{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm_notebook as tqdm\n",
    "from hippocampus.plotting import tsplot_boot\n",
    "import matplotlib.pyplot as plt\n",
    "from definitions import ROOT_FOLDER\n",
    "import os\n",
    "import pandas as pd\n",
    "from hippocampus.agents import CombinedAgent\n",
    "from hippocampus.environments import HexWaterMaze\n",
    "import numpy as np\n",
    "import random\n",
    "import seaborn as sns\n",
    "from multiprocessing import Pool\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some hyperparameters \n",
    "\n",
    "eta = .07  # learning rate for reliability estimators\n",
    "learning_rate = .1  # step size for value and SR learning\n",
    "inv_temp = 10  # inverse temperature parameter for softmax action selection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a22ffd320>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = HexWaterMaze(6)\n",
    "g.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "agent = CombinedAgent(g, init_sr='rw', lesion_dls=False, lesion_hpc=False)\n",
    "agent_results = []\n",
    "agent_ets = []\n",
    "session = 0\n",
    "res['session'] = session\n",
    "\n",
    "for ep in tqdm(range(44)):\n",
    "    agent.env.starting_state = np.random.choice(agent.env.grid.edge_states)\n",
    "    #if ep == 5: \n",
    "    #    g.set_platform_state(52)\n",
    "    if ep == 4: \n",
    "        g.set_platform_state(possible_platform_states[0])\n",
    "    if ep == 8: \n",
    "        g.set_platform_state(possible_platform_states[3])\n",
    "    if ep == 12: \n",
    "        g.set_platform_state(possible_platform_states[6])\n",
    "    if ep == 16:\n",
    "        g.set_platform_state(possible_platform_states[3])\n",
    "    if ep == 20: \n",
    "        g.set_platform_state(possible_platform_states[4])\n",
    "    if ep == 24: \n",
    "        g.set_platform_state(possible_platform_states[5])\n",
    "    if ep == 18: \n",
    "        g.set_platform_state(possible_platform_states[6])\n",
    "    if ep == 32:\n",
    "        g.set_platform_state(possible_platform_states[7])\n",
    "    if ep == 36: \n",
    "        g.set_platform_state(possible_platform_states[0])\n",
    "    if ep == 40: \n",
    "        g.set_platform_state(possible_platform_states[3])\n",
    "\n",
    "    res = agent.one_episode(random_policy=False)\n",
    "    res['trial'] = ep\n",
    "    res['escape time'] = res.time.max()\n",
    "    agent_results.append(res)\n",
    "    agent_ets.append(res.time.max())\n",
    "    \n",
    "    if ep % 4 == 0:\n",
    "        session += 1\n",
    "\n",
    "\n",
    "df = pd.concat(agent_results)\n",
    "```"
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    "all_results = []\n",
    "\n",
    "possible_platform_states = [48, 45, 42, 39, 60, 57, 54, 57]\n",
    "\n",
    "\n",
    "for n_agent in tqdm(range(20)):\n",
    "    \n",
    "    #inv_temp = 5\n",
    "    #possible_platform_states = [192, 185, 181, 174, 216, 210, 203, 197]  # for the r = 10 case\n",
    "    \n",
    "    #random.seed(n_agent)\n",
    "    random.shuffle(possible_platform_states)\n",
    "    g.set_platform_state(possible_platform_states[6]) \n",
    "\n",
    "\n",
    "    agent = CombinedAgent(g, init_sr='rw', lesion_dls=False, lesion_hpc=True, eta=eta,\n",
    "                         learning_rate=learning_rate, inv_temp=inv_temp)\n",
    "    agent.DLS.eta = .1\n",
    "    agent_results = []\n",
    "    agent_ets = []\n",
    "    session = 0\n",
    "\n",
    "    total_trial_count = 0\n",
    "\n",
    "    for ses in tqdm(range(11), leave=False):\n",
    "        for trial in tqdm(range(4),leave=False):\n",
    "            if trial == 0: \n",
    "                g.set_platform_state(possible_platform_states[ses % len(possible_platform_states)])\n",
    "            res = agent.one_episode(random_policy=False)\n",
    "            res['trial'] = trial\n",
    "            res['escape time'] = res.time.max()\n",
    "            res['session'] = ses\n",
    "            res['total trial'] = total_trial_count\n",
    "            agent_results.append(res)\n",
    "            agent_ets.append(res.time.max())\n",
    "\n",
    "            total_trial_count += 1\n",
    "            \n",
    "        #inv_temp += 1\n",
    "    agent_df = pd.concat(agent_results)\n",
    "    agent_df['agent'] = n_agent\n",
    "    agent_df['total time']= np.arange(len(agent_df))\n",
    "\n",
    "    all_results.append(agent_df)\n",
    "\n",
    "df = pd.concat(all_results)\n"
   ]
  },
  {
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   "execution_count": 5,
   "metadata": {},
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DLS reliability</th>\n",
       "      <th>HPC reliability</th>\n",
       "      <th>M_hat</th>\n",
       "      <th>P(SR)</th>\n",
       "      <th>RPE</th>\n",
       "      <th>R_hat</th>\n",
       "      <th>SPE</th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "      <th>choice</th>\n",
       "      <th>reward</th>\n",
       "      <th>state</th>\n",
       "      <th>time</th>\n",
       "      <th>platform</th>\n",
       "      <th>trial</th>\n",
       "      <th>escape time</th>\n",
       "      <th>session</th>\n",
       "      <th>total trial</th>\n",
       "      <th>agent</th>\n",
       "      <th>total time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[[1.5544961667901593, 0.5893681385311593, 0.58...</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[[1.5550365232716035, 0.6795758460662984, 0.58...</td>\n",
       "      <td>0.838377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.005403564814442152, 0.9020770753513905, -0....</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[[1.5550365232716035, 0.6795758460662984, 0.58...</td>\n",
       "      <td>0.815209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[-0.3055120278127675, 0.01005920312884312, -0....</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[[1.5550365232716035, 0.6795758460662984, 0.58...</td>\n",
       "      <td>0.806498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[-0.09748960686700103, -0.25572224121976794, -...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[[1.5550365232716035, 0.6795758460662984, 0.58...</td>\n",
       "      <td>0.803223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[-0.07362054018453487, -0.13603059410435517, -...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DLS reliability  HPC reliability  \\\n",
       "0              0.0              0.8   \n",
       "1              0.0              0.8   \n",
       "2              0.0              0.8   \n",
       "3              0.0              0.8   \n",
       "4              0.0              0.8   \n",
       "\n",
       "                                               M_hat     P(SR)  RPE  \\\n",
       "0  [[1.5544961667901593, 0.5893681385311593, 0.58...  0.900000  0.0   \n",
       "1  [[1.5550365232716035, 0.6795758460662984, 0.58...  0.838377  0.0   \n",
       "2  [[1.5550365232716035, 0.6795758460662984, 0.58...  0.815209  0.0   \n",
       "3  [[1.5550365232716035, 0.6795758460662984, 0.58...  0.806498  0.0   \n",
       "4  [[1.5550365232716035, 0.6795758460662984, 0.58...  0.803223  0.0   \n",
       "\n",
       "                                               R_hat  \\\n",
       "0  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "3  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "4  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                                 SPE  alpha  beta choice  \\\n",
       "0                                                  0    0.5  0.25   None   \n",
       "1  [0.005403564814442152, 0.9020770753513905, -0....    0.5  0.25   None   \n",
       "2  [-0.3055120278127675, 0.01005920312884312, -0....    0.5  0.25   None   \n",
       "3  [-0.09748960686700103, -0.25572224121976794, -...    0.5  0.25   None   \n",
       "4  [-0.07362054018453487, -0.13603059410435517, -...    0.5  0.25   None   \n",
       "\n",
       "   reward  state  time  platform  trial  escape time  session  total trial  \\\n",
       "0     0.0    0.0   0.0       NaN      0         49.0        0            0   \n",
       "1     0.0    1.0   1.0      45.0      0         49.0        0            0   \n",
       "2     0.0    8.0   2.0      45.0      0         49.0        0            0   \n",
       "3     0.0   22.0   3.0      45.0      0         49.0        0            0   \n",
       "4     0.0   41.0   4.0      45.0      0         49.0        0            0   \n",
       "\n",
       "   agent  total time  \n",
       "0      0           0  \n",
       "1      0           1  \n",
       "2      0           2  \n",
       "3      0           3  \n",
       "4      0           4  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2349f1d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure()\n",
    "sns.lineplot(data=df, x='total trial', y='escape time')\n",
    "#df.plot(x='total trial', y='escape time', hue='agent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df.plot(x='total trial',y=['DLS reliability', 'HPC reliability'])\n",
    "plt.figure()\n",
    "sns.lineplot(data=df, x='total trial', y='DLS reliability')\n",
    "sns.lineplot(data=df, x='total trial', y='HPC reliability')\n",
    "\n",
    "plt.ylim([0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b8590b588>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "sns.lineplot(data=df, x='total trial', y='P(SR)')\n",
    "\n",
    "sns.lineplot(data=df, x='total trial', y='HPC reliability')\n",
    "sns.lineplot(data=df, x='total trial', y='DLS reliability')\n",
    "sns.lineplot(data=df, x='total trial', y='alpha')\n",
    "sns.lineplot(data=df, x='total trial', y='beta')\n",
    "\n",
    "plt.legend(['P(SR)', 'HPC reliab', 'DLS reliab', 'alpha', 'beta'])\n",
    "\n",
    "\n",
    "\n",
    "#df.plot(x='total trial', y=['P(SR)', 'HPC reliability', 'DLS reliability', 'alpha', 'beta'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "sns.lineplot(data=df, x='total trial', y='P(SR)', hue='agent')\n",
    "plt.ylim([0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a2a8cfda0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(agent.get_alpha(np.linspace(0,1)))\n",
    "plt.plot(agent.get_beta(np.linspace(0,1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_alpha(chi_mf, alpha1=.1, A=1., B=3):\n",
    "    #A = 2* (2 * alpha1 + 1)\n",
    "    B = np.log((alpha1 ** -1) * A - 1)\n",
    "    return A / (1 + np.exp(B * chi_mf))\n",
    "\n",
    "def get_beta(chi_mb, beta1=.01, A=.5):\n",
    "    #A = 2 * beta1 + 1.\n",
    "    B = np.log(beta1 ** -1 * A - 1)\n",
    "    return A / (1 + np.exp(B * chi_mb))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "949e9bb7c55943039fc20b4f3858c0ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.01, description='alpha1', max=0.03, min=-0.01), FloatSlider(value=1.…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x = np.linspace(0, 1)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "line, = ax.plot(x, get_alpha(x), c='r')\n",
    "line2, = ax.plot(x, get_beta(x), c='b')\n",
    "plt.legend(['alpha (MF--> SR)', 'beta (SR --> MF)'])\n",
    "\n",
    "def update(alpha1 = .01, Aa=1., beta1=.1, Ab=.5):\n",
    "    line.set_ydata(get_alpha(x, alpha1=alpha1, A=Aa))\n",
    "    line2.set_ydata(get_beta(x, beta1=beta1, A=Ab))\n",
    "    fig.canvas.draw_idle()\n",
    "\n",
    "interact(update);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## plot trajectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessegeerts/Projects/hippocampus/hippocampus/environments.py:693: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c_mappable = c_mappable / c_mappable.max()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "g.plot_occupancy_on_grid(df[df['trial']==5], show_state_idx=True)\n",
    "80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df = df[df['trial']==5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: state, dtype: float64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trial_df['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the pearce plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DLS reliability</th>\n",
       "      <th>HPC reliability</th>\n",
       "      <th>M_hat</th>\n",
       "      <th>P(SR)</th>\n",
       "      <th>RPE</th>\n",
       "      <th>R_hat</th>\n",
       "      <th>SPE</th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "      <th>choice</th>\n",
       "      <th>reward</th>\n",
       "      <th>state</th>\n",
       "      <th>time</th>\n",
       "      <th>platform</th>\n",
       "      <th>trial</th>\n",
       "      <th>escape time</th>\n",
       "      <th>session</th>\n",
       "      <th>total trial</th>\n",
       "      <th>agent</th>\n",
       "      <th>total time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[[1.5544961667901593, 0.5893681385311593, 0.58...</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[[1.5550365232716035, 0.6795758460662984, 0.58...</td>\n",
       "      <td>0.838377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.005403564814442152, 0.9020770753513905, -0....</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[[1.5550365232716035, 0.6795758460662984, 0.58...</td>\n",
       "      <td>0.815209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[-0.3055120278127675, 0.01005920312884312, -0....</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[[1.5550365232716035, 0.6795758460662984, 0.58...</td>\n",
       "      <td>0.806498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[-0.09748960686700103, -0.25572224121976794, -...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[[1.5550365232716035, 0.6795758460662984, 0.58...</td>\n",
       "      <td>0.803223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[-0.07362054018453487, -0.13603059410435517, -...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DLS reliability  HPC reliability  \\\n",
       "0              0.0              0.8   \n",
       "1              0.0              0.8   \n",
       "2              0.0              0.8   \n",
       "3              0.0              0.8   \n",
       "4              0.0              0.8   \n",
       "\n",
       "                                               M_hat     P(SR)  RPE  \\\n",
       "0  [[1.5544961667901593, 0.5893681385311593, 0.58...  0.900000  0.0   \n",
       "1  [[1.5550365232716035, 0.6795758460662984, 0.58...  0.838377  0.0   \n",
       "2  [[1.5550365232716035, 0.6795758460662984, 0.58...  0.815209  0.0   \n",
       "3  [[1.5550365232716035, 0.6795758460662984, 0.58...  0.806498  0.0   \n",
       "4  [[1.5550365232716035, 0.6795758460662984, 0.58...  0.803223  0.0   \n",
       "\n",
       "                                               R_hat  \\\n",
       "0  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "3  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "4  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                                 SPE  alpha  beta choice  \\\n",
       "0                                                  0    0.5  0.25   None   \n",
       "1  [0.005403564814442152, 0.9020770753513905, -0....    0.5  0.25   None   \n",
       "2  [-0.3055120278127675, 0.01005920312884312, -0....    0.5  0.25   None   \n",
       "3  [-0.09748960686700103, -0.25572224121976794, -...    0.5  0.25   None   \n",
       "4  [-0.07362054018453487, -0.13603059410435517, -...    0.5  0.25   None   \n",
       "\n",
       "   reward  state  time  platform  trial  escape time  session  total trial  \\\n",
       "0     0.0    0.0   0.0       NaN      0         49.0        0            0   \n",
       "1     0.0    1.0   1.0      45.0      0         49.0        0            0   \n",
       "2     0.0    8.0   2.0      45.0      0         49.0        0            0   \n",
       "3     0.0   22.0   3.0      45.0      0         49.0        0            0   \n",
       "4     0.0   41.0   4.0      45.0      0         49.0        0            0   \n",
       "\n",
       "   agent  total time  \n",
       "0      0           0  \n",
       "1      0           1  \n",
       "2      0           2  \n",
       "3      0           3  \n",
       "4      0           4  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function lineplot in module seaborn.relational:\n",
      "\n",
      "lineplot(x=None, y=None, hue=None, size=None, style=None, data=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, dashes=True, markers=None, style_order=None, units=None, estimator='mean', ci=95, n_boot=1000, sort=True, err_style='band', err_kws=None, legend='brief', ax=None, **kwargs)\n",
      "    Draw a line plot with possibility of several semantic groupings.\n",
      "    \n",
      "    The relationship between ``x`` and ``y`` can be shown for different subsets\n",
      "    of the data using the ``hue``, ``size``, and ``style`` parameters. These\n",
      "    parameters control what visual semantics are used to identify the different\n",
      "    subsets. It is possible to show up to three dimensions independently by\n",
      "    using all three semantic types, but this style of plot can be hard to\n",
      "    interpret and is often ineffective. Using redundant semantics (i.e. both\n",
      "    ``hue`` and ``style`` for the same variable) can be helpful for making\n",
      "    graphics more accessible.\n",
      "    \n",
      "    See the :ref:`tutorial <relational_tutorial>` for more information.    \n",
      "    \n",
      "    By default, the plot aggregates over multiple ``y`` values at each value of\n",
      "    ``x`` and shows an estimate of the central tendency and a confidence\n",
      "    interval for that estimate.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : names of variables in ``data`` or vector data, optional\n",
      "        Input data variables; must be numeric. Can pass data directly or\n",
      "        reference columns in ``data``.    \n",
      "    hue : name of variables in ``data`` or vector data, optional\n",
      "        Grouping variable that will produce lines with different colors.\n",
      "        Can be either categorical or numeric, although color mapping will\n",
      "        behave differently in latter case.\n",
      "    size : name of variables in ``data`` or vector data, optional\n",
      "        Grouping variable that will produce lines with different widths.\n",
      "        Can be either categorical or numeric, although size mapping will\n",
      "        behave differently in latter case.\n",
      "    style : name of variables in ``data`` or vector data, optional\n",
      "        Grouping variable that will produce lines with different dashes\n",
      "        and/or markers. Can have a numeric dtype but will always be treated\n",
      "        as categorical.\n",
      "    data : DataFrame\n",
      "        Tidy (\"long-form\") dataframe where each column is a variable and each\n",
      "        row is an observation.    \n",
      "    palette : palette name, list, or dict, optional\n",
      "        Colors to use for the different levels of the ``hue`` variable. Should\n",
      "        be something that can be interpreted by :func:`color_palette`, or a\n",
      "        dictionary mapping hue levels to matplotlib colors.    \n",
      "    hue_order : list, optional\n",
      "        Specified order for the appearance of the ``hue`` variable levels,\n",
      "        otherwise they are determined from the data. Not relevant when the\n",
      "        ``hue`` variable is numeric.    \n",
      "    hue_norm : tuple or Normalize object, optional\n",
      "        Normalization in data units for colormap applied to the ``hue``\n",
      "        variable when it is numeric. Not relevant if it is categorical.    \n",
      "    sizes : list, dict, or tuple, optional\n",
      "        An object that determines how sizes are chosen when ``size`` is used.\n",
      "        It can always be a list of size values or a dict mapping levels of the\n",
      "        ``size`` variable to sizes. When ``size``  is numeric, it can also be\n",
      "        a tuple specifying the minimum and maximum size to use such that other\n",
      "        values are normalized within this range.    \n",
      "    size_order : list, optional\n",
      "        Specified order for appearance of the ``size`` variable levels,\n",
      "        otherwise they are determined from the data. Not relevant when the\n",
      "        ``size`` variable is numeric.    \n",
      "    size_norm : tuple or Normalize object, optional\n",
      "        Normalization in data units for scaling plot objects when the\n",
      "        ``size`` variable is numeric.    \n",
      "    dashes : boolean, list, or dictionary, optional\n",
      "        Object determining how to draw the lines for different levels of the\n",
      "        ``style`` variable. Setting to ``True`` will use default dash codes, or\n",
      "        you can pass a list of dash codes or a dictionary mapping levels of the\n",
      "        ``style`` variable to dash codes. Setting to ``False`` will use solid\n",
      "        lines for all subsets. Dashes are specified as in matplotlib: a tuple\n",
      "        of ``(segment, gap)`` lengths, or an empty string to draw a solid line.\n",
      "    markers : boolean, list, or dictionary, optional\n",
      "        Object determining how to draw the markers for different levels of the\n",
      "        ``style`` variable. Setting to ``True`` will use default markers, or\n",
      "        you can pass a list of markers or a dictionary mapping levels of the\n",
      "        ``style`` variable to markers. Setting to ``False`` will draw\n",
      "        marker-less lines.  Markers are specified as in matplotlib.    \n",
      "    style_order : list, optional\n",
      "        Specified order for appearance of the ``style`` variable levels\n",
      "        otherwise they are determined from the data. Not relevant when the\n",
      "        ``style`` variable is numeric.    \n",
      "    units : {long_form_var}\n",
      "        Grouping variable identifying sampling units. When used, a separate\n",
      "        line will be drawn for each unit with appropriate semantics, but no\n",
      "        legend entry will be added. Useful for showing distribution of\n",
      "        experimental replicates when exact identities are not needed.\n",
      "    \n",
      "    estimator : name of pandas method or callable or None, optional\n",
      "        Method for aggregating across multiple observations of the ``y``\n",
      "        variable at the same ``x`` level. If ``None``, all observations will\n",
      "        be drawn.    \n",
      "    ci : int or \"sd\" or None, optional\n",
      "        Size of the confidence interval to draw when aggregating with an\n",
      "        estimator. \"sd\" means to draw the standard deviation of the data.\n",
      "        Setting to ``None`` will skip bootstrapping.    \n",
      "    n_boot : int, optional\n",
      "        Number of bootstraps to use for computing the confidence interval.    \n",
      "    sort : boolean, optional\n",
      "        If True, the data will be sorted by the x and y variables, otherwise\n",
      "        lines will connect points in the order they appear in the dataset.\n",
      "    err_style : \"band\" or \"bars\", optional\n",
      "        Whether to draw the confidence intervals with translucent error bands\n",
      "        or discrete error bars.\n",
      "    err_band : dict of keyword arguments\n",
      "        Additional paramters to control the aesthetics of the error bars. The\n",
      "        kwargs are passed either to ``ax.fill_between`` or ``ax.errorbar``,\n",
      "        depending on the ``err_style``.\n",
      "    legend : \"brief\", \"full\", or False, optional\n",
      "        How to draw the legend. If \"brief\", numeric ``hue`` and ``size``\n",
      "        variables will be represented with a sample of evenly spaced values.\n",
      "        If \"full\", every group will get an entry in the legend. If ``False``,\n",
      "        no legend data is added and no legend is drawn.    \n",
      "    ax : matplotlib Axes, optional\n",
      "        Axes object to draw the plot onto, otherwise uses the current Axes.    \n",
      "    kwargs : key, value mappings\n",
      "        Other keyword arguments are passed down to ``plt.plot`` at draw time.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    ax : matplotlib Axes\n",
      "        Returns the Axes object with the plot drawn onto it.    \n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scatterplot : Show the relationship between two variables without\n",
      "                  emphasizing continuity of the ``x`` variable.\n",
      "    pointplot : Show the relationship between two variables when one is\n",
      "                categorical.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    \n",
      "    Draw a single line plot with error bands showing a confidence interval:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> import seaborn as sns; sns.set()\n",
      "        >>> import matplotlib.pyplot as plt\n",
      "        >>> fmri = sns.load_dataset(\"fmri\")\n",
      "        >>> ax = sns.lineplot(x=\"timepoint\", y=\"signal\", data=fmri)\n",
      "    \n",
      "    Group by another variable and show the groups with different colors:\n",
      "    \n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.lineplot(x=\"timepoint\", y=\"signal\", hue=\"event\",\n",
      "        ...                   data=fmri)\n",
      "    \n",
      "    Show the grouping variable with both color and line dashing:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.lineplot(x=\"timepoint\", y=\"signal\",\n",
      "        ...                   hue=\"event\", style=\"event\", data=fmri)\n",
      "    \n",
      "    Use color and line dashing to represent two different grouping variables:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.lineplot(x=\"timepoint\", y=\"signal\",\n",
      "        ...                   hue=\"region\", style=\"event\", data=fmri)\n",
      "    \n",
      "    Use markers instead of the dashes to identify groups:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.lineplot(x=\"timepoint\", y=\"signal\",\n",
      "        ...                   hue=\"event\", style=\"event\",\n",
      "        ...                   markers=True, dashes=False, data=fmri)\n",
      "    \n",
      "    Show error bars instead of error bands and plot the standard error:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.lineplot(x=\"timepoint\", y=\"signal\", hue=\"event\",\n",
      "        ...                   err_style=\"bars\", ci=68, data=fmri)\n",
      "    \n",
      "    Show experimental replicates instead of aggregating:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.lineplot(x=\"timepoint\", y=\"signal\", hue=\"event\",\n",
      "        ...                   units=\"subject\", estimator=None, lw=1,\n",
      "        ...                   data=fmri.query(\"region == 'frontal'\"))\n",
      "    \n",
      "    Use a quantitative color mapping:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> dots = sns.load_dataset(\"dots\").query(\"align == 'dots'\")\n",
      "        >>> ax = sns.lineplot(x=\"time\", y=\"firing_rate\",\n",
      "        ...                   hue=\"coherence\", style=\"choice\",\n",
      "        ...                   data=dots)\n",
      "    \n",
      "    Use a different normalization for the colormap:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> from matplotlib.colors import LogNorm\n",
      "        >>> ax = sns.lineplot(x=\"time\", y=\"firing_rate\",\n",
      "        ...                   hue=\"coherence\", style=\"choice\",\n",
      "        ...                   hue_norm=LogNorm(), data=dots)\n",
      "    \n",
      "    Use a different color palette:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.lineplot(x=\"time\", y=\"firing_rate\",\n",
      "        ...                   hue=\"coherence\", style=\"choice\",\n",
      "        ...                   palette=\"ch:2.5,.25\", data=dots)\n",
      "    \n",
      "    Use specific color values, treating the hue variable as categorical:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> palette = sns.color_palette(\"mako_r\", 6)\n",
      "        >>> ax = sns.lineplot(x=\"time\", y=\"firing_rate\",\n",
      "        ...                   hue=\"coherence\", style=\"choice\",\n",
      "        ...                   palette=palette, data=dots)\n",
      "    \n",
      "    Change the width of the lines with a quantitative variable:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.lineplot(x=\"time\", y=\"firing_rate\",\n",
      "        ...                   size=\"coherence\", hue=\"choice\",\n",
      "        ...                   legend=\"full\", data=dots)\n",
      "    \n",
      "    Change the range of line widths used to normalize the size variable:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.lineplot(x=\"time\", y=\"firing_rate\",\n",
      "        ...                   size=\"coherence\", hue=\"choice\",\n",
      "        ...                   sizes=(.25, 2.5), data=dots)\n",
      "    \n",
      "    Plot from a wide-form DataFrame:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> import numpy as np, pandas as pd; plt.close(\"all\")\n",
      "        >>> index = pd.date_range(\"1 1 2000\", periods=100,\n",
      "        ...                       freq=\"m\", name=\"date\")\n",
      "        >>> data = np.random.randn(100, 4).cumsum(axis=0)\n",
      "        >>> wide_df = pd.DataFrame(data, index, [\"a\", \"b\", \"c\", \"d\"])\n",
      "        >>> ax = sns.lineplot(data=wide_df)\n",
      "    \n",
      "    Plot from a list of Series:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> list_data = [wide_df.loc[:\"2005\", \"a\"], wide_df.loc[\"2003\":, \"b\"]]\n",
      "        >>> ax = sns.lineplot(data=list_data)\n",
      "    \n",
      "    Plot a single Series, pass kwargs to ``plt.plot``:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.lineplot(data=wide_df[\"a\"], color=\"coral\", label=\"line\")\n",
      "    \n",
      "    Draw lines at points as they appear in the dataset:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> x, y = np.random.randn(2, 5000).cumsum(axis=1)\n",
      "        >>> ax = sns.lineplot(x=x, y=y, sort=False, lw=1)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(sns.lineplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_and_last = df[ np.logical_or(df.trial == 0, df.trial==3)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b86fe8cc0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.lineplot(data=first_and_last, x='session', y='escape time', hue='trial', units='agent', estimator=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b86386a90>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.lineplot(data=first_and_last, x='session', y='escape time', hue='trial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from definitions import ROOT_FOLDER\n",
    "from datetime import datetime\n",
    "results_folder = os.path.join(ROOT_FOLDER, 'results/pearce')\n",
    "if not os.path.exists(results_folder):\n",
    "    os.makedirs(results_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save \n",
    "df.to_csv(os.path.join(results_folder,'results_r6_{}.csv'.format(str(datetime.now()))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}