfdYe3H16naw74AtHg6OJudsfmXqANaVV1qec7yJ\npUKCGnG4AhjovR/ovZ/svb8UKwrKakg5DagNnOm9n+i9fwO4G4jZcfRgLVtmm6r89782Wfajj6xn\nL85uuMHalNautY3qxo0LO6LwvPsuvPiiHcn55z/juSrJlnTtar2lq1bZyiW5JlU47LdfuHFI5qVy\nmouTLl97zU7PP9/63ZOkRg07Ql2tGjz4oO0PkKueecYO/jhnK04dcUTYEVXNlVfaz1G9uhUS55xj\noxCydRmf4+CcqwGsAvp5719Pu/4RoIv3frNOQefcc0BD7/0xadftA3wJtPXez9rk9jk3x2HBAjjy\nSJtc2awZvP8+7LFH2FFlRlGRjaK88Yb1yA4fnns98MuW2dD4nDnxm69SEale6JYtrRe6evWwI8qO\nVats5RHvLddRXsBAKu7zz+3o6x57wNixYUeTPT/8AG3a2PmZM6F5qT0F8Xf99dYa3LGjvQfXqhV2\nRNn10Ud20LKwEB55xNqLk2LQIPj972H1alsN7D//yb38QvhzHHYEqgELNrl+AdC0jPs0LeP2bgv3\nyRnff29vSqNHQ/v29iaVlKIB7GjOiy/avhM//liyZX0uue46Kxp69LDlS5OqTx+bJD17trXZ5Yqv\nv7YCeY89VDQk0V57WRE8bhwsXx52NNnz6KP2d923b3KLBrCd7zt2tDkst94adjTZ9d13cOKJVjRc\nc02yigawA7KDB1sXx5tv2pKyufQarowcXP8hXr77DvbfH6ZNsxUehg0rOcKTJLVq2Tr/u+1mq8/0\n6lXA6tVhR5UdQ4bYZm81algLT7VqYUcUnLy8ksLollsKQo0lm1ItDnGaSJgJuTLHoXZt+/9cXAyj\nRoUdTXasXAlPPglQwOWXhx1NsGrVKllG/J574Jtvwo4oO+bOtQ/Sy5YVcNJJyd249Te/gc8+s43i\nPvnEWmoXLQo7qugKolFgEVAENNnk+ibA/DLuM7+M2/uy7tO/f39at24NQP369enatSv5+flAyZtV\nEi4PHQrz5hWw557w6af51KsXrfgyfXnQINhrrwLGjx/Daafl8+qrMGRIdOLL9OXVq+GUU+zy9dfn\ns9tu0YoviMtt2xaw3XZ2dHbUKFi5MlrxBXH57bcB8tlvv2jEo8uZv7zffvmMGgX//ncBeXnhxxP0\n5YkT8/nlF2jZcsyGgzzRii+Iy5ddBg88UEDfvvbz16wZrfgyeXnvvfM56ij44YcCWrcew3PP5ZOX\nF534grg8bBgccEABo0fDhAn59OoVrfgyeTl1fubMmVSY9z7jX8AI4PFNrpsM3F7G7S8AlgA10667\nHvihjNv7XPLvf3u/enXYUWTP+PHe16vnPXj/xz+GHU2wrr3Wfs4uXbxfsybsaLLnqqvs5z7llLAj\nCV5xsfdNm9rPO3ly2NFIUF580XJ8zDFhRxK8oiLvO3Swn/fll8OOJntWrPC+bVv7uW+9NexogrN+\nvfd9+tjP2b699wsXhh1R9syd6/2gQWFHkX0bPleX6zN+IBvAOedOAp4HLgI+x1ZTOgvo7L2f45y7\nE+juvT90w+3rApOAAuAOoAPwDPAX7/0DpTy+DyJuiY5PPrG5DuvXJ28yVsrXX9syjsXF1sqSS8t0\nzpplezvk5dl8h2bNwo4oOLNnQ6tWtlzlzz8nb7UsMTNmQLt20KiRLWaR5Dx/+CEcfrjNa5gxw9os\nc0WqlaVGDZt32KVL2BFllvcwYAA8/jjssIO9N+2yS9hRSdDCnhyN9/4V4HLgBmA00BM40ns/Z8NN\nmgJt0m6/DPgtsBMwCngY+FtpRYPkhry8Ap56ys5feqmtxpMka9faJnhFRbZ3Qy4VDWAfpHv2LKCw\nMNUnnVzpy7Am+cNkadKHxZOuTRsrGhYutAUtkuyxx+z0ggvg888LQo0l2w4+2JaeXb8ezjoreUt4\n3nuvFQ3bbANvvWVFQy69jmXrAikcALz3j3vv23rva3vvu3vvP0/73lne+3ab3H689z7fe7+t935n\n7/3tQcUm8XDGGXDzzXZE/uST7Qh9UvzlL/Dtt3bU/bbbwo4mHCecYKePPw7r1oUbS5C0f0NucC43\n9nOYPRveftuOuJ97btjRhOOee2y0ZdQoeCBBhzdfecVWTgLb46Bnz3DjkWgKrHAQqYrURJ4//9kK\niIWCNVAAAB9JSURBVFWrbLO72bPDjSsThg61N568PHjhhdxdnvPyy/Pp3Bnmz7dNDZMqlwuH1Os4\nV+RC4fDEE3Yw58QToUmT3Msx2J4sTzxh52+6CaZODTeeTPj8c3uvBXt/Oumkku/lYo6lbCocJNKc\ns12Ue/e2D5h9+sDSpWFHVXnLltk/Z+9t74ZcPqLjHFx8sZ1/5JFwYwnK2rUlSzf26BFuLBK8pBcO\na9fyawvpgAHhxhK2Pn3g9NNhzRrbdbi4OOyIKm/KFPjd7yy/F14IV10VdkQSZSocJJLSeypr1oTX\nX4dOnWD8eNvlMa6tLZdfbjusdutm7Uq5rKCggNNPt6N3w4bBmDFhR5R5Y8fam3GnTrbBUK7Jtd7o\n7t2tIB49mkTuQ/P66/DTT7aR4f7723W5luN0Dzxgoy5Dh9pmeHH000+2CdrixbZnw0MPbT4XK5dz\nLJtT4SCx0KABvPceNG5suzyef74dtY+Tf/0LnnnGJp39619WEOW6OnVsgiEkc9Th8w0zu3KxTSkX\nbb+9bWJZWJjMjeBSk6IHDMi9if6ladiw5Hdy9dXxO/ixapWNNMyYYbufv/SS7YAusiUqHCSSSuup\nbN3aVleqXRuefRZuj9H0+UmTbAUSsCM6nTuHG08UpHKcanl48UU76pUkn31mp716hRtHWHKxNzqV\n6yFDwo0j08aOtUK4bl049dSS63Mxx+lOOAHOO89GFk86ydpR46CoCE47Db78Elq2tPfWOnVKv22u\n51g2psJBYqV7d/jPf+xo15//bAVE1K1aBX37wsqV8H//Z28yUmLXXW1N+DVr4Omnw44mc4qLrYUB\n4KCDwo1FsieV61TRmBSpI+tnnln2B8xc9eCD1r41dSr84Q/RHw33Hq64whalqFcPBg1K9l46klkq\nHCSSttRTeeyx9o8abDnAN9/MTkyV4b1NAB43zj4gP/GEhvhT0nN8ySV2+thjdiQsCcaNsxGU5s1t\ntCwX5WJv9IEH2unw4bbWfxIsXWrtlbD5pOhczPGmateGV1+1gurll+Ef/wg7oi275RZ4+GFrl/3v\nf7c+Aq4cSzoVDhJLl1wCN95oHzJPPtnmPUTRgw/avIZateyNZfvtw44omo44wva0mDnT5rIkQapV\n5aCDVCzmkqZNoUMHG2lMyt4zzz9vP8/BB0PHjmFHE0277morAAJcdhl8+mm48ZTlwQetcMjLs9H7\n3r3DjkjiRoWDRFJ5eipvvdWO5q9bZ6MQUVsC8b334I9/tPPPPGND2VIiPcfVqpUcyUzKJOlUq0ou\ntynlam90ktqVvN94UvSmcjXHpenXz5YyLSy01f+mTAk7oo0984yt7Ae2rG5qE86tUY4lnQoHiS3n\n7OjJaafZ/IHDDrP2gCgYN87eRIqLbdnVfv3Cjij6zjrLhvw//BAmTw47mqrxfuMRB8ktqZwnYYL0\np5/a4g477WQHaGTL7rrLVir65Rc45hg7jYKBA22/CYD77y9ZzU6kolQ4SCSVt6cyL8+OovTrB8uX\n2yTb1ITUsMyebethL19ubVS5vl9DWTbNccOGVgRCfNdET5k0ydZHb9IEdtkl7GjCk6u90amVlYYN\ni/+cndRr8fzzS1+qM1dzXJZq1WyFuD32sBGH446zNq8wPfmkFQ3eW2GTGnUoL+VY0qlwkNirXh1e\neME+dK5YYf3yYc15+PFH6xmdPRt+8xsratTfXn4XXWSnzz5rhVdcaX5Dbmve3ObsLFsWv7X90/34\nI/zvf/Y/VqvBlV+dOvD22zZKM2SIFQ9r1oQTy8MPW9EHcO+9cO214cQhyaHCQSKpoj2V1avbh80z\nz7SjO0ceaRP6smn+fJs8OGMG7L23zXGoXTu7McRJaTnec09blWb58uznL5M0v8Hkcm90EtqVnnjC\nRkxOOKHs5TpzOcdb0rIlfPKJjTp+9JHNeVi7NnvPX1xsm9JdeqldfuCBkjl3FaUcSzoVDpIY1apZ\nH+dVV9kyiGeeaatHZGNN7e+/t5GGKVPsw++HH0L9+sE/bxKllmZ95JHor4deGu9VOEj8J0ivW2ct\nLlAyEigV06GDjX7vuKMdSDr++OyMpK5ebW2y995rB9WeecZWehLJBBUOEkmV7anMy4O//c36cvPy\n4OabbTfPJUsyGt5Ghg+Hffe1vvbddrOjSw0bBvd8SVFWjo87zob4J02yI3ZxM306zJ0LO+wAnTqF\nHU24crk3OjXPYehQO/obN2+8AQsW2P+01N4UpcnlHJdHly7w8cf2/2DQINh/f5g1K7jnmzbN/vZe\ne802d3v/fejfv2qPqRxLOhUOkkgDBlhv7vbb2z/Qrl2DWXHphRdspGHhQlvVadgwaNQo88+TS2rU\ngAsusPNxXJo19R7bq5cVr5KbWreGFi1sE8Dvvgs7mopLvfYuukjzdKpqzz1tufAOHexvoUePYN6P\nXnwRunWDr76yv7/PP4dDDsn880hu09uaRFImeiqPPhpGj4bu3e0IT69eNjFs2bKqxzdvnvWsnnGG\nDelfdBG8+64d4ZHy2VKOzzvPCoi33gr26FwQPvzQTg89NNw4oiCXe6OdK/kbSP1NxMXYsfahs27d\nkpXOypLLOa6I9u3hiy/sg/xPP9kozlVXZWbFpdmzbWXB1AIhffvae1+XLlV/bFCOZWMqHCTR2rWz\nUYBrrrFJfvfcY8tjPvlk5ZZJXLvWJgx27mxD+XXq2OVHHil9qUKpnKZNrcWsuBj+8Y+woym/wkJr\nVQNb3UtyW+pv4P33w42jolJLsJ55pv2Pk8xo0MDala6+2i7fdx/svrsddKrMfK4VK+Cmm2wk4+WX\nbTGOf/7TzmuOnQRFhYNEUiZ7KmvWhLvvhi+/hJ497WjP+efbUO5NN9kqSFuzYAHcdpvd54ILbM7E\nkUfC+PHwhz9kLNScsrUcX3yxnT71lE32i4ORI+1vY5ddbDnOXJfrvdGHHmrtakOH2oe8OFiyxFpe\noPSdojeV6zmuqBo17ADWiBFWNMyYYaPjXbrYQaiVK7d8f++tFenCC23Z39tvt6VeTz4ZJkyAc8/N\nfGuZcizpdIxUckaPHjb68MorcOONNons9tvtq3NnmwfRrZsdqSkstNGFceOsF3XChJLH2X13uP56\n+0et3t/g7LuvLWv79dfw0kvx2Ok0dWRZow0CtkjCvvtai8qnn9pOwlH37LPWPnPIIdCxY9jRJFf3\n7lYAPPSQLZU6caIdlLr4YthrLzvI1b69jWRXr257aowZA998s3H75v77WyHSs2d4P4vkFudjuN6h\nc87HMW6JjuJiW1/96adt8vTWNufZZhub/Hz55TYZWgVDdjz3nK0I0q2bFRBR/7336AGjRlnrQZ8+\nYUcjUXDrrbZ7/IAB0d8RvbjYioWpU60V8/jjw44oN6xfb+9DDz9sI+NbW4Vrhx3g9NNtN+jddstO\njJJszjm89+V6h1XhIDlvzRobWRg92iYFrllTcpSnTRs7otOtmxUPkl1r1thw/M8/28jPb34TdkRl\nW7jQNnuqWdPi3W67sCOSKBg50kYd2rSxpXqjXPx+8IGNlrVoYS00mreVfcuW2d/MF1/YpqKFhfbV\noEHJqHiHDsqNZFZFCgf96UkkFRQUZG0lh1q1YJ997Euypzw5rlXLVli66y6bgB7lwuGjj6z/uFcv\nFQ0p2XwdR9Xee9sR4u+/t/bIXXYJO6KypUZELrig/B9MlePMqlvX5sZEaVU25VjSaXK0iETaBRfY\nBNNXX7UjcFGl+Q1SmmrVrM0Ror260syZ8M47NmJ27rlhRyMiUaXCQSJJRzeSr7w5btUKjj3W+oCf\nfDLYmCqruNjaPECFQzq9jk0clmV9/HEbMevbFxo3Lv/9lOPkU44lneY4iEjkffKJrfLSrJmtKFKj\nRtgRbeybb6wlpUULiy/KfeySffPn299u7dq2k3StWmFHtLE4zSUSkcyryBwHjThIJGnd6OSrSI57\n97Ylc+fNs9Veoubdd+30iCNUNKTT69g0bWqTWlevtiI4av79bysaunWD/far2H2V4+RTjiWdCgcR\niTznSjaEe+SRcGMpzauv2qmWr5SypP42Xnst3Dg25T38/e92/vLLVfiKyJapVUlEYmHFCth5Z1uu\ncPRoW5owCiZNgk6dbOPABQtscqnIpqL6d5JagnWnnWzlp6jEJSLZo1YlEUmcOnVsMziI1qhDarTh\nuOP0oUvK1rGjbda1ZAkMHhx2NCXuu89OL7lEf78isnUqHCSS1FOZfJXJ8UUX2emLL9ok0yhIFQ59\n+4YbRxTpdbyx1N9I6m/m/9u7+2grqzqB49+fqCE6iKOpjebyJXVMM9LUbBCBxJdarnK0mF5IxHfN\nmHGZWqSj4VskGjSV49IyKWV0nNIYNRNETSOFvCpqE0ak5kskiiJaCnv+2Ac5XcFzL/c557nnud/P\nWnfdc849z/P8Fr97uOd39v7tXbaHHsr7jwwYAMcfv3bnMMfVZ45Vz8JBUtvYaSc46KC8Csz3vld2\nNHn6ycMP5+knvWnDJvVOKwuHn/wkLy9ctpW9DWPH5p2JJakRexwktZXp0+HQQ2HbbfNOvP36lRfL\nhAlw9tlw5JFw1VXlxaH2sdtu8MgjcMst5e758cwzeY+UN96A+fNhhx3Ki0VSuexxkFRZhxwC22+f\nd7ote8qH05TUXb1lutKkSXnU47DDLBokdZ2Fg3ol51RW39rmuF8/OPPMfPu88/KuzWVYOU1p441h\n5MhyYujtfB2/1crC4cc/Lm+60qJF8N3v5tvjx/fsXOa4+syx6lk4SGo7Rx6Zd2l+5JH8BqwM116b\nv7uakrrjve+FXXeFF17IS6GW4ZJLYNky+NjHYI89yolBUnuyx0FSW/r2t/OmcIMHw69/3dqNq15/\nPfdYPP10XlpzxIjWXVvtb+JEOOOM/MZ9+vTWXnvx4tzbsHQpzJ4N++zT2utL6n3scZBUeUcfDe96\nF3R0tP7N1/TpuWjYeWcYPry111b7O+qoPEp18825V6eVJk/ORcOBB1o0SOo+Cwf1Ss6prL6e5rh/\nfzj99Hz7a1+DVg5CrpwffsIJrR3paDe+jlfvne/MvQ4pweWXt+66S5bkwgHgrLOKOac5rj5zrHoW\nDpLa1nHHweabw5w5eW38Vpg/P2+a1b8/fP7zrbmmqueEE/L3K6+Ev/61NdecODEXD8OGwZAhrbmm\npGqxx0FSW1vZ6/Ce9+Rm6WY3Kn/pS3DxxTBmDHz/+829lqorJdh9d5g3D6ZNg1Gjmnu9J5/MGyi+\n9hr88pfwoQ8193qS2oc9DpL6jOOOy70Gjz8Ol13W3Gu99tqqYuHEE5t7LVVbxKrfoZVT35pp/Pj8\n+ztqlEWDpLVn4aBeyTmV1VdUjtdbL0/BADj33LzMZbNcdx08/3xewnKvvZp3narwdfz2Pvc52HBD\nuPPOPPLQLHPnwtSpeTTuwguLPbc5rj5zrHoWDpLa3qGH5nnbixfDBRc05xqvvw4TJuTbX/iCTdHq\nuYED854kAOec05xrpASnnZZvf/GLsN12zbmOpL7BHgdJlTB3Lnzwg/lT1YceytOXinTZZXlqyU47\n5V6Kddct9vzqm/74x9yf89prcN99xY9k3XADHHEEbLppns43aFCx55fU/krtcYiI9SPiWxGxKCKW\nRsSNEbFVg2OOiYi7ImJxRLwQETMj4p+Kjk1Sde25J4wdm1eoGTMGli8v7tyvvJKnQQGcf75Fg4qz\n1VYwbly+feaZxS4rvGjRqj6K886zaJDUc82YqjQZOAwYBQwBBgLTI952YH9/YBowHNgb+D/gZxGx\nQxPiUxtwTmX1NSPHkyblN2KzZ8MllxR33ilT4Nln84jG4YcXd96q83XcNWeckd/Uz5wJt99e3HlP\nPjkXDyNG5EUEmsEcV585Vr1CC4eIGAiMBU5LKc1MKXUAo4HdgQPWdFxKaXRK6TsppQdTSvNTSicC\nLwMHFxmfpGobNAiuuCLfPussePTRnp9z8WL4+tfz7YsusrdBxdtkkzzaAPn7ihU9P+d118H118NG\nG+W9Itaxo1FSAQrtcYiI4cDtwOYppefrHp8HXJ9SOreL53kH8CxwckrpmtX83B4HSWt07LG5gNhr\nL7jnnrzyUk/PNXIk3HZbcTFK9ZYtgx13hKefzv00xx+/9ud69lnYbbe8AlhPzyWp+srscdgSWF5f\nNNQ8V/tZV51HHnG4qajAJPUdkybBNtvA/ffDMces/bzxH/4wFw3veEfe9E1qlgEDVv2OjRsHDzyw\ndud55ZW8ytjzz8MBBzRvipKkvqlLhUNETIiIFW/ztTwihhYRUESMA44FDkspLS3inGo/zqmsvmbm\neODAvJrMgAFw9dV52lJ3PfLIqk9qp0zJu/yqe3wdd8+nP51HuP7yF/jkJ2HJku4d/8Yb8KlPwZw5\nednVqVObP7XOHFefOVa9rq4NcikwtcFzngD2BfpFxKadRh22AO5qdJGI+FfgXODglNLct3vumDFj\n2HbbbQEYNGgQgwcPZtiwYcCqX3Lvt+/9jo6OXhWP94u/v1Izr3f99XDoobM4/3x497uHcfzxXTv+\n1Vfh1FOHsWwZjBw5ix13BGjtv4/3++b9I46YxR13wOOPD+Ooo+CUU2YR0fj4/fcfxoknws03z2Lg\nQLj11mFsuWXz4+3o6Cj138v7zb/v3+Pq3V95e+HChXRX0T0OA4FFwJEppWm1x7YG/gAclFJa43oR\nEXEq8O/AR1NK9zS4jj0Okrrkiivyp7jrrJNHHr761bdfTvXJJ/OntrNnw667wq9+lXf3lVrld7/L\nywsvWQKf/WzuU9hoozU/f8kSOOkkuOYa6N8/r860776ti1dSeyutxyGl9BJwJTAxIj4SER8ArgY6\ngBl1Ac6IiPPr7n8JuBA4Gng8IraofQ0sMj5Jfc8xx+TVkFLKezHsvz+s6UOWW2+FD3wgFw1bb52n\nO1k0qNV22AGuvTb/7v3oR7D33mteIezee2Hw4Fw0DBiQV1OyaJDULIXvHB0R6wEXA58BNiCvsnRy\nSumPdc9ZANyRUjq6dv/3wDarOd0PUkpjV3MNRxwqbtasWW8OramaWp3jmTNh9Oi8as0GG8Dw4fCR\nj+Qdpu+/H+6+G+64IxcYBx+c54dvtlnLwqskX8c989hjedfnRx/Nv7MHHgj77Qe77JJ/Z2fMyKuG\nrVgBe+yRi4eid0xvxBxXnzmuvu6MOBS+/2lK6XVgXO1rTc/ZvtP97YqOQ5LqjRgBDz2UG55vuAFu\nvjl/1evXL49KfPnLrnuv8u2yC9x3X979eepUuPHG/FWvXz84/XSYMAHWX7+cOCX1HYWPOLSCIw6S\neuKpp/LowowZsGBBnk++334wZAhsvnnZ0UlvtWAB/OIXeWTsN7/JU+pGjMhT7zbZpOzoJLWz7ow4\nWDhIkiRJfVSZG8BJhahfMkzVZI6rzxxXnzmuPnOsehYOkiRJkhpyqpIkSZLURzlVSZIkSVKhLBzU\nKzmnsvrMcfWZ4+ozx9VnjlXPwkGSJElSQ/Y4SJIkSX2UPQ6SJEmSCmXhoF7JOZXVZ46rzxxXnzmu\nPnOsehYOkiRJkhqyx0GSJEnqo+xxkCRJklQoCwf1Ss6prD5zXH3muPrMcfWZY9WzcJAkSZLUkD0O\nkiRJUh9lj4MkSZKkQlk4qFdyTmX1mePqM8fVZ46rzxyrnoWDJEmSpIbscZAkSZL6KHscJEmSJBXK\nwkG9knMqq88cV585rj5zXH3mWPUsHCRJkiQ1ZI+DJEmS1EfZ4yBJkiSpUBYO6pWcU1l95rj6zHH1\nmePqM8eqZ+EgSZIkqSF7HCRJkqQ+yh4HSZIkSYWycFCv5JzK6jPH1WeOq88cV585Vj0LB0mSJEkN\n2eMgSZIk9VH2OEiSJEkqlIWDeiXnVFafOa4+c1x95rj6zLHqWThIkiRJasgeB0mSJKmPssdBkiRJ\nUqEsHNQrOaey+sxx9Znj6jPH1WeOVc/CQZIkSVJD9jhIkiRJfZQ9DpIkSZIKZeGgXsk5ldVnjqvP\nHFefOa4+c6x6Fg6SJEmSGrLHQZIkSeqjSu1xiIj1I+JbEbEoIpZGxI0RsVU3jv90RKyIiJuKjk2S\nJEnS2mnGVKXJwGHAKGAIMBCYHhENK5mI2B6YCNzVhLjURpxTWX3muPrMcfWZ4+ozx6pXaOEQEQOB\nscBpKaWZKaUOYDSwO3BAg2PXBa4BvgL8vsi41H46OjrKDkFNZo6rzxxXnzmuPnOsekWPOOwJrAv8\nfOUDKaWngMeADzc49gJgQUppasExqQ29+OKLZYegJjPH1WeOq88cV585Vr11Cz7flsDylNLznR5/\nrvaz1YqIA4EjgPcXHI8kSZKkAnRpxCEiJtQaltf0tTwihq5NABGxGfB94MiU0strcw5Vz8KFC8sO\nQU1mjqvPHFefOa4+c6x6XVqONSL+HtiswdOeAPYFbgc2rx91iIh5wPUppXNXc+79gZnAcmBlA/XK\ngmY5sGtKaX6nY1yLVZIkSSpAV5djLXQfh1pz9CLy6MG02mNbA38ADkop3b6aYzYAtuv08PnAIOAk\nYH5K6Y3CgpQkSZLUbYX2OKSUXoqIK4GJEbEIWAxMAjqAGSufFxEzgNkppfEppVeBR+vPExEvAv1S\nSo8VGZ8kSZKktVN0czTAOOB1YBqwAXnq0uhOWz1vByxswrUlSZIkNUGhU5UkSZIkVVMzdo5uqYjY\nOyJui4iXI+KliPhFrZlbFRMRt9RW8frnsmNRMSJik4iYEhGPRcSyiHgiIr7ja7i9RcRJEbEgIl6N\niDkRMaTsmFSMiPhyRNwXEUsi4k8RcVNE7Fp2XGqeWs5XRMSUsmNRcSJiy4i4qvY6fjUi5kXEfo2O\na+vCISL2AX5GXpVpb2AP4GLyVClVSEScBrwBOERWLf9Q+zoN2A34LDCUvIu82lBEjAK+CZwHDAbu\nBW6pLZSh9jcU+A/yKorDyf8v3x4Rg0qNSk0RER8CjgUeLDsWFSciNgbuIb+nOgT4R+AU4E8Nj23n\nqUoRcQ8wI6V0dtmxqHkiYi/gv8k7k/8JOCKl9D/lRqVmiYhDgJ8Cg1JKS8uOR90TEbOBjpTSCXWP\n/Za8JPf48iJTM0TEhsAS4OMppf8tOx4Vp/bmci5wNHAO8HBK6YulBqVCRMQFwH4ppYYjDJ217YhD\nRLyT/InHcxFxd0Q8FxF3RcSIsmNTcSLi74AfAcemlP5cdjxqiY2BvwDLyg5E3RMR65EL/J93+tFt\nwIdbH5FaYCD5vcQLZQeiwl0OXJdSurPsQFS4jwO/iohptffPD0TEyV05sG0LB2D72vdzgCuAA4G7\ngZ9FxPvKCkqF+y5wc0rptrIDUfPVpjt8Dbg8pbSi7HjUbZsB/YDnOj3+HLBl68NRC0wGfg38suxA\nVJyIOJb8PuurZceiptievFfa78jvn78JXBQRJzU6sNcVDhExodaEs6av5RExlFWxX5ZS+kFK6cHa\nMPj9wAlrvoLK1tUcR8Ro4P3A6WXHrO7pxuu4/pgNyVOUngTOKCVwSV0WEZeQR5IOT+0871l/IyJ2\nIm/E+xk/wKmsdYC5tf3UHkwp/QCYAjQcdWjGPg49dSkwtcFznmDVp1edN4l7FNim6KBUqK7k+Eng\nKGAX4JWIv9kJ/bqIuDelNHS1R6o36OrrGHizaLgFWA4cmlL6axNjU/P8mZzDLTo9vgXwbOvDUbNE\nxKXAp4BhKaU/lB2PCrUvsCnwaN3f3n7A0Ig4AdgwpeQiNO3tGd76/vkxoGEPS68rHFJKi8k7Tjey\nMCKeBnbu9PhOwEOFB6bCdDXHEfEV4BudHp4HnArc1ITQVJBuvI6JiI3IRUMCPppSsrehTaWUXo+I\nucBI4Ia6H40Eri8nKhUtIiYDnyQXDfPLjkeF+zF59ka9q4DfAudbNFTCPbz1/fPOQMMPAXpd4dBN\n3wDOiYiHgQeAUcA+5HlbanMppWfIVfGbap9+PJVSWlhGTCpWrWj4ObAR8Ang72oN8QCL/QPVli4B\nro6I+8l/nE4E3gX8Z6lRqRAR8W3gc+TmyiURsXJ0aWlK6ZXyIlNRUkovkWdvvCkiXiH/n9z5U2q1\np0uBe2of0P4XeTuDU4AzGx3Y1oVDSmlyRKxP3rthU+AR4OCU0rxyI1MTOY+2WvYk78EC+dMsgCDn\neThwVxlBae2llK6rbeA3nlwwzAMOSSk9WW5kKsiJ5NfnjE6Pn0te2EDV5N/eCkkpzYmITwAXkhvg\nnwDGp5Qua3RsW+/jIEmSJKk1et2qSpIkSZJ6HwsHSZIkSQ1ZOEiSJElqyMJBkiRJUkMWDpIkSZIa\nsnCQJEmS1JCFgyRJkqSGLBwkSZIkNWThIEmSJKkhCwdJkiRJDVk4SJIKERGbRcTTEXF23WO7R8Sr\nEXF4mbFJknouUkplxyBJqoiIOBD4KTAUeBCYA8xOKR1TamCSpB6zcJAkFSoiLgE+DtwJDAEGp5SW\nlRuVJKmnLBwkSYWKiPXJow3vAfZNKc0pOSRJUgHscZAkFW074N1AAnYoORZJUkEccZAkFSYi1gVm\nA78B7gPOAXZPKT1VZlySpJ6zcJAkFSYiLgL+BXhfSunliLgF6J9SGl5yaJKkHnKqkiSpEBExFPg3\nYHRK6eXaw2OAXSLijNICkyQVwhEHSZIkSQ054iBJkiSpIQsHSZIkSQ1ZOEiSJElqyMJBkiRJUkMW\nDpIkSZIasnCQJEmS1JCFgyRJkqSGLBwkSZIkNWThIEmSJKmh/wf3MitN9sikYwAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102eb210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# put new_plot to separate figures for lines in matplotlib\n",
    "function_plot1.plot(new_plot=True)\n",
    "function_plot2.plot(new_plot=True, xlabel='x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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++0tz5ki77OL6GvbYI++RVaYizjgsWeKSh3793OrOaaE5GgDSZay1eY+hbMYY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\nxpjGVdtov2fS40MxcN3o8BHj7Awe7LZ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lMwYAkEgc4CmuGx0+Ypy/tBOHUGKcZ7nSO++4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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113085090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uu8OiRTBrFrRvH3Y01fP00zBoEBx9NLzyStjRiIjEk+Y4iIjINpYutaKhXj2bIB13PXrY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\nDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130850d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function_plot1.plot(new_plot=True)\n",
    "function_plot2.plot(new_plot=True, xlabel='x', show_legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"plotdiv\" id=\"3ac83fdb-9282-4c6b-a5a6-33ecf8efb9f6\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"3ac83fdb-9282-4c6b-a5a6-33ecf8efb9f6\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '3ac83fdb-9282-4c6b-a5a6-33ecf8efb9f6' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
       "            var docs_json = 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       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'aa9ceb6e-f49f-45d7-8c69-4e8d006bc058' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
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       "            var render_items = [{\"docid\":\"ce8c058a-c6e3-471d-be3e-ca9f3aed44ff\",\"elementid\":\"aa9ceb6e-f49f-45d7-8c69-4e8d006bc058\",\"modelid\":\"67223458-27c0-414b-8c75-f7b281110979\",\"notebook_comms_target\":\"3777114a-d04e-40c5-a0b8-7a60bd681dae\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function_plot1.plot_bokeh()\n",
    "function_plot2.plot_bokeh(title='bokeh', xlabel='x', ylabel='points', xlim=(-5, 0), fontsize=13,\n",
    "                          show_legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GridPlot\n",
    "\n",
    "Organizes lots of plots in grid. Used when need to show many plots of same nature, this saves space :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rep.plotting import GridPlot, HStackPlot, VStackPlot\n",
    "grid = GridPlot(2, function_plot1, function_plot2, function_plot3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2Y4e0fbvUpo3UqVP270lnfPQ4T/qJvAHRstY+LukaSTdJmitprKTTrLUfVD+l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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11346b590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parameters doesn't work here, set them for each plot in grid\n",
    "grid.plot(title='grid', xlabel='x', ylim=(0.2, 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2Y4e0fbvUpo3UqVP270lnfPQ4T/qJvAHRstY+LukaSTdJmitprKTTrLUfVD+l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\nkkrPYnyVsqdufsxTb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25+zFNuK5Kjt99O/eK7d4eWLQsXU7nxPMqPecrNHOVm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\nnF13QeAZlpl5Zme/n+uaa+Z5dmb29ufsAvecuY8kq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SqJCy+En/8cvv1t+Ne/8q6mdJwZ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\nSZIkSZIkqWcOxkuSJEmSJEmS1LP/AWZYhbK1c58tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114528110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function_plot1.xlabel = 'x'\n",
    "function_plot2.xlabel = 'y'\n",
    "# these parameters will be ignored\n",
    "function_plot1.figsize = (8, 6)\n",
    "function_plot2.figsize = (4, 3)\n",
    "\n",
    "grid = GridPlot(2, function_plot1, function_plot2, function_plot3)\n",
    "# Only inner parameters of plot is used in grid, so grid ignores all parameters in plot(...)\n",
    "# fot the exception of `show_legend` and `fontsize`.\n",
    "\n",
    "grid.plot(xlabel='try', ylim=(0.2, 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# can be done with mpld3\n",
    "# grid.plot(show_legend=False, fontsize=10, figsize=[12, 10])\n",
    "# mpld3.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"plotdiv\" id=\"d57db189-63cf-437a-9f7f-7a500c12df31\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"d57db189-63cf-437a-9f7f-7a500c12df31\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'd57db189-63cf-437a-9f7f-7a500c12df31' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
       "            var docs_json = 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       "            var render_items = [{\"docid\":\"4ffc5b58-c3d5-4d5d-af47-50fe9ec90320\",\"elementid\":\"d57db189-63cf-437a-9f7f-7a500c12df31\",\"modelid\":\"70beecb7-4fab-4c2b-98b9-5fb68009e8b8\",\"notebook_comms_target\":\"b88c90f3-0edb-4578-a58d-0ae7ef5f55ce\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid.plot_bokeh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HStackPLot, VStackPlot\n",
    "\n",
    "Incorporates other plots and aligns them horizontally / vertically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hstack = HStackPlot(function_plot1, function_plot2, function_plot3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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okC1SDr8L2cZA8+bu/OLF/jxnVXgF6rJmZHuF7JUrw4lHREQkrYKakZ2VQraI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BApIg3hS0a2j43syu691xY+69QJrrgi7WpEEjMTWAJ0qvb9TsB3tfzMd7U8PljKz9C/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\n8X3/SDxRXGwFxWxmg/AyKEcUMbb6FNrWNV0C/AQUo753IX1+X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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130a6110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hstack.plot(title='stack', xlabel='x', ylim=(0.2, 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# hstack.plot(show_legend=False, fontsize=20)\n",
    "# mpld3.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"plotdiv\" id=\"e5c3b3e3-1414-41ac-b970-2385cb2abeaa\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"e5c3b3e3-1414-41ac-b970-2385cb2abeaa\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'e5c3b3e3-1414-41ac-b970-2385cb2abeaa' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
       "            var docs_json = 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       "            var render_items = [{\"docid\":\"9c3a0afa-358b-4576-ae01-7deaca63b22e\",\"elementid\":\"e5c3b3e3-1414-41ac-b970-2385cb2abeaa\",\"modelid\":\"cdc9528d-d45b-43ea-b317-bd073c9f74f6\",\"notebook_comms_target\":\"6d9d4d5e-a11d-4970-9fd5-dc02c56fdc52\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hstack.plot_bokeh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vstack = VStackPlot(function_plot1, function_plot2, function_plot3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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IFt+/m75goSrDyCDVqyrKFkfv3m6bOa7BV15/Hb77XTcNSN++8OCDcPDBUVtl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\nh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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113960490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vstack.plot(new_plot=True, figsize=(6, 12), title='stack', xlabel='x', ylim=(0.2, 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el7576246253401766259843474\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el7576246253401766259843474\", {\"axes\": [{\"xlim\": [-6.0, 6.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"x\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [0.5, -0.091397849462365954], \"rotation\": -0.0, \"id\": \"el757624660147088\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-6.0, 6.0], \"ylim\": [-1.0, 1.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el7576246253401766259843474\", {\"axes\": [{\"xlim\": [-6.0, 6.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"x\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [0.5, -0.091397849462365954], \"rotation\": -0.0, \"id\": \"el757624660147088\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-6.0, 6.0], \"ylim\": [-1.0, 1.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, 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[4.84924623115578, -0.28012255534302044, 0.02993885328464845], [4.899497487437186, -0.2800549588622857, 0.020173693248607624], [4.949748743718594, -0.2799938238860639, 0.010110166538635845], [5.0, -0.27993853292596743, 3.898171832519376e-17]]}, \"id\": \"el757624625340176\"});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el7576246253401766259843474\", {\"axes\": [{\"xlim\": [-6.0, 6.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"x\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [0.5, -0.091397849462365954], \"rotation\": -0.0, \"id\": \"el757624660147088\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-6.0, 6.0], \"ylim\": [-1.0, 1.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 5, \"tickvalues\": null}], \"lines\": [{\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data01\", \"id\": \"el757624660634448\"}], \"markers\": [], \"id\": \"el757624660094096\", \"ydomain\": [-1.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.67205882352941182, 0.77500000000000002, 0.2279411764705882]}, {\"xlim\": [-6.0, 6.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"y\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [0.5, -0.091397849462365732], \"rotation\": -0.0, \"id\": \"el757624660693200\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-6.0, 6.0], \"ylim\": [-0.40000000000000002, 1.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 9, \"tickvalues\": null}], \"lines\": [{\"color\": \"#0000FF\", \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data01\", \"id\": \"el757624659702928\"}], \"markers\": [], \"id\": \"el757624660634256\", \"ydomain\": [-0.40000000000000002, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.39852941176470591, 0.77500000000000002, 0.2279411764705882]}, {\"xlim\": [-1.0, 0.80000000000000004], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-1.0, 0.80000000000000004], \"ylim\": [-1.0, 0.60000000000000009], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 10, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 10, \"tickvalues\": null}], \"lines\": [{\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2.0, \"data\": \"data02\", \"id\": \"el757624661174096\"}], \"markers\": [], \"id\": \"el757624659701328\", \"ydomain\": [-1.0, 0.60000000000000009], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.12499999999999989, 0.77500000000000002, 0.22794117647058831]}], \"height\": 960.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data02\": [[0.0, 0.0], [0.004884232059242633, 0.0011815941647041822], [0.009874836315429457, 0.001869534182052197], [0.014934416900382167, 0.00205674015202407], [0.02002516672155112, 0.0017386486222424492], [0.02510902611269302, 0.0009132344370492149], [0.03014784256120325, -0.0004189770093639589], [0.035103530948177634, -0.002254907164732868], [0.03993823373528108, -0.004588931371924417], [0.04461448053233996, -0.007412895567575154], [0.04909534648125092, -0.010716142577698987], [0.05334460889530847, -0.014485547929462446], [0.05732690159838505, -0.01870556506623023], [0.06100786641553631, -0.02335827982123012], [0.06435430127552773, 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstack.plot(new_plot=True, figsize=(6, 12), show_legend=False, fontsize=10)\n",
    "mpld3.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# vstack.plot_bokeh(figsize=(8, 13))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>ColorMap</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rep.plotting import ColorMap\n",
    "matrix = numpy.ndarray(shape=(3, 3), buffer=numpy.random.random(size=9))\n",
    "cm = ColorMap(matrix, labels=['feature {}'.format(index) for index in range(3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11452b250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm.plot(show_legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el7576246259332648320982414\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el7576246259332648320982414\", {\"axes\": [{\"xlim\": [0.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 3.0], \"ylim\": [0.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": false}, \"fontsize\": 14.0, \"position\": \"bottom\", \"nticks\": 3, \"tickvalues\": [0.5, 1.5, 2.5]}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": false}, \"fontsize\": 14.0, \"position\": \"left\", \"nticks\": 3, \"tickvalues\": [0.5, 1.5, 2.5]}], \"lines\": [], \"markers\": [], \"id\": \"el757624613596624\", \"ydomain\": [0.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0], [1.0, 0.0], [0.0, 0.0]], [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"]], [[[1.0, 0.0], [1.0, 1.0], [2.0, 1.0], [2.0, 0.0], [1.0, 0.0]], [\"M\", \"L\", \"L\", \"L\", 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el7576246259332648320982414\", {\"axes\": [{\"xlim\": [0.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 3.0], \"ylim\": [0.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": false}, \"fontsize\": 14.0, \"position\": \"bottom\", \"nticks\": 3, \"tickvalues\": [0.5, 1.5, 2.5]}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": false}, \"fontsize\": 14.0, \"position\": \"left\", \"nticks\": 3, \"tickvalues\": [0.5, 1.5, 2.5]}], \"lines\": [], \"markers\": [], \"id\": \"el757624613596624\", \"ydomain\": [0.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0], [1.0, 0.0], [0.0, 0.0]], [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"]], [[[1.0, 0.0], [1.0, 1.0], [2.0, 1.0], [2.0, 0.0], [1.0, 0.0]], [\"M\", \"L\", \"L\", \"L\", 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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el7576246259332648320982414\", {\"axes\": [{\"xlim\": [0.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 3.0], \"ylim\": [0.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": false}, \"fontsize\": 14.0, \"position\": \"bottom\", \"nticks\": 3, \"tickvalues\": [0.5, 1.5, 2.5]}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": false}, \"fontsize\": 14.0, \"position\": \"left\", \"nticks\": 3, \"tickvalues\": [0.5, 1.5, 2.5]}], \"lines\": [], \"markers\": [], \"id\": \"el757624613596624\", \"ydomain\": [0.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0], [1.0, 0.0], [0.0, 0.0]], [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"]], [[[1.0, 0.0], [1.0, 1.0], [2.0, 1.0], [2.0, 0.0], [1.0, 0.0]], [\"M\", \"L\", \"L\", 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       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 28,
     "metadata": {},
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   ],
   "source": [
    "cm.plot(show_legend=False)\n",
    "mpld3.display()"
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  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
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   },
   "outputs": [
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       "            var render_items = [{\"docid\":\"de70d626-eb40-4dbe-85fe-f8ed8ef9b6e0\",\"elementid\":\"09b9ea08-0b8b-45fe-84fd-e447200d0c03\",\"modelid\":\"b33486a0-f913-4322-82e4-c43ad38a4eaa\",\"notebook_comms_target\":\"9dc09fc0-d34c-425b-a405-c4f9ab439211\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm.plot_bokeh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>BarPlot</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rep.plotting import BarPlot\n",
    "data = {'normal': (numpy.random.normal(0, 0.5, 100), numpy.ones(100), 'filled'),\n",
    "        'gamma': (numpy.random.gamma(1.0, 2.0, 100), numpy.ones(100), '')}\n",
    "bar = BarPlot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1139f67d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el7576246143907365529181089\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el7576246143907365529181089\", {\"axes\": [{\"xlim\": [-2.0, 10.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624643997776\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"gamma\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el757624650758672\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"normal\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.831989247311828], \"rotation\": -0.0, \"id\": \"el757624643996304\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-2.0, 10.0], \"ylim\": [0.0, 1.2000000000000002], \"paths\": [{\"edgecolor\": \"#008000\", \"facecolor\": \"none\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el757624650758736\"}, {\"edgecolor\": \"#008000\", \"facecolor\": \"none\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el757624650760208\"}, {\"edgecolor\": \"#008000\", \"facecolor\": \"none\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 2, \"data\": \"data01\", \"id\": \"el757624650761872\"}, {\"edgecolor\": \"#008000\", \"facecolor\": \"none\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el757624650751312\"}, {\"edgecolor\": \"#008000\", \"facecolor\": \"none\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 2, \"data\": \"data03\", \"id\": \"el757624650752976\"}, {\"edgecolor\": \"#008000\", \"facecolor\": \"none\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el757624650869392\"}, {\"edgecolor\": \"#008000\", \"facecolor\": \"none\", \"edgewidth\": 1.0, 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el7576246143907365529181089\", {\"axes\": [{\"xlim\": [-2.0, 10.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624643997776\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"gamma\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el757624650758672\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"normal\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.831989247311828], 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\"el757624644217296\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FFFF\", \"edgewidth\": 0.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 17, \"data\": \"data10\", \"id\": \"el757624644218960\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FFFF\", \"edgewidth\": 0.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 18, \"data\": \"data10\", \"id\": \"el757624644220624\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FFFF\", \"edgewidth\": 0.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 3, \"data\": \"data11\", \"id\": \"el757624643988880\"}, {\"edgecolor\": \"#008000\", \"facecolor\": \"none\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el757624643995920\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FFFF\", \"edgewidth\": 0.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el757624643997200\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el757624650758352\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, 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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el7576246143907365529181089\", {\"axes\": [{\"xlim\": [-2.0, 10.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624643997776\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"gamma\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el757624650758672\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"normal\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.831989247311828], \"rotation\": -0.0, \"id\": \"el757624643996304\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-2.0, 10.0], \"ylim\": [0.0, 1.2000000000000002], \"paths\": [{\"edgecolor\": \"#008000\", \"facecolor\": \"none\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el757624650758736\"}, {\"edgecolor\": \"#008000\", \"facecolor\": \"none\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el757624650760208\"}, {\"edgecolor\": \"#008000\", \"facecolor\": \"none\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 2, \"data\": 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0.7987621852004525], [-0.7048542752550945, 0.3192303440563397, -0.6108782464766228, 0.610810127643509, 0.7987621852004525], [-0.7988303040335661, 0.3192303440563397, -0.7048542752550945, 0.5168340988650373, 0.7047861564219808]], \"data12\": [[-0.9867823615905096, 0.0, 0.7987621852004525], [-0.8928063328120379, 0.0, 0.8927382139789242], [-0.8928063328120379, 0.21282022937089315, 0.8927382139789242], [-0.9867823615905096, 0.21282022937089315, 0.7987621852004525]]}, \"id\": \"el757624614390736\"});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar.plot()\n",
    "mpld3.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rep.plotting import BarComparePlot\n",
    "data = {'normal': {'one': 23, 'two': 34, 'three': 45},\n",
    "        'gamma': {'one': 11, 'two': 23, 'three': 33}}\n",
    "bar_c = BarComparePlot(data, sortby='normal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11532c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_c.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el7576246357037607031523096\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el7576246357037607031523096\", {\"axes\": [{\"xlim\": [0.0, 16.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624650251088\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"gamma\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el757624650250576\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"normal\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.831989247311828], \"rotation\": -0.0, \"id\": \"el757624650252176\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 16.0], \"ylim\": [0.0, 45.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el757624661798608\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el757624661766992\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el757624661764368\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el757624661765008\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el757624661851600\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 2, \"data\": \"data02\", \"id\": \"el757624661852688\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el757624650250960\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el757624650252496\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el757624613735952\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"bottom\", \"nticks\": 3, \"tickvalues\": [0, 7, 14]}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"left\", \"nticks\": 10, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el757624636184464\", \"ydomain\": [0.0, 45.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data06\": [[0.7296146953405018, 0.9112903225806451, 0.831989247311828], [0.8012992831541219, 0.9112903225806451, 0.831989247311828], [0.8012992831541219, 0.9489247311827956, 0.8696236559139785], [0.7296146953405018, 0.9489247311827956, 0.8696236559139785]], \"data07\": [[0.7152777777777778, 0.7983870967741935], [0.982078853046595, 0.7983870967741935], [0.982078853046595, 0.9731182795698926], [0.7152777777777778, 0.9731182795698926]], \"data04\": [[1.0, 0.0], [2.0, 0.0], [2.0, 45.0], [1.0, 45.0]], \"data05\": [[8.0, 0.0], [9.0, 0.0], [9.0, 34.0], [8.0, 34.0]], \"data02\": [[7.0, 0.0, 15.0], [8.0, 0.0, 16.0], [8.0, 23.0, 16.0], [7.0, 23.0, 15.0]], \"data03\": [[14.0, 0.0], [15.0, 0.0], [15.0, 11.0], [14.0, 11.0]], \"data01\": [[0.0, 0.0], [1.0, 0.0], [1.0, 33.0], [0.0, 33.0]]}, \"id\": \"el757624635703760\"});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el7576246357037607031523096\", {\"axes\": [{\"xlim\": [0.0, 16.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624650251088\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"gamma\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el757624650250576\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"normal\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.831989247311828], \"rotation\": -0.0, \"id\": \"el757624650252176\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 16.0], \"ylim\": [0.0, 45.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el757624661798608\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el757624661766992\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el757624661764368\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el757624661765008\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el757624661851600\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 2, \"data\": \"data02\", \"id\": \"el757624661852688\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el757624650250960\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el757624650252496\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el757624613735952\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"bottom\", \"nticks\": 3, \"tickvalues\": [0, 7, 14]}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"left\", \"nticks\": 10, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el757624636184464\", \"ydomain\": [0.0, 45.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data06\": [[0.7296146953405018, 0.9112903225806451, 0.831989247311828], [0.8012992831541219, 0.9112903225806451, 0.831989247311828], [0.8012992831541219, 0.9489247311827956, 0.8696236559139785], [0.7296146953405018, 0.9489247311827956, 0.8696236559139785]], \"data07\": [[0.7152777777777778, 0.7983870967741935], [0.982078853046595, 0.7983870967741935], [0.982078853046595, 0.9731182795698926], [0.7152777777777778, 0.9731182795698926]], \"data04\": [[1.0, 0.0], [2.0, 0.0], [2.0, 45.0], [1.0, 45.0]], \"data05\": [[8.0, 0.0], [9.0, 0.0], [9.0, 34.0], [8.0, 34.0]], \"data02\": [[7.0, 0.0, 15.0], [8.0, 0.0, 16.0], [8.0, 23.0, 16.0], [7.0, 23.0, 15.0]], \"data03\": [[14.0, 0.0], [15.0, 0.0], [15.0, 11.0], [14.0, 11.0]], \"data01\": [[0.0, 0.0], [1.0, 0.0], [1.0, 33.0], [0.0, 33.0]]}, \"id\": \"el757624635703760\"});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el7576246357037607031523096\", {\"axes\": [{\"xlim\": [0.0, 16.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624650251088\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"gamma\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el757624650250576\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"normal\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82997311827956988, 0.831989247311828], \"rotation\": -0.0, \"id\": \"el757624650252176\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 16.0], \"ylim\": [0.0, 45.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el757624661798608\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el757624661766992\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el757624661764368\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el757624661765008\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el757624661851600\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 2, \"data\": \"data02\", \"id\": \"el757624661852688\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#000080\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el757624650250960\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#00FF00\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el757624650252496\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el757624613735952\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"bottom\", \"nticks\": 3, \"tickvalues\": [0, 7, 14]}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"left\", \"nticks\": 10, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el757624636184464\", \"ydomain\": [0.0, 45.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data06\": [[0.7296146953405018, 0.9112903225806451, 0.831989247311828], [0.8012992831541219, 0.9112903225806451, 0.831989247311828], [0.8012992831541219, 0.9489247311827956, 0.8696236559139785], [0.7296146953405018, 0.9489247311827956, 0.8696236559139785]], \"data07\": [[0.7152777777777778, 0.7983870967741935], [0.982078853046595, 0.7983870967741935], [0.982078853046595, 0.9731182795698926], [0.7152777777777778, 0.9731182795698926]], \"data04\": [[1.0, 0.0], [2.0, 0.0], [2.0, 45.0], [1.0, 45.0]], \"data05\": [[8.0, 0.0], [9.0, 0.0], [9.0, 34.0], [8.0, 34.0]], \"data02\": [[7.0, 0.0, 15.0], [8.0, 0.0, 16.0], [8.0, 23.0, 16.0], [7.0, 23.0, 15.0]], \"data03\": [[14.0, 0.0], [15.0, 0.0], [15.0, 11.0], [14.0, 11.0]], \"data01\": [[0.0, 0.0], [1.0, 0.0], [1.0, 33.0], [0.0, 33.0]]}, \"id\": \"el757624635703760\"});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_c.plot()\n",
    "mpld3.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# bar_c.plot_bokeh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ErrorPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rep.plotting import ErrorPlot\n",
    "err = ErrorPlot({'uniform': (numpy.random.random(size=3), numpy.random.random(size=3), \n",
    "                             numpy.random.random(size=3), numpy.random.random(size=3))}, size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x103b1bdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "err.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/axelr/.conda/envs/rep/lib/python2.7/site-packages/mpld3/mplexporter/exporter.py:171: UserWarning: Legend element <matplotlib.collections.LineCollection object at 0x113066790> not impemented\n",
      "  warnings.warn(\"Legend element %s not impemented\" % child)\n",
      "/Users/axelr/.conda/envs/rep/lib/python2.7/site-packages/mpld3/mplexporter/exporter.py:171: UserWarning: Legend element <matplotlib.collections.LineCollection object at 0x114580490> not impemented\n",
      "  warnings.warn(\"Legend element %s not impemented\" % child)\n"
     ]
    },
    {
     "data": {
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       "\n",
       "\n",
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       "\n",
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       "\n",
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       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
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\"xindex\": 0, \"alphas\": [null], \"facecolors\": []}, {\"paths\": [[[[0.9364318023702357, -0.2167587504053844], [0.9364318023702357, 0.9342440291799124]], [\"M\", \"L\"]], [[[0.023540660062376095, 0.8398033265212695], [0.023540660062376095, 0.9060384332937578]], [\"M\", \"L\"]], [[[0.46284233993046986, 0.4706486283838357], [0.46284233993046986, 1.1708641413036824]], [\"M\", \"L\"]]], \"edgecolors\": [\"#0000FF\"], \"edgewidths\": [1.0], \"offsets\": \"data03\", \"yindex\": 0, \"id\": \"el757624613980496\", \"pathtransforms\": [], \"pathcoordinates\": \"data\", \"offsetcoordinates\": \"display\", \"zorder\": 2, \"xindex\": 0, \"alphas\": [null], \"facecolors\": []}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data06\": [[0.711581541218638, 0.8776881720430109], [0.982078853046595, 0.8776881720430109], [0.982078853046595, 0.9731182795698926], [0.711581541218638, 0.9731182795698926]], \"data04\": [[0.71875, 0.9301075268817204, 0.75459229390681, 0.736671146953405], [0.768929211469534, 0.9301075268817204, 0.804771505376344, 0.786850358422939]], \"data05\": [[0.736671146953405, 0.9032258064516129, 0.9569892473118279], [0.786850358422939, 0.9032258064516129, 0.9569892473118279]], \"data02\": [[0.9364318023702357, -0.2167587504053844, 0.9342440291799124], [0.023540660062376095, 0.8398033265212695, 0.9060384332937578], [0.46284233993046986, 0.4706486283838357, 1.1708641413036824]], \"data03\": [[0.0, 0.0]], \"data01\": [[0.825784745044032, 0.35874263938726403, 1.0470788596964393, 0.9364318023702357], [-0.5150068901987862, 0.8729208799075137, 0.5620882103235384, 0.023540660062376095], [0.41945079464647694, 0.820756384843759, 0.5062338852144628, 0.46284233993046986]]}, \"id\": \"el757624614201168\"});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el7576246142011689730699017\", {\"axes\": [{\"xlim\": [-0.60000000000000009, 1.2], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624636281232\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"uniform\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82627688172043001, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el757624614155856\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-0.60000000000000009, 1.2], \"ylim\": [-0.40000000000000002, 1.2000000000000002], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el757624614153104\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"left\", \"nticks\": 10, \"tickvalues\": null}], \"lines\": [], \"markers\": [{\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 1, \"coordinates\": \"data\", \"zorder\": 2, \"markerpath\": [[[0.0, 3.0], [0.0, -3.0]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el757624615550544pts\"}, {\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 1, \"coordinates\": \"data\", \"zorder\": 2, \"markerpath\": [[[0.0, 3.0], [0.0, -3.0]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 2, \"data\": \"data01\", \"id\": \"el757624615550992pts\"}, {\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 1, \"coordinates\": \"data\", \"zorder\": 2, \"markerpath\": [[[3.0, 1.8369701987210297e-16], [-3.0, -1.8369701987210297e-16]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el757624613983568pts\"}, {\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 2, \"coordinates\": \"data\", \"zorder\": 2, \"markerpath\": [[[3.0, 1.8369701987210297e-16], [-3.0, -1.8369701987210297e-16]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el757624613982480pts\"}, {\"edgecolor\": 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\"id\": \"el757624635818128pts\"}, {\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 1, \"coordinates\": \"axes\", \"zorder\": 1000002.0, \"markerpath\": [[[0.0, 3.0], [0.0, -3.0]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el757624614155344pts\"}, {\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 1, \"coordinates\": \"axes\", \"zorder\": 1000002.0, \"markerpath\": [[[0.0, 3.0], [0.0, -3.0]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 2, \"data\": \"data04\", \"id\": \"el757624636282000pts\"}, {\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 1, \"coordinates\": \"axes\", \"zorder\": 1000002.0, \"markerpath\": [[[3.0, 1.8369701987210297e-16], [-3.0, -1.8369701987210297e-16]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el757624636282640pts\"}, {\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 2, \"coordinates\": \"axes\", \"zorder\": 1000002.0, \"markerpath\": [[[3.0, 1.8369701987210297e-16], [-3.0, -1.8369701987210297e-16]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el757624636280272pts\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 1, \"coordinates\": \"axes\", \"zorder\": 1000002.0, \"markerpath\": [[[0.0, 2.5], [0.66300775, 2.5], [1.2989496769621336, 2.2365842289706044], [1.7677669529663689, 1.7677669529663689], [2.2365842289706044, 1.2989496769621336], [2.5, 0.66300775], [2.5, 0.0], [2.5, -0.66300775], [2.2365842289706044, -1.2989496769621336], [1.7677669529663689, -1.7677669529663689], [1.2989496769621336, -2.2365842289706044], [0.66300775, -2.5], [0.0, -2.5], [-0.66300775, -2.5], [-1.2989496769621336, -2.2365842289706044], [-1.7677669529663689, -1.7677669529663689], [-2.2365842289706044, -1.2989496769621336], [-2.5, -0.66300775], [-2.5, 0.0], [-2.5, 0.66300775], [-2.2365842289706044, 1.2989496769621336], [-1.7677669529663689, 1.7677669529663689], [-1.2989496769621336, 2.2365842289706044], [-0.66300775, 2.5], [0.0, 2.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]], \"alpha\": 1, \"xindex\": 3, \"data\": \"data04\", \"id\": \"el757624614152656pts\"}], \"id\": \"el757624650616848\", \"ydomain\": [-0.40000000000000002, 1.2000000000000002], \"collections\": [{\"paths\": [[[[0.825784745044032, 0.35874263938726403], [1.0470788596964393, 0.35874263938726403]], [\"M\", \"L\"]], [[[-0.5150068901987862, 0.8729208799075137], [0.5620882103235384, 0.8729208799075137]], [\"M\", \"L\"]], [[[0.41945079464647694, 0.820756384843759], [0.5062338852144628, 0.820756384843759]], [\"M\", \"L\"]]], \"edgecolors\": [\"#0000FF\"], \"edgewidths\": [1.0], \"offsets\": \"data03\", \"yindex\": 1, \"id\": \"el757624615550928\", \"pathtransforms\": [], \"pathcoordinates\": \"data\", \"offsetcoordinates\": \"display\", \"zorder\": 2, \"xindex\": 0, \"alphas\": [null], \"facecolors\": []}, {\"paths\": [[[[0.9364318023702357, -0.2167587504053844], [0.9364318023702357, 0.9342440291799124]], [\"M\", \"L\"]], [[[0.023540660062376095, 0.8398033265212695], [0.023540660062376095, 0.9060384332937578]], [\"M\", \"L\"]], [[[0.46284233993046986, 0.4706486283838357], [0.46284233993046986, 1.1708641413036824]], [\"M\", \"L\"]]], \"edgecolors\": [\"#0000FF\"], \"edgewidths\": [1.0], \"offsets\": \"data03\", \"yindex\": 0, \"id\": \"el757624613980496\", \"pathtransforms\": [], \"pathcoordinates\": \"data\", \"offsetcoordinates\": \"display\", \"zorder\": 2, \"xindex\": 0, \"alphas\": [null], \"facecolors\": []}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data06\": [[0.711581541218638, 0.8776881720430109], [0.982078853046595, 0.8776881720430109], [0.982078853046595, 0.9731182795698926], [0.711581541218638, 0.9731182795698926]], \"data04\": [[0.71875, 0.9301075268817204, 0.75459229390681, 0.736671146953405], [0.768929211469534, 0.9301075268817204, 0.804771505376344, 0.786850358422939]], \"data05\": [[0.736671146953405, 0.9032258064516129, 0.9569892473118279], [0.786850358422939, 0.9032258064516129, 0.9569892473118279]], \"data02\": [[0.9364318023702357, -0.2167587504053844, 0.9342440291799124], [0.023540660062376095, 0.8398033265212695, 0.9060384332937578], [0.46284233993046986, 0.4706486283838357, 1.1708641413036824]], \"data03\": [[0.0, 0.0]], \"data01\": [[0.825784745044032, 0.35874263938726403, 1.0470788596964393, 0.9364318023702357], [-0.5150068901987862, 0.8729208799075137, 0.5620882103235384, 0.023540660062376095], [0.41945079464647694, 0.820756384843759, 0.5062338852144628, 0.46284233993046986]]}, \"id\": \"el757624614201168\"});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el7576246142011689730699017\", {\"axes\": [{\"xlim\": [-0.60000000000000009, 1.2], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624636281232\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"uniform\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.82627688172043001, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el757624614155856\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-0.60000000000000009, 1.2], \"ylim\": [-0.40000000000000002, 1.2000000000000002], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el757624614153104\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"left\", \"nticks\": 10, \"tickvalues\": null}], \"lines\": [], \"markers\": [{\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 1, \"coordinates\": \"data\", \"zorder\": 2, \"markerpath\": [[[0.0, 3.0], [0.0, -3.0]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el757624615550544pts\"}, {\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 1, \"coordinates\": \"data\", \"zorder\": 2, \"markerpath\": [[[0.0, 3.0], [0.0, -3.0]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 2, \"data\": \"data01\", \"id\": \"el757624615550992pts\"}, {\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 1, \"coordinates\": \"data\", \"zorder\": 2, \"markerpath\": [[[3.0, 1.8369701987210297e-16], [-3.0, -1.8369701987210297e-16]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el757624613983568pts\"}, {\"edgecolor\": \"#0000FF\", \"facecolor\": \"#0000FF\", \"edgewidth\": 0.5, \"yindex\": 2, \"coordinates\": \"data\", \"zorder\": 2, \"markerpath\": [[[3.0, 1.8369701987210297e-16], [-3.0, -1.8369701987210297e-16]], [\"M\", \"L\"]], \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el757624613982480pts\"}, {\"edgecolor\": 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err.plot()\n",
    "mpld3.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>ScatterPlot</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rep.plotting import ScatterPlot\n",
    "x_val = numpy.random.uniform(0.0, 1000.0, 100)\n",
    "y_val = numpy.random.uniform(0.0, 1000.0, 100)\n",
    "sp = ScatterPlot({'first': (x_val, y_val),\n",
    "                  'second': (y_val, x_val)},\n",
    "                 alpha=1, size=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fvaeSnfl0j0EOitNrN8VS/qpK9URSykf3f2C8UXn1Gjdj0CPLyDunL/+Tf5j5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\nG6geVCjyR+Jr8g1dTmtYh4vPa8gtI+6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113055090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/axelr/.conda/envs/rep/lib/python2.7/site-packages/mpld3/mplexporter/exporter.py:171: UserWarning: Legend element <matplotlib.collections.PathCollection object at 0x1130a6d50> not impemented\n",
      "  warnings.warn(\"Legend element %s not impemented\" % child)\n",
      "/Users/axelr/.conda/envs/rep/lib/python2.7/site-packages/mpld3/mplexporter/exporter.py:171: UserWarning: Legend element <matplotlib.collections.PathCollection object at 0x1130a6e90> not impemented\n",
      "  warnings.warn(\"Legend element %s not impemented\" % child)\n"
     ]
    },
    {
     "data": {
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       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
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       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
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       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
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       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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       "   // already loaded: just create the figure\n",
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       "       \n",
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[640.4698322854863, 828.3383893588], [425.21495329943525, 400.95081601211115]]}, \"id\": \"el757624626175440\"});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el7576246261754402712228601\", {\"axes\": [{\"xlim\": [-200.0, 1200.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624659600976\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"second\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.83938172043010739, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el757624614417168\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"first\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.83938172043010739, 0.831989247311828], \"rotation\": -0.0, \"id\": \"el757624614418192\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-200.0, 1200.0], \"ylim\": [-200.0, 1200.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el757624614414928\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#000000\", \"alpha\": 1.0, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 14.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], 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\"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el757624623566608\", \"pathtransforms\": [[6.085806194501846, 0.0, 0.0, 6.085806194501846, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [1.0], \"facecolors\": [\"#FF1493\"]}, {\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], 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[640.4698322854863, 828.3383893588], [425.21495329943525, 400.95081601211115]]}, \"id\": \"el757624626175440\"});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el7576246261754402712228601\", {\"axes\": [{\"xlim\": [-200.0, 1200.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"None\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [-0.16129032258064516, -0.16129032258064516], \"rotation\": -0.0, \"id\": \"el757624659600976\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"second\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.83938172043010739, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el757624614417168\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"first\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.83938172043010739, 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     "execution_count": 42,
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   "source": [
    "sp.plot()\n",
    "mpld3.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
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       "            var render_items = [{\"docid\":\"84148ff8-b8ce-4532-9ec4-5e72f78698cf\",\"elementid\":\"ee453366-9804-456c-b6e0-90f20fdae078\",\"modelid\":\"e4a4547c-4bfe-49a4-b753-fbda0f3271ff\",\"notebook_comms_target\":\"ca7c7faf-7c98-4f1a-a22e-62f2c6755adb\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp.plot_bokeh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function2D_Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rep.plotting import Function2D_Plot\n",
    "def func(x, y):\n",
    "    return numpy.sin(x + y*y)\n",
    "\n",
    "func_plot = Function2D_Plot(func, xlim=(0, 5), ylim=(0, 5), xsteps=100, ysteps=100, vmin=0.5, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/axelr/.conda/envs/rep/lib/python2.7/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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hsplRTbuBgO08tpsDwK7xRtv57pSadofYu2UyUukXjXNNO6qP59Q4V51OpPA0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\nBb6H6vZxAh7DMDrLOIXU19t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Y2ucHXXm3QW+qGe7zppoRd9/BPn9+sJ9NIv53bxidZDNu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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffff990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "func_plot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram2d Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rep.plotting import Histogram2D_Plot\n",
    "hist2d_plot = Histogram2D_Plot([numpy.random.normal(size=1000), numpy.random.normal(size=1000)], bins=15, \n",
    "                               range=[[-1.5, 1.5], [-1, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<rep.plotting.Histogram2D_Plot at 0x113945050>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ga/b5a+uavdItZeU9e+ecS6HMM1qm4T37gnjNPn9es89fW9fsU/5XVt6zD+Ds\nscMbbjNt/SLGNtgu1OyZt83PPuPgsiVr2HWfPQOkgSVDtg9ynHtv7fnGGRtWL6TPE+t73Ob7V58f\nJMvts58LcpxlK9ewuH/2n9dnJuyaPUxA44eHmc0z1OyZIZS5RJOGN/YFia3WGdt3DAB9dty91RGa\nFtt5ju19HFKZe+1peGPvnHMpRF6y95p9UWKrdcb2HQMkZZzYxHaeY3sfh5RliuMy8J69c86l0Cfy\nrr039gWJrdYZWy0ZvGZfhNjex0HF3dZ7Y++cc2nE/gWt1+wLElutM7ZaMnjNvgixvY9DyjrFcat5\nz94551IocTueijf2BYmt1hlbLRm8Zl+E2N7HQUXe2ntj75xzKXjN3qUSW60ztloyeM2+CLG9j0PK\nMuulpJ0l3StprqTZks4pNr337J1zLp1sHfvXgM+b2aOStgOmS7rTzJ4Iki0Fb+wLElutM7ZaMnjN\nvgixvY9DynjzkmeBZyt/fknSPGAY4I29c86VSahhlZJ2BUYDD4U5YjrRNvb/euNjmY9x0XF7B0iS\nbmriZXMebtiLu+ZndwfJc8ZZ78l8jGVzHmb9wHcGSAMLpz0e5DiN/l5pznEox+67U5Dj3H3PxFL1\n7gf03aLH56dMnsi48Uc0PM4fH18eKlJp1GvrH5kyiWlT032XUSnh/B4418xeCpUtjWgbe+ecK1Sd\n1v5dh07gXYduKm/95MpLut9d2oykof+lmd0SPmDPgo/GkbSFpB9Iek7SS5JukTSswT6nSXpd0obK\n/6t/7rmbEZEy9d7SiC0veOYipOnVv1UFuFPVNcDjZnZVQZHfII+hl1cB/wicBIwH+gG3SQ0rXi8D\ng2uWIWa2Lod8zjnXtIxDLw8D/hn4B0kzJc2Q9L5C84c8mKR+wBnAF8zsXjN7FPgYsB/QqJBsZvac\nma2sLiGztVps46ljywueuQhTJk9sdYTWyTChvZk9aGZ9zGy0mR1gZgea2f8UkrsidM9+DMn3AHdV\nHzCzpcA84NAG+24taZGkJZL+JGl04GzOOddrsd9wPHRjPxjYYGaruzy+ovJcPfNJPhEcD3wEeBV4\nUNLbA+drmdhqs7HlBc9chLau2Uc+62Wqxl7ShTVfnHa3bJB0eG9DmNlUM/ulmc0yswdJ6v1/AT7b\n22M651xI7XJbwiuAXzbYZjEwDugjaccuvftBwANpQ5nZ65KmA3UviXzsuu+w9Y5DAdh86+3oN3wP\ndtxjDAC9/gxfAAAKyklEQVSrF0wHaLhOZZx9db6P6tWBza5X667VXlp366v++gT7f+DUHrevqs7x\nUr0itNn1NHnS5O1/8AlNnc9661n/PpvmvHlPw/M3bJ+DGv79Fs6YCsDuBx7S8vWu2Xv6+/W0PmtN\nf/Y76DAAZj38IECv16t1+Wovvna9tmbf3fPV9YWLVrfs/N534zUsXfg4Ow7ZmaDK3JKnIDMLd7Dk\nC9rngNPM7DeVx3YGngbea2aprxqqNPYzzeysbp6z436U/eIzv6iqvpAXVd1769QgxwlxUdXeg7YO\nkiWUUBdVvX/PAQHSwL5D+/f4fIwXVX12/G6YWaamWpLNW/5yqm33Hrpt5tfLQ9CLqszsRUk/B/5d\n0nPA88BlwKPAPdXtJN0DTDWzr1XWvwlMBRaSDNU8F9gH+JeQ+VopttrssH0OYtHKQi/wyyy2cwzx\nZW7nmn29YZWxyOMK2nOB9cBvgK2Bu4GP2Rs/QowEFtWsbw/8hORL3DXATGCCmU3PIZ9zzjUv8sY+\n+EVVZrbezM41s53MbDsz+5CZLeuyzW5mdmbN+ufNbKSZbW1mg83sWDOLawByA7GNp44tL3jmIrTz\nOPvYh1763DjOOZdCmYdVpuF3qipIbLXZ2PKCZy5CO9fsYx96GXQ0TlEk2Svrs+e+b36YGRkajWBI\na/byNUGOc/qVqUe59mjgkO2DHKdMhg/tF+Q4uw7cLshxQo2iuW3+qiDH+cpR5bqOcUDfLTMfY+vN\nFWQ0zl9Wrk217TsGblPK0Tjesy9IbLXOdcvntjpC015alP0eB0WrjnGPRWzv45C8Zu+cc20g9qGX\n3rMvSGy1zi2Gjmp1hKZtt+v+rY7QtOpVq7GI7X0cVIaivaSfS1ohaVYhWbvhjb1zzqWQsYzzC+C9\nBcZ9E2/sCxJbrdNr9sXwmn08ssx6aWaTgRcKDdyF1+ydcy6FyEv23tgXJbZap9fsi+E1+3jU67VP\nmTyRKZPDDHfOkzf2zjmXSvet/bjxRzJu/JEb16/494sKytMcr9kXJLZap9fsi+E1+3hkueF4RUsv\nsvXG3jnnUsjyBa2kG4D/BfaQtFjS6UVmBy/jFCa2WqfX7IvhNft4ZLk61sxOCRilV7yxd865NCIf\njuNlnILEVuv0mn0xvGYfj9hnvfSevXPOpRD7fPbRTnH85zkrWh1joyF9y3UT62c6XwlynCf/lu4G\ny42MHx5mGt80N3ZvpGw3HJ+3IszPqmxTE4e64fhZB4/MfIxQUxyv7FyfatuBfTcv5RTH3rN3zrkU\nStd6N8lr9gWZNmVSqyM0JbZaMsR3P1eIL3Nb1+wzDL0sA+/ZO+dcCmW+MUka3rMvyNhxE1odoSmx\njf+G+O7nCvFlbutx9pH37L2xd865NuCNfUG8Zp+/2OrfEF9mr9nH27P3mr1zzqXQUeaWPAXv2RfE\na/b5i63+DfFlbuuafcqlrII39pLOlnSvpBckvS5pl5T7nShprqRXJc2R9KHQ2Zxzrtcib+3z6Nlv\nA9wBfAtIdXmupHHAb4BfAvsDNwC/k/SuHPK1hNfs8xdb/Rviy9zWNftsNxxH0vskPSFpgaQLCowO\n5NDYm9lVZnYp0ExrcS5wr5ldYmbzzex7wP3A50Lna5X5c2e1OkJTnnxiTqsjNG3VX59odYSmxZZ5\n7uz4JpsLJeN89h3A1cB7gVHAyZL2Ki59eWr244A7uzx2B3BoC7LkorNzTasjNOXlzhdbHaFpf1/b\n2eoITYst84tr4nofh5SxinMQsNDMnjaz9SSVjA/mm/iNytLYDwa6zmy2ovK4c861XrbWfhhQO5Pf\n0spjhUnV2Eu6sPJla71lg6TD8w4bs+VLFrc6QlNWLIsrL0DnymWtjtC02DIvWfx0qyO0TIeUaimr\nVFMcS9oBaDRP7WIze7VmnzHAw8BIM+ux5ZD0NPCfZnZZzWNfAD5tZm+a41RSfPMyO+daJsAUx4uA\nESk3X2Fmb6hKSDoE+LaZva+y/uUkll2aJVczUl1UZWbPA8/nmGMKcDRwWc1jR5PcoLe7POX99emc\ne8sxs10zHuIR4B2SRgDPAB8BTs6aqxnBr6CVNIik1r4nSQVrlKS3kfT8X6hscw8w1cy+VtntKmBi\nZTjSH4ETgCOB+K7scc65Lsxsg6TPkAxE6QB+bmbziswQ/E5Vkr5F92PsTzez6yvbPAXcZ2Zn1ux3\nAvBdYDfgSeCrZnZL0HDOOdemorwtoXPOueaUZehlj3ozBYOk02pGCtWOGtqijHkr+7VsyghJW0j6\ngaTnJL0k6RZJPQ4NK/ocS/qUpKckvSJpmqTxDbbfR9L9ktZKWiLpG3nkapAhdWZJI+qMdDumoKwT\nKj/3pZXXPjXFPi07x83mbfX5bbUoGnt6MQVDxcsk3x9UlyFmti58vDeJccqIq4B/BE4CxgP9gNuk\nhmPJCjnHkk4CriQp9Y0m+fL+dkk719m+L3AXyZdhY0iu0v6ipPNCZ6un2cwVBhxDzfkE7s05atV2\nwGzgHGBto41LcI6bylvRyvPbWmYWzULyhtoA7JJi29OAFyPK+xvgji6P3QX8qoCc/YC/Ax+peWzn\nSvajy3COganAj7s8tgC4qM72nwT+BmxR89jXgCUF/vybzTwCeB04sKiMPWTvBE5tsE3Lz3GTeUtz\nfluxxNKz762tJS2qfLz8k6TRrQ7Ug1ZOGTGGZGTWXdUHzGwpMC/F6+d+jiVtXsl4V5en7uwh3yHA\nJHvjp4w7gKGV4W+56mXmqpslrZA0WdKJuQQMo6XnOINYzm9Qb+XGfj5wBnA8yZjWV4EHJb29panq\na+WUEYOBDWa2usnXL+ocDwD60Nz5qXc+1cM+IfUm80vA+cCHgWOBe4AbJZ2SV8iMWn2OmxXb+Q2q\nZXeqknQhyUe+egw4yswe6M3xzWwqycfo6utNAWYCn6UXs2nmnTcPaTP39vihz3G7q/yyvaLmoRmS\ndgS+RPIdjsug3c9vK29LeAXJl5E9CTZBi5m9Lmk6sHsvD5F33meBQV0eG1R5vLfSZh4H9JG0Y5fe\n/SAg9S+vAOe4nlUk3x80c37qnU/rYZ+QepO5Ow8Dp4cKFVirz3EIZT6/QbWssbf8p2Dozv4kPc+m\nFZC3qSkj0kibudJAv1Z5vd9UHtsZ2Jvm7ksAGc5xPWa2vpLxaOCmmqeOBn5XZ7cpwCWStqipKR8D\nLDez3Gfz6mXm7hxAMtqljFp6jgMp8/kNq9XfEKdZSHoL+wOnkHybfmxl/W0129xDzSgH4Jskb7yR\nlW2vIRlxMqakeccB64ALSKaa+Eol79iCzvH/I+nlv5vkH8C9wHQqF961+hyT1FlfBc4E9iIZKvoi\nsHPl+YuBu2u27wcsJ/l4PopkCo41wOcKfN82m/lUkvlS9gL2AL5Q2f+cgvJuW/k5jiYZUvv1yvrw\nMp7jXuRt6flt9dLyACl/qN+qNJobuiyn1mzzFMl8E9X1y4G/Aq+QfKS8HTiorHkrj50APF55A84F\nPljgOd680hg9R/JF1h+BYV22aek5Bj5RyfAKycRSh9U89wvgyS7bjyK549laYBnw9Ra8d1NnrjRG\nc0mGEf6NpMRwcoFZj6jzvr2mjOe42bytPr+tXny6BOecawNv5aGXzjnnKryxd865NuCNvXPOtQFv\n7J1zrg14Y++cc23AG3vnnGsD3tg751wb8MbeOefagDf2zjnXBv4/LwaZkbEkaDcAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1139f8dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist2d_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el7576246259351201982244122\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el7576246259351201982244122\", {\"axes\": [{\"xlim\": [-1.5, 1.5], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [{\"coordinates\": \"data\", \"zorder\": 0, \"extent\": [-1.5, 1.5, -1.0, 2.0], \"alpha\": null, \"data\": 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "         \n",
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\"#084286\", \"#084185\", \"#084083\", \"#083F82\", \"#083E80\", \"#083D7E\", \"#083C7D\", \"#083B7B\", \"#083A7A\", \"#083978\", \"#083877\", \"#083775\", \"#083674\", \"#083572\", \"#083471\", \"#08336F\", \"#08326E\", \"#08316C\", \"#08306B\"]}], \"xscale\": \"linear\", \"bbox\": [0.78374999999999995, 0.12499999999999989, 0.025833333333333375, 0.77500000000000013]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data01\": [[0.0, 0.0]]}, \"id\": \"el757624625935120\"});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el7576246259351201982244122\", {\"axes\": [{\"xlim\": [-1.5, 1.5], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [{\"coordinates\": \"data\", \"zorder\": 0, \"extent\": [-1.5, 1.5, -1.0, 2.0], \"alpha\": null, \"data\": 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist2d_plot.plot()\n",
    "mpld3.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib interactive\n",
    "\n",
    "One more plotting option is to use magic function for interactive matplotlib\n",
    "\n",
    "* interactive figures shall be created with pyplot.figure, so use `new_plot=True` for correctness \n",
    "* after saving to file/refreshing interactive plot is replaced with image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# hist2d_plot.plot(new_plot=True)"
   ]
  }
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   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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