9.3. Results - traffic prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original NE: POINT (31.5 66.8)\n",
      "Original SW: POINT (8.5 53)\n",
      "Transformed NE: (697681.2863658136, 7416224.9393330095)\n",
      "Transformed SW: (-735260.4063631196, 6033964.466017238)\n",
      "col_count 573\n",
      "row_count 552\n",
      "grid_size 316296\n",
      "Original NE: POINT (31.5 66.8)\n",
      "Original SW: POINT (8.5 53)\n",
      "Transformed NE: (697681.2863658136, 7416224.9393330095)\n",
      "Transformed SW: (-735260.4063631196, 6033964.466017238)\n",
      "col_count 286\n",
      "row_count 276\n",
      "grid_size 78936\n"
     ]
    }
   ],
   "source": [
    "##### from pygradu import gridify\n",
    "from pygradu import shortest_path\n",
    "from pygradu import gridify\n",
    "from pygradu import portcalls\n",
    "\n",
    "import shapely.geometry\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import importlib\n",
    "\n",
    "RESULTS_DIR = 'data/results/'\n",
    "DATASET_DIR = 'data/datasets/'\n",
    "SHAPES_DIR = 'data/shapes/'\n",
    "\n",
    "importlib.reload(gridify)\n",
    "\n",
    "grid_2500m = gridify.area_to_grid(side_length=2500)\n",
    "grid_5km = gridify.area_to_grid(side_length=5000)\n",
    "\n",
    "validation_voyages_summer = pd.read_csv(DATASET_DIR + 'validation_set_summer.csv', index_col=0, parse_dates = ['timestamp', 'ata', 'atd'])\n",
    "validation_voyages_winter = pd.read_csv(DATASET_DIR + 'validation_set_winter.csv', index_col=0, parse_dates = ['timestamp', 'ata', 'atd'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from shapely.geometry import shape, Point\n",
    "# depending on your version, use: from shapely.geometry import shape, Point\n",
    "\n",
    "# load GeoJSON file containing sectors\n",
    "with open(SHAPES_DIR + 'sea_areas.geojson') as f:\n",
    "    sea_areas = json.load(f)\n",
    "\n",
    "def get_sea_area_from_latlon(sea_areas, lat, lon):\n",
    "    point = Point(lon, lat)\n",
    "    # check each polygon to see if it contains the point\n",
    "    for feature in sea_areas['features']:\n",
    "        polygon = shape(feature['geometry'])\n",
    "        if polygon.contains(point):\n",
    "            return feature['properties']['id']\n",
    "    return -1\n",
    "\n",
    "def get_sea_area_name_from_latlon(sea_areas, lat, lon):\n",
    "    point = Point(lon, lat)\n",
    "    # check each polygon to see if it contains the point\n",
    "    for feature in sea_areas['features']:\n",
    "        polygon = shape(feature['geometry'])\n",
    "        if polygon.contains(point):\n",
    "            return feature['properties']['name']\n",
    "    return 'South'\n",
    "\n",
    "def get_sea_area_name(sea_areas, _id):\n",
    "    # check each polygon to see if it contains the point\n",
    "    for feature in sea_areas['features']:\n",
    "        polygon = shape(feature['geometry'])\n",
    "        if int(feature['properties']['id']) == int(_id):\n",
    "            return feature['properties']['name']\n",
    "    return 'South'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper columns and variables for result examination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>voyage</th>\n",
       "      <th>vessel_type</th>\n",
       "      <th>end_port</th>\n",
       "      <th>end_port_sea_area</th>\n",
       "      <th>start_time</th>\n",
       "      <th>pred_time</th>\n",
       "      <th>mins_to_future</th>\n",
       "      <th>actual_lat</th>\n",
       "      <th>actual_lon</th>\n",
       "      <th>pred_lat</th>\n",
       "      <th>pred_lon</th>\n",
       "      <th>actual_speed</th>\n",
       "      <th>pred_speed</th>\n",
       "      <th>acc_distance_nm</th>\n",
       "      <th>error_rate_lat</th>\n",
       "      <th>error_rate_lon</th>\n",
       "      <th>error_rate_speed</th>\n",
       "      <th>correct_node</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>GC</td>\n",
       "      <td>19942</td>\n",
       "      <td>-1</td>\n",
       "      <td>2019-06-07 11:27:49.000000</td>\n",
       "      <td>2019-06-07 11:27:49.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.974982</td>\n",
       "      <td>11.340805</td>\n",
       "      <td>53.974982</td>\n",
       "      <td>11.340805</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.649501</td>\n",
       "      <td>3.819239e-13</td>\n",
       "      <td>1.316430e-14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.421189</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>GC</td>\n",
       "      <td>19942</td>\n",
       "      <td>-1</td>\n",
       "      <td>2019-06-07 11:27:49.000000</td>\n",
       "      <td>2019-06-07 11:35:43.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>53.992868</td>\n",
       "      <td>11.331420</td>\n",
       "      <td>53.992051</td>\n",
       "      <td>11.357935</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.649501</td>\n",
       "      <td>9.371762e-01</td>\n",
       "      <td>1.513326e-03</td>\n",
       "      <td>0.233991</td>\n",
       "      <td>0.421189</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>GC</td>\n",
       "      <td>19942</td>\n",
       "      <td>-1</td>\n",
       "      <td>2019-06-07 11:27:49.000000</td>\n",
       "      <td>2019-06-07 11:41:43.000000</td>\n",
       "      <td>13.900000</td>\n",
       "      <td>54.007890</td>\n",
       "      <td>11.331562</td>\n",
       "      <td>54.005014</td>\n",
       "      <td>11.370954</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.649501</td>\n",
       "      <td>1.400651e+00</td>\n",
       "      <td>5.325419e-03</td>\n",
       "      <td>0.347631</td>\n",
       "      <td>0.421189</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>GC</td>\n",
       "      <td>19942</td>\n",
       "      <td>-1</td>\n",
       "      <td>2019-06-07 11:27:49.000000</td>\n",
       "      <td>2019-06-07 11:47:53.000000</td>\n",
       "      <td>20.066667</td>\n",
       "      <td>54.018665</td>\n",
       "      <td>11.345753</td>\n",
       "      <td>54.018335</td>\n",
       "      <td>11.384343</td>\n",
       "      <td>4.42</td>\n",
       "      <td>4.649501</td>\n",
       "      <td>1.361401e+00</td>\n",
       "      <td>6.107745e-04</td>\n",
       "      <td>0.340124</td>\n",
       "      <td>5.192332</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>GC</td>\n",
       "      <td>19942</td>\n",
       "      <td>-1</td>\n",
       "      <td>2019-06-07 11:27:49.000000</td>\n",
       "      <td>2019-06-07 11:52:59.000000</td>\n",
       "      <td>25.166667</td>\n",
       "      <td>54.020092</td>\n",
       "      <td>11.367012</td>\n",
       "      <td>54.029351</td>\n",
       "      <td>11.395423</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.649501</td>\n",
       "      <td>1.145939e+00</td>\n",
       "      <td>1.714044e-02</td>\n",
       "      <td>0.249943</td>\n",
       "      <td>2.637993</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   voyage vessel_type  end_port  end_port_sea_area  \\\n",
       "0      14          GC     19942                 -1   \n",
       "1      14          GC     19942                 -1   \n",
       "2      14          GC     19942                 -1   \n",
       "3      14          GC     19942                 -1   \n",
       "4      14          GC     19942                 -1   \n",
       "\n",
       "                   start_time                   pred_time  mins_to_future  \\\n",
       "0  2019-06-07 11:27:49.000000  2019-06-07 11:27:49.000000        0.000000   \n",
       "1  2019-06-07 11:27:49.000000  2019-06-07 11:35:43.000000        7.900000   \n",
       "2  2019-06-07 11:27:49.000000  2019-06-07 11:41:43.000000       13.900000   \n",
       "3  2019-06-07 11:27:49.000000  2019-06-07 11:47:53.000000       20.066667   \n",
       "4  2019-06-07 11:27:49.000000  2019-06-07 11:52:59.000000       25.166667   \n",
       "\n",
       "   actual_lat  actual_lon   pred_lat   pred_lon  actual_speed  pred_speed  \\\n",
       "0   53.974982   11.340805  53.974982  11.340805          4.63    4.649501   \n",
       "1   53.992868   11.331420  53.992051  11.357935          4.63    4.649501   \n",
       "2   54.007890   11.331562  54.005014  11.370954          4.63    4.649501   \n",
       "3   54.018665   11.345753  54.018335  11.384343          4.42    4.649501   \n",
       "4   54.020092   11.367012  54.029351  11.395423          4.53    4.649501   \n",
       "\n",
       "   acc_distance_nm  error_rate_lat  error_rate_lon  error_rate_speed  \\\n",
       "0     3.819239e-13    1.316430e-14        0.000000          0.421189   \n",
       "1     9.371762e-01    1.513326e-03        0.233991          0.421189   \n",
       "2     1.400651e+00    5.325419e-03        0.347631          0.421189   \n",
       "3     1.361401e+00    6.107745e-04        0.340124          5.192332   \n",
       "4     1.145939e+00    1.714044e-02        0.249943          2.637993   \n",
       "\n",
       "   correct_node  \n",
       "0             1  \n",
       "1             1  \n",
       "2             1  \n",
       "3             0  \n",
       "4             0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summer_results_5km_adj = pd.read_csv(RESULTS_DIR + 'summer_results_5km_adj_to_end.csv', index_col=0)\n",
    "summer_results_5km_ship = pd.read_csv(RESULTS_DIR + 'summer_results_5km_ship_to_end.csv', index_col=0)\n",
    "summer_results_2500m_adj = pd.read_csv(RESULTS_DIR + 'summer_results_2500m_adj_to_end.csv', index_col=0)\n",
    "\n",
    "winter_results_5km_adj_no_dirway = pd.read_csv(RESULTS_DIR + 'winter_results_to_end_5km_adj_no_dirways.csv', index_col=0)\n",
    "winter_results_5km_adj_dirway = pd.read_csv(RESULTS_DIR + 'winter_results_to_end_5km_adj_dirways.csv', index_col=0)\n",
    "winter_results_5km_ship_no_dirway = pd.read_csv(RESULTS_DIR + 'winter_results_5km_ship_to_end_no_dirways.csv', index_col=0)\n",
    "winter_results_5km_ship_dirway = pd.read_csv(RESULTS_DIR + 'winter_results_5km_ship_to_end_dirways.csv', index_col=0)\n",
    "winter_results_2500m_adj_no_dirway = pd.read_csv(RESULTS_DIR + 'winter_results_2500m_ship_to_end_no_dirways.csv', index_col=0)\n",
    "winter_results_2500m_adj_dirway = pd.read_csv(RESULTS_DIR + 'winter_results_2500m_ship_to_end_dirways.csv', index_col=0)\n",
    "summer_results_5km_adj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 21.6 s, sys: 89.8 ms, total: 21.7 s\n",
      "Wall time: 21.9 s\n",
      "CPU times: user 11 s, sys: 43.9 ms, total: 11 s\n",
      "Wall time: 11.1 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>voyage</th>\n",
       "      <th>voyage_length</th>\n",
       "      <th>start_end_length</th>\n",
       "      <th>difference</th>\n",
       "      <th>travel_time</th>\n",
       "      <th>avg_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3299.927273</td>\n",
       "      <td>516.031082</td>\n",
       "      <td>408.732554</td>\n",
       "      <td>26.281408</td>\n",
       "      <td>1422.086162</td>\n",
       "      <td>6.296045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1756.757853</td>\n",
       "      <td>343.622339</td>\n",
       "      <td>256.768676</td>\n",
       "      <td>31.315226</td>\n",
       "      <td>1013.877433</td>\n",
       "      <td>1.895353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>9.368543</td>\n",
       "      <td>9.184511</td>\n",
       "      <td>0.453766</td>\n",
       "      <td>67.950000</td>\n",
       "      <td>1.564000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2254.250000</td>\n",
       "      <td>284.000496</td>\n",
       "      <td>259.338044</td>\n",
       "      <td>5.739473</td>\n",
       "      <td>638.200000</td>\n",
       "      <td>5.034446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2996.000000</td>\n",
       "      <td>404.163412</td>\n",
       "      <td>324.668310</td>\n",
       "      <td>14.700501</td>\n",
       "      <td>1118.750000</td>\n",
       "      <td>5.986336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4895.000000</td>\n",
       "      <td>635.502617</td>\n",
       "      <td>498.671228</td>\n",
       "      <td>31.509834</td>\n",
       "      <td>1857.175000</td>\n",
       "      <td>7.939009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6576.000000</td>\n",
       "      <td>2029.400648</td>\n",
       "      <td>1317.256220</td>\n",
       "      <td>173.280797</td>\n",
       "      <td>5353.933333</td>\n",
       "      <td>10.909583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            voyage  voyage_length  start_end_length  difference  travel_time  \\\n",
       "count   330.000000     330.000000        330.000000  330.000000   330.000000   \n",
       "mean   3299.927273     516.031082        408.732554   26.281408  1422.086162   \n",
       "std    1756.757853     343.622339        256.768676   31.315226  1013.877433   \n",
       "min      14.000000       9.368543          9.184511    0.453766    67.950000   \n",
       "25%    2254.250000     284.000496        259.338044    5.739473   638.200000   \n",
       "50%    2996.000000     404.163412        324.668310   14.700501  1118.750000   \n",
       "75%    4895.000000     635.502617        498.671228   31.509834  1857.175000   \n",
       "max    6576.000000    2029.400648       1317.256220  173.280797  5353.933333   \n",
       "\n",
       "        avg_speed  \n",
       "count  330.000000  \n",
       "mean     6.296045  \n",
       "std      1.895353  \n",
       "min      1.564000  \n",
       "25%      5.034446  \n",
       "50%      5.986336  \n",
       "75%      7.939009  \n",
       "max     10.909583  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(portcalls)\n",
    "\n",
    "%time voyages_summer_lengths = portcalls.get_voyage_lengths(validation_voyages_summer)\n",
    "%time voyages_winter_lengths = portcalls.get_voyage_lengths(validation_voyages_winter)\n",
    "voyages_summer_lengths.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157\n"
     ]
    }
   ],
   "source": [
    "ports = pd.read_csv(DATASET_DIR + 'ports.csv')\n",
    "ports = ports[['port_id', 'lat', 'lon']]\n",
    "print(len(ports))\n",
    "\n",
    "ais_ports = pd.read_csv(DATASET_DIR + 'ports_calculated_from_ais.csv')\n",
    "ais_ports = ais_ports[['id', 'lat', 'lon']]\n",
    "ais_ports.columns=['port_id', 'lat', 'lon']\n",
    "\n",
    "ports = ports.append(ais_ports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "importlib.reload(portcalls)\n",
    "\n",
    "voyages_summer = summer_results_5km_adj[['voyage', 'vessel_type', 'end_port', 'end_port_sea_area', 'start_time']].drop_duplicates()\n",
    "voyages_summer['start_lat'] = voyages_summer.apply(lambda row: summer_results_5km_adj[summer_results_5km_adj.voyage == row.voyage].head(1).iloc[0].actual_lat, axis=1)\n",
    "voyages_summer['start_lon'] = voyages_summer.apply(lambda row: summer_results_5km_adj[summer_results_5km_adj.voyage == row.voyage].head(1).iloc[0].actual_lon, axis=1)\n",
    "voyages_summer['start_sea_area'] = voyages_summer.apply(lambda row: get_sea_area_from_latlon(sea_areas, row.start_lat, row.start_lon), axis=1)\n",
    "voyages_summer['start_port'] = voyages_summer.apply(lambda row: portcalls.get_port_id(row, ports), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "importlib.reload(portcalls)\n",
    "\n",
    "voyages_winter = winter_results_5km_adj_no_dirway[['voyage', 'vessel_type', 'end_port', 'end_port_sea_area', 'start_time']].drop_duplicates()\n",
    "voyages_winter['start_lat'] = voyages_winter.apply(lambda row: winter_results_5km_adj_no_dirway[winter_results_5km_adj_no_dirway.voyage == row.voyage].head(1).iloc[0].actual_lat, axis=1)\n",
    "voyages_winter['start_lon'] = voyages_winter.apply(lambda row: winter_results_5km_adj_no_dirway[winter_results_5km_adj_no_dirway.voyage == row.voyage].head(1).iloc[0].actual_lon, axis=1)\n",
    "voyages_winter['start_sea_area'] = voyages_winter.apply(lambda row: get_sea_area_from_latlon(sea_areas, row.start_lat, row.start_lon), axis=1)\n",
    "voyages_winter['start_port'] = voyages_summer.apply(lambda row: portcalls.get_port_id(row, ports), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing median results of different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>mins_to_future</th>\n",
       "      <th>acc_distance_nm</th>\n",
       "      <th>correct_node</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>716.4666666666667</td>\n",
       "      <td>13.719029596672332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>summer_results_5km_ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>754.85</td>\n",
       "      <td>15.263242325973387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>summer_results_5km_adj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>784.6333333333332</td>\n",
       "      <td>15.737912061138902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>summer_results_2500m_adj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1570.8333333333333</td>\n",
       "      <td>24.29263770063627</td>\n",
       "      <td>0.0</td>\n",
       "      <td>winter_results_5km_adj_no_dirway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1588.95</td>\n",
       "      <td>25.642677561746712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>winter_results_5km_adj_dirway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1510.0333333333333</td>\n",
       "      <td>25.715536712968166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>winter_results_5km_ship_dirway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1520.2</td>\n",
       "      <td>26.725314098169125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>winter_results_2500m_adj_no_dirway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1542.6</td>\n",
       "      <td>27.44212143562052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>winter_results_2500m_adj_dirway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1495.0</td>\n",
       "      <td>28.079261895082464</td>\n",
       "      <td>0.0</td>\n",
       "      <td>winter_results_5km_ship_no_dirway</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mins_to_future     acc_distance_nm correct_node  \\\n",
       "1   716.4666666666667  13.719029596672332          0.0   \n",
       "0              754.85  15.263242325973387          0.0   \n",
       "2   784.6333333333332  15.737912061138902          0.0   \n",
       "3  1570.8333333333333   24.29263770063627          0.0   \n",
       "4             1588.95  25.642677561746712          0.0   \n",
       "6  1510.0333333333333  25.715536712968166          0.0   \n",
       "7              1520.2  26.725314098169125          0.0   \n",
       "8              1542.6   27.44212143562052          0.0   \n",
       "5              1495.0  28.079261895082464          0.0   \n",
       "\n",
       "                                model  \n",
       "1             summer_results_5km_ship  \n",
       "0              summer_results_5km_adj  \n",
       "2            summer_results_2500m_adj  \n",
       "3    winter_results_5km_adj_no_dirway  \n",
       "4       winter_results_5km_adj_dirway  \n",
       "6      winter_results_5km_ship_dirway  \n",
       "7  winter_results_2500m_adj_no_dirway  \n",
       "8     winter_results_2500m_adj_dirway  \n",
       "5   winter_results_5km_ship_no_dirway  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = []\n",
    "results.append(np.append(summer_results_5km_adj.describe().loc[['50%'], ['mins_to_future', 'acc_distance_nm', 'correct_node']].values, 'summer_results_5km_adj'))\n",
    "results.append(np.append(summer_results_5km_ship.describe().loc[['50%'], ['mins_to_future', 'acc_distance_nm', 'correct_node']].values, 'summer_results_5km_ship'))\n",
    "results.append(np.append(summer_results_2500m_adj.describe().loc[['50%'], ['mins_to_future', 'acc_distance_nm', 'correct_node']].values, 'summer_results_2500m_adj'))\n",
    "\n",
    "results.append(np.append(winter_results_5km_adj_no_dirway.describe().loc[['50%'], ['mins_to_future', 'acc_distance_nm', 'correct_node']].values, 'winter_results_5km_adj_no_dirway'))\n",
    "results.append(np.append(winter_results_5km_adj_dirway.describe().loc[['50%'], ['mins_to_future', 'acc_distance_nm', 'correct_node']].values, 'winter_results_5km_adj_dirway'))\n",
    "results.append(np.append(winter_results_5km_ship_no_dirway.describe().loc[['50%'], ['mins_to_future', 'acc_distance_nm', 'correct_node']].values, 'winter_results_5km_ship_no_dirway'))\n",
    "results.append(np.append(winter_results_5km_ship_dirway.describe().loc[['50%'], ['mins_to_future', 'acc_distance_nm', 'correct_node']].values, 'winter_results_5km_ship_dirway'))\n",
    "results.append(np.append(winter_results_2500m_adj_no_dirway.describe().loc[['50%'], ['mins_to_future', 'acc_distance_nm', 'correct_node']].values, 'winter_results_2500m_adj_no_dirway'))\n",
    "results.append(np.append(winter_results_2500m_adj_dirway.describe().loc[['50%'], ['mins_to_future', 'acc_distance_nm', 'correct_node']].values, 'winter_results_2500m_adj_dirway'))\n",
    "\n",
    "results = pd.DataFrame(data=results, columns=['mins_to_future', 'acc_distance_nm', 'correct_node', 'model'])\n",
    "results.sort_values(by='acc_distance_nm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summer result examination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 16s, sys: 1.07 s, total: 1min 17s\n",
      "Wall time: 1min 27s\n"
     ]
    },
    {
     "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",
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       "    }\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>voyage</th>\n",
       "      <th>end_port</th>\n",
       "      <th>end_port_sea_area</th>\n",
       "      <th>start_port</th>\n",
       "      <th>start_sea_area</th>\n",
       "      <th>median_acc_distance_nm</th>\n",
       "      <th>mean_acc_distance_nm</th>\n",
       "      <th>median_mins_to_future</th>\n",
       "      <th>mean_mins_to_future</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>330.000000</td>\n",
       "      <td>3.300000e+02</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>2.800000e+02</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3299.927273</td>\n",
       "      <td>1.045565e+06</td>\n",
       "      <td>6.090909</td>\n",
       "      <td>8.433189e+05</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>17.827735</td>\n",
       "      <td>17.548266</td>\n",
       "      <td>642.407828</td>\n",
       "      <td>640.686367</td>\n",
       "      <td>516.031082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1756.757853</td>\n",
       "      <td>2.493300e+06</td>\n",
       "      <td>4.083949</td>\n",
       "      <td>2.438744e+06</td>\n",
       "      <td>77.179366</td>\n",
       "      <td>19.305590</td>\n",
       "      <td>18.217136</td>\n",
       "      <td>459.301232</td>\n",
       "      <td>452.067479</td>\n",
       "      <td>343.622339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>9.230000e+02</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>9.150000e+02</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.187049</td>\n",
       "      <td>0.174912</td>\n",
       "      <td>26.966667</td>\n",
       "      <td>27.228788</td>\n",
       "      <td>9.368543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2254.250000</td>\n",
       "      <td>1.007000e+03</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.750000e+02</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.655007</td>\n",
       "      <td>6.139788</td>\n",
       "      <td>290.387500</td>\n",
       "      <td>293.152047</td>\n",
       "      <td>284.000496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2996.000000</td>\n",
       "      <td>2.345000e+03</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.051000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11.379913</td>\n",
       "      <td>11.195742</td>\n",
       "      <td>536.741667</td>\n",
       "      <td>533.535148</td>\n",
       "      <td>404.163412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4895.000000</td>\n",
       "      <td>1.976225e+04</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.411950e+04</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>22.472049</td>\n",
       "      <td>22.159794</td>\n",
       "      <td>832.081250</td>\n",
       "      <td>859.208609</td>\n",
       "      <td>635.502617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6576.000000</td>\n",
       "      <td>8.616642e+06</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.583330e+07</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>142.514841</td>\n",
       "      <td>128.089890</td>\n",
       "      <td>2687.983333</td>\n",
       "      <td>2594.233273</td>\n",
       "      <td>2029.400648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            voyage      end_port  end_port_sea_area    start_port  \\\n",
       "count   330.000000  3.300000e+02         330.000000  2.800000e+02   \n",
       "mean   3299.927273  1.045565e+06           6.090909  8.433189e+05   \n",
       "std    1756.757853  2.493300e+06           4.083949  2.438744e+06   \n",
       "min      14.000000  9.230000e+02          -1.000000  9.150000e+02   \n",
       "25%    2254.250000  1.007000e+03           3.000000  9.750000e+02   \n",
       "50%    2996.000000  2.345000e+03           6.000000  1.051000e+03   \n",
       "75%    4895.000000  1.976225e+04          10.000000  1.411950e+04   \n",
       "max    6576.000000  8.616642e+06          13.000000  1.583330e+07   \n",
       "\n",
       "       start_sea_area  median_acc_distance_nm  mean_acc_distance_nm  \\\n",
       "count      330.000000              330.000000            330.000000   \n",
       "mean        13.333333               17.827735             17.548266   \n",
       "std         77.179366               19.305590             18.217136   \n",
       "min         -1.000000                0.187049              0.174912   \n",
       "25%          5.000000                5.655007              6.139788   \n",
       "50%          8.000000               11.379913             11.195742   \n",
       "75%         11.000000               22.472049             22.159794   \n",
       "max        999.000000              142.514841            128.089890   \n",
       "\n",
       "       median_mins_to_future  mean_mins_to_future       length  \n",
       "count             330.000000           330.000000   330.000000  \n",
       "mean              642.407828           640.686367   516.031082  \n",
       "std               459.301232           452.067479   343.622339  \n",
       "min                26.966667            27.228788     9.368543  \n",
       "25%               290.387500           293.152047   284.000496  \n",
       "50%               536.741667           533.535148   404.163412  \n",
       "75%               832.081250           859.208609   635.502617  \n",
       "max              2687.983333          2594.233273  2029.400648  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append start ports to summer_results_5km_ship\n",
    "\n",
    "%time summer_results_5km_ship['start_port_sea_area'] = summer_results_5km_ship.apply(lambda row: voyages_summer[voyages_summer.voyage == row.voyage].iloc[0].start_sea_area, axis=1)\n",
    "\n",
    "acc_by_voyage = []\n",
    "\n",
    "for index, row in voyages_summer.iterrows():\n",
    "    voyage_results = summer_results_5km_ship[summer_results_5km_ship.voyage == row.voyage]\n",
    "    \n",
    "    voyage_results = voyage_results.describe().loc[['mean', '50%', 'std'], ['mins_to_future', 'acc_distance_nm', 'correct_node']]\n",
    "    length = voyages_summer_lengths[voyages_summer_lengths.voyage == row.voyage].iloc[0].voyage_length\n",
    "    start_port = voyages_summer[voyages_summer.voyage == row.voyage].iloc[0].start_port\n",
    "    acc_by_voyage.append([row.voyage, row.vessel_type, row.end_port, row.end_port_sea_area, start_port, row.start_sea_area, voyage_results.loc['50%', 'acc_distance_nm'], voyage_results.loc['mean', 'acc_distance_nm'], voyage_results.loc['50%', 'mins_to_future'], voyage_results.loc['mean', 'mins_to_future'], length])\n",
    "    \n",
    "acc_by_voyage = pd.DataFrame(data=acc_by_voyage, columns=['voyage', 'vessel_type', 'end_port', 'end_port_sea_area', 'start_port', 'start_sea_area', 'median_acc_distance_nm', 'mean_acc_distance_nm', 'median_mins_to_future', 'mean_mins_to_future', 'length'])\n",
    "acc_by_voyage.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc_by_voyage[['mean_acc_distance_nm']].hist(figsize=(10,10), bins=20, grid=False, xlabelsize=15, ylabelsize=15)\n",
    "plt.title('')\n",
    "plt.xticks(np.arange(0, 150, 15)) \n",
    "plt.yticks(np.arange(0, 90, 10)) \n",
    "plt.xlabel(\"Mean error (nm) of voyage\", fontsize=23)\n",
    "plt.ylabel(\"Frequency\",fontsize=23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of summer bad voyages = 111\n"
     ]
    }
   ],
   "source": [
    "acc_by_route = acc_by_voyage.groupby(['start_port', 'end_port'], as_index=False).mean()\n",
    "bad_routes = acc_by_route[acc_by_route.median_acc_distance_nm > 25]\n",
    "bad_voyages = acc_by_voyage[acc_by_voyage.start_port.isin(bad_routes.start_port) & acc_by_voyage.end_port.isin(bad_routes.end_port)].voyage.values\n",
    "print('Number of summer bad voyages =', len(bad_voyages))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results by different routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_port</th>\n",
       "      <th>end_port</th>\n",
       "      <th>voyage</th>\n",
       "      <th>end_port_sea_area</th>\n",
       "      <th>start_sea_area</th>\n",
       "      <th>median_acc_distance_nm</th>\n",
       "      <th>mean_acc_distance_nm</th>\n",
       "      <th>median_mins_to_future</th>\n",
       "      <th>mean_mins_to_future</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1043.0</td>\n",
       "      <td>7546455</td>\n",
       "      <td>4425.285714</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.276629</td>\n",
       "      <td>0.345191</td>\n",
       "      <td>34.972619</td>\n",
       "      <td>35.050886</td>\n",
       "      <td>11.598525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>7546470.0</td>\n",
       "      <td>7546473</td>\n",
       "      <td>2289.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.919550</td>\n",
       "      <td>0.921423</td>\n",
       "      <td>123.452083</td>\n",
       "      <td>123.434149</td>\n",
       "      <td>73.543277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>7546470.0</td>\n",
       "      <td>7546485</td>\n",
       "      <td>2257.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.820975</td>\n",
       "      <td>2.228836</td>\n",
       "      <td>331.437500</td>\n",
       "      <td>330.788532</td>\n",
       "      <td>174.273163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1043.0</td>\n",
       "      <td>951</td>\n",
       "      <td>1582.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.513912</td>\n",
       "      <td>4.135208</td>\n",
       "      <td>647.666667</td>\n",
       "      <td>650.719364</td>\n",
       "      <td>361.032081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>979.0</td>\n",
       "      <td>967</td>\n",
       "      <td>5702.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.634311</td>\n",
       "      <td>3.662950</td>\n",
       "      <td>788.591667</td>\n",
       "      <td>789.010658</td>\n",
       "      <td>557.083198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>3108068.0</td>\n",
       "      <td>1013</td>\n",
       "      <td>3543.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.768965</td>\n",
       "      <td>4.530031</td>\n",
       "      <td>429.258333</td>\n",
       "      <td>427.710654</td>\n",
       "      <td>336.574701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>7546455.0</td>\n",
       "      <td>7546473</td>\n",
       "      <td>4668.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.272348</td>\n",
       "      <td>3.717961</td>\n",
       "      <td>392.400000</td>\n",
       "      <td>391.589669</td>\n",
       "      <td>219.361094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>953.0</td>\n",
       "      <td>2345</td>\n",
       "      <td>3581.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.471620</td>\n",
       "      <td>3.528142</td>\n",
       "      <td>525.650000</td>\n",
       "      <td>516.433235</td>\n",
       "      <td>324.356839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1182.0</td>\n",
       "      <td>2803</td>\n",
       "      <td>3617.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.507708</td>\n",
       "      <td>4.121115</td>\n",
       "      <td>477.400000</td>\n",
       "      <td>450.398111</td>\n",
       "      <td>273.146546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>979.0</td>\n",
       "      <td>24228</td>\n",
       "      <td>5958.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.548798</td>\n",
       "      <td>3.735759</td>\n",
       "      <td>398.650000</td>\n",
       "      <td>404.829487</td>\n",
       "      <td>287.499618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     start_port  end_port       voyage  end_port_sea_area  start_sea_area  \\\n",
       "69       1043.0   7546455  4425.285714               13.0            13.0   \n",
       "171   7546470.0   7546473  2289.000000               13.0            13.0   \n",
       "172   7546470.0   7546485  2257.000000               13.0            13.0   \n",
       "68       1043.0       951  1582.000000                5.0            13.0   \n",
       "43        979.0       967  5702.000000                3.0             5.0   \n",
       "159   3108068.0      1013  3543.000000                4.0             5.0   \n",
       "168   7546455.0   7546473  4668.000000               13.0            13.0   \n",
       "33        953.0      2345  3581.000000               10.0             8.0   \n",
       "107      1182.0      2803  3617.000000               11.0            11.0   \n",
       "45        979.0     24228  5958.000000                7.0             5.0   \n",
       "\n",
       "     median_acc_distance_nm  mean_acc_distance_nm  median_mins_to_future  \\\n",
       "69                 0.276629              0.345191              34.972619   \n",
       "171                0.919550              0.921423             123.452083   \n",
       "172                1.820975              2.228836             331.437500   \n",
       "68                 2.513912              4.135208             647.666667   \n",
       "43                 2.634311              3.662950             788.591667   \n",
       "159                2.768965              4.530031             429.258333   \n",
       "168                3.272348              3.717961             392.400000   \n",
       "33                 3.471620              3.528142             525.650000   \n",
       "107                3.507708              4.121115             477.400000   \n",
       "45                 3.548798              3.735759             398.650000   \n",
       "\n",
       "     mean_mins_to_future      length  \n",
       "69             35.050886   11.598525  \n",
       "171           123.434149   73.543277  \n",
       "172           330.788532  174.273163  \n",
       "68            650.719364  361.032081  \n",
       "43            789.010658  557.083198  \n",
       "159           427.710654  336.574701  \n",
       "168           391.589669  219.361094  \n",
       "33            516.433235  324.356839  \n",
       "107           450.398111  273.146546  \n",
       "45            404.829487  287.499618  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 10 Best predicted routes\n",
    "acc_by_route.sort_values(by='median_acc_distance_nm').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_port</th>\n",
       "      <th>end_port</th>\n",
       "      <th>voyage</th>\n",
       "      <th>end_port_sea_area</th>\n",
       "      <th>start_sea_area</th>\n",
       "      <th>median_acc_distance_nm</th>\n",
       "      <th>mean_acc_distance_nm</th>\n",
       "      <th>median_mins_to_future</th>\n",
       "      <th>mean_mins_to_future</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>24228.0</td>\n",
       "      <td>977</td>\n",
       "      <td>3491.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>97.560470</td>\n",
       "      <td>76.593302</td>\n",
       "      <td>778.750000</td>\n",
       "      <td>764.923966</td>\n",
       "      <td>668.300710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1081.0</td>\n",
       "      <td>935</td>\n",
       "      <td>6502.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>91.337493</td>\n",
       "      <td>80.551126</td>\n",
       "      <td>2687.983333</td>\n",
       "      <td>2594.233273</td>\n",
       "      <td>2029.400648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>8886.0</td>\n",
       "      <td>1242</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>89.717544</td>\n",
       "      <td>79.364666</td>\n",
       "      <td>813.700000</td>\n",
       "      <td>834.010256</td>\n",
       "      <td>688.375756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>937.0</td>\n",
       "      <td>2345</td>\n",
       "      <td>4581.500000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>85.596240</td>\n",
       "      <td>75.150600</td>\n",
       "      <td>588.225000</td>\n",
       "      <td>582.712146</td>\n",
       "      <td>720.973072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>953.0</td>\n",
       "      <td>1013</td>\n",
       "      <td>3663.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>84.797563</td>\n",
       "      <td>79.289357</td>\n",
       "      <td>793.333333</td>\n",
       "      <td>751.855732</td>\n",
       "      <td>702.830093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>953.0</td>\n",
       "      <td>977</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>78.178722</td>\n",
       "      <td>76.036897</td>\n",
       "      <td>1132.183333</td>\n",
       "      <td>1105.121130</td>\n",
       "      <td>904.444474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1083.0</td>\n",
       "      <td>3347</td>\n",
       "      <td>1970.333333</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>74.978014</td>\n",
       "      <td>74.251207</td>\n",
       "      <td>823.477778</td>\n",
       "      <td>894.872474</td>\n",
       "      <td>1102.493112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1029.0</td>\n",
       "      <td>999</td>\n",
       "      <td>2617.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>73.456862</td>\n",
       "      <td>65.320086</td>\n",
       "      <td>1305.833333</td>\n",
       "      <td>1302.488616</td>\n",
       "      <td>1073.752750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>929.0</td>\n",
       "      <td>4081153</td>\n",
       "      <td>3129.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>72.889488</td>\n",
       "      <td>70.439491</td>\n",
       "      <td>1639.241667</td>\n",
       "      <td>1617.282471</td>\n",
       "      <td>1112.094828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>25182.0</td>\n",
       "      <td>935</td>\n",
       "      <td>3126.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>68.746026</td>\n",
       "      <td>66.565021</td>\n",
       "      <td>2366.533333</td>\n",
       "      <td>2230.838366</td>\n",
       "      <td>1936.563744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     start_port  end_port       voyage  end_port_sea_area  start_sea_area  \\\n",
       "135     24228.0       977  3491.000000                5.0             7.0   \n",
       "93       1081.0       935  6502.000000                1.0            -1.0   \n",
       "122      8886.0      1242   971.000000               10.0            11.0   \n",
       "21        937.0      2345  4581.500000               10.0            11.0   \n",
       "32        953.0      1013  3663.000000                4.0             8.0   \n",
       "31        953.0       977  1450.000000                5.0             8.0   \n",
       "94       1083.0      3347  1970.333333               -1.0             3.0   \n",
       "66       1029.0       999  2617.000000                1.0             8.0   \n",
       "17        929.0   4081153  3129.000000                3.0            11.0   \n",
       "140     25182.0       935  3126.000000                1.0            -1.0   \n",
       "\n",
       "     median_acc_distance_nm  mean_acc_distance_nm  median_mins_to_future  \\\n",
       "135               97.560470             76.593302             778.750000   \n",
       "93                91.337493             80.551126            2687.983333   \n",
       "122               89.717544             79.364666             813.700000   \n",
       "21                85.596240             75.150600             588.225000   \n",
       "32                84.797563             79.289357             793.333333   \n",
       "31                78.178722             76.036897            1132.183333   \n",
       "94                74.978014             74.251207             823.477778   \n",
       "66                73.456862             65.320086            1305.833333   \n",
       "17                72.889488             70.439491            1639.241667   \n",
       "140               68.746026             66.565021            2366.533333   \n",
       "\n",
       "     mean_mins_to_future       length  \n",
       "135           764.923966   668.300710  \n",
       "93           2594.233273  2029.400648  \n",
       "122           834.010256   688.375756  \n",
       "21            582.712146   720.973072  \n",
       "32            751.855732   702.830093  \n",
       "31           1105.121130   904.444474  \n",
       "94            894.872474  1102.493112  \n",
       "66           1302.488616  1073.752750  \n",
       "17           1617.282471  1112.094828  \n",
       "140          2230.838366  1936.563744  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 10 Worst predicted routes\n",
    "acc_by_route.sort_values(by='median_acc_distance_nm', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Winter result examination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>voyage</th>\n",
       "      <th>end_port</th>\n",
       "      <th>end_port_sea_area</th>\n",
       "      <th>start_port</th>\n",
       "      <th>start_sea_area</th>\n",
       "      <th>median_acc_distance_nm</th>\n",
       "      <th>mean_acc_distance_nm</th>\n",
       "      <th>median_mins_to_future</th>\n",
       "      <th>mean_mins_to_future</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>310.728395</td>\n",
       "      <td>4.187657e+04</td>\n",
       "      <td>1.530864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.172840</td>\n",
       "      <td>31.290260</td>\n",
       "      <td>32.006126</td>\n",
       "      <td>1353.757716</td>\n",
       "      <td>1360.127769</td>\n",
       "      <td>1114.098253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>181.453301</td>\n",
       "      <td>3.342383e+05</td>\n",
       "      <td>2.627196</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.787447</td>\n",
       "      <td>28.495452</td>\n",
       "      <td>27.464566</td>\n",
       "      <td>704.439667</td>\n",
       "      <td>703.059341</td>\n",
       "      <td>594.746713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.310000e+02</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.154120</td>\n",
       "      <td>1.545767</td>\n",
       "      <td>103.458333</td>\n",
       "      <td>103.806250</td>\n",
       "      <td>80.657340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>143.000000</td>\n",
       "      <td>9.990000e+02</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.543669</td>\n",
       "      <td>13.025306</td>\n",
       "      <td>725.683333</td>\n",
       "      <td>727.582357</td>\n",
       "      <td>534.406915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>341.000000</td>\n",
       "      <td>1.073000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.566662</td>\n",
       "      <td>23.450054</td>\n",
       "      <td>1458.616667</td>\n",
       "      <td>1528.282250</td>\n",
       "      <td>1138.545480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>438.000000</td>\n",
       "      <td>6.652000e+03</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.027634</td>\n",
       "      <td>40.488257</td>\n",
       "      <td>1803.516667</td>\n",
       "      <td>1911.968476</td>\n",
       "      <td>1674.517523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>627.000000</td>\n",
       "      <td>3.012222e+06</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>134.201452</td>\n",
       "      <td>134.266838</td>\n",
       "      <td>2632.566667</td>\n",
       "      <td>2536.528345</td>\n",
       "      <td>1966.384680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           voyage      end_port  end_port_sea_area  start_port  \\\n",
       "count   81.000000  8.100000e+01          81.000000         0.0   \n",
       "mean   310.728395  4.187657e+04           1.530864         NaN   \n",
       "std    181.453301  3.342383e+05           2.627196         NaN   \n",
       "min      5.000000  9.310000e+02          -1.000000         NaN   \n",
       "25%    143.000000  9.990000e+02          -1.000000         NaN   \n",
       "50%    341.000000  1.073000e+03           1.000000         NaN   \n",
       "75%    438.000000  6.652000e+03           2.000000         NaN   \n",
       "max    627.000000  3.012222e+06           9.000000         NaN   \n",
       "\n",
       "       start_sea_area  median_acc_distance_nm  mean_acc_distance_nm  \\\n",
       "count       81.000000               81.000000             81.000000   \n",
       "mean         3.172840               31.290260             32.006126   \n",
       "std          3.787447               28.495452             27.464566   \n",
       "min         -1.000000                1.154120              1.545767   \n",
       "25%          1.000000               11.543669             13.025306   \n",
       "50%          1.000000               21.566662             23.450054   \n",
       "75%          3.000000               39.027634             40.488257   \n",
       "max         11.000000              134.201452            134.266838   \n",
       "\n",
       "       median_mins_to_future  mean_mins_to_future       length  \n",
       "count              81.000000            81.000000    81.000000  \n",
       "mean             1353.757716          1360.127769  1114.098253  \n",
       "std               704.439667           703.059341   594.746713  \n",
       "min               103.458333           103.806250    80.657340  \n",
       "25%               725.683333           727.582357   534.406915  \n",
       "50%              1458.616667          1528.282250  1138.545480  \n",
       "75%              1803.516667          1911.968476  1674.517523  \n",
       "max              2632.566667          2536.528345  1966.384680  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_by_voyage_winter = []\n",
    "for index, row in voyages_winter.iterrows():\n",
    "    voyage_results = winter_results_5km_ship_dirway[winter_results_5km_ship_dirway.voyage == row.voyage]\n",
    "    \n",
    "    voyage_results = voyage_results.describe().loc[['mean', '50%', 'std'], ['mins_to_future', 'acc_distance_nm', 'correct_node']]\n",
    "    length = voyages_winter_lengths[voyages_winter_lengths.voyage == row.voyage].iloc[0].voyage_length\n",
    "    start_port = voyages_winter[voyages_winter.voyage == row.voyage].iloc[0].start_port\n",
    "    acc_by_voyage_winter.append([row.voyage, row.vessel_type, row.end_port, row.end_port_sea_area, start_port, row.start_sea_area, voyage_results.loc['50%', 'acc_distance_nm'], voyage_results.loc['mean', 'acc_distance_nm'], voyage_results.loc['50%', 'mins_to_future'], voyage_results.loc['mean', 'mins_to_future'], length])\n",
    "    \n",
    "acc_by_voyage_winter = pd.DataFrame(data=acc_by_voyage_winter, columns=['voyage', 'vessel_type', 'end_port', 'end_port_sea_area', 'start_port', 'start_sea_area', 'median_acc_distance_nm', 'mean_acc_distance_nm', 'median_mins_to_future', 'mean_mins_to_future', 'length'])\n",
    "acc_by_voyage_winter.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        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>start_sea_area</th>\n",
       "      <th>end_port</th>\n",
       "      <th>voyage</th>\n",
       "      <th>end_port_sea_area</th>\n",
       "      <th>start_port</th>\n",
       "      <th>median_acc_distance_nm</th>\n",
       "      <th>mean_acc_distance_nm</th>\n",
       "      <th>median_mins_to_future</th>\n",
       "      <th>mean_mins_to_future</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>1047</td>\n",
       "      <td>575.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.154120</td>\n",
       "      <td>1.545767</td>\n",
       "      <td>103.458333</td>\n",
       "      <td>103.806250</td>\n",
       "      <td>80.657340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>959</td>\n",
       "      <td>382.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.837414</td>\n",
       "      <td>2.817017</td>\n",
       "      <td>142.933333</td>\n",
       "      <td>143.772727</td>\n",
       "      <td>103.952690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>1109</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.360111</td>\n",
       "      <td>3.706977</td>\n",
       "      <td>681.250000</td>\n",
       "      <td>677.741862</td>\n",
       "      <td>478.823139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>1073</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.752657</td>\n",
       "      <td>4.548791</td>\n",
       "      <td>123.966667</td>\n",
       "      <td>123.303546</td>\n",
       "      <td>112.534701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>935</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.221587</td>\n",
       "      <td>16.334239</td>\n",
       "      <td>824.350000</td>\n",
       "      <td>783.154426</td>\n",
       "      <td>566.226600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>1041</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.870667</td>\n",
       "      <td>9.389512</td>\n",
       "      <td>555.416667</td>\n",
       "      <td>557.564794</td>\n",
       "      <td>426.519036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>11</td>\n",
       "      <td>999</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.732660</td>\n",
       "      <td>12.249679</td>\n",
       "      <td>2420.500000</td>\n",
       "      <td>2422.516938</td>\n",
       "      <td>1778.804769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>17602</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.038882</td>\n",
       "      <td>7.797875</td>\n",
       "      <td>1544.783333</td>\n",
       "      <td>1553.986034</td>\n",
       "      <td>1159.349451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>931</td>\n",
       "      <td>623.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.157920</td>\n",
       "      <td>7.437350</td>\n",
       "      <td>1759.150000</td>\n",
       "      <td>1691.948614</td>\n",
       "      <td>1138.545480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>975</td>\n",
       "      <td>302.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.175998</td>\n",
       "      <td>10.758505</td>\n",
       "      <td>325.208333</td>\n",
       "      <td>327.810972</td>\n",
       "      <td>253.071202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    start_sea_area  end_port      voyage  end_port_sea_area  start_port  \\\n",
       "13               1      1047  575.000000                1.0         NaN   \n",
       "6                1       959  382.000000                1.0         NaN   \n",
       "18               1      1109  122.000000                3.0         NaN   \n",
       "17               1      1073   46.000000                1.0         NaN   \n",
       "26               3       935    5.000000                1.0         NaN   \n",
       "12               1      1041  119.000000                3.0         NaN   \n",
       "36              11       999  364.000000                1.0         NaN   \n",
       "22               1     17602   89.000000                7.0         NaN   \n",
       "2                1       931  623.000000                5.0         NaN   \n",
       "8                1       975  302.333333                1.0         NaN   \n",
       "\n",
       "    median_acc_distance_nm  mean_acc_distance_nm  median_mins_to_future  \\\n",
       "13                1.154120              1.545767             103.458333   \n",
       "6                 2.837414              2.817017             142.933333   \n",
       "18                3.360111              3.706977             681.250000   \n",
       "17                3.752657              4.548791             123.966667   \n",
       "26                4.221587             16.334239             824.350000   \n",
       "12                4.870667              9.389512             555.416667   \n",
       "36                6.732660             12.249679            2420.500000   \n",
       "22                7.038882              7.797875            1544.783333   \n",
       "2                 8.157920              7.437350            1759.150000   \n",
       "8                 9.175998             10.758505             325.208333   \n",
       "\n",
       "    mean_mins_to_future       length  \n",
       "13           103.806250    80.657340  \n",
       "6            143.772727   103.952690  \n",
       "18           677.741862   478.823139  \n",
       "17           123.303546   112.534701  \n",
       "26           783.154426   566.226600  \n",
       "12           557.564794   426.519036  \n",
       "36          2422.516938  1778.804769  \n",
       "22          1553.986034  1159.349451  \n",
       "2           1691.948614  1138.545480  \n",
       "8            327.810972   253.071202  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_by_route = acc_by_voyage_winter.groupby(['start_sea_area', 'end_port'], as_index=False).mean()\n",
    "acc_by_route.sort_values(by='median_acc_distance_nm').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results by sea areas in summer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sea_area_accuracies = []\n",
    "for sea_area in summer_results_5km_ship.end_port_sea_area.drop_duplicates().values:\n",
    "    sea_area_results = summer_results_5km_ship[(summer_results_5km_ship.start_port_sea_area == sea_area) | (summer_results_5km_ship.end_port_sea_area == sea_area)]\n",
    "    count = len(summer_results_5km_ship[(summer_results_5km_ship.start_port_sea_area == sea_area)].voyage.drop_duplicates())\n",
    "    count += len(summer_results_5km_ship[(summer_results_5km_ship.end_port_sea_area == sea_area)].voyage.drop_duplicates())\n",
    "    accuracy = sea_area_results.describe().loc[['mean', '50%', 'std'], ['mins_to_future', 'acc_distance_nm', 'correct_node']]\n",
    "    \n",
    "    sea_area_accuracies.append([sea_area, accuracy.loc['50%', 'acc_distance_nm'], accuracy.loc['mean', 'acc_distance_nm'], accuracy.loc['std', 'acc_distance_nm'], accuracy.loc['50%', 'mins_to_future'], accuracy.loc['mean', 'mins_to_future'], accuracy.loc['std', 'mins_to_future'], count])\n",
    "\n",
    "sea_area_accuracies = pd.DataFrame(data=sea_area_accuracies, columns=['sea_area', 'median_acc_distance_nm', 'mean_acc_distance_nm', 'std_acc_distance_nm', 'median_mins_to_future', 'mean_mins_to_future', 'std_mins_to_future', 'count'])\n",
    "sea_area_accuracies = sea_area_accuracies.sort_values(by='median_acc_distance_nm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sea_area_name</th>\n",
       "      <th>median_acc_distance_nm</th>\n",
       "      <th>mean_acc_distance_nm</th>\n",
       "      <th>std_acc_distance_nm</th>\n",
       "      <th>median_mins_to_future</th>\n",
       "      <th>mean_mins_to_future</th>\n",
       "      <th>std_mins_to_future</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saimaa</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>372.7</td>\n",
       "      <td>307.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Åland Sea</td>\n",
       "      <td>10.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>527.8</td>\n",
       "      <td>628.4</td>\n",
       "      <td>465.9</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gulf of Finland</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>664.6</td>\n",
       "      <td>819.6</td>\n",
       "      <td>647.5</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gulf of Riga</td>\n",
       "      <td>11.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>794.4</td>\n",
       "      <td>873.6</td>\n",
       "      <td>594.1</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bothnian Sea &amp; The Quark</td>\n",
       "      <td>11.9</td>\n",
       "      <td>23.1</td>\n",
       "      <td>27.9</td>\n",
       "      <td>896.8</td>\n",
       "      <td>1034.1</td>\n",
       "      <td>758.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gotland Basin</td>\n",
       "      <td>12.6</td>\n",
       "      <td>21.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>584.0</td>\n",
       "      <td>760.3</td>\n",
       "      <td>619.8</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Northern Baltic Proper</td>\n",
       "      <td>15.1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>18.1</td>\n",
       "      <td>406.8</td>\n",
       "      <td>582.7</td>\n",
       "      <td>533.3</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arkona Basin &amp; Bomholm Basin</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>828.7</td>\n",
       "      <td>1089.7</td>\n",
       "      <td>923.7</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gdansk Basin</td>\n",
       "      <td>17.5</td>\n",
       "      <td>25.3</td>\n",
       "      <td>27.8</td>\n",
       "      <td>618.6</td>\n",
       "      <td>813.5</td>\n",
       "      <td>711.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>South</td>\n",
       "      <td>18.8</td>\n",
       "      <td>33.8</td>\n",
       "      <td>33.8</td>\n",
       "      <td>839.5</td>\n",
       "      <td>1138.9</td>\n",
       "      <td>1006.5</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bothnian Bay</td>\n",
       "      <td>28.3</td>\n",
       "      <td>38.1</td>\n",
       "      <td>33.8</td>\n",
       "      <td>1311.2</td>\n",
       "      <td>1531.9</td>\n",
       "      <td>1104.9</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sea_area_name  median_acc_distance_nm  \\\n",
       "10                        Saimaa                     2.8   \n",
       "7                      Åland Sea                    10.8   \n",
       "4                Gulf of Finland                    11.0   \n",
       "2                   Gulf of Riga                    11.7   \n",
       "9       Bothnian Sea & The Quark                    11.9   \n",
       "6                  Gotland Basin                    12.6   \n",
       "8         Northern Baltic Proper                    15.1   \n",
       "5   Arkona Basin & Bomholm Basin                    15.6   \n",
       "3                   Gdansk Basin                    17.5   \n",
       "0                          South                    18.8   \n",
       "1                   Bothnian Bay                    28.3   \n",
       "\n",
       "    mean_acc_distance_nm  std_acc_distance_nm  median_mins_to_future  \\\n",
       "10                   4.4                  4.4                  304.5   \n",
       "7                   18.0                 19.9                  527.8   \n",
       "4                   18.8                 22.2                  664.6   \n",
       "2                   18.0                 19.5                  794.4   \n",
       "9                   23.1                 27.9                  896.8   \n",
       "6                   21.1                 22.4                  584.0   \n",
       "8                   19.6                 18.1                  406.8   \n",
       "5                   26.0                 29.0                  828.7   \n",
       "3                   25.3                 27.8                  618.6   \n",
       "0                   33.8                 33.8                  839.5   \n",
       "1                   38.1                 33.8                 1311.2   \n",
       "\n",
       "    mean_mins_to_future  std_mins_to_future  count  \n",
       "10                372.7               307.0     60  \n",
       "7                 628.4               465.9     50  \n",
       "4                 819.6               647.5     86  \n",
       "2                 873.6               594.1     33  \n",
       "9                1034.1               758.0     56  \n",
       "6                 760.3               619.8     77  \n",
       "8                 582.7               533.3     47  \n",
       "5                1089.7               923.7     96  \n",
       "3                 813.5               711.0     66  \n",
       "0                1138.9              1006.5     49  \n",
       "1                1531.9              1104.9     38  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_area_accuracies['sea_area_name'] = sea_area_accuracies.apply(lambda row: get_sea_area_name(sea_areas, row.sea_area), axis=1)\n",
    "sea_area_accuracies = sea_area_accuracies[['sea_area_name', 'median_acc_distance_nm', 'mean_acc_distance_nm',  'std_acc_distance_nm', 'median_mins_to_future', 'mean_mins_to_future', 'std_mins_to_future', 'count']]\n",
    "sea_area_accuracies.round(1)#.to_excel('summer_tr_results.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_intervals(results, col_x, col_y, interval):\n",
    "    bins = []\n",
    "    labels = []\n",
    "\n",
    "    i = 0\n",
    "    while i < results[col_x].max():\n",
    "        bins.append(i)\n",
    "        labels.append(int(i))\n",
    "        i += interval\n",
    "    del labels[0]\n",
    "\n",
    "    results['interval'] = pd.cut(results[col_x], bins=bins, labels=labels)\n",
    "\n",
    "    x = [0]\n",
    "    y = [0]\n",
    "\n",
    "    for group, voyage_results in results.groupby('interval'):\n",
    "        if(len(voyage_results)) < 3 or group/60 > 72:\n",
    "            continue\n",
    "        y.append(voyage_results[col_y].mean())\n",
    "        if np.isnan(group):\n",
    "            group = 0\n",
    "        x.append(group/60)\n",
    "        \n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summer traffic prediction accuracy over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.interpolate import make_interp_spline, BSpline\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(30,15))\n",
    "plt.rcParams.update({'font.size': 36})\n",
    "\n",
    "x, y = plot_intervals(summer_results_5km_ship,'mins_to_future', 'acc_distance_nm', 60)\n",
    "plt.plot(x, y, alpha=1, linewidth=3, c='tab:red', label='Summer')\n",
    "\n",
    "x, y = plot_intervals(winter_results_5km_ship_dirway,'mins_to_future', 'acc_distance_nm', 60)\n",
    "plt.plot(x, y, alpha=1, linewidth=3, c='tab:blue', label='Winter (Dirways ON)')\n",
    "\n",
    "x, y = plot_intervals(winter_results_5km_ship_no_dirway,'mins_to_future', 'acc_distance_nm', 60)\n",
    "plt.plot(x, y, alpha=1, linewidth=3, c='tab:cyan', label='Winter (Dirways OFF')\n",
    "\n",
    "plt.xticks(np.arange(0, 75, 5)) \n",
    "plt.yticks(np.arange(0, 100, 5)) \n",
    "plt.grid(axis='y', alpha=1)\n",
    "plt.title(f\"Traffic Prediction Error over Prediction Time to Future\")\n",
    "plt.ylabel(\"Mean Error in Nautical Miles\")\n",
    "plt.xlabel(\"Time to Future in Hours\")\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "newLabels, newHandles = [], []\n",
    "for handle, label in zip(handles, labels):\n",
    "  if label not in newLabels:\n",
    "    newLabels.append(label)\n",
    "    newHandles.append(handle)\n",
    "plt.legend(newHandles, newLabels)\n",
    "plt.show()"
   ]
  },
  {
   "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>vessel_type</th>\n",
       "      <th>median_acc_distance_nm</th>\n",
       "      <th>mean_acc_distance_nm</th>\n",
       "      <th>std_acc_distance_nm</th>\n",
       "      <th>median_mins_to_future</th>\n",
       "      <th>mean_mins_to_future</th>\n",
       "      <th>std_mins_to_future</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PAS</td>\n",
       "      <td>8.5</td>\n",
       "      <td>14.9</td>\n",
       "      <td>21.1</td>\n",
       "      <td>309.8</td>\n",
       "      <td>349.2</td>\n",
       "      <td>255.5</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CONT</td>\n",
       "      <td>11.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>13.1</td>\n",
       "      <td>511.1</td>\n",
       "      <td>540.8</td>\n",
       "      <td>345.9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GC</td>\n",
       "      <td>13.4</td>\n",
       "      <td>24.2</td>\n",
       "      <td>27.9</td>\n",
       "      <td>865.9</td>\n",
       "      <td>1078.0</td>\n",
       "      <td>878.7</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T</td>\n",
       "      <td>16.7</td>\n",
       "      <td>27.8</td>\n",
       "      <td>25.6</td>\n",
       "      <td>844.6</td>\n",
       "      <td>1073.5</td>\n",
       "      <td>873.2</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RORO</td>\n",
       "      <td>24.1</td>\n",
       "      <td>35.3</td>\n",
       "      <td>34.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>944.8</td>\n",
       "      <td>613.2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  vessel_type  median_acc_distance_nm  mean_acc_distance_nm  \\\n",
       "2         PAS                     8.5                  14.9   \n",
       "0        CONT                    11.2                  14.8   \n",
       "1          GC                    13.4                  24.2   \n",
       "4           T                    16.7                  27.8   \n",
       "3        RORO                    24.1                  35.3   \n",
       "\n",
       "   std_acc_distance_nm  median_mins_to_future  mean_mins_to_future  \\\n",
       "2                 21.1                  309.8                349.2   \n",
       "0                 13.1                  511.1                540.8   \n",
       "1                 27.9                  865.9               1078.0   \n",
       "4                 25.6                  844.6               1073.5   \n",
       "3                 34.0                  879.0                944.8   \n",
       "\n",
       "   std_mins_to_future  count  \n",
       "2               255.5     96  \n",
       "0               345.9     11  \n",
       "1               878.7    159  \n",
       "4               873.2     43  \n",
       "3               613.2     21  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "vessel_type_accuracies = []\n",
    "for vessel_type, vessel_type_results in summer_results_5km_ship.groupby('vessel_type'):\n",
    "    #vessel_type_results = vessel_type_results[~vessel_type_results.voyage.isin(bad_voyages)]\n",
    "    count = len(vessel_type_results.voyage.drop_duplicates())\n",
    "    accuracy = vessel_type_results.describe().loc[['mean', '50%', 'std'], ['mins_to_future', 'acc_distance_nm', 'correct_node']]\n",
    "    \n",
    "    #voyage_lengths = actual_voyage_lengths[actual_voyage_lengths.voyage.isin(sea_area_results.voyage.drop_duplicates().values)].voyage_length\n",
    "    vessel_type_accuracies.append([vessel_type, accuracy.loc['50%', 'acc_distance_nm'], accuracy.loc['mean', 'acc_distance_nm'], accuracy.loc['std', 'acc_distance_nm'], accuracy.loc['50%', 'mins_to_future'], accuracy.loc['mean', 'mins_to_future'], accuracy.loc['std', 'mins_to_future'], count])\n",
    "    #print(calculate_route_accuracy_km2(sea_area_results, test_voyages).describe())\n",
    "\n",
    "vessel_type_accuracies = pd.DataFrame(data=vessel_type_accuracies, columns=['vessel_type', 'median_acc_distance_nm', 'mean_acc_distance_nm', 'std_acc_distance_nm', 'median_mins_to_future', 'mean_mins_to_future', 'std_mins_to_future', 'count'])\n",
    "vessel_type_accuracies = vessel_type_accuracies.sort_values(by='median_acc_distance_nm')\n",
    "vessel_type_accuracies.round(1)#.to_excel('summer_tr_results_vessel_type.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find out most travelled port to port -pair and visualise lat and lon prediction accuracy"
   ]
  },
  {
   "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></th>\n",
       "      <th></th>\n",
       "      <th>voyage</th>\n",
       "      <th>vessel_type</th>\n",
       "      <th>end_port_sea_area</th>\n",
       "      <th>start_time</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lon</th>\n",
       "      <th>start_sea_area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_port</th>\n",
       "      <th>end_port</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1242.0</th>\n",
       "      <th>1019</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     voyage  vessel_type  end_port_sea_area  start_time  \\\n",
       "start_port end_port                                                       \n",
       "1242.0     1019           8            8                  8           8   \n",
       "\n",
       "                     start_lat  start_lon  start_sea_area  \n",
       "start_port end_port                                        \n",
       "1242.0     1019              8          8               8  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(portcalls)\n",
    "\n",
    "voyages_summer['start_port'] = voyages_summer.apply(lambda row: portcalls.get_port_id(row, ports), axis=1)\n",
    "# Filter out short voyages\n",
    "voyages_summer_long = voyages_summer[voyages_summer.voyage.isin(voyages_summer_lengths[voyages_summer_lengths.travel_time > 1000].voyage)]\n",
    "count = voyages_summer_long.groupby(['start_port', 'end_port']).count() \n",
    "most_travelled = count[count.voyage == count.voyage.max()]\n",
    "most_travelled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>voyage</th>\n",
       "      <th>end_port</th>\n",
       "      <th>end_port_sea_area</th>\n",
       "      <th>mins_to_future</th>\n",
       "      <th>actual_lat</th>\n",
       "      <th>actual_lon</th>\n",
       "      <th>pred_lat</th>\n",
       "      <th>pred_lon</th>\n",
       "      <th>actual_speed</th>\n",
       "      <th>pred_speed</th>\n",
       "      <th>acc_distance_nm</th>\n",
       "      <th>error_rate_lat</th>\n",
       "      <th>error_rate_lon</th>\n",
       "      <th>error_rate_speed</th>\n",
       "      <th>correct_node</th>\n",
       "      <th>start_port_sea_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1801.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "      <td>1804.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6288.161308</td>\n",
       "      <td>1080.065410</td>\n",
       "      <td>7.095344</td>\n",
       "      <td>528.879160</td>\n",
       "      <td>56.738400</td>\n",
       "      <td>18.226280</td>\n",
       "      <td>56.574439</td>\n",
       "      <td>18.240659</td>\n",
       "      <td>8.200299</td>\n",
       "      <td>8.129098</td>\n",
       "      <td>23.587784</td>\n",
       "      <td>0.582989</td>\n",
       "      <td>1.578198</td>\n",
       "      <td>261.030478</td>\n",
       "      <td>0.029379</td>\n",
       "      <td>8.904656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>83.419674</td>\n",
       "      <td>99.469024</td>\n",
       "      <td>1.784198</td>\n",
       "      <td>318.199613</td>\n",
       "      <td>1.414434</td>\n",
       "      <td>0.369455</td>\n",
       "      <td>1.302480</td>\n",
       "      <td>0.494115</td>\n",
       "      <td>1.528295</td>\n",
       "      <td>1.228321</td>\n",
       "      <td>11.072455</td>\n",
       "      <td>0.285487</td>\n",
       "      <td>1.564218</td>\n",
       "      <td>1861.422695</td>\n",
       "      <td>0.168914</td>\n",
       "      <td>1.784198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6174.000000</td>\n",
       "      <td>1019.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.403948</td>\n",
       "      <td>17.681000</td>\n",
       "      <td>54.403948</td>\n",
       "      <td>17.116176</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.535369</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008525</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6177.000000</td>\n",
       "      <td>1019.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>238.941667</td>\n",
       "      <td>55.451106</td>\n",
       "      <td>17.940794</td>\n",
       "      <td>55.424374</td>\n",
       "      <td>17.890619</td>\n",
       "      <td>8.330000</td>\n",
       "      <td>7.535267</td>\n",
       "      <td>16.032574</td>\n",
       "      <td>0.411644</td>\n",
       "      <td>0.423764</td>\n",
       "      <td>5.200597</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6343.000000</td>\n",
       "      <td>1019.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>531.891667</td>\n",
       "      <td>56.767167</td>\n",
       "      <td>18.063531</td>\n",
       "      <td>56.523303</td>\n",
       "      <td>18.235830</td>\n",
       "      <td>8.590000</td>\n",
       "      <td>8.275413</td>\n",
       "      <td>23.846810</td>\n",
       "      <td>0.570243</td>\n",
       "      <td>1.072146</td>\n",
       "      <td>9.498918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6355.000000</td>\n",
       "      <td>1242.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>815.575000</td>\n",
       "      <td>58.060825</td>\n",
       "      <td>18.568489</td>\n",
       "      <td>57.728955</td>\n",
       "      <td>18.648139</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>8.949237</td>\n",
       "      <td>32.280602</td>\n",
       "      <td>0.812338</td>\n",
       "      <td>2.112295</td>\n",
       "      <td>19.849988</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6357.000000</td>\n",
       "      <td>1242.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1164.012755</td>\n",
       "      <td>58.916000</td>\n",
       "      <td>18.992597</td>\n",
       "      <td>58.916000</td>\n",
       "      <td>19.066960</td>\n",
       "      <td>10.080000</td>\n",
       "      <td>10.666649</td>\n",
       "      <td>46.461625</td>\n",
       "      <td>1.148671</td>\n",
       "      <td>7.006421</td>\n",
       "      <td>19377.475712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            voyage     end_port  end_port_sea_area  mins_to_future  \\\n",
       "count  1804.000000  1804.000000        1804.000000     1804.000000   \n",
       "mean   6288.161308  1080.065410           7.095344      528.879160   \n",
       "std      83.419674    99.469024           1.784198      318.199613   \n",
       "min    6174.000000  1019.000000           6.000000        0.000000   \n",
       "25%    6177.000000  1019.000000           6.000000      238.941667   \n",
       "50%    6343.000000  1019.000000           6.000000      531.891667   \n",
       "75%    6355.000000  1242.000000          10.000000      815.575000   \n",
       "max    6357.000000  1242.000000          10.000000     1164.012755   \n",
       "\n",
       "        actual_lat   actual_lon     pred_lat     pred_lon  actual_speed  \\\n",
       "count  1804.000000  1804.000000  1804.000000  1804.000000   1804.000000   \n",
       "mean     56.738400    18.226280    56.574439    18.240659      8.200299   \n",
       "std       1.414434     0.369455     1.302480     0.494115      1.528295   \n",
       "min      54.403948    17.681000    54.403948    17.116176      0.000000   \n",
       "25%      55.451106    17.940794    55.424374    17.890619      8.330000   \n",
       "50%      56.767167    18.063531    56.523303    18.235830      8.590000   \n",
       "75%      58.060825    18.568489    57.728955    18.648139      8.800000   \n",
       "max      58.916000    18.992597    58.916000    19.066960     10.080000   \n",
       "\n",
       "        pred_speed  acc_distance_nm  error_rate_lat  error_rate_lon  \\\n",
       "count  1804.000000      1804.000000     1804.000000     1804.000000   \n",
       "mean      8.129098        23.587784        0.582989        1.578198   \n",
       "std       1.228321        11.072455        0.285487        1.564218   \n",
       "min       4.535369         0.000000        0.000000        0.000000   \n",
       "25%       7.535267        16.032574        0.411644        0.423764   \n",
       "50%       8.275413        23.846810        0.570243        1.072146   \n",
       "75%       8.949237        32.280602        0.812338        2.112295   \n",
       "max      10.666649        46.461625        1.148671        7.006421   \n",
       "\n",
       "       error_rate_speed  correct_node  start_port_sea_area  \n",
       "count       1801.000000   1804.000000          1804.000000  \n",
       "mean         261.030478      0.029379             8.904656  \n",
       "std         1861.422695      0.168914             1.784198  \n",
       "min            0.008525      0.000000             6.000000  \n",
       "25%            5.200597      0.000000             6.000000  \n",
       "50%            9.498918      0.000000            10.000000  \n",
       "75%           19.849988      0.000000            10.000000  \n",
       "max        19377.475712      1.000000            10.000000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ports = list(most_travelled.iloc[0].name)\n",
    "\n",
    "single_route_voyages = voyages_summer[(voyages_summer.start_port.isin(test_ports)) & (voyages_summer.end_port.isin(test_ports))].voyage.values\n",
    "single_route_results = summer_results_5km_ship[summer_results_5km_ship.voyage.isin(single_route_voyages)]\n",
    "single_route_results.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65725    6174\n",
       "65876    6176\n",
       "66048    6177\n",
       "66388    6184\n",
       "67251    6341\n",
       "Name: voyage, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_route_results.voyage.drop_duplicates().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = []\n",
    "labels = []\n",
    "interval = 15\n",
    "\n",
    "i = 0\n",
    "while i < 121*15:\n",
    "    bins.append(i)\n",
    "    labels.append(int(i))\n",
    "    i += interval\n",
    "del labels[0]\n",
    "\n",
    "single_route_results['interval'] = pd.cut(single_route_results['mins_to_future'], bins=bins, labels=labels)\n",
    "\n",
    "y = []\n",
    "y_pred = []\n",
    "x = []\n",
    "\n",
    "fig = plt.figure(figsize=(20,15))\n",
    "for group, results in single_route_results[single_route_results.voyage == 6174].groupby('interval'):\n",
    "    if np.isnan(group):\n",
    "        group = 0\n",
    "    if len(results) == 0:\n",
    "        continue\n",
    "    y.append(results.iloc[0].actual_lat)\n",
    "    y_pred.append(results.iloc[0].pred_lat)\n",
    "    label = vessel_type\n",
    "    x.append(group/60)\n",
    "    plt.plot(x, y, alpha=1, c='blue', linewidth=2, label='Actual')\n",
    "    plt.plot(x, y_pred, alpha=1, c='orange', linewidth=2, label='Pred')\n",
    "\n",
    "plt.title(f\"Predicted lat vs actual lat over time\")\n",
    "plt.ylabel(\"Lat\")\n",
    "plt.xlabel(\"Voyage Duration in Hours\")\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "newLabels, newHandles = [], []\n",
    "for handle, label in zip(handles, labels):\n",
    "  if label not in newLabels:\n",
    "    newLabels.append(label)\n",
    "    newHandles.append(handle)\n",
    "plt.legend(newHandles, newLabels)\n",
    "plt.show()\n",
    "\n",
    "y = []\n",
    "y_pred = []\n",
    "x = []\n",
    "\n",
    "fig = plt.figure(figsize=(20,15))\n",
    "for group, results in single_route_results[single_route_results.voyage == 6174].groupby('interval'):\n",
    "    if np.isnan(group):\n",
    "        group = 0\n",
    "    if len(results) == 0:\n",
    "        continue\n",
    "    y.append(results.iloc[0].actual_lon)\n",
    "    y_pred.append(results.iloc[0].pred_lon)\n",
    "    label = vessel_type\n",
    "    x.append(group/60)\n",
    "    plt.plot(x, y, alpha=1, c='blue', linewidth=2, label='Actual')\n",
    "    plt.plot(x, y_pred, alpha=1, c='orange', linewidth=2, label='Pred')\n",
    "\n",
    "plt.title(f\"Predicted lon vs actual lat over time\")\n",
    "plt.ylabel(\"Lon\")\n",
    "plt.xlabel(\"Voyage Duration in Hours\")\n",
    "#plt.yticks(np.arange(17, 23, 0.5)) \n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "newLabels, newHandles = [], []\n",
    "for handle, label in zip(handles, labels):\n",
    "  if label not in newLabels:\n",
    "    newLabels.append(label)\n",
    "    newHandles.append(handle)\n",
    "plt.legend(newHandles, newLabels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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