pyFTS/pyFTS/notebooks/Ismail & Efendi - ImprovedWeightedFTS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Order Improved Weighted Fuzzy Time Series by Efendi, Ismail and Deris (2013)\n",
    "\n",
    "R. Efendi, Z. Ismail, and M. M. Deris, “Improved weight Fuzzy Time Series as used in the exchange rates forecasting of \n",
    "US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, p. 1350005, 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Common Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n",
      "/usr/lib/python3/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pylab as plt\n",
    "from pyFTS.benchmarks import benchmarks as bchmk\n",
    "from pyFTS.models import ismailefendi\n",
    "\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyFTS.data import Enrollments\n",
    "\n",
    "enrollments = Enrollments.get_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the partitioning effects on original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAF+CAYAAACPsKJfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nId6R86AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e54d69b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, ismailefendi.ImprovedWeightedFTS, \n",
    "                              range(4,12), [1], tam=[10, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the partitioning effects on transformed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF+CAYAAACI8nxKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfwaiZEYuH1vRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e3464710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyFTS.common import Transformations\n",
    "diff = Transformations.Differential(1)\n",
    "\n",
    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, ismailefendi.ImprovedWeightedFTS, \n",
    "                              range(2,10), [1], transformation=diff, tam=[10, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing the partitioning schemas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e1517e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyFTS.partitioners import Grid, Util as pUtil\n",
    "\n",
    "fuzzy_sets = Grid.GridPartitioner(enrollments, 11)\n",
    "fuzzy_sets2 = Grid.GridPartitioner(enrollments, 4, transformation=diff)\n",
    "\n",
    "pUtil.plot_partitioners(enrollments, [fuzzy_sets,fuzzy_sets2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting a model on original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improved Weighted FTS:\n",
      "A2 -> A2(0.667),A3(0.333)\n",
      "A3 -> A4(1.0)\n",
      "A4 -> A4(0.714),A5(0.286)\n",
      "A5 -> A4(0.333),A6(0.667)\n",
      "A6 -> A5(0.333),A6(0.333),A7(0.333)\n",
      "A7 -> A8(1.0)\n",
      "A8 -> A9(1.0)\n",
      "A9 -> A8(0.5),A9(0.5)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model1 = ismailefendi.ImprovedWeightedFTS(\"FTS\", partitioner=fuzzy_sets)\n",
    "model1.fit(enrollments)\n",
    "\n",
    "print(model1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting a model on transformed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improved Weighted FTS:\n",
      "A0 -> A1(1.0)\n",
      "A1 -> A0(0.143),A1(0.286),A2(0.429),A3(0.143)\n",
      "A2 -> A1(0.444),A2(0.222),A3(0.333)\n",
      "A3 -> A2(0.75),A3(0.25)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model2 = ismailefendi.ImprovedWeightedFTS(\"FTS Diff\", partitioner=fuzzy_sets2)\n",
    "model2.append_transformation(diff)\n",
    "model2.fit(enrollments)\n",
    "\n",
    "print(model2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[13769.148484848485,\n",
       " 13769.148484848485,\n",
       " 13769.148484848485,\n",
       " 15211.754545454547,\n",
       " 15459.058441558444,\n",
       " 15459.058441558444,\n",
       " 15459.058441558444,\n",
       " 16365.839393939395,\n",
       " 16942.88181818182,\n",
       " 16942.88181818182,\n",
       " 16365.839393939395,\n",
       " 15459.058441558444,\n",
       " 15459.058441558444,\n",
       " 15459.058441558444,\n",
       " 15459.058441558444,\n",
       " 16365.839393939395,\n",
       " 16942.88181818182,\n",
       " 18674.009090909094,\n",
       " 19539.57272727273,\n",
       " 19106.790909090912,\n",
       " 19106.790909090912,\n",
       " 19539.57272727273]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.predict(enrollments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[12801.572222222223,\n",
       " 13113.492857142857,\n",
       " 13613.572222222223,\n",
       " 14246.492857142857,\n",
       " 15010.492857142857,\n",
       " 15280.6125,\n",
       " 15349.572222222223,\n",
       " 15607.572222222223,\n",
       " 16357.492857142857,\n",
       " 16665.57222222222,\n",
       " 16357.6125,\n",
       " 15402.6125,\n",
       " 15243.572222222223,\n",
       " 15114.6125,\n",
       " 14909.572222222223,\n",
       " 15534.492857142857,\n",
       " 16409.492857142857,\n",
       " 17347.55,\n",
       " 18520.492857142857,\n",
       " 19074.57222222222,\n",
       " 19083.57222222222,\n",
       " 18845.6125]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict(enrollments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQcAAAE/CAYAAAD7W3XlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNElen1Fw/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e3464630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bchmk.plot_compared_series(enrollments, [model1, model2], bchmk.colors, intervals=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model\t\t& Order     & RMSE\t\t& SMAPE      & Theil's U\t\t\\\\ \n",
      "IWFTS FTS\t\t& 1\t\t& 485.63\t\t& 1.17\t\t& 0.79\t\\\\ \n",
      "IWFTS FTS Diff\t\t& 1\t\t& 880.72\t\t& 2.27\t\t& 1.44\t\\\\ \n",
      "\n"
     ]
    }
   ],
   "source": [
    "bchmk.print_point_statistics(enrollments, [model1, model2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Residual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot convert float NaN to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2216\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1135\u001b[0m                                                                 renderer)\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m         \"\"\"\n\u001b[0;32m--> 917\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1959\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1960\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1961\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1903\u001b[0;31m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[1;32m   1904\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[1;32m   1905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2060\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e2d76be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyFTS.benchmarks import ResidualAnalysis as ra\n",
    "\n",
    "ra.plot_residuals(enrollments, [model1, model2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}