Valhalla_to_Folium.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import folium\n",
    "from folium import Map, Marker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take a sample trajectory and convert to HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>trj_id</th>\n",
       "      <th>driving_mode</th>\n",
       "      <th>osname</th>\n",
       "      <th>pingtimestamp</th>\n",
       "      <th>rawlat</th>\n",
       "      <th>rawlng</th>\n",
       "      <th>speed</th>\n",
       "      <th>bearing</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>62155</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555652978</td>\n",
       "      <td>-6.152760</td>\n",
       "      <td>106.817673</td>\n",
       "      <td>8.696515</td>\n",
       "      <td>167</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13:49:38</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>62155</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555652979</td>\n",
       "      <td>-6.152830</td>\n",
       "      <td>106.817696</td>\n",
       "      <td>8.006205</td>\n",
       "      <td>167</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13:49:39</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>62155</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555652980</td>\n",
       "      <td>-6.152903</td>\n",
       "      <td>106.817711</td>\n",
       "      <td>8.006205</td>\n",
       "      <td>167</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13:49:40</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>62155</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555652981</td>\n",
       "      <td>-6.152982</td>\n",
       "      <td>106.817726</td>\n",
       "      <td>8.249675</td>\n",
       "      <td>167</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13:49:41</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>62155</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555652982</td>\n",
       "      <td>-6.153068</td>\n",
       "      <td>106.817749</td>\n",
       "      <td>9.676609</td>\n",
       "      <td>167</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13:49:42</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>62155</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555654026</td>\n",
       "      <td>-6.165999</td>\n",
       "      <td>106.800819</td>\n",
       "      <td>4.879760</td>\n",
       "      <td>92</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14:07:06</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>62155</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555654028</td>\n",
       "      <td>-6.165983</td>\n",
       "      <td>106.800926</td>\n",
       "      <td>5.049112</td>\n",
       "      <td>93</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14:07:08</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>62155</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555654029</td>\n",
       "      <td>-6.165990</td>\n",
       "      <td>106.801079</td>\n",
       "      <td>5.597365</td>\n",
       "      <td>93</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14:07:09</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>62155</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555654030</td>\n",
       "      <td>-6.165983</td>\n",
       "      <td>106.801140</td>\n",
       "      <td>5.597365</td>\n",
       "      <td>93</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14:07:10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>62155</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>1555654031</td>\n",
       "      <td>-6.165991</td>\n",
       "      <td>106.801170</td>\n",
       "      <td>3.443098</td>\n",
       "      <td>94</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14:07:11</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>645 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    trj_id driving_mode osname  pingtimestamp    rawlat      rawlng     speed  \\\n",
       "397  62155          car    ios     1555652978 -6.152760  106.817673  8.696515   \n",
       "328  62155          car    ios     1555652979 -6.152830  106.817696  8.006205   \n",
       "304  62155          car    ios     1555652980 -6.152903  106.817711  8.006205   \n",
       "104  62155          car    ios     1555652981 -6.152982  106.817726  8.249675   \n",
       "642  62155          car    ios     1555652982 -6.153068  106.817749  9.676609   \n",
       "..     ...          ...    ...            ...       ...         ...       ...   \n",
       "141  62155          car    ios     1555654026 -6.165999  106.800819  4.879760   \n",
       "418  62155          car    ios     1555654028 -6.165983  106.800926  5.049112   \n",
       "486  62155          car    ios     1555654029 -6.165990  106.801079  5.597365   \n",
       "238  62155          car    ios     1555654030 -6.165983  106.801140  5.597365   \n",
       "461  62155          car    ios     1555654031 -6.165991  106.801170  3.443098   \n",
       "\n",
       "     bearing  accuracy      time  day_of_week  month  year  \n",
       "397      167       8.0  13:49:38            4      4  2019  \n",
       "328      167       4.0  13:49:39            4      4  2019  \n",
       "304      167       6.0  13:49:40            4      4  2019  \n",
       "104      167       6.0  13:49:41            4      4  2019  \n",
       "642      167       4.0  13:49:42            4      4  2019  \n",
       "..       ...       ...       ...          ...    ...   ...  \n",
       "141       92      12.0  14:07:06            4      4  2019  \n",
       "418       93      16.0  14:07:08            4      4  2019  \n",
       "486       93      16.0  14:07:09            4      4  2019  \n",
       "238       93      24.0  14:07:10            4      4  2019  \n",
       "461       94      16.0  14:07:11            4      4  2019  \n",
       "\n",
       "[645 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_trj_id = \"62155\"\n",
    "df = pd.read_feather(f'data/{sample_trj_id}.ftr').sort_values(by='pingtimestamp')\n",
    "#df = df[df['distance'] != 0]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trj_id</th>\n",
       "      <th>driving_mode</th>\n",
       "      <th>osname</th>\n",
       "      <th>pingtimestamp</th>\n",
       "      <th>rawlat</th>\n",
       "      <th>rawlng</th>\n",
       "      <th>speed</th>\n",
       "      <th>bearing</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>645</td>\n",
       "      <td>645</td>\n",
       "      <td>645</td>\n",
       "      <td>6.450000e+02</td>\n",
       "      <td>645.000000</td>\n",
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       "      <td>645.0</td>\n",
       "      <td>645.0</td>\n",
       "      <td>645.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>645</td>\n",
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>62155</td>\n",
       "      <td>car</td>\n",
       "      <td>ios</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13:49:38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>645</td>\n",
       "      <td>645</td>\n",
       "      <td>645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.555654e+09</td>\n",
       "      <td>-6.163095</td>\n",
       "      <td>106.809105</td>\n",
       "      <td>8.774761</td>\n",
       "      <td>220.291473</td>\n",
       "      <td>6.508527</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.439817e+02</td>\n",
       "      <td>0.003555</td>\n",
       "      <td>0.009152</td>\n",
       "      <td>4.107210</td>\n",
       "      <td>72.316089</td>\n",
       "      <td>2.047269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.555653e+09</td>\n",
       "      <td>-6.166734</td>\n",
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       "      <td>0.155046</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.555653e+09</td>\n",
       "      <td>-6.166095</td>\n",
       "      <td>106.800285</td>\n",
       "      <td>6.324945</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.555654e+09</td>\n",
       "      <td>-6.165110</td>\n",
       "      <td>106.812294</td>\n",
       "      <td>8.683396</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.555654e+09</td>\n",
       "      <td>-6.160484</td>\n",
       "      <td>106.817955</td>\n",
       "      <td>11.946131</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.555654e+09</td>\n",
       "      <td>-6.152760</td>\n",
       "      <td>106.819962</td>\n",
       "      <td>19.464340</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       trj_id driving_mode osname  pingtimestamp      rawlat      rawlng  \\\n",
       "count     645          645    645   6.450000e+02  645.000000  645.000000   \n",
       "unique      1            1      1            NaN         NaN         NaN   \n",
       "top     62155          car    ios            NaN         NaN         NaN   \n",
       "freq      645          645    645            NaN         NaN         NaN   \n",
       "mean      NaN          NaN    NaN   1.555654e+09   -6.163095  106.809105   \n",
       "std       NaN          NaN    NaN   3.439817e+02    0.003555    0.009152   \n",
       "min       NaN          NaN    NaN   1.555653e+09   -6.166734  106.791298   \n",
       "25%       NaN          NaN    NaN   1.555653e+09   -6.166095  106.800285   \n",
       "50%       NaN          NaN    NaN   1.555654e+09   -6.165110  106.812294   \n",
       "75%       NaN          NaN    NaN   1.555654e+09   -6.160484  106.817955   \n",
       "max       NaN          NaN    NaN   1.555654e+09   -6.152760  106.819962   \n",
       "\n",
       "             speed     bearing    accuracy      time  day_of_week  month  \\\n",
       "count   645.000000  645.000000  645.000000       645        645.0  645.0   \n",
       "unique         NaN         NaN         NaN       645          NaN    NaN   \n",
       "top            NaN         NaN         NaN  13:49:38          NaN    NaN   \n",
       "freq           NaN         NaN         NaN         1          NaN    NaN   \n",
       "mean      8.774761  220.291473    6.508527       NaN          4.0    4.0   \n",
       "std       4.107210   72.316089    2.047269       NaN          0.0    0.0   \n",
       "min       0.155046   31.000000    3.000000       NaN          4.0    4.0   \n",
       "25%       6.324945  166.000000    6.000000       NaN          4.0    4.0   \n",
       "50%       8.683396  262.000000    6.000000       NaN          4.0    4.0   \n",
       "75%      11.946131  266.000000    8.000000       NaN          4.0    4.0   \n",
       "max      19.464340  351.000000   24.000000       NaN          4.0    4.0   \n",
       "\n",
       "          year  \n",
       "count    645.0  \n",
       "unique     NaN  \n",
       "top        NaN  \n",
       "freq       NaN  \n",
       "mean    2019.0  \n",
       "std        0.0  \n",
       "min     2019.0  \n",
       "25%     2019.0  \n",
       "50%     2019.0  \n",
       "75%     2019.0  \n",
       "max     2019.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-6.165109634399414, 106.81229400634766]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.loc[0, 'location']\n",
    "[df.quantile(0.5)['rawlat'], df.quantile(0.5)['rawlng']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Polyline over Marker due to the number of pings = number of points to be connected.\n",
    "\n",
    "Marker is more laggy if there are too many of them in the same HTML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<folium.map.LayerControl at 0x2a56e11bdc0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgp_map = folium.Map(\n",
    "    location=[df.quantile(0.5)['rawlat'], df.quantile(0.5)['rawlng']],\n",
    "    tiles=\"openstreetmap\",\n",
    "    zoom_start=17\n",
    "    )\n",
    "\n",
    "\"\"\"\n",
    "for idx, row in df.iterrows():\n",
    "    Marker([row['lat'], row['lon']], icon=folium.Icon(color=\"red\")).add_to(sgp_map)\n",
    "\n",
    "for idx, row in df.iterrows():\n",
    "    Marker([row['location'][1], row['location'][0]], icon=folium.Icon(color=\"green\")).add_to(sgp_map)\n",
    "\"\"\"\n",
    "\n",
    "# Creating feature groups\n",
    "f1 = folium.FeatureGroup(\"Red\")\n",
    "f2 = folium.FeatureGroup(\"Green\")\n",
    "\n",
    "# Adding lines to the different feature groups\n",
    "folium.vector_layers.PolyLine(list(zip(df['rawlat'], df['rawlng'])), popup='<b>Original Path</b>', color='red', weight=5).add_to(f1)\n",
    "#folium.vector_layers.PolyLine(list(map(lambda x: x[::-1], df['location'])), popup='<b>Valhalla Path</b>', color='green',weight=5).add_to(f2)\n",
    "\n",
    "f1.add_to(sgp_map)\n",
    "#f2.add_to(sgp_map)\n",
    "folium.LayerControl().add_to(sgp_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgp_map.save(f'data/valhalla/line/{sample_trj_id}.html')"
   ]
  }
 ],
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