diff --git a/module3/exo3/Work-peer-reviewed.ipynb b/module3/exo3/Work-peer-reviewed.ipynb
index 61df2b08da2021c7bf2074d0206a172b48e459bf..0b4fb86ba22c5cac0f7e29e95071215e98718acb 100644
--- a/module3/exo3/Work-peer-reviewed.ipynb
+++ b/module3/exo3/Work-peer-reviewed.ipynb
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,12 +137,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error in callback <function install_repl_displayhook.<locals>.post_execute at 0x7f418d85e730> (for post_execute):\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "can't multiply sequence by non-int of type 'float'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m    147\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\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    148\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\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[0;32m--> 149\u001b[0;31m                     \u001b[0mdraw_all\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    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0;31m# IPython >= 2\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/_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mf_mgr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\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    135\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mf_mgr\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[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m                 \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\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    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[0matexit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestroy_all\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;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2053\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2054\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\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[0;32m-> 2055\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\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   2056\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2057\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_cursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\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/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   1188\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   1189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1190\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   1191\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1192\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\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-> 1028\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;31m# iter_ticks calls the locator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1029\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   1030\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/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    976\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    977\u001b[0m         \u001b[0mminorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminor\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[0;32m--> 978\u001b[0;31m         \u001b[0mminorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_minor_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminorLocs\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    979\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminor\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[0mminorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         minorLabels = [self.minor.formatter(val, i)\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_minor_ticks\u001b[0;34m(self, numticks)\u001b[0m\n\u001b[1;32m   1413\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mnumticks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1414\u001b[0m             \u001b[0;31m# update the new tick label properties from the old\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1415\u001b[0;31m             \u001b[0mtick\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1416\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gridOnMinor\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_get_tick\u001b[0;34m(self, major)\u001b[0m\n\u001b[1;32m   1790\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1791\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1792\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mXTick\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[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1793\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1794\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\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[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kw)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick1line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick1line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick2line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick2line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgridline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_gridline\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    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_text1\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/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_gridline\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    501\u001b[0m                           \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grid_alpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m                           \u001b[0mmarkersize\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--> 503\u001b[0;31m                           **self._grid_kw)\n\u001b[0m\u001b[1;32m    504\u001b[0m         \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\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[0mget_xaxis_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'grid'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGRIDLINE_INTERPOLATION_STEPS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_us_dashOffset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linestyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinestyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_drawstyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrawstyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linewidth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinewidth\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/lines.py\u001b[0m in \u001b[0;36mset_linestyle\u001b[0;34m(self, ls)\u001b[0m\n\u001b[1;32m   1123\u001b[0m         \u001b[0;31m# compute the linewidth scaled dashes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1124\u001b[0m         self._dashOffset, self._dashSeq = _scale_dashes(\n\u001b[0;32m-> 1125\u001b[0;31m             self._us_dashOffset, self._us_dashSeq, self._linewidth)\n\u001b[0m\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1127\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdocstring\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdedent_interpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m_scale_dashes\u001b[0;34m(offset, dashes, lw)\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0mscaled_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaled_dashes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moffset\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---> 68\u001b[0;31m         \u001b[0mscaled_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdashes\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[1;32m     70\u001b[0m         scaled_dashes = [x * lw if x is not None else None\n",
+      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "can't multiply sequence by non-int of type 'float'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\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   1188\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   1189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1190\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   1191\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1192\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\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-> 1028\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;31m# iter_ticks calls the locator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1029\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   1030\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/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    976\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    977\u001b[0m         \u001b[0mminorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminor\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[0;32m--> 978\u001b[0;31m         \u001b[0mminorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_minor_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminorLocs\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    979\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminor\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[0mminorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         minorLabels = [self.minor.formatter(val, i)\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_minor_ticks\u001b[0;34m(self, numticks)\u001b[0m\n\u001b[1;32m   1413\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mnumticks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1414\u001b[0m             \u001b[0;31m# update the new tick label properties from the old\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1415\u001b[0;31m             \u001b[0mtick\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1416\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gridOnMinor\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_get_tick\u001b[0;34m(self, major)\u001b[0m\n\u001b[1;32m   1790\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1791\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1792\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mXTick\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[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1793\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1794\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\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[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kw)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick1line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick1line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick2line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick2line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgridline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_gridline\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    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_text1\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/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_gridline\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    501\u001b[0m                           \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grid_alpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m                           \u001b[0mmarkersize\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--> 503\u001b[0;31m                           **self._grid_kw)\n\u001b[0m\u001b[1;32m    504\u001b[0m         \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\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[0mget_xaxis_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'grid'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGRIDLINE_INTERPOLATION_STEPS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_us_dashOffset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linestyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinestyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_drawstyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrawstyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linewidth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinewidth\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/lines.py\u001b[0m in \u001b[0;36mset_linestyle\u001b[0;34m(self, ls)\u001b[0m\n\u001b[1;32m   1123\u001b[0m         \u001b[0;31m# compute the linewidth scaled dashes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1124\u001b[0m         self._dashOffset, self._dashSeq = _scale_dashes(\n\u001b[0;32m-> 1125\u001b[0;31m             self._us_dashOffset, self._us_dashSeq, self._linewidth)\n\u001b[0m\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1127\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdocstring\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdedent_interpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m_scale_dashes\u001b[0;34m(offset, dashes, lw)\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0mscaled_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaled_dashes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moffset\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---> 68\u001b[0;31m         \u001b[0mscaled_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdashes\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[1;32m     70\u001b[0m         scaled_dashes = [x * lw if x is not None else None\n",
+      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -152,9 +222,41 @@
      },
      "output_type": "display_data"
     },
+    {
+     "ename": "TypeError",
+     "evalue": "can't multiply sequence by non-int of type 'float'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\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   1188\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   1189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1190\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   1191\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1192\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\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-> 1028\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;31m# iter_ticks calls the locator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1029\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   1030\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/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    976\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    977\u001b[0m         \u001b[0mminorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminor\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[0;32m--> 978\u001b[0;31m         \u001b[0mminorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_minor_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminorLocs\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    979\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminor\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[0mminorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         minorLabels = [self.minor.formatter(val, i)\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_minor_ticks\u001b[0;34m(self, numticks)\u001b[0m\n\u001b[1;32m   1413\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mnumticks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1414\u001b[0m             \u001b[0;31m# update the new tick label properties from the old\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1415\u001b[0;31m             \u001b[0mtick\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1416\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gridOnMinor\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_get_tick\u001b[0;34m(self, major)\u001b[0m\n\u001b[1;32m   1790\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1791\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1792\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mXTick\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[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1793\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1794\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\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[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kw)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick1line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick1line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick2line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick2line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgridline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_gridline\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    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_text1\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/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_gridline\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    501\u001b[0m                           \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grid_alpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m                           \u001b[0mmarkersize\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--> 503\u001b[0;31m                           **self._grid_kw)\n\u001b[0m\u001b[1;32m    504\u001b[0m         \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\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[0mget_xaxis_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'grid'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGRIDLINE_INTERPOLATION_STEPS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_us_dashOffset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linestyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinestyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_drawstyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrawstyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linewidth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinewidth\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/lines.py\u001b[0m in \u001b[0;36mset_linestyle\u001b[0;34m(self, ls)\u001b[0m\n\u001b[1;32m   1123\u001b[0m         \u001b[0;31m# compute the linewidth scaled dashes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1124\u001b[0m         self._dashOffset, self._dashSeq = _scale_dashes(\n\u001b[0;32m-> 1125\u001b[0;31m             self._us_dashOffset, self._us_dashSeq, self._linewidth)\n\u001b[0m\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1127\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdocstring\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdedent_interpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m_scale_dashes\u001b[0;34m(offset, dashes, lw)\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0mscaled_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaled_dashes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moffset\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---> 68\u001b[0;31m         \u001b[0mscaled_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdashes\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[1;32m     70\u001b[0m         scaled_dashes = [x * lw if x is not None else None\n",
+      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]