From f64daed759ad7464d1dd6abda0b06a8fd166faf1 Mon Sep 17 00:00:00 2001
From: wassname <wassname@users.noreply.github.com>
Date: Wed, 15 Nov 2017 16:40:33 +0800
Subject: [PATCH] show video

---
 .../videos/beales_20171115_07-18-03_loss.webm | Bin 0 -> 221395 bytes
 main.ipynb                                    | 190 +++++++++++++-----
 readme.md                                     |   5 +
 3 files changed, 146 insertions(+), 49 deletions(-)
 create mode 100644 docs/videos/beales_20171115_07-18-03_loss.webm

diff --git a/docs/videos/beales_20171115_07-18-03_loss.webm b/docs/videos/beales_20171115_07-18-03_loss.webm
new file mode 100644
index 0000000000000000000000000000000000000000..9afc2408b75e8c07933157d6c0ae90ce799ca31a
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diff --git a/main.ipynb b/main.ipynb
index 5300c9b..13b8249 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -1184,7 +1184,7 @@
     "ax.plot(*minima_, 'r*', markersize=10)\n",
     "ax.plot(*problem.x0, 'r+', markersize=10)\n",
     "\n",
-    "ax.set_title('{} (github.com/wassname/viz_torch_optim)'.format(problem.f.__name__))\n",
+    "ax.set_title('{} function'.format(problem.f.__name__))\n",
     "ax.set_xlabel('$x$')\n",
     "ax.set_ylabel('$y$')\n",
     "\n",
@@ -1198,11 +1198,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 93,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-15T08:18:34.088803Z",
-     "start_time": "2017-11-15T08:18:33.437574Z"
+     "end_time": "2017-11-15T08:34:01.409768Z",
+     "start_time": "2017-11-15T08:33:55.424783Z"
     }
    },
    "outputs": [
@@ -1214,25 +1214,32 @@
      ]
     },
     {
-     "ename": "ValueError",
-     "evalue": "Invalid file object: <_io.BufferedReader name=76>",
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-85-e778a87d7032>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msave_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'videos/{name:}_{ts:}.webm'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# display\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-93-1c93b3c569f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msave_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'videos/{name:}_{ts:}.webm'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbitrate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcodec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'vp9'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;31m# display\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m html=\"\"\"<video {options}>\n",
       "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs)\u001b[0m\n\u001b[1;32m   1061\u001b[0m                         \u001b[0;31m# TODO: See if turning off blit is really necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m                         \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_next_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\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[0;32m-> 1063\u001b[0;31m                     \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1064\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m         \u001b[0;31m# Reconnect signal for first draw if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.6.0/lib/python3.6/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     98\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    101\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't stop after throw()\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msaving\u001b[0;34m(self, *args, **kw)\u001b[0m\n\u001b[1;32m    287\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinish\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    290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run\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~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mfinish\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    307\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfinish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m         \u001b[0;34m'Finish any processing for writing the movie.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcleanup\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    310\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mcleanup\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    346\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;34m'Clean-up and collect the process used to write the movie file.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_proc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\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    349\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_frame_sink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m         verbose.report('MovieWriter -- '\n",
-      "\u001b[0;32m~/.pyenv/versions/3.6.0/lib/python3.6/subprocess.py\u001b[0m in \u001b[0;36mcommunicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m    834\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 836\u001b[0;31m                 \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_communicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mendtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    837\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    838\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_communication_started\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.6.0/lib/python3.6/subprocess.py\u001b[0m in \u001b[0;36m_communicate\u001b[0;34m(self, input, endtime, orig_timeout)\u001b[0m\n\u001b[1;32m   1482\u001b[0m                     \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEVENT_WRITE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1483\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1484\u001b[0;31m                     \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEVENT_READ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1485\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1486\u001b[0m                     \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEVENT_READ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.6.0/lib/python3.6/selectors.py\u001b[0m in \u001b[0;36mregister\u001b[0;34m(self, fileobj, events, data)\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    352\u001b[0m             \u001b[0mpoll_events\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mevents\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mEVENT_READ\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.6.0/lib/python3.6/selectors.py\u001b[0m in \u001b[0;36mregister\u001b[0;34m(self, fileobj, events, data)\u001b[0m\n\u001b[1;32m    235\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid events: {!r}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSelectorKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fileobj_lookup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfd\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fd_to_key\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.6.0/lib/python3.6/selectors.py\u001b[0m in \u001b[0;36m_fileobj_lookup\u001b[0;34m(self, fileobj)\u001b[0m\n\u001b[1;32m    222\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[0;32mreturn\u001b[0m \u001b[0m_fileobj_to_fd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\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[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m             \u001b[0;31m# Do an exhaustive search.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.6.0/lib/python3.6/selectors.py\u001b[0m in \u001b[0;36m_fileobj_to_fd\u001b[0;34m(fileobj)\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m             raise ValueError(\"Invalid file object: \"\n\u001b[0;32m---> 39\u001b[0;31m                              \"{!r}\".format(fileobj)) from None\n\u001b[0m\u001b[1;32m     40\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfd\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[1;32m     41\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid file descriptor: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: Invalid file object: <_io.BufferedReader name=76>"
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mgrab_frame\u001b[0;34m(self, **savefig_kwargs)\u001b[0m\n\u001b[1;32m    326\u001b[0m             \u001b[0;31m# frame format and dpi.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m             self.fig.savefig(self._frame_sink(), format=self.frame_format,\n\u001b[0;32m--> 328\u001b[0;31m                              dpi=self.dpi, **savefig_kwargs)\n\u001b[0m\u001b[1;32m    329\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mRuntimeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m)\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    330\u001b[0m             \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_proc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1571\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_frameon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1572\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1573\u001b[0;31m         \u001b[0mself\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[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   1574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1575\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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   2250\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   2251\u001b[0m                 \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2252\u001b[0;31m                 **kwargs)\n\u001b[0m\u001b[1;32m   2253\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2254\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_raw\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    525\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_raw\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--> 526\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    527\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    528\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~/.pyenv/versions/3.6.0/envs/jupyter3/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    462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\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    465\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\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[0m\n\u001b[0;32m---> 63\u001b[0;31m         \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     64\u001b[0m         \u001b[0mafter\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[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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   1142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1144\u001b[0;31m                 renderer, self, dsu, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1146\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~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[1;32m    137\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    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\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    140\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\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~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m         \u001b[0;31m# Then rest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m         \u001b[0mAxes\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    294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_axis_position\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~/.pyenv/versions/3.6.0/envs/jupyter3/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     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\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[0m\n\u001b[0;32m---> 63\u001b[0;31m         \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     64\u001b[0m         \u001b[0mafter\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[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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   2424\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   2425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2426\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[0mdsu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2428\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~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[1;32m    137\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    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\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    140\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\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~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/mpl_toolkits/mplot3d/art3d.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m     \u001b[0;32mdef\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mCollection\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    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\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[0m\n\u001b[0;32m---> 63\u001b[0;31m         \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     64\u001b[0m         \u001b[0mafter\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[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    352\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_linewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_linestyles\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_antialiaseds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_urls\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m                 self._offset_position)\n\u001b[0m\u001b[1;32m    355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw_path_collection\u001b[0;34m(self, *kl, **kw)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_path_collection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mkl\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_path_collection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mkl\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    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_update_methods\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~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/path.py\u001b[0m in \u001b[0;36mvertices\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_nonfinite\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vertices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvertices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m         \"\"\"\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -1292,13 +1299,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 94,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2017-11-15T08:27:18.730Z"
+     "end_time": "2017-11-15T08:34:19.080189Z",
+     "start_time": "2017-11-15T08:34:11.947969Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f8c0dc707f0>"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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I0KlTp3QebotHunqZqqpK1fr1KEeOAKZ714ibOAqieLQMxjAQFQWjlkzdeBiG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FG31m7Q4RB1FWjmmVf8H7zs/OPsDcaMD83j3d/fZzAYsFgslgcoYGl/8d6zt7e3vE9P9Q+Hw2Uf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\nNGb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Sj2tjZnaRatSQT9bk5QptHHGxhHVE64r25AQNnnjampn6oIKuXHSef4n2d/6E8TNP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\nV8uNlAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8c237ab470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig = plt.figure(figsize=(8, 5))\n",
     "ax = plt.axes(projection='3d', elev=50, azim=azim)\n",
@@ -1307,7 +1336,7 @@
     "ax.plot(*minima_, problem.f(minima_).numpy(), 'r*', markersize=10)\n",
     "ax.plot(*_x0, problem.f(_x0).numpy(), 'r+', markersize=10)\n",
     "\n",
-    "ax.set_title('{} (github.com/wassname/viz_torch_optim)'.format(problem.f.__name__))\n",
+    "ax.set_title('{} function'.format(problem.f.__name__))\n",
     "ax.set_xlabel('$x$')\n",
     "ax.set_ylabel('$y$')\n",
     "ax.set_zlabel('$z$')\n",
@@ -1337,10 +1366,18 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2017-11-15T08:27:20.517Z"
+     "start_time": "2017-11-15T08:34:21.482Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "videos/beales_20171115_07-18-03_3d.webm\n"
+     ]
+    }
+   ],
    "source": [
     "# anim.to_html5_video()\n",
     "save_file3d = save_file.replace('.webm', '_3d.webm')\n",
@@ -1388,10 +1425,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2017-11-15T07:06:34.656905Z",
-     "start_time": "2017-11-15T07:06:34.653458Z"
-    }
+    "collapsed": true
    },
    "outputs": [],
    "source": []
@@ -1415,29 +1449,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 91,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-15T08:18:45.537954Z",
-     "start_time": "2017-11-15T08:18:45.270205Z"
+     "end_time": "2017-11-15T08:32:15.927234Z",
+     "start_time": "2017-11-15T08:32:15.631727Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f8c1632bb70>"
+       "<matplotlib.legend.Legend at 0x7f8c2262dbe0>"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Kdy2SJCXV4XrC0ikzM5P8/HwA+vbtS2VlJa+//jpf/vKXa7fZu3cvAAMHDmTSpElcddVV\nXHnllXUe7/nnn+epp57innvuAWDPnj386U9/AmDEiBF06tQJgMWLFzNhwgQyMjLo1q0bF1xwAUuX\nLuXiiy/m5ptvZu/evfz6179myJAhtGvXrs5zvfrqq3zzm98EoE+fPvTp0weA3/3ud6xatYqBAwcC\nsG/fPgYMGMCaNWvo3r07hYWFABx33HEAfPDBB0ydOpXy8nIyMjL4wx/+AMAFF1zAjTfeyJYtW5g3\nbx5jx46lZcuG6O86VIOcMcb4XeC7f7X4j0C/hji/JEn6ZG3atKmdzsjIYNOmTXTs2JHy8vKPbfvQ\nQw+xZMkSnnnmGfr27UtZWdnHtokxMm/ePHr16nXI8iVLlnDMMccctp62bdsydOhQnnvuOUpKShg/\nfvxnvqYYIyNGjKC4uPiQ5StWrKhz+/vuu49u3brx5ptv8tFHH9G2bdvadddccw3/+Z//yZw5c5g9\ne/ZnrqU+ONKBJEk6xHHHHUdmZiZPPvkkUBN+3nzzTQDeeecdzjvvPIqKiujatSvr16+nQ4cO7Ny5\ns3b/UaNG8ZOf/IQYa249//3vf1/neQYPHkxJSQnV1dVs2bKFV199lX79avpyxo0bx+zZs3nttdcY\nPfqTn1UcMmQIP/vZzwBYuXIly5cvB6B///785je/4e233wZqetD+8Ic/0KtXLzZu3MjSpUsB2Llz\nJ1VVVezYsYPu3bvTokUL/uM//oPq6urac0yaNIkf/ehHAPTu3fsztmb9MLBJkqSPeeKJJ5g1axZ5\neXlkZ2ezYEHNrea33norubm55OTkcP7555OXl8eFF17IqlWryM/Pp6SkhDvvvJP9+/fTp08fsrOz\nufPOO+s8xxVXXEGfPn3Iy8tj2LBh3H333Zx00kkAjBw5kldeeYXhw4fTuvUnD4b09a9/nV27dpGV\nlcX06dPp27cvAF27duWxxx5jwoQJ9OnThwEDBrB69Wpat25NSUkJN910E3l5eYwYMYI9e/Zw4403\n8vjjj5OXl8fq1asP6Qns1q0bWVlZTJ48ub6a9zMLB9JvU1BQUBCPxt9WkSQ1TxUVFWRlpefpUNWf\nDz/8kNzcXJYtW8bxxx9f5zZ1vZchhLIYY0F91GAPmyRJ0id48cUXycrK4qabbvrEsNYQGv4xB0mS\npM/oueeeY9q0aYcsy8zM5Je//GVazzt8+HDWrVuX1nMcCQObJElKvFGjRjFq1KjGLqPR+JWoJElS\nwhnYJEmSEs7AJkmSlHAGNkmSmrn58+cTQmD16tV1rp80aRJz58494uNVVlaSk5NzxNuUl5fz7LPP\nHnnBzZCBTZKkZq64uJhBgwZ9bBinhmJgOzwDmyRJzdiuXbtYvHgxs2bNYs6cOUDNUFRTp06lV69e\nDB8+nM2bN9duX1RURGFhITk5OUyZMqV2+KmysjLy8vLIy8vjgQceqN2+urqaW2+9lcLCQvr06cNP\nf/rTQ86/b98+pk+fTklJSe1ICW+88QYDBgzgnHPO4fzzz2fNmjUN0BLJ5s96SJKUAA/8w8K0HPcb\nDw371PULFixg9OjR9OzZk86dO1NWVsa6detYs2YNq1atYtOmTfTu3ZvrrrsOgKlTpzJ9+nQAJk6c\nyNNPP82ll17K5MmTmTlzJkOGDOHWW2+tPf6sWbM4/vjjWbp0KXv37mXgwIGMHDmSEAIArVu3pqio\niNLSUmbOnAnA+++/z2uvvUbLli158cUXuf3225k3b146mqfJMLBJktSMFRcXc/PNNwMwfvx4iouL\nqaqqYsKECWRkZHDyySczbNj/hr5FixZx99138+GHH7Jt2zays7MZPHgw27dvZ8iQIUBNkPvVr34F\nwPPPP8/y5ctr74HbsWMHa9eupWfPnp9Y044dO7j22mtZu3YtIQT279+frstvMgxskiQlwOF6wtJh\n27ZtLFy4kBUrVhBCoLq6mhACV1xxRZ3bHxgkvbS0lNNOO40ZM2awZ8+eTz1HjJGf/OQnH/vR28rK\nyk/c58477+TCCy/kl7/8JZWVlQwdOvSzXtpRx3vYJElqpubOncvEiRNZt24dlZWVrF+/nszMTDp3\n7kxJSQnV1dVs3LiRRYsWAdSGsy5durBr167aXrOOHTvSsWNHFi9eDMATTzxRe45Ro0bx4IMP1vaS\n/eEPf+CDDz44pI4OHTqwc+fO2vkdO3ZwyimnAPDYY4+l5+KbGAObJEnNVHFx8cd608aOHcvGjRvp\n0aMHvXv35pprrmHAgAFATTC74YYbyMnJYdSoURQWFtbuN3v2bL7xjW+Qn59f+yACwNe+9jV69+7N\nueeeS05ODn//939PVVXVIee88MILWbVqVe1DB9/+9rf5zne+wznnnPOxbZurcHCjJl1BQUEsLS1t\n7DIkSaoXFRUVZGVlNXYZqgd1vZchhLIYY0F9HN8eNkmSpIQzsEmSJCWcgU2SJCnhDGySJEkJZ2CT\nJElKOAObJElSwhnYJElq5ubPn08IgdWrV9e5ftKkSbU/kttQZsyYwT333NOg50wyA5skSc1ccXEx\ngwYNori4OK3n8Udw/3YGNkmSmrFdu3axePFiZs2axZw5c4Ca8T+nTp1Kr169GD58OJs3b67dvqio\niMLCQnJycpgyZUrtqAZLly6lT58+5Ofnc+utt5KTkwPUDC112WWXMWzYMC666CJ27drFRRddxLnn\nnktubi4LFiyoPfb3v/99evbsyaBBg1izZk0DtkLyOfi7JEkJcO+4L6bluN8qefpT1y9YsIDRo0fT\ns2dPOnfuTFlZGevWrWPNmjWsWrWKTZs20bt3b6677joApk6dyvTp0wGYOHEiTz/9NJdeeimTJ0/m\nkUceYcCAAdx2222HnGPZsmUsX76cTp06UVVVxS9/+UuOO+44tm7dSv/+/bnssstYtmwZc+bMoby8\nnKqqKs4991z69u2bljZpiuxhkySpGSsuLmb8+PEAjB8/nuLiYl599VUmTJhARkYGJ598MsOGDavd\nftGiRZx33nnk5uaycOFC3nrrLbZv387OnTtrxxz9yle+csg5RowYQadOnYCa3rvbb7+dPn36MHz4\ncN599102bdrEa6+9xhVXXEH79u057rjjuOyyyxqoBZoGe9gkSUqAw/WEpcO2bdtYuHAhK1asIIRA\ndXU1IYSPDQh/wJ49e7jxxhspLS3ltNNOY8aMGezZs+ew5znmmGNqp5944gm2bNlCWVkZrVq14swz\nzzyiYzR39rBJktRMzZ07l4kTJ7Ju3ToqKytZv349mZmZdO7cmZKSEqqrq9m4cSOLFi0CqA1WXbp0\nYdeuXbVPjnbs2JEOHTqwZMkSgNp74eqyY8cOTjzxRFq1asWiRYtYt24dAEOGDGH+/Pns3r2bnTt3\n8l//9V/pvPQmxx42SZKaqeLiYqZNm3bIsrFjx1JRUUGPHj3o3bs3p59+eu1XnR07duSGG24gJyeH\nk046icLCwtr9Zs2axQ033ECLFi244IILOP744+s859VXX82ll15Kbm4uBQUFnH322QCce+65jBs3\njry8PE488cRDji0IB57uaAoKCgpiaWlpY5chSVK9qKioICsrq7HLqBe7du3i2GOPBeAHP/gBGzdu\n5P7772/kqhpOXe9lCKEsxlhQH8e3h02SJH1uzzzzDP/yL/9CVVUVZ5xxBo899lhjl3RUMbBJkqTP\nbdy4cYwbN66xyzhq+dCBJElSwhnYJEmSEs7AJkmSlHAGNkmSpIQzsEmS1MzNnz+fEAKrV6+uc/2k\nSZNqfyRXjcPAJklSM1dcXMygQYMoLi5u7FL0CQxskiQ1Y7t27WLx4sXMmjWrdkipGCNTp06lV69e\nDB8+nM2bN9duX1RURGFhITk5OUyZMoUDP8A/dOhQbrnlFgoKCsjKymLp0qVceeWV9OjRgzvuuKN2\n/8svv5y+ffuSnZ3Nww8/DMC6devo0aMHW7du5aOPPmLw4ME8//zzDdgKyefvsEmSlAAbbnstLcc9\n9QeDP3X9ggULGD16ND179qRz586UlZWxbt061qxZw6pVq9i0aRO9e/fmuuuuA2Dq1KlMnz4dgIkT\nJ/L0009z6aWXAtC6dWtKS0u5//77GTNmDGVlZXTq1IkvfOEL3HLLLXTu3JlHH32UTp06sXv3bgoL\nCxk7dixnnHEG06ZN4+tf/zr9+vWjd+/ejBw5Mi3t0VTZwyZJUjNWXFzM+PHjARg/fjzFxcW8+uqr\nTJgwgYyMDE4++WSGDRtWu/2iRYs477zzyM3NZeHChbz11lu16y677DIAcnNzyc7Opnv37rRp04az\nzjqL9evXA/DjH/+YvLw8+vfvz/r161m7di0AX/va13j//fd56KGHuOeeexrq8psMe9gkSUqAw/WE\npcO2bdtYuHAhK1asIIRAdXU1IQSuuOKKOrffs2cPN954I6WlpZx22mnMmDGDPXv21K5v06YNAC1a\ntKidPjBfVVXFyy+/zIsvvshvf/tb2rdvz9ChQ2v3//DDD9mwYQNQ8zVthw4d0nXZTZI9bJIkNVNz\n585l4sSJrFu3jsrKStavX09mZiadO3empKSE6upqNm7cyKJFiwBqw1WXLl3YtWvXZ35ydMeOHZxw\nwgm0b9+e1atX87vf/a523bRp07j66qspKirihhtuqL+LPErYwyZJUjNVXFzMtGnTDlk2duxYKioq\n6NGjB7179+b0009nwIABAHTs2JEbbriBnJwcTjrpJAoLCz/T+UaPHs1DDz1EVlYWvXr1on///gC8\n8sorLF26lN/85jdkZGQwb948Zs+ezeTJk+vnQo8C4cDTHU1BQUFBLC0tbewyJEmqFxUVFWRlZTV2\nGaoHdb2XIYSyGGNBfRzfr0QlSZISzsAmSZKUcAY2SZKkhDOwSZIkJZyBTZIkKeEMbJIkSQlnYJMk\nqRnLyMggPz+fnJwcLr30UrZv3w5AZWUlIYRDBm7funUrrVq1YurUqQCsWbOGoUOHkp+fT1ZWFlOm\nTGmUa2gODGySJDVj7dq1o7y8nJUrV9KpUyceeOCB2nWZmZk888wztfNPPvkk2dnZtfPf/OY3ueWW\nWygvL6eiooKbbrrpiM8bY+Sjjz6qn4toBgxskiQJgAEDBvDuu+/Wzrdv356srCwO/Gh9SUkJV111\nVe36jRs3cuqpp9bO5+bmAvDYY48xZswYhg4dSo8ePbjrrruAml67Xr16cc0115CTk8P69espLi4m\nNzeXnJycQ0ZdOPbYY7nlllvIzs7moosuYsuWLWm99qRzaCpJkhJgxowZjXrc6upqXnrpJa6//vpD\nlo8fP545c+bQrVs3MjIyOPnkk/nzn/8MwC233MKwYcM4//zzGTlyJJMnT6Zjx44AvPHGG6xcuZL2\n7dtTWFjIJZdcQpcuXVi7di2PP/44/fv3589//jPTpk2jrKyME044gZEjRzJ//nwuv/xyPvjgAwoK\nCrjvvvsoKirirrvuYubMmfXaNk1Jg/SwhRA6hhDmhhBWhxAqQggDQgidQggvhBDWpv6e0BC1SJKk\n/7V7927y8/M56aST2LRpEyNGjDhk/ejRo3nhhReYM2cO48aNO2Td5MmTqaio4Mtf/jIvv/wy/fv3\nZ+/evQCMGDGCzp07065dO6688koWL14MwBlnnFE7hujSpUsZOnQoXbt2pWXLllx99dW8+uqrALRo\n0aL2fF/96ldr92+uGqqH7X7g1zHGL4UQWgPtgduBl2KMPwgh3AbcBkz7tINIknS0SlcP2+EcuIft\nww8/ZNSoUTzwwAN885vfrF3funVr+vbty7333suqVat46qmnDtn/5JNP5rrrruO6664jJyeHlStX\nAhBCOGS7A/PHHHPM31TnXx+vuUl7D1sI4XhgCDALIMa4L8a4HRgDPJ7a7HHg8nTXIkmS6ta+fXt+\n/OMfc++991JVVXXIum9961v88Ic/pFOnTocs//Wvf83+/fsB+Mtf/sJ7773HKaecAsALL7zAtm3b\n2L17N/Pnz2fgwIEfO2e/fv145ZVX2Lp1K9XV1RQXF3PBBRcA8NFHHzF37lwAfvaznzFo0KB6v+am\npCF62DKBLcDsEEIeUAbcDHSLMW5MbfMXoFsD1CJJkj7BOeecQ58+fSguLmbw4MG1y7Ozsw95OvSA\n559/npu0DXR0AAARTUlEQVRvvpm2bdsC8K//+q+cdNJJQE0YGzt2LBs2bOCrX/0qBQUFVFZWHrJ/\n9+7d+cEPfsCFF15IjJFLLrmEMWPGADU9cW+88Qbf+973OPHEEykpKUnTVTcNIcaY3hOEUAD8DhgY\nY1wSQrgfeB+4KcbY8aDt/ifG+LH72EIIU4ApAKeffnrfdevWpbVeSZIaSkVFBVlZWY1dRr177LHH\nKC0t/VwPCRx77LHs2rWrHqtKr7reyxBCWYyxoD6O3xAPHWwANsQYl6Tm5wLnAptCCN0BUn8317Vz\njPHhGGNBjLGga9euDVCuJElSsqQ9sMUY/wKsDyH0Si26CFgFPAVcm1p2LbAg3bVIkqT0mzRp0uf+\nCY6m1LvWEBrqKdGbgCdST4j+EZhMTVj8eQjhemAdcNWn7C9JktRsNUhgizGWA3V9h3tRQ5xfkiSp\nKXNoKkmSpIQzsEmSJCWcgU2SpGZu/vz5hBBYvXp1nesnTZpU+yO2ahwGNkmSmrni4mIGDRpEcXFx\nY5eiT2BgkySpGdu1axeLFy9m1qxZzJkzB4AYI1OnTqVXr14MHz6czZv/96dSi4qKKCwsJCcnhylT\npnDgB/iHDh3KLbfcQkFBAVlZWSxdupQrr7ySHj16cMcddzTKtR1NGupnPSRJ0qd4aeEX0nLci4a9\n86nrFyxYwOjRo+nZsyedO3emrKyMdevWsWbNGlatWsWmTZvo3bs31113HQBTp05l+vTpAEycOJGn\nn36aSy+9FKgZKL60tJT777+fMWPGUFZWRqdOnfjCF77ALbfcQufOndNyjc2BPWySJDVjxcXFjB8/\nHoDx48dTXFzMq6++yoQJE8jIyODkk09m2LBhtdsvWrSI8847j9zcXBYuXMhbb71Vu+6yyy4DIDc3\nl+zsbLp3706bNm0466yzWL9+fcNe2FHGHjZJkhLgcD1h6bBt2zYWLlzIihUrCCFQXV1NCIErrrii\nzu337NnDjTfeSGlpKaeddhozZsxgz549tevbtGkDQIsWLWqnD8xXVVWl92KOcvawSZLUTM2dO5eJ\nEyeybt06KisrWb9+PZmZmXTu3JmSkhKqq6vZuHEjixYtAqgNZ126dGHXrl0+OdqA7GGTJKmZKi4u\nZtq0aYcsGzt2LBUVFfTo0YPevXtz+umnM2DAAAA6duzIDTfcQE5ODieddBKFhYWNUXazFA483dEU\nFBQUxNLS0sYuQ5KkelFRUUFWVlZjl6F6UNd7GUIoizHWNTTnZ+ZXopIkSQlnYJMkSUo4A5skSVLC\nGdgkSWpETelectWtId5DA5skSY2kbdu2vPfee4a2JizGyHvvvUfbtm3Teh5/1kOSpEZy6qmnsmHD\nBrZs2dLYpehzaNu2Laeeempaz2FgkySpkbRq1YrMzMzGLkNNgF+JSpIkJZyBTZIkKeEMbJIkSQln\nYJMkSUo4A5skSVLCGdgkSZISzsAmSZKUcAY2SZKkhDOwSZIkJZyBTZIkKeEMbJIkSQlnYJMkSUo4\nA5skSVLCGdgkSZISzsAmSZKUcIcNbCGEF0IIeQ1RjCRJkj7uSHrYpgE/CiHMDiF0T3dBkiRJOtRh\nA1uMcVmM8ULgaeDXIYTvhhDapb80SZIkwRHewxZCCMAa4EHgJmBtCGFiOguTJElSjSO5h+03wLvA\nfcApwCRgKNAvhPBwOouTJEkStDyCbaYAq2KM8a+W3xRCqEhDTZIkSTrIYQNbjPGtT1l9ST3WIkmS\npDp8rt9hizH+sb4KkSRJUt384VxJkqSEM7BJkiQlnIFNkiQp4QxskiRJCWdgkyRJSjgDmyRJUsIZ\n2CRJkhLOwCZJkpRwBjZJkqSEM7BJkiQlnIFNkiQp4QxskiRJCWdgkyRJSjgDmyRJUsIZ2CRJkhLO\nwCZJkpRwBjZJkqSEM7BJkiQlXIMFthBCRgjh9yGEp1PzmSGEJSGEt0MIJSGE1g1ViyRJUlPSkD1s\nNwMVB83/ELgvxvh3wP8A1zdgLZIkSU1GgwS2EMKpwCXAv6fmAzAMmJva5HHg8oaoRZIkqalpqB62\nHwHfBj5KzXcGtscYq1LzG4BTGqgWSZKkJiXtgS2E8EVgc4yx7G/cf0oIoTSEULply5Z6rk6SJCn5\nGqKHbSBwWQihEphDzVeh9wMdQwgtU9ucCrxb184xxodjjAUxxoKuXbs2QLmSJEnJkvbAFmP8Tozx\n1BjjmcB4YGGM8WpgEfCl1GbXAgvSXYskSVJT1Ji/wzYN+McQwtvU3NM2qxFrkSRJSqyWh9+k/sQY\nXwZeTk3/EejXkOeXJElqihzpQJIkKeEMbJIkSQlnYJMkSUo4A5skSVLCGdgkSZISzsAmSZKUcAY2\nSZKkhDOwSZIkJZyBTZIkKeEMbJIkSQlnYJMkSUo4A5skSVLCGdgkSZISzsAmSZKUcAY2SZKkhDOw\nSZIkJZyBTZIkKeEMbJIkSQlnYJMkSUo4A5skSVLCGdgkSZISzsAmSZKUcAY2SZKkhDOwSZIkJZyB\nTZIkKeEMbJIkSQlnYJMkSUo4A5skSVLCGdgkSZISzsAmSZKUcAY2SZKkhDOwSZIkJZyBTZIkKeEM\nbJIkSQlnYJMkSUo4A5skSVLCGdgkSZISzsAmSZKUcAY2SZKkhDOwSZIkJZyBTZIkKeEMbJIkSQln\nYJMkSUo4A5skSVLCGdgkSZISzsAmSZKUcAY2SZKkhDOwSZIkJZyBTZIkKeEMbJIkSQlnYJMkSUo4\nA5skSVLCGdgkSZISzsAmSZKUcAY2SZKkhDOwSZIkJZyBTZIkKeEMbJIkSQmX9sAWQjgthLAohLAq\nhPBWCOHm1PJOIYQXQghrU39PSHctkiRJTVFD9LBVAd+KMfYG+gPfCCH0Bm4DXoox9gBeSs1LkiTp\nr6Q9sMUYN8YYl6WmdwIVwCnAGODx1GaPA5enuxZJkqSmqEHvYQshnAmcAywBusUYN6ZW/QXo1pC1\nSJIkNRUNFthCCMcC84D/G2N8/+B1McYIxE/Yb0oIoTSEULply5YGqFSSJClZGiSwhRBaURPWnogx\n/iK1eFMIoXtqfXdgc137xhgfjjEWxBgLunbt2hDlSpIkJUpDPCUagFlARYzx3w5a9RRwbWr6WmBB\numuRJElqilo2wDkGAhOBFSGE8tSy24EfAD8PIVwPrAOuaoBaJEmSmpy0B7YY42IgfMLqi9J9fkmS\npKbOkQ4kSZISzsAmSZKUcAY2SZKkhDOwSZIkJZyBTZIkKeEMbJIkSQlnYJMkSUo4A5skSVLCGdgk\nSZISzsAmSZKUcAY2SZKkhDOwSZIkJZyBTZIkKeEMbJIkSQlnYJMkSUo4A5skSVLCGdgkSZISzsAm\nSZKUcAY2SZKkhDOwSZIkJZyBTZIkKeEMbJIkSQlnYJMkSUo4A5skSVLCGdgkSZISzsAmSZKUcAY2\nSZKkhDOwSZIkJZyBTZIkKeEMbJIkSQlnYJMkSUo4A5skSVLCGdgkSZISzsAmSZKUcAY2SZKkhDOw\nSZIkJZyBTZIkKeEMbJIkSQlnYJMkSUo4A5skSVLCGdgkSZISzsAmSZKUcAY2SZKkhDOwSZIkJZyB\nTZIkKeEMbJIkSQlnYJMkSUo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194tf/ILExERSUlJIS0tj5cqVFBcXc/fdd9O+fXvS0tJIS0vjF7/4RWiZmJgY0tLSSExM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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8c0f505780>"
+       "<matplotlib.figure.Figure at 0x7f8c23e81b70>"
       ]
      },
      "metadata": {},
@@ -1448,7 +1482,7 @@
     "fig, ax = plt.subplots(figsize=(10, 6))\n",
     "ax.set_xlabel('$step$')\n",
     "ax.set_ylabel('$loss$')\n",
-    "ax.set_title('{} (github.com/wassname/viz_torch_optim)'.format(problem.f.__name__))\n",
+    "ax.set_title('{} function'.format(problem.f.__name__))\n",
     "\n",
     "ax.set_xlim((0, loss_paths[:,0,:].max()))\n",
     "ax.set_ylim((0, loss_paths[:,1,:].max()))\n",
@@ -1460,10 +1494,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2017-11-15T08:18:45.947Z"
+     "end_time": "2017-11-15T08:30:34.172637Z",
+     "start_time": "2017-11-15T08:18:45.948924Z"
     },
     "scrolled": true
    },
@@ -1472,8 +1507,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "videos/beales_20171115_07-18-03_loss.webm\n"
+      "videos/beales_20171115_07-18-03_loss.webm\n",
+      "<video controls autoplay>\n",
+      "  <source type=\"video/webm\" src=\"videos/beales_20171115_07-18-03_loss.webm\">\n",
+      "  Your browser does not support the video tag.\n",
+      "</video>\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<video controls autoplay>\n",
+       "  <source type=\"video/webm\" src=\"videos/beales_20171115_07-18-03_loss.webm\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -1496,19 +1551,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 88,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-15T08:17:34.598851Z",
-     "start_time": "2017-11-15T08:17:34.575898Z"
-    },
-    "collapsed": true
+     "end_time": "2017-11-15T08:31:32.095580Z",
+     "start_time": "2017-11-15T08:30:46.339999Z"
+    }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "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<ipython-input-88-72db69b641d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# or save as gif\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msave_file_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msave_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.webm'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_loss.gif'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_file_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbitrate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcodec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gif'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs)\u001b[0m\n\u001b[1;32m   1060\u001b[0m                     \u001b[0;32mfor\u001b[0m \u001b[0manim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_anim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1061\u001b[0m                         \u001b[0;31m# TODO: See if turning off blit is really necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1062\u001b[0;31m                         \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_next_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\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   1063\u001b[0m                     \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1064\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_next_frame\u001b[0;34m(self, framedata, blit)\u001b[0m\n\u001b[1;32m   1098\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pre_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init_draw\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~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_post_draw\u001b[0;34m(self, framedata, blit)\u001b[0m\n\u001b[1;32m   1123\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit_cache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1124\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1125\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fig\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   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1127\u001b[0m     \u001b[0;31m# The rest of the code in this class is to facilitate easy blitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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   2038\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   2039\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-> 2040\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   2041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2042\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~/.pyenv/versions/3.6.0/envs/jupyter3/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    462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\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    465\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\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[0m\n\u001b[0;32m---> 63\u001b[0;31m         \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     64\u001b[0m         \u001b[0mafter\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[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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   1142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1144\u001b[0;31m                 renderer, self, dsu, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1146\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~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[1;32m    137\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    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\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    140\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\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~/.pyenv/versions/3.6.0/envs/jupyter3/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     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\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[0m\n\u001b[0;32m---> 63\u001b[0;31m         \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     64\u001b[0m         \u001b[0mafter\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[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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   2424\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   2425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2426\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[0mdsu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2428\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~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[1;32m    137\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    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\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    140\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\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~/.pyenv/versions/3.6.0/envs/jupyter3/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     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\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[0m\n\u001b[0;32m---> 63\u001b[0;31m         \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     64\u001b[0m         \u001b[0mafter\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[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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   1134\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   1135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1136\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   1137\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1138\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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    967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\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--> 969\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    970\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\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~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\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--> 969\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    970\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\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~/.pyenv/versions/3.6.0/envs/jupyter3/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    910\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m         \"\"\"\n\u001b[0;32m--> 912\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1784\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1785\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1786\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1788\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1792\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1793\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1794\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1795\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1796\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1734\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m                 nbins = max(min(self.axis.get_tick_space(), 9),\n\u001b[0m\u001b[1;32m   1737\u001b[0m                             max(1, self._min_n_ticks - 1))\n\u001b[1;32m   1738\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2018\u001b[0m         \u001b[0mends\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[0mtransAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[0;36m1\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2019\u001b[0m         \u001b[0mlength\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mends\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mends\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m72.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2020\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[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2021\u001b[0m         \u001b[0;31m# There is a heuristic here that the aspect ratio of tick text\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2022\u001b[0m         \u001b[0;31m# is no more than 3:1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/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   1727\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1728\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-> 1729\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   1730\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1731\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~/.pyenv/versions/3.6.0/envs/jupyter3/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)\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\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[0;32m--> 151\u001b[0;31m         \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[0m\u001b[1;32m    152\u001b[0m         \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[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_tick2line\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    434\u001b[0m                           zorder=self._zorder)\n\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m         \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'tick2'\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    437\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_artist_props\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mget_xaxis_transform\u001b[0;34m(self, which)\u001b[0m\n\u001b[1;32m    707\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mwhich\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tick2'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    708\u001b[0m             \u001b[0;31m# for cartesian projection, this is top spine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'top'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_spine_transform\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    710\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    711\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'unknown value for which'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/spines.py\u001b[0m in \u001b[0;36mget_spine_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    425\u001b[0m             \u001b[0mbase_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_yaxis_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[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspine_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'top'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bottom'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m             \u001b[0mbase_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[0m\n\u001b[0m\u001b[1;32m    428\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             raise ValueError('unknown spine spine_type: %s' %\n",
+      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36maxes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0mresides\u001b[0m \u001b[0;32min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m         \"\"\"\n\u001b[0;32m--> 242\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
-    "# or save as gif\n",
-    "save_file_loss = save_file.replace('.webm', '_loss.gif')\n",
-    "anim.save(save_file_loss, fps=1000, bitrate=1000, codec='gif')"
+    "# # or save as gif\n",
+    "# save_file_loss = save_file.replace('.webm', '_loss.gif')\n",
+    "# anim.save(save_file_loss, fps=1000, bitrate=1000, codec='gif')"
    ]
   },
   {
diff --git a/readme.md b/readme.md
index 04da5ea..f0463fd 100644
--- a/readme.md
+++ b/readme.md
@@ -6,6 +6,11 @@ This project generates animations of pytorch optimizers solving toy problems. Ex
 
 ![](docs/videos/beales.gif)
 
+<video controls autoplay>
+  <source type="video/webm" src="docs/videos/beales_20171115_07-18-03_loss.webm">
+  Your browser does not support the video tag.
+</video>
+
 # Usage:
 
 - `git clone https://github.com/wassname/viz_torch_optim`