diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 893a5aa4ebc44a7ce464070244ff51c9de51db57..625da3cb05a6565584fd5d29e83e2894297cb7f2 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "source": [
     "# Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle\n",
     "\n",
@@ -32,8 +35,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
+   "execution_count": 138,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "outputs": [],
    "source": [
     "# import des bibliothèques\n",
@@ -46,8 +52,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 139,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -73,7 +82,7 @@
        " '.ipynb_checkpoints']"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -98,8 +107,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 140,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "outputs": [],
    "source": [
     "# lecture du fichier\n",
@@ -108,8 +120,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 141,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "outputs": [
     {
      "data": {
@@ -181,7 +196,7 @@
        "5  1585   41.5   5.15"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -192,7 +207,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "source": [
     "### Vérification des données\n",
     "\n",
@@ -206,8 +224,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 142,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "outputs": [
     {
      "data": {
@@ -265,7 +286,7 @@
        "53  1821   54.0    NaN"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -277,15 +298,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "source": [
     "Les 3 dernières lignes sont vides pour la colonne *Wages*, ce qui explique l'arrêt de la ligne rouge et de la surface bleue dans le document originale."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
+   "execution_count": 143,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -308,42 +335,50 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "source": [
     "Seule l'année 1820 présente une rupture. En effet l'enregistrement suivant est 1821. Ce qui explique la barre moins large sur le graphique original, pour les années 1820 à 1821.\n",
     "\n",
-    "### Création des unités\n",
+    "### Commentaire sur les unités\n",
     "\n",
-    "Pour rappel les untités originales des données sont les suivantes :"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wheat_unit = 'Shillings pour un quart de boisseau'\n",
-    "wages_unit = 'Shillings par semaine'"
+    "Pour rappel les untités originales des données sont les suivantes :\n",
+    "* Blé (Wheat) : Shillings pour un quart de boisseau (8,6 kg)\n",
+    "* Salaire (Wages) : Shillings par semaine\n",
+    "\n",
+    "Avant 1971, la monnaie anglaise était découpée de la manière suivante :\n",
+    "* 1 Livre valait 20 shillings\n",
+    "* 1 Shilling valait 12 penses"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "source": [
     "## Reproduction du graphique original\n",
     "\n",
     "Ci-dessous le graphique original est reproduit à l'aide de `Matplotlib`.\n",
-    "Pour représenter des dégradés voir ce lien : https://matplotlib.org/3.2.1/gallery/lines_bars_and_markers/gradient_bar.html?highlight=gradient"
+    "(Pour représenter des dégradés voir ce lien : https://matplotlib.org/3.2.1/gallery/lines_bars_and_markers/gradient_bar.html?highlight=gradient)\n",
+    "\n",
+    "Les années 1820 et 1821 sont représentées séparémment du reste des données afin d'avoir une barre de largeur différente (voir le code ci-dessous)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "outputs": [],
    "source": [
     "from matplotlib.ticker import MultipleLocator\n",
+    "from matplotlib.dates import DateFormatter\n",
     "\n",
     "fig1, ax1 = plt.subplots(1,1)\n",
     "\n",
@@ -351,6 +386,7 @@
     "\n",
     "# === représentation du prix du blé === #\n",
     "ax1.bar(rawdata['Year'][:-2], rawdata['Wheat'][:-2], align='edge', width=5, color='dimgrey')\n",
+    "# bar de largeur 1 année pour les deux années 1820 et 1821\n",
     "ax1.bar(rawdata['Year'][-2:], rawdata['Wheat'][-2:], align='edge', width=1, color='dimgrey')\n",
     "\n",
     "# graduation proche de l'original, avec les siècles comme majeure\n",
@@ -387,17 +423,24 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "source": [
     "## Graphique avec deux axes des ordonnées\n",
     "Le graphique précédent est repris et amélioré avec deux axes distincts cette fois-ci.\n",
-    "Le jeu de couleur est changé pour un affichage plus lisible."
+    "Le jeu de couleur est changé pour un affichage plus lisible.\n",
+    "La conversion vers l'unité moderne est ajouté en légende de l'axe y, pour information."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
+   "execution_count": 161,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "outputs": [
     {
      "data": {
@@ -405,13 +448,13 @@
        "Text(0.5,1,'Chart Showing at One View\\nthe Price of the Quarter of Wheat, and Wages of Labour\\nby the Week, from 1565 to 1821')"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1097.28x548.64 with 2 Axes>"
       ]
@@ -439,7 +482,7 @@
     "ax11.grid(True, which='both')\n",
     "\n",
     "ax11.set_xlabel('5 Years each division')\n",
-    "ax11.set_ylabel('Price of the Quarter of Wheat in Shillings')\n",
+    "ax11.set_ylabel('Price of the Quarter (8.6 kg) of Wheat in Shillings')\n",
     "\n",
     "# les marges de l'axe x sont diminuées\n",
     "ax11.set_xlim([int(rawdata['Year'][0:1]), int(rawdata['Year'][-1:]+5)])\n",
@@ -447,7 +490,7 @@
     "# === représentation du salaire === #\n",
     "# l'axe 2 partage l'axe x de l'axe 1\n",
     "ax12 = ax11.twinx()\n",
-    "ax12.plot(rawdata['Year'], rawdata['Wages'], 'r', linewidth=2)\n",
+    "ax12.plot(rawdata['Year'], rawdata['Wages'], 'r', linewidth=2, marker='x')\n",
     "\n",
     "# les deux axes ont des limites différentes pour montrer que les axes sont différents\n",
     "myylim = ax11.get_ylim()\n",
@@ -462,31 +505,195 @@
     "by the Week, from 1565 to 1821\"\"\")\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## Représentation du pouvoir d'achat\n",
+    "\n",
+    "Le pouvoir d'achat est définie ici comme la quantité de blé qu'un ouvrier peut acheter avec son salaire hebdomadaire. \n",
+    "\n",
+    "Le prix du blé par boisseau est converti en prix du blé par kg pour une représentation plus moderne.\n",
+    "Le pouvoir d'achat sera donc exprimé en kg de blé par semaine.\n",
+    "\n",
+    "Pour rappel, les années 1815, 1820, 1821 n'ont pas de salaire hebdomadaire renseigné. Ces lignes sont retirées pour le calcul du pouvoir d'achat."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
+   "execution_count": 164,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lignes supprimées :\n",
+      "    Year  Wheat  Wages\n",
+      "51  1815   78.0    NaN\n",
+      "52  1820   54.0    NaN\n",
+      "53  1821   54.0    NaN\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "53    1821\n",
-       "Name: Year, dtype: int64"
+       "1    1.048780\n",
+       "2    0.965111\n",
+       "3    1.040190\n",
+       "4    0.898612\n",
+       "5    1.067229\n",
+       "dtype: float64"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "rawdata['Year'][-1:]"
+    "# === on supprime les années sans salaires === #\n",
+    "print(\"lignes supprimées :\")\n",
+    "print(rawdata[rawdata.isnull().any(axis=1)])\n",
+    "data = rawdata.dropna().copy()\n",
+    "\n",
+    "# === facteurs de conversion === #\n",
+    "quarter_to_mass = 8.6  # in kg\n",
+    "shilling_to_pence = 12\n",
+    "\n",
+    "purchasing_power = data['Wages']/(data['Wheat']/quarter_to_mass)\n",
+    "purchasing_power.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'DateFormatter' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-175-d61e95af2cba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0max31\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_minor_locator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMultipleLocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0max31\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_major_formatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDateFormatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%Y'\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     15\u001b[0m \u001b[0max31\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_minor_formatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDateFormatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'DateFormatter' is not defined"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error in callback <function flush_figures at 0x7ff2a9a526a8> (for post_execute):\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mflush_figures\u001b[0;34m()\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;31m# ignore the tracking, just draw and close all figures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mshow\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[0m\u001b[1;32m    122\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m             \u001b[0;31m# safely show traceback if in IPython, else raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     41\u001b[0m             display(\n\u001b[1;32m     42\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m                 \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    311\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\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[1;32m    312\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    314\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\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    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m</opt/conda/lib/python3.6/site-packages/decorator.py:decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\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    342\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    343\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/opt/conda/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\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--> 248\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    249\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    250\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/opt/conda/lib/python3.6/site-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    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\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    133\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    134\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/opt/conda/lib/python3.6/site-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   2210\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   2211\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-> 2212\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2213\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   2214\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/opt/conda/lib/python3.6/site-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    515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\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--> 517\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    518\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    519\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/opt/conda/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    435\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    436\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--> 437\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    438\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-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/opt/conda/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1491\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1492\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1493\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1495\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/opt/conda/lib/python3.6/site-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    139\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    140\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--> 141\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    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\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/opt/conda/lib/python3.6/site-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/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2633\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   2634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2635\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   2636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2637\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/opt/conda/lib/python3.6/site-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    139\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    140\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--> 141\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    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\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/opt/conda/lib/python3.6/site-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/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1200\u001b[0m         \u001b[0;31m# the actual bbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1202\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_label_position\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   1203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1204\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\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[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_label_position\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1915\u001b[0m                 \u001b[0;31m# use axes if spine doesn't exist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1916\u001b[0m                 \u001b[0mspinebbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1917\u001b[0;31m             \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbboxes\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mspinebbox\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   1918\u001b[0m             \u001b[0mbottom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36munion\u001b[0;34m(bboxes)\u001b[0m\n\u001b[1;32m    748\u001b[0m         \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxmax\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m         \u001b[0my0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mymin\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 750\u001b[0;31m         \u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mymax\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\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    751\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\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[1;32m    752\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    748\u001b[0m         \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxmax\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m         \u001b[0my0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mymin\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 750\u001b[0;31m         \u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mymax\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\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    751\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\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[1;32m    752\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mymax\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mymax\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtop\u001b[0m \u001b[0medge\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mbounding\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         \"\"\"\n\u001b[0;32m--> 376\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\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    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mamax\u001b[0;34m(a, axis, out, keepdims, initial)\u001b[0m\n\u001b[1;32m   2332\u001b[0m     \"\"\"\n\u001b[1;32m   2333\u001b[0m     return _wrapreduction(a, np.maximum, 'max', axis, None, out, keepdims=keepdims,\n\u001b[0;32m-> 2334\u001b[0;31m                           initial=initial)\n\u001b[0m\u001b[1;32m   2335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m     81\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "fig3, ax31 = plt.subplots(1,1)\n",
+    "fig3.set_size_inches(2.54*6, 2.54*3)\n",
+    "\n",
+    "ax31.bar(data['Year'], purchasing_power, align='edge', width=5)\n",
+    "\n",
+    "# les mêmes limites que précédemment sont conservées pour comparaison\n",
+    "ax31.set_xlim([int(rawdata['Year'][0:1]), int(rawdata['Year'][-1:]+5)])\n",
+    "\n",
+    "# graduation proche de l'original, avec les siècles comme majeure\n",
+    "# et une graduation mineure tous les 5 ans\n",
+    "ax31.xaxis.set_major_locator(MultipleLocator(100))\n",
+    "ax31.xaxis.set_minor_locator(MultipleLocator(5))\n",
+    "\n",
+    "ax31.xaxis.set_major_formatter(DateFormatter('%Y'))\n",
+    "ax31.xaxis.set_minor_formatter(DateFormatter('%y'))\n",
+    "\n",
+    "\n",
+    "ax31.grid(True, which='both')\n",
+    "\n",
+    "ax31.set_xlabel(\"Année\")\n",
+    "ax31.set_ylabel(\"Pouvoir d'achat en masse de blé hebdomadaire [kg/sem]\")\n",
+    "\n",
+    "ax31.set_title(\"Pouvoir d'achat d'un ouvrier\\n en équivalent masse de blé hebdomadaire\\n de 1565 à 1810\")\n",
+    "\n",
+    "fig3.autofmt_xdate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "52   NaN\n",
+       "53   NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "purchasing_power[-2:]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 63,
-   "metadata": {},
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "outputs": [
     {
      "data": {
@@ -906,12 +1113,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
+  "hide_code_all_hidden": false,
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",