prototypes/TemporalCoupling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We sort by timestamp ascending because we need a monotonic time data for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>timestamp</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-09 10:13:17 +0100</td>\n",
       "      <td>org.springframework.samples.petclinic.util.Ent...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-16 03:30:20 +0100</td>\n",
       "      <td>org.springframework.samples.petclinic.web.Owne...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-16 03:57:50 +0100</td>\n",
       "      <td>org.springframework.samples.petclinic.web.PetC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-16 04:02:37 +0100</td>\n",
       "      <td>org.springframework.samples.petclinic.web.PetC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-16 04:07:39 +0100</td>\n",
       "      <td>org.springframework.samples.petclinic.web.Visi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   timestamp  \\\n",
       "0  2013-01-09 10:13:17 +0100   \n",
       "1  2013-01-16 03:30:20 +0100   \n",
       "2  2013-01-16 03:57:50 +0100   \n",
       "3  2013-01-16 04:02:37 +0100   \n",
       "4  2013-01-16 04:07:39 +0100   \n",
       "\n",
       "                                                type  \n",
       "0  org.springframework.samples.petclinic.util.Ent...  \n",
       "1  org.springframework.samples.petclinic.web.Owne...  \n",
       "2  org.springframework.samples.petclinic.web.PetC...  \n",
       "3  org.springframework.samples.petclinic.web.PetC...  \n",
       "4  org.springframework.samples.petclinic.web.Visi...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import py2neo\n",
    "import pandas as pd\n",
    "\n",
    "graph = py2neo.Graph()\n",
    "\n",
    "query = \"\"\"\n",
    "MATCH\n",
    "(t:Type)-->(ch:Change)<--(co:Commit)\n",
    "RETURN t.fqn as type, co.date + \" \" + co.time as timestamp\n",
    "ORDER BY timestamp\n",
    "\"\"\"\n",
    "\n",
    "changes = pd.DataFrame(graph.data(query))\n",
    "changes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-09 09:13:17</th>\n",
       "      <td>org.springframework.samples.petclinic.util.Ent...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-16 02:30:20</th>\n",
       "      <td>org.springframework.samples.petclinic.web.Owne...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-16 02:57:50</th>\n",
       "      <td>org.springframework.samples.petclinic.web.PetC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-16 03:02:37</th>\n",
       "      <td>org.springframework.samples.petclinic.web.PetC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-16 03:07:39</th>\n",
       "      <td>org.springframework.samples.petclinic.web.Visi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  type\n",
       "timestamp                                                             \n",
       "2013-01-09 09:13:17  org.springframework.samples.petclinic.util.Ent...\n",
       "2013-01-16 02:30:20  org.springframework.samples.petclinic.web.Owne...\n",
       "2013-01-16 02:57:50  org.springframework.samples.petclinic.web.PetC...\n",
       "2013-01-16 03:02:37  org.springframework.samples.petclinic.web.PetC...\n",
       "2013-01-16 03:07:39  org.springframework.samples.petclinic.web.Visi..."
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "changes.timestamp = pd.to_datetime(changes.timestamp)\n",
    "changes_over_time = changes.set_index('timestamp')\n",
    "changes_over_time.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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></th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">org.springframework.samples.petclinic.PetclinicInitializer</th>\n",
       "      <th>2016-07-06 16:00:00</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-06 17:00:00</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-06 18:00:00</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-06 19:00:00</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-06 20:00:00</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                        type\n",
       "type                                               timestamp                \n",
       "org.springframework.samples.petclinic.Petclinic... 2016-07-06 16:00:00     1\n",
       "                                                   2016-07-06 17:00:00     0\n",
       "                                                   2016-07-06 18:00:00     0\n",
       "                                                   2016-07-06 19:00:00     0\n",
       "                                                   2016-07-06 20:00:00     0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dependend_types_within_1hour = \\\n",
    "  changes_over_time.groupby('type').resample(\"1h\").count()\n",
    "dependend_types_within_1hour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th>2013-01-09 09:00:00</th>\n",
       "      <th>2013-01-09 10:00:00</th>\n",
       "      <th>2013-01-09 11:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.PetclinicInitializer</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.BaseEntity</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.NamedEntity</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.Owner</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.Person</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  type  \\\n",
       "timestamp                                          2013-01-09 09:00:00   \n",
       "type                                                                     \n",
       "org.springframework.samples.petclinic.Petclinic...                 0.0   \n",
       "org.springframework.samples.petclinic.model.Bas...                 0.0   \n",
       "org.springframework.samples.petclinic.model.Nam...                 0.0   \n",
       "org.springframework.samples.petclinic.model.Owner                  0.0   \n",
       "org.springframework.samples.petclinic.model.Person                 0.0   \n",
       "\n",
       "                                                                        \\\n",
       "timestamp                                          2013-01-09 10:00:00   \n",
       "type                                                                     \n",
       "org.springframework.samples.petclinic.Petclinic...                 0.0   \n",
       "org.springframework.samples.petclinic.model.Bas...                 0.0   \n",
       "org.springframework.samples.petclinic.model.Nam...                 0.0   \n",
       "org.springframework.samples.petclinic.model.Owner                  0.0   \n",
       "org.springframework.samples.petclinic.model.Person                 0.0   \n",
       "\n",
       "                                                                        \n",
       "timestamp                                          2013-01-09 11:00:00  \n",
       "type                                                                    \n",
       "org.springframework.samples.petclinic.Petclinic...                 0.0  \n",
       "org.springframework.samples.petclinic.model.Bas...                 0.0  \n",
       "org.springframework.samples.petclinic.model.Nam...                 0.0  \n",
       "org.springframework.samples.petclinic.model.Owner                  0.0  \n",
       "org.springframework.samples.petclinic.model.Person                 0.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dependency_matrix = dependend_types_within_1hour.unstack().fillna(0)\n",
    "dependency_matrix.iloc[:5,:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.47973402,  1.        ,  1.        ,  1.        ],\n",
       "       [ 0.47973402,  0.        ,  0.43804851,  0.54116853,  0.58960866],\n",
       "       [ 1.        ,  0.43804851,  0.        ,  0.18350342,  0.26970326],\n",
       "       [ 1.        ,  0.54116853,  0.18350342,  0.        ,  0.25464401],\n",
       "       [ 1.        ,  0.58960866,  0.26970326,  0.25464401,  0.        ]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_distances\n",
    "\n",
    "distance_matrix = cosine_distances(dependency_matrix)\n",
    "distance_matrix[:5,:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>type</th>\n",
       "      <th>org.springframework.samples.petclinic.PetclinicInitializer</th>\n",
       "      <th>org.springframework.samples.petclinic.model.BaseEntity</th>\n",
       "      <th>org.springframework.samples.petclinic.model.NamedEntity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.PetclinicInitializer</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.479734</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.BaseEntity</th>\n",
       "      <td>0.479734</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.438049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.NamedEntity</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.438049</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type                                                org.springframework.samples.petclinic.PetclinicInitializer  \\\n",
       "type                                                                                                             \n",
       "org.springframework.samples.petclinic.Petclinic...                                           0.000000            \n",
       "org.springframework.samples.petclinic.model.Bas...                                           0.479734            \n",
       "org.springframework.samples.petclinic.model.Nam...                                           1.000000            \n",
       "\n",
       "type                                                org.springframework.samples.petclinic.model.BaseEntity  \\\n",
       "type                                                                                                         \n",
       "org.springframework.samples.petclinic.Petclinic...                                           0.479734        \n",
       "org.springframework.samples.petclinic.model.Bas...                                           0.000000        \n",
       "org.springframework.samples.petclinic.model.Nam...                                           0.438049        \n",
       "\n",
       "type                                                org.springframework.samples.petclinic.model.NamedEntity  \n",
       "type                                                                                                         \n",
       "org.springframework.samples.petclinic.Petclinic...                                           1.000000        \n",
       "org.springframework.samples.petclinic.model.Bas...                                           0.438049        \n",
       "org.springframework.samples.petclinic.model.Nam...                                           0.000000        "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_df = pd.DataFrame(\n",
    "    distance_matrix,\n",
    "    index=dependency_matrix.index,\n",
    "    columns=dependency_matrix.index)\n",
    "distance_df.iloc[:3,:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.13633063,  0.66974029],\n",
       "       [-0.03844349,  0.18250675],\n",
       "       [-0.0028786 , -0.33508867],\n",
       "       [ 0.02943364, -0.31556507],\n",
       "       [-0.15292003, -0.4830353 ]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import MDS\n",
    "\n",
    "model = MDS(dissimilarity='precomputed')\n",
    "T = model.fit_transform(distance_df)\n",
    "T[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21e12d07f28>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21e0bd799b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.scatter(T[:,0], T[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.136331</td>\n",
       "      <td>0.669740</td>\n",
       "      <td>org.springframework.samples.petclinic.Petclini...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.038443</td>\n",
       "      <td>0.182507</td>\n",
       "      <td>org.springframework.samples.petclinic.model.Ba...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.002879</td>\n",
       "      <td>-0.335089</td>\n",
       "      <td>org.springframework.samples.petclinic.model.Na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.029434</td>\n",
       "      <td>-0.315565</td>\n",
       "      <td>org.springframework.samples.petclinic.model.Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.152920</td>\n",
       "      <td>-0.483035</td>\n",
       "      <td>org.springframework.samples.petclinic.model.Pe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y                                              class\n",
       "0 -0.136331  0.669740  org.springframework.samples.petclinic.Petclini...\n",
       "1 -0.038443  0.182507  org.springframework.samples.petclinic.model.Ba...\n",
       "2 -0.002879 -0.335089  org.springframework.samples.petclinic.model.Na...\n",
       "3  0.029434 -0.315565  org.springframework.samples.petclinic.model.Owner\n",
       "4 -0.152920 -0.483035  org.springframework.samples.petclinic.model.Pe..."
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_df = pd.DataFrame(T, columns=['x','y'])\n",
    "T_df['class'] = distance_df.index\n",
    "T_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, ColumnDataSource\n",
    "from bokeh.models import HoverTool\n",
    "\n",
    "def scatter_with_hover(df, x, y,fig_width=500, fig_height=500):\n",
    "   \n",
    "    fig = figure(width=fig_width, height=fig_height)\n",
    "    source = ColumnDataSource(data=df)\n",
    "    name = 'main'\n",
    "    fig.scatter(x, y, source=source, name='main')\n",
    "    hover = HoverTool(names=[name])\n",
    "    hover.tooltips = [(c, '@' + c) for c in df.columns]\n",
    "    hover.tooltips.append(('index', '$index'))\n",
    "    fig.add_tools(hover)\n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"f38295b1-4186-4660-bf16-a6d08fc48ca6\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"f38295b1-4186-4660-bf16-a6d08fc48ca6\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"f38295b1-4186-4660-bf16-a6d08fc48ca6\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'f38295b1-4186-4660-bf16-a6d08fc48ca6' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"f38295b1-4186-4660-bf16-a6d08fc48ca6\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"f38295b1-4186-4660-bf16-a6d08fc48ca6\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"f38295b1-4186-4660-bf16-a6d08fc48ca6\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'f38295b1-4186-4660-bf16-a6d08fc48ca6' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"f38295b1-4186-4660-bf16-a6d08fc48ca6\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"b567c813-0766-4f4d-8f44-d3f9d8863d0e\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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Application\",\"version\":\"0.12.13\"}};\n",
       "  var render_items = [{\"docid\":\"95171af6-2f91-43c7-bc6a-d131ff3163bc\",\"elementid\":\"b567c813-0766-4f4d-8f44-d3f9d8863d0e\",\"modelid\":\"1cacdc00-7a09-4f3c-a55e-3525f0c650e2\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1cacdc00-7a09-4f3c-a55e-3525f0c650e2"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import output_notebook, show\n",
    "\n",
    "fig = scatter_with_hover(T_df,\"x\",\"y\")\n",
    "output_notebook()\n",
    "show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot perform __truediv__ with this index type: <class 'pandas.core.indexes.base.Index'>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-58-645201547863>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mtypes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdistance_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdistance_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mcolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhsv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtypes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\dev\\apps\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36minvalid_op\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m   3886\u001b[0m             \u001b[1;32mdef\u001b[0m \u001b[0minvalid_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3887\u001b[0m                 raise TypeError(\"cannot perform {name} with this index type: \"\n\u001b[1;32m-> 3888\u001b[1;33m                                 \"{typ}\".format(name=name, typ=type(self)))\n\u001b[0m\u001b[0;32m   3889\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3890\u001b[0m             \u001b[0minvalid_op\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot perform __truediv__ with this index type: <class 'pandas.core.indexes.base.Index'>"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "types = distance_df.index / distance_df.index.max()\n",
    "colors = [x for x in cm.hsv(types)]\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.scatter(T[:,0], T[:,1], c=colors)"
   ]
  }
 ],
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   "display_name": "Python 3",
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