lateral_distributions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import geopandas as gpd\n",
    "import dask_geopandas as dgd\n",
    "import matplotlib.pyplot as plt\n",
    "from shapely.geometry import Point, LineString\n",
    "from shapely import box\n",
    "import datashader as ds\n",
    "from tqdm import tqdm\n",
    "from dotenv import load_dotenv\n",
    "import dask.dataframe as dd\n",
    "import dask.distributed\n",
    "import dask_gateway\n",
    "from dask.distributed import PipInstall, CondaInstall, WorkerPlugin, Client, get_worker\n",
    "from dask import delayed\n",
    "from shapely.affinity import rotate\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.io import output_notebook, export_png, save\n",
    "from bokeh.models import ColumnDataSource, Range1d, LinearAxis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gateway = dask_gateway.Gateway()\n",
    "cluster_options = gateway.cluster_options()\n",
    "cluster = gateway.new_cluster(cluster_options)\n",
    "cluster.adapt(minimum=1, maximum=100)\n",
    "client = dask.distributed.Client(cluster)\n",
    "client\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading data from Azure Blob Storage\n",
    "%load_ext dotenv\n",
    "load_dotenv()\n",
    "\n",
    "folder_name = '202205_202209_drought_velocity_profiles'\n",
    "pathname = 'abfs://ais/parquet/' + folder_name \n",
    "sas_token2 = os.environ['SAS_TOKEN2']\n",
    "\n",
    "\n",
    "ddf = dd.read_parquet(pathname, \n",
    "                      engine='pyarrow',\n",
    "                      storage_options={\"account_name\": \"rwsais\", \"sas_token\": sas_token2})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Single trajectory extraction for plotting in QGIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj_id = 19454\n",
    "\n",
    "traj_id_df = ddf[ddf['traj_id'] == traj_id].compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj_id_gdf = gpd.GeoDataFrame(traj_id_df, geometry=gpd.points_from_xy(traj_id_df.longitude, traj_id_df.latitude))\n",
    "traj_id_gdf.to_file('traj_right.geojson', driver='GeoJSON') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crossing zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "polygon_gdf = gpd.read_file('box_trein_big.geojson')\n",
    "polygon_train = polygon_gdf.geometry[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "polygon_oversteek = gpd.read_file('oversteek_goed.geojson')\n",
    "polygon_oversteek = polygon_oversteek.geometry[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "polygon_waalbrug = gpd.read_file('box_waalbrug.geojson')\n",
    "polygon_waalbrug = polygon_waalbrug.geometry[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# set which bridge\n",
    "polygon = polygon_waalbrug\n",
    "\n",
    "if polygon == polygon_oversteek:\n",
    "    angle = -33\n",
    "elif polygon == polygon_train:\n",
    "    angle = -28\n",
    "else:\n",
    "    angle = 25.5\n",
    "\n",
    "centroid = polygon.centroid\n",
    "minx, miny, maxx, maxy = polygon.bounds\n",
    "orientation_line = LineString([(centroid.x, miny), (centroid.x, maxy)])\n",
    "orientation_line\n",
    "\n",
    "\n",
    "new_line = rotate(orientation_line, angle, origin=centroid, use_radians=False)\n",
    "# show new_line and polygon in same plot\n",
    "fig, ax = plt.subplots()\n",
    "gpd.GeoSeries([new_line]).plot(ax=ax)\n",
    "gpd.GeoSeries([polygon]).plot(ax=ax, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sog</th>\n",
       "      <th>cog</th>\n",
       "      <th>rot</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>maneuver</th>\n",
       "      <th>seconds</th>\n",
       "      <th>name</th>\n",
       "      <th>draughtInland</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>datetime</th>\n",
       "      <th>time_diff</th>\n",
       "      <th>traj_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>speed</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>geometry</th>\n",
       "      <th>lateral_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>5.4</td>\n",
       "      <td>73.199997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.871022</td>\n",
       "      <td>51.850990</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-05-02 11:07:41+00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>41.067437</td>\n",
       "      <td>14.784277</td>\n",
       "      <td>-0.151760</td>\n",
       "      <td>POINT (5.87102 51.85099)</td>\n",
       "      <td>51.342029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>5.4</td>\n",
       "      <td>73.199997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.871455</td>\n",
       "      <td>51.851170</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-05-02 11:07:53+00:00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>52.068469</td>\n",
       "      <td>15.620541</td>\n",
       "      <td>0.019358</td>\n",
       "      <td>POINT (5.87146 51.85117)</td>\n",
       "      <td>31.440754</td>\n",
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       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>9.8</td>\n",
       "      <td>242.699997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.870843</td>\n",
       "      <td>51.851383</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-05-12 04:16:02+00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>83.464713</td>\n",
       "      <td>30.047297</td>\n",
       "      <td>0.092475</td>\n",
       "      <td>POINT (5.87084 51.85138)</td>\n",
       "      <td>7.728596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>4.7</td>\n",
       "      <td>71.099998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.870788</td>\n",
       "      <td>51.851925</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-05-24 18:07:43+00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>32.570858</td>\n",
       "      <td>11.725509</td>\n",
       "      <td>-0.043552</td>\n",
       "      <td>POINT (5.87079 51.85192)</td>\n",
       "      <td>-52.398663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>4.6</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.870888</td>\n",
       "      <td>51.851917</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-06-11 09:42:33+00:00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>34.158119</td>\n",
       "      <td>13.663247</td>\n",
       "      <td>-0.058283</td>\n",
       "      <td>POINT (5.87089 51.85192)</td>\n",
       "      <td>-51.551800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sog         cog  rot  longitude   latitude  maneuver  seconds  \\\n",
       "87   5.4   73.199997  0.0   5.871022  51.850990         0     42.0   \n",
       "88   5.4   73.199997  0.0   5.871455  51.851170         0     52.0   \n",
       "208  9.8  242.699997  0.0   5.870843  51.851383         0      2.0   \n",
       "361  4.7   71.099998  0.0   5.870788  51.851925         0     45.0   \n",
       "548  4.6   70.000000  0.0   5.870888  51.851917         0     34.0   \n",
       "\n",
       "               name  draughtInland  length  width                  datetime  \\\n",
       "87   testschip-1001            0.4   110.0   12.0 2022-05-02 11:07:41+00:00   \n",
       "88   testschip-1001            0.4   110.0   12.0 2022-05-02 11:07:53+00:00   \n",
       "208  testschip-1001            0.4   110.0   12.0 2022-05-12 04:16:02+00:00   \n",
       "361  testschip-1001            0.4   110.0   12.0 2022-05-24 18:07:43+00:00   \n",
       "548  testschip-1001            0.4   110.0   12.0 2022-06-11 09:42:33+00:00   \n",
       "\n",
       "     time_diff  traj_id   distance      speed  acceleration  \\\n",
       "87        10.0       -1  41.067437  14.784277     -0.151760   \n",
       "88        12.0       -1  52.068469  15.620541      0.019358   \n",
       "208       10.0        0  83.464713  30.047297      0.092475   \n",
       "361       10.0        1  32.570858  11.725509     -0.043552   \n",
       "548        9.0        2  34.158119  13.663247     -0.058283   \n",
       "\n",
       "                     geometry  lateral_pos  \n",
       "87   POINT (5.87102 51.85099)    51.342029  \n",
       "88   POINT (5.87146 51.85117)    31.440754  \n",
       "208  POINT (5.87084 51.85138)     7.728596  \n",
       "361  POINT (5.87079 51.85192)   -52.398663  \n",
       "548  POINT (5.87089 51.85192)   -51.551800  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from shapely.affinity import rotate\n",
    "\n",
    "def filter_within_polygon(partition, polygon, angle):\n",
    "    \"\"\"Apply spatial filter to a partition of data, add signed lateral positions relative to polygon centroid.\"\"\"\n",
    "    # Convert the partition to a GeoDataFrame\n",
    "    gdf = gpd.GeoDataFrame(partition, geometry=gpd.points_from_xy(partition.longitude, partition.latitude))\n",
    "    \n",
    "    # Perform spatial filtering\n",
    "    filtered_gdf = gdf[gdf.geometry.within(polygon)]\n",
    "        \n",
    "    # Calculate the centroid and main orientation of the polygon\n",
    "    centroid = polygon.centroid\n",
    "    minx, miny, maxx, maxy = polygon.bounds\n",
    "\n",
    "     # Create a reference line from north to south through the centroid\n",
    "    orientation_line = LineString([(centroid.x, miny), (centroid.x, maxy)])\n",
    "    # Rotate the orientation line by the specified angle around the centroid\n",
    "    rotated_orientation_line = rotate(orientation_line, angle, origin=centroid)\n",
    "\n",
    "    # Calculate signed lateral positions relative to the rotated orientation line\n",
    "    # Project each point onto the rotated orientation line and calculate distance from centroid\n",
    "    def calculate_signed_distance(geom):\n",
    "        # Rotate the point by the same angle to align with the rotated orientation line\n",
    "        rotated_point = rotate(geom, angle, origin=centroid)\n",
    "        \n",
    "        # Project the rotated point onto the rotated orientation line\n",
    "        projected_point = rotated_orientation_line.interpolate(rotated_orientation_line.project(rotated_point))\n",
    "        \n",
    "        # Calculate the distance from the projected point to the centroid\n",
    "        distance = projected_point.distance(centroid)\n",
    "        \n",
    "        # Determine the sign based on the y-coordinate of the projected point\n",
    "        sign = 1 if projected_point.y < centroid.y else -1\n",
    "        \n",
    "        # Return the signed distance\n",
    "        return sign * distance*111000\n",
    "\n",
    "    # Apply the function to calculate lateral positions\n",
    "    filtered_gdf['lateral_pos'] = filtered_gdf['geometry'].apply(calculate_signed_distance)\n",
    "    \n",
    "    return filtered_gdf\n",
    "\n",
    "existing_meta = ddf.dtypes.to_dict()\n",
    "new_meta = {'geometry': 'object',\n",
    "            #'rotated_geometry': 'object',\n",
    "            'lateral_pos': 'float64'\n",
    "            }\n",
    "    \n",
    "full_meta = {**existing_meta, **new_meta}\n",
    "\n",
    "ddf_within = ddf.map_partitions(filter_within_polygon, polygon=polygon, angle=angle, meta=full_meta)\n",
    "ddf_within.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sog</th>\n",
       "      <th>cog</th>\n",
       "      <th>rot</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>maneuver</th>\n",
       "      <th>seconds</th>\n",
       "      <th>name</th>\n",
       "      <th>draughtInland</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>datetime</th>\n",
       "      <th>time_diff</th>\n",
       "      <th>traj_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>speed</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>geometry</th>\n",
       "      <th>lateral_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87</td>\n",
       "      <td>5.4</td>\n",
       "      <td>73.199997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.871022</td>\n",
       "      <td>51.850990</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-05-02 11:07:41+00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>41.067437</td>\n",
       "      <td>14.784277</td>\n",
       "      <td>-0.151760</td>\n",
       "      <td>POINT (5.87102 51.85099)</td>\n",
       "      <td>51.342029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88</td>\n",
       "      <td>5.4</td>\n",
       "      <td>73.199997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.871455</td>\n",
       "      <td>51.851170</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-05-02 11:07:53+00:00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>52.068469</td>\n",
       "      <td>15.620541</td>\n",
       "      <td>0.019358</td>\n",
       "      <td>POINT (5.87146 51.85117)</td>\n",
       "      <td>31.440754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>208</td>\n",
       "      <td>9.8</td>\n",
       "      <td>242.699997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.870843</td>\n",
       "      <td>51.851383</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-05-12 04:16:02+00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>83.464713</td>\n",
       "      <td>30.047297</td>\n",
       "      <td>0.092475</td>\n",
       "      <td>POINT (5.87084 51.85138)</td>\n",
       "      <td>7.728596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>361</td>\n",
       "      <td>4.7</td>\n",
       "      <td>71.099998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.870788</td>\n",
       "      <td>51.851925</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-05-24 18:07:43+00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>32.570858</td>\n",
       "      <td>11.725509</td>\n",
       "      <td>-0.043552</td>\n",
       "      <td>POINT (5.87079 51.85192)</td>\n",
       "      <td>-52.398663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>548</td>\n",
       "      <td>4.6</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.870888</td>\n",
       "      <td>51.851917</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-06-11 09:42:33+00:00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>34.158119</td>\n",
       "      <td>13.663247</td>\n",
       "      <td>-0.058283</td>\n",
       "      <td>POINT (5.87089 51.85192)</td>\n",
       "      <td>-51.551800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  sog         cog  rot  longitude   latitude  maneuver  seconds  \\\n",
       "0     87  5.4   73.199997  0.0   5.871022  51.850990         0     42.0   \n",
       "1     88  5.4   73.199997  0.0   5.871455  51.851170         0     52.0   \n",
       "2    208  9.8  242.699997  0.0   5.870843  51.851383         0      2.0   \n",
       "3    361  4.7   71.099998  0.0   5.870788  51.851925         0     45.0   \n",
       "4    548  4.6   70.000000  0.0   5.870888  51.851917         0     34.0   \n",
       "\n",
       "             name  draughtInland  length  width                  datetime  \\\n",
       "0  testschip-1001            0.4   110.0   12.0 2022-05-02 11:07:41+00:00   \n",
       "1  testschip-1001            0.4   110.0   12.0 2022-05-02 11:07:53+00:00   \n",
       "2  testschip-1001            0.4   110.0   12.0 2022-05-12 04:16:02+00:00   \n",
       "3  testschip-1001            0.4   110.0   12.0 2022-05-24 18:07:43+00:00   \n",
       "4  testschip-1001            0.4   110.0   12.0 2022-06-11 09:42:33+00:00   \n",
       "\n",
       "   time_diff  traj_id   distance      speed  acceleration  \\\n",
       "0       10.0       -1  41.067437  14.784277     -0.151760   \n",
       "1       12.0       -1  52.068469  15.620541      0.019358   \n",
       "2       10.0        0  83.464713  30.047297      0.092475   \n",
       "3       10.0        1  32.570858  11.725509     -0.043552   \n",
       "4        9.0        2  34.158119  13.663247     -0.058283   \n",
       "\n",
       "                   geometry  lateral_pos  \n",
       "0  POINT (5.87102 51.85099)    51.342029  \n",
       "1  POINT (5.87146 51.85117)    31.440754  \n",
       "2  POINT (5.87084 51.85138)     7.728596  \n",
       "3  POINT (5.87079 51.85192)   -52.398663  \n",
       "4  POINT (5.87089 51.85192)   -51.551800  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set index as column datetime\n",
    "ddf_within = ddf_within.reset_index()\n",
    "ddf_within.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sog</th>\n",
       "      <th>cog</th>\n",
       "      <th>rot</th>\n",
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       "      <th>datetime</th>\n",
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       "      <th>distance</th>\n",
       "      <th>speed</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>geometry</th>\n",
       "      <th>lateral_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87</td>\n",
       "      <td>5.4</td>\n",
       "      <td>73.199997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.871022</td>\n",
       "      <td>51.850990</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-05-02 11:07:41+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>41.067437</td>\n",
       "      <td>14.784277</td>\n",
       "      <td>-0.151760</td>\n",
       "      <td>POINT (5.87102 51.85099)</td>\n",
       "      <td>51.342029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>208</td>\n",
       "      <td>9.8</td>\n",
       "      <td>242.699997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.870843</td>\n",
       "      <td>51.851383</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-05-12 04:16:02+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>83.464713</td>\n",
       "      <td>30.047297</td>\n",
       "      <td>0.092475</td>\n",
       "      <td>POINT (5.87084 51.85138)</td>\n",
       "      <td>7.728596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>361</td>\n",
       "      <td>4.7</td>\n",
       "      <td>71.099998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.870788</td>\n",
       "      <td>51.851925</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-05-24 18:07:43+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>32.570858</td>\n",
       "      <td>11.725509</td>\n",
       "      <td>-0.043552</td>\n",
       "      <td>POINT (5.87079 51.85192)</td>\n",
       "      <td>-52.398663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>548</td>\n",
       "      <td>4.6</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.870888</td>\n",
       "      <td>51.851917</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-06-11 09:42:33+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>34.158119</td>\n",
       "      <td>13.663247</td>\n",
       "      <td>-0.058283</td>\n",
       "      <td>POINT (5.87089 51.85192)</td>\n",
       "      <td>-51.551800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>678</td>\n",
       "      <td>9.6</td>\n",
       "      <td>249.100006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.871437</td>\n",
       "      <td>51.851662</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>testschip-1001</td>\n",
       "      <td>0.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2022-06-22 10:22:31+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>93.963431</td>\n",
       "      <td>30.751668</td>\n",
       "      <td>0.078224</td>\n",
       "      <td>POINT (5.87144 51.85166)</td>\n",
       "      <td>-23.181896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  sog         cog  rot  longitude   latitude  maneuver  seconds  \\\n",
       "0     87  5.4   73.199997  0.0   5.871022  51.850990         0     42.0   \n",
       "1    208  9.8  242.699997  0.0   5.870843  51.851383         0      2.0   \n",
       "2    361  4.7   71.099998  0.0   5.870788  51.851925         0     45.0   \n",
       "3    548  4.6   70.000000  0.0   5.870888  51.851917         0     34.0   \n",
       "4    678  9.6  249.100006  0.0   5.871437  51.851662         0     32.0   \n",
       "\n",
       "             name  draughtInland  length  width                  datetime  \\\n",
       "0  testschip-1001            0.4   110.0   12.0 2022-05-02 11:07:41+00:00   \n",
       "1  testschip-1001            0.4   110.0   12.0 2022-05-12 04:16:02+00:00   \n",
       "2  testschip-1001            0.4   110.0   12.0 2022-05-24 18:07:43+00:00   \n",
       "3  testschip-1001            0.4   110.0   12.0 2022-06-11 09:42:33+00:00   \n",
       "4  testschip-1001            0.4   110.0   12.0 2022-06-22 10:22:31+00:00   \n",
       "\n",
       "   time_diff  traj_id   distance      speed  acceleration  \\\n",
       "0        NaN       -1  41.067437  14.784277     -0.151760   \n",
       "1        NaN        0  83.464713  30.047297      0.092475   \n",
       "2        NaN        1  32.570858  11.725509     -0.043552   \n",
       "3        NaN        2  34.158119  13.663247     -0.058283   \n",
       "4        NaN        3  93.963431  30.751668      0.078224   \n",
       "\n",
       "                   geometry  lateral_pos  \n",
       "0  POINT (5.87102 51.85099)    51.342029  \n",
       "1  POINT (5.87084 51.85138)     7.728596  \n",
       "2  POINT (5.87079 51.85192)   -52.398663  \n",
       "3  POINT (5.87089 51.85192)   -51.551800  \n",
       "4  POINT (5.87144 51.85166)   -23.181896  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def filter_duplicates_within(partition):\n",
    "    \"\"\"Filter out points that have the same trajectory_id and are within 30 seconds of each other.\"\"\"\n",
    "    partition = partition.sort_values('datetime')\n",
    "\n",
    "    # Group by 'traj_id' and then apply the filtering logic\n",
    "    def filter_within_group(group):\n",
    "        # Calculate the time difference between consecutive points\n",
    "        group['time_diff'] = group['datetime'].diff().dt.total_seconds().abs()\n",
    "        # Keep the first point and any point that is at least 30 seconds away from the previous point\n",
    "        return group[(group['time_diff'] > 30) | (group['time_diff'].isna())]\n",
    "    \n",
    "\n",
    "    # Apply the filtering function to each group\n",
    "    return partition.groupby('traj_id').apply(filter_within_group).reset_index(drop=True)\n",
    "ddf_within = ddf_within.map_partitions(filter_duplicates_within, meta=ddf_within)\n",
    "ddf_within.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First two bridges\n",
    "ddf_within['upstream'] = (ddf_within['cog'] > 30) & (ddf_within['cog'] < 210)\n",
    "\n",
    "#third brige between 30 and 160\n",
    "#ddf_within['upstream'] = (ddf_within['cog'] > 30) & (ddf_within['cog'] < 160)   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create two separate dask dataframes where one is in period of 01-05-2022 until 1-07-2022 and the other one from 15-07-2022 to 15-08-2022\n",
    "normal_period_ddf = ddf_within[(ddf_within['datetime'] >= '2022-05-01') & (ddf_within['datetime'] < '2022-07-01')]\n",
    "drought_period_ddf = ddf_within[(ddf_within['datetime'] >= '2022-07-01') & (ddf_within['datetime'] < '2022-08-31')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_period_df = normal_period_ddf.compute()\n",
    "drought_period_df = drought_period_ddf.compute()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MmaMdO3Zox44dGjRokO655x4z9Dz//POaP3++srKytH37djkcDg0ZMkSnT582t5Genq61a9cqOztbeXl5qqysVHJysurq6sya1NRUFRYWKicnRzk5OSosLFRaWlqL7y8AAGidbIZhGJ5uxLeFhIRo3rx5mjBhgqKiopSenq4nnnhC0vneoIiICM2dO1cPPfSQnE6nOnbsqFWrVmnMmDGSpGPHjik6Olrr16/X0KFDtW/fPvXo0UP5+fnq27evJCk/P18JCQn6z3/+o27dujWqXRUVFbLb7XI6nQoKCmrSfV6Qe+CKNY8NiWvS7wQAwAoa+/e71YwhqqurU3Z2ts6cOaOEhAQVFRWptLRUSUlJZo2fn5/uvPNObdmyRZJUUFCg2tpal5qoqCjFx8ebNR999JHsdrsZhiSpX79+stvtZk1DqqurVVFR4TIBAIDrk8cD0e7du9W+fXv5+fnpF7/4hdauXasePXqotLRUkhQREeFSHxERYa4rLS2Vr6+vgoODL1sTHh5e73vDw8PNmoZkZmaaY47sdruio6OvaT8BAEDr5fFA1K1bNxUWFio/P1+//OUvNW7cOH366afmepvN5lJvGEa9ZRe7uKah+ittZ+bMmXI6neZ09OjRxu4SAABoYzweiHx9fdW1a1f16dNHmZmZuuWWW/Tiiy/K4XBIUr1enLKyMrPXyOFwqKamRuXl5ZetOX78eL3vPXHiRL3ep2/z8/Mz7367MAEAgOuTxwPRxQzDUHV1tbp06SKHw6Hc3FxzXU1NjTZv3qz+/ftLknr37i0fHx+XmpKSEu3Zs8esSUhIkNPp1LZt28yarVu3yul0mjUAAMDavD355U8++aSGDx+u6OhonT59WtnZ2dq0aZNycnJks9mUnp6u2bNnKzY2VrGxsZo9e7batWun1NRUSZLdbtfEiROVkZGh0NBQhYSEaMaMGerVq5cSExMlSd27d9ewYcM0adIkLVmyRJI0efJkJScnN/oOMwAAcH3zaCA6fvy40tLSVFJSIrvdrptvvlk5OTkaMmSIJOnxxx9XVVWVHn74YZWXl6tv3756//33FRgYaG5jwYIF8vb21ujRo1VVVaXBgwdr+fLl8vLyMmvWrFmjqVOnmnejpaSkKCsrq2V3FgAAtFqt7jlErRXPIQIAoO1pc88hAgAA8BQCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDyPBqLMzEz98Ic/VGBgoMLDwzVixAjt37/fpWb8+PGy2WwuU79+/VxqqqurNWXKFIWFhSkgIEApKSkqLi52qSkvL1daWprsdrvsdrvS0tJ06tSp5t5FAADQBng0EG3evFmPPPKI8vPzlZubq2+++UZJSUk6c+aMS92wYcNUUlJiTuvXr3dZn56errVr1yo7O1t5eXmqrKxUcnKy6urqzJrU1FQVFhYqJydHOTk5KiwsVFpaWovsJwAAaN28PfnlOTk5LvPLli1TeHi4CgoKdMcdd5jL/fz85HA4GtyG0+nU0qVLtWrVKiUmJkqSVq9erejoaG3YsEFDhw7Vvn37lJOTo/z8fPXt21eS9MorryghIUH79+9Xt27dmmkPAQBAW9CqxhA5nU5JUkhIiMvyTZs2KTw8XHFxcZo0aZLKysrMdQUFBaqtrVVSUpK5LCoqSvHx8dqyZYsk6aOPPpLdbjfDkCT169dPdrvdrLlYdXW1KioqXCYAAHB9ajWByDAMTZ8+Xbfffrvi4+PN5cOHD9eaNWv0wQcf6IUXXtD27ds1aNAgVVdXS5JKS0vl6+ur4OBgl+1FRESotLTUrAkPD6/3neHh4WbNxTIzM83xRna7XdHR0U21qwAAoJXx6CWzb3v00Uf1ySefKC8vz2X5mDFjzH/Hx8erT58+iomJ0TvvvKNRo0ZdcnuGYchms5nz3/73pWq+bebMmZo+fbo5X1FRQSgCAOA61Sp6iKZMmaI333xTGzdu1I033njZ2sjISMXExOjgwYOSJIfDoZqaGpWXl7vUlZWVKSIiwqw5fvx4vW2dOHHCrLmYn5+fgoKCXCYAAHB98mggMgxDjz76qN544w198MEH6tKlyxU/c/LkSR09elSRkZGSpN69e8vHx0e5ublmTUlJifbs2aP+/ftLkhISEuR0OrVt2zazZuvWrXI6nWYNAACwLo9eMnvkkUf06quv6h//+IcCAwPN8Tx2u13+/v6qrKzUrFmzdO+99yoyMlKff/65nnzySYWFhWnkyJFm7cSJE5WRkaHQ0FCFhIRoxowZ6tWrl3nXWffu3TVs2DBNmjRJS5YskSRNnjxZycnJ3GEGAAA8G4gWL14sSRowYIDL8mXLlmn8+PHy8vLS7t27tXLlSp06dUqRkZEaOHCgXnvtNQUGBpr1CxYskLe3t0aPHq2qqioNHjxYy5cvl5eXl1mzZs0aTZ061bwbLSUlRVlZWc2/kwAAoNWzGYZheLoRbUFFRYXsdrucTmeTjydakHvgijWPDYlr0u8EAMAKGvv3u1UMqgYAAPAkAhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8twJRUVFRU7cDAADAY9wKRF27dtXAgQO1evVqnT17tqnbBAAA0KLcCkS7du3SD37wA2VkZMjhcOihhx7Stm3bmrptAAAALcKtQBQfH6/58+friy++0LJly1RaWqrbb79dPXv21Pz583XixImmbicAAECzuaZB1d7e3ho5cqT++te/au7cuTp06JBmzJihG2+8UQ888IBKSkqaqp0AAADN5poC0Y4dO/Twww8rMjJS8+fP14wZM3To0CF98MEH+uKLL3TPPfc0VTsBAACajbc7H5o/f76WLVum/fv366677tLKlSt111136YYbzuerLl26aMmSJbrpppuatLEAAADNwa1AtHjxYk2YMEE///nP5XA4Gqzp1KmTli5dek2NAwAAaAluBaKDBw9escbX11fjxo1zZ/MAAAAtyq0xRMuWLdPrr79eb/nrr7+uFStWXHOjAAAAWpJbgWjOnDkKCwurtzw8PFyzZ8++5kYBAAC0JLcC0eHDh9WlS5d6y2NiYnTkyJFrbhQAAEBLcisQhYeH65NPPqm3fNeuXQoNDb3mRgEAALQktwLR2LFjNXXqVG3cuFF1dXWqq6vTBx98oGnTpmns2LFN3UYAAIBm5dZdZs8++6wOHz6swYMHy9v7/CbOnTunBx54gDFEAACgzXErEPn6+uq1117TM888o127dsnf31+9evVSTExMU7cPAACg2bkViC6Ii4tTXFxcU7UFAADAI9waQ1RXV6elS5cqNTVViYmJGjRokMvUWJmZmfrhD3+owMBAhYeHa8SIEdq/f79LjWEYmjVrlqKiouTv768BAwZo7969LjXV1dWaMmWKwsLCFBAQoJSUFBUXF7vUlJeXKy0tTXa7XXa7XWlpaTp16pQ7uw8AAK4zbgWiadOmadq0aaqrq1N8fLxuueUWl6mxNm/erEceeUT5+fnKzc3VN998o6SkJJ05c8asef755zV//nxlZWVp+/btcjgcGjJkiE6fPm3WpKena+3atcrOzlZeXp4qKyuVnJysuro6syY1NVWFhYXKyclRTk6OCgsLlZaW5s7uAwCA64zNMAzjaj8UFhZmvtC1KZ04cULh4eHavHmz7rjjDhmGoaioKKWnp+uJJ56QdL43KCIiQnPnztVDDz0kp9Opjh07atWqVRozZowk6dixY4qOjtb69es1dOhQ7du3Tz169FB+fr769u0rScrPz1dCQoL+85//qFu3bldsW0VFhex2u5xOp4KCgpp0vxfkHrhizWNDuDQJAMDVauzfb7d6iHx9fdW1a1e3G3cpTqdTkhQSEiJJKioqUmlpqZKSkswaPz8/3XnnndqyZYskqaCgQLW1tS41UVFRio+PN2s++ugj2e12MwxJUr9+/WS3282ai1VXV6uiosJlAgAA1ye3AlFGRoZefPFFudG5dEmGYWj69Om6/fbbFR8fL0kqLS2VJEVERLjURkREmOtKS0vl6+ur4ODgy9aEh4fX+87w8HCz5mKZmZnmeCO73a7o6Ohr20EAANBquXWXWV5enjZu3Kh3331XPXv2lI+Pj8v6N95446q3+eijj+qTTz5RXl5evXU2m81l3jCMessudnFNQ/WX287MmTM1ffp0c76iooJQBADAdcqtQNShQweNHDmyyRoxZcoUvfnmm/rwww914403mssdDoek8z08kZGR5vKysjKz18jhcKimpkbl5eUuvURlZWXq37+/WXP8+PF633vixIl6vU8X+Pn5yc/P79p3DgAAtHpuBaJly5Y1yZcbhqEpU6Zo7dq12rRpU70Xxnbp0kUOh0O5ubn6wQ9+IEmqqanR5s2bNXfuXElS79695ePjo9zcXI0ePVqSVFJSoj179uj555+XJCUkJMjpdGrbtm360Y9+JEnaunWrnE6nGZoAAIB1uf1gxm+++UabNm3SoUOHlJqaqsDAQB07dkxBQUFq3759o7bxyCOP6NVXX9U//vEPBQYGmuN57Ha7/P39ZbPZlJ6ertmzZys2NlaxsbGaPXu22rVrp9TUVLN24sSJysjIUGhoqEJCQjRjxgz16tVLiYmJkqTu3btr2LBhmjRpkpYsWSJJmjx5spKTkxt1hxkAALi+uRWIDh8+rGHDhunIkSOqrq7WkCFDFBgYqOeff15nz57VH//4x0ZtZ/HixZKkAQMGuCxftmyZxo8fL0l6/PHHVVVVpYcffljl5eXq27ev3n//fQUGBpr1CxYskLe3t0aPHq2qqioNHjxYy5cvl5eXl1mzZs0aTZ061bwbLSUlRVlZWe7sPgAAuM649RyiESNGKDAwUEuXLlVoaKh27dql7373u9q8ebMefPBBHTx4sDna6lE8hwgAgLansX+/3b7L7N///rd8fX1dlsfExOiLL75wZ5MAAAAe49ZziM6dO+fyWowLiouLXS5lAQAAtAVuBaIhQ4Zo4cKF5rzNZlNlZaWefvrpJn+dBwAAQHNz65LZggULNHDgQPXo0UNnz55VamqqDh48qLCwMP3lL39p6jYCANCqMRa07XMrEEVFRamwsFB/+ctftHPnTp07d04TJ07U/fffL39//6ZuIwAAQLNy+zlE/v7+mjBhgiZMmNCU7QEAAGhxbgWilStXXnb9Aw884FZjAAAAPMGtQDRt2jSX+draWn399dfy9fVVu3btCEQAAKBNcesus/LycpepsrJS+/fv1+23386gagAA0Oa4FYgaEhsbqzlz5tTrPQIAAGjtmiwQSZKXl5eOHTvWlJsEAABodm6NIXrzzTdd5g3DUElJibKysnTbbbc1ScMAAABailuBaMSIES7zNptNHTt21KBBg/TCCy80RbsAAABajFuB6Ny5c03dDgAAAI9p0jFEAAAAbZFbPUTTp09vdO38+fPd+QoAAIAW41Yg+vjjj7Vz505988036tatmyTpwIED8vLy0q233mrW2Wy2pmklAABAM3IrEN19990KDAzUihUrFBwcLOn8wxp//vOf68c//rEyMjKatJEAAADNya0xRC+88IIyMzPNMCRJwcHBevbZZ7nLDAAAtDluBaKKigodP3683vKysjKdPn36mhsFAADQktwKRCNHjtTPf/5z/e1vf1NxcbGKi4v1t7/9TRMnTtSoUaOauo0AAADNyq0xRH/84x81Y8YM/exnP1Ntbe35DXl7a+LEiZo3b16TNhAAAKC5uRWI2rVrp0WLFmnevHk6dOiQDMNQ165dFRAQ0NTtAwAAaHbX9GDGkpISlZSUKC4uTgEBATIMo6naBQAA0GLcCkQnT57U4MGDFRcXp7vuukslJSWSpAcffJBb7gEAQJvjViB67LHH5OPjoyNHjqhdu3bm8jFjxignJ6fJGgcAANAS3BpD9P777+u9997TjTfe6LI8NjZWhw8fbpKGAQAAtBS3eojOnDnj0jN0wZdffik/P79rbhQAAEBLcisQ3XHHHVq5cqU5b7PZdO7cOc2bN08DBw5sssYBAAC0BLcumc2bN08DBgzQjh07VFNTo8cff1x79+7VV199pX//+99N3UYAAIBm5VYPUY8ePfTJJ5/oRz/6kYYMGaIzZ85o1KhR+vjjj/W9732vqdsIAADQrK66h6i2tlZJSUlasmSJfve73zVHmwAAAFrUVfcQ+fj4aM+ePbLZbM3RHgAAgBbn1iWzBx54QEuXLm3qtgAAAHiEW4Oqa2pq9Kc//Um5ubnq06dPvXeYzZ8/v0kaBwAA0BKuKhD997//VefOnbVnzx7deuutkqQDBw641HApDQAAtDVXFYhiY2NVUlKijRs3Sjr/qo4//OEPioiIaJbGAQAAtISrGkN08dvs3333XZ05c6ZJGwQAANDS3BpUfcHFAelqffjhh7r77rsVFRUlm82mdevWuawfP368bDaby9SvXz+Xmurqak2ZMkVhYWEKCAhQSkqKiouLXWrKy8uVlpYmu90uu92utLQ0nTp16praDgAArh9XFYguhJKLl7nrzJkzuuWWW5SVlXXJmmHDhqmkpMSc1q9f77I+PT1da9euVXZ2tvLy8lRZWank5GTV1dWZNampqSosLFROTo5ycnJUWFiotLQ0t9sNAACuL1c1hsgwDI0fP958gevZs2f1i1/8ot5dZm+88Uajtjd8+HANHz78sjV+fn5yOBwNrnM6nVq6dKlWrVqlxMRESdLq1asVHR2tDRs2aOjQodq3b59ycnKUn5+vvn37SpJeeeUVJSQkaP/+/erWrVuj2goAAK5fV9VDNG7cOIWHh5uXnn72s58pKirKnL8wNaVNmzYpPDxccXFxmjRpksrKysx1BQUF5pOzL4iKilJ8fLy2bNkiSfroo49kt9vNMCRJ/fr1k91uN2saUl1drYqKCpcJAABcn66qh2jZsmXN1Y4GDR8+XPfdd59iYmJUVFSkp556SoMGDVJBQYH8/PxUWloqX19fBQcHu3wuIiJCpaWlkqTS0lKFh4fX23Z4eLhZ05DMzExeTQIAgEW49WDGljJmzBjz3/Hx8erTp49iYmL0zjvvaNSoUZf8nGEYLmObGhrndHHNxWbOnKnp06eb8xUVFYqOjr7aXQAAAG3ANd1l1tIiIyMVExOjgwcPSpIcDodqampUXl7uUldWVmY+G8nhcOj48eP1tnXixInLPj/Jz89PQUFBLhMAALg+talAdPLkSR09elSRkZGSpN69e8vHx0e5ublmTUlJifbs2aP+/ftLkhISEuR0OrVt2zazZuvWrXI6nWYNAACwNo9eMqusrNRnn31mzhcVFamwsFAhISEKCQnRrFmzdO+99yoyMlKff/65nnzySYWFhWnkyJGSJLvdrokTJyojI0OhoaEKCQnRjBkz1KtXL/Ous+7du2vYsGGaNGmSlixZIkmaPHmykpOTucMMAABI8nAg2rFjhwYOHGjOXxizM27cOC1evFi7d+/WypUrderUKUVGRmrgwIF67bXXFBgYaH5mwYIF8vb21ujRo1VVVaXBgwdr+fLl8vLyMmvWrFmjqVOnmnejpaSkXPbZRwAAwFpsxrU+btoiKioqZLfb5XQ6m3w80YLcA1eseWxIXJN+JwCg6fB7vPVq7N/vNjWGCAAAoDkQiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOV5NBB9+OGHuvvuuxUVFSWbzaZ169a5rDcMQ7NmzVJUVJT8/f01YMAA7d2716WmurpaU6ZMUVhYmAICApSSkqLi4mKXmvLycqWlpclut8tutystLU2nTp1q5r0DAABthUcD0ZkzZ3TLLbcoKyurwfXPP/+85s+fr6ysLG3fvl0Oh0NDhgzR6dOnzZr09HStXbtW2dnZysvLU2VlpZKTk1VXV2fWpKamqrCwUDk5OcrJyVFhYaHS0tKaff8AAEDb4O3JLx8+fLiGDx/e4DrDMLRw4UL95je/0ahRoyRJK1asUEREhF599VU99NBDcjqdWrp0qVatWqXExERJ0urVqxUdHa0NGzZo6NCh2rdvn3JycpSfn6++fftKkl555RUlJCRo//796tatW8vsLAAAaLVa7RiioqIilZaWKikpyVzm5+enO++8U1u2bJEkFRQUqLa21qUmKipK8fHxZs1HH30ku91uhiFJ6tevn+x2u1nTkOrqalVUVLhMAADg+tRqA1FpaakkKSIiwmV5RESEua60tFS+vr4KDg6+bE14eHi97YeHh5s1DcnMzDTHHNntdkVHR1/T/gAAgNar1QaiC2w2m8u8YRj1ll3s4pqG6q+0nZkzZ8rpdJrT0aNHr7LlAACgrWi1gcjhcEhSvV6csrIys9fI4XCopqZG5eXll605fvx4ve2fOHGiXu/Tt/n5+SkoKMhlAgAA16dWG4i6dOkih8Oh3Nxcc1lNTY02b96s/v37S5J69+4tHx8fl5qSkhLt2bPHrElISJDT6dS2bdvMmq1bt8rpdJo1AADA2jx6l1llZaU+++wzc76oqEiFhYUKCQlRp06dlJ6ertmzZys2NlaxsbGaPXu22rVrp9TUVEmS3W7XxIkTlZGRodDQUIWEhGjGjBnq1auXeddZ9+7dNWzYME2aNElLliyRJE2ePFnJycncYQYAACR5OBDt2LFDAwcONOenT58uSRo3bpyWL1+uxx9/XFVVVXr44YdVXl6uvn376v3331dgYKD5mQULFsjb21ujR49WVVWVBg8erOXLl8vLy8usWbNmjaZOnWrejZaSknLJZx8BAADrsRmGYXi6EW1BRUWF7Ha7nE5nk48nWpB74Io1jw2Ja9LvBAA0HX6Pt16N/fvdascQAQAAtBQCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsLxWHYhmzZolm83mMjkcDnO9YRiaNWuWoqKi5O/vrwEDBmjv3r0u26iurtaUKVMUFhamgIAApaSkqLi4uKV3BQAAtGKtOhBJUs+ePVVSUmJOu3fvNtc9//zzmj9/vrKysrR9+3Y5HA4NGTJEp0+fNmvS09O1du1aZWdnKy8vT5WVlUpOTlZdXZ0ndgcAALRC3p5uwJV4e3u79ApdYBiGFi5cqN/85jcaNWqUJGnFihWKiIjQq6++qoceekhOp1NLly7VqlWrlJiYKElavXq1oqOjtWHDBg0dOrRF9wUAALROrb6H6ODBg4qKilKXLl00duxY/fe//5UkFRUVqbS0VElJSWatn5+f7rzzTm3ZskWSVFBQoNraWpeaqKgoxcfHmzWXUl1drYqKCpcJAABcn1p1IOrbt69Wrlyp9957T6+88opKS0vVv39/nTx5UqWlpZKkiIgIl89ERESY60pLS+Xr66vg4OBL1lxKZmam7Ha7OUVHRzfhngEAgNakVQei4cOH695771WvXr2UmJiod955R9L5S2MX2Gw2l88YhlFv2cUaUzNz5kw5nU5zOnr0qJt7AQAAWrtWHYguFhAQoF69eungwYPmuKKLe3rKysrMXiOHw6GamhqVl5dfsuZS/Pz8FBQU5DIBAIDrU5sKRNXV1dq3b58iIyPVpUsXORwO5ebmmutramq0efNm9e/fX5LUu3dv+fj4uNSUlJRoz549Zg0AAECrvstsxowZuvvuu9WpUyeVlZXp2WefVUVFhcaNGyebzab09HTNnj1bsbGxio2N1ezZs9WuXTulpqZKkux2uyZOnKiMjAyFhoYqJCREM2bMMC/BAQAASK08EBUXF+unP/2pvvzyS3Xs2FH9+vVTfn6+YmJiJEmPP/64qqqq9PDDD6u8vFx9+/bV+++/r8DAQHMbCxYskLe3t0aPHq2qqioNHjxYy5cvl5eXl6d2CwAAtDI2wzAMTzeiLaioqJDdbpfT6Wzy8UQLcg9cseaxIXFN+p0AgKbD7/HWq7F/v9vUGCIAAIDmQCACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACW5+3pBqBxFuQeuGLNY0PiWqAlAABcf+ghAgAAlkcgAgAAlsclMwAAWgmGR3gOPUQAAMDyCEQAAMDyCEQAAMDyGEMEAGLsBmB19BABAADLIxABAADLIxABAADLIxABAADLIxABAADL4y4zAADQrNrCXZyW6iFatGiRunTpou985zvq3bu3/vWvf3m6SQAAoBWwTA/Ra6+9pvT0dC1atEi33XablixZouHDh+vTTz9Vp06dPN28FtMWUjoAAC3NMoFo/vz5mjhxoh588EFJ0sKFC/Xee+9p8eLFyszM9HDrgGtD0G0ZHGfg+mWJQFRTU6OCggL9+te/dlmelJSkLVu2NPiZ6upqVVdXm/NOp1OSVFFR0eTtO3umskm205i2Nea7mmMf0bz4uV67lvzvsC36fx98dsWaRwZ1bYGWtLzG7HtjtOTv6Nb28/Lk76gL2zUM4/KFhgV88cUXhiTj3//+t8vy5557zoiLi2vwM08//bQhiYmJiYmJiek6mI4ePXrZrGCJHqILbDaby7xhGPWWXTBz5kxNnz7dnD937py++uorhYaGXvIz7qioqFB0dLSOHj2qoKCgJtvu9YhjdXU4Xo3HsWo8jlXjcawarzmPlWEYOn36tKKioi5bZ4lAFBYWJi8vL5WWlrosLysrU0RERIOf8fPzk5+fn8uyDh06NFcTFRQUxH8wjcSxujocr8bjWDUex6rxOFaN11zHym63X7HGErfd+/r6qnfv3srNzXVZnpubq/79+3uoVQAAoLWwRA+RJE2fPl1paWnq06ePEhIS9PLLL+vIkSP6xS9+4emmAQAAD7NMIBozZoxOnjyp3//+9yopKVF8fLzWr1+vmJgYj7bLz89PTz/9dL3Lc6iPY3V1OF6Nx7FqPI5V43GsGq81HCubYVzpPjQAAIDrmyXGEAEAAFwOgQgAAFgegQgAAFgegQgAAFgegaiZfPjhh7r77rsVFRUlm82mdevWuax/4403NHToUIWFhclms6mwsLDeNqqrqzVlyhSFhYUpICBAKSkpKi4ubpkdaEFNcawGDBggm83mMo0dO7ZldqAFXe5Y1dbW6oknnlCvXr0UEBCgqKgoPfDAAzp27JjLNjivGn+sOK/OmzVrlm666SYFBAQoODhYiYmJ2rp1q0sN59V5jTlWnFf1PfTQQ7LZbFq4cKHL8pY8rwhEzeTMmTO65ZZblJWVdcn1t912m+bMmXPJbaSnp2vt2rXKzs5WXl6eKisrlZycrLq6uuZqtkc0xbGSpEmTJqmkpMSclixZ0hzN9ajLHauvv/5aO3fu1FNPPaWdO3fqjTfe0IEDB5SSkuJSx3nV+GMlcV5JUlxcnLKysrR7927l5eWpc+fOSkpK0okTJ8wazqvzGnOsJM6rb1u3bp22bt3a4Ks1WvS8apK3p+KyJBlr165tcF1RUZEhyfj4449dlp86dcrw8fExsrOzzWVffPGFccMNNxg5OTnN2FrPcudYGYZh3Hnnnca0adOatW2tzeWO1QXbtm0zJBmHDx82DIPz6nIuPlaGwXl1KU6n05BkbNiwwTAMzqvLufhYGQbn1bcVFxcb//M//2Ps2bPHiImJMRYsWGCua+nzih6iVqqgoEC1tbVKSkoyl0VFRSk+Pl5btmzxYMtarzVr1igsLEw9e/bUjBkzdPr0aU83yeOcTqdsNpv5Hj7Oq0u7+FhdwHnlqqamRi+//LLsdrtuueUWSZxXl9LQsbqA8+r8S9PT0tL0q1/9Sj179qy3vqXPK8s8qbqtKS0tla+vr4KDg12WR0RE1HtJLaT7779fXbp0kcPh0J49ezRz5kzt2rWr3vvrrOTs2bP69a9/rdTUVPNliZxXDWvoWEmcV9/29ttva+zYsfr6668VGRmp3NxchYWFSeK8utjljpXEeXXB3Llz5e3tralTpza4vqXPKwJRG2MYhmw2m6eb0epMmjTJ/Hd8fLxiY2PVp08f7dy5U7feeqsHW+YZtbW1Gjt2rM6dO6dFixZdsd7K59XljhXn1f8ZOHCgCgsL9eWXX+qVV17R6NGjtXXrVoWHh1/yM1Y9r650rDivzvf+vPjii9q5c+dVnyPNdV5xyayVcjgcqqmpUXl5ucvysrIyRUREeKhVbcett94qHx8fHTx40NNNaXG1tbUaPXq0ioqKlJub69LjwXnl6nLHqiFWPq8CAgLUtWtX9evXT0uXLpW3t7eWLl0qifPqYpc7Vg2x4nn1r3/9S2VlZerUqZO8vb3l7e2tw4cPKyMjQ507d5bU8ucVgaiV6t27t3x8fFy6UEtKSrRnzx7179/fgy1rG/bu3ava2lpFRkZ6uikt6sIf+IMHD2rDhg0KDQ11Wc959X+udKwaYtXzqiGGYai6uloS59WVfPtYNcSK51VaWpo++eQTFRYWmlNUVJR+9atf6b333pPU8ucVl8yaSWVlpT777DNzvqioSIWFhQoJCVGnTp301Vdf6ciRI+ZzT/bv3y/pfCJ2OByy2+2aOHGiMjIyFBoaqpCQEM2YMUO9evVSYmKiR/apuVzrsTp06JDWrFmju+66S2FhYfr000+VkZGhH/zgB7rttts8sk/N5XLHKioqSj/5yU+0c+dOvf3226qrqzOvs4eEhMjX15fz6iqOFefV+WMVGhqq5557TikpKYqMjNTJkye1aNEiFRcX67777pMkzqurOFacV//3u/3i/wnx8fGRw+FQt27dJHngvGry+9ZgGIZhbNy40ZBUbxo3bpxhGIaxbNmyBtc//fTT5jaqqqqMRx991AgJCTH8/f2N5ORk48iRI57ZoWZ0rcfqyJEjxh133GGEhIQYvr6+xve+9z1j6tSpxsmTJz23U83kcsfqwmMJGpo2btxoboPzqnHHivPq/LGqqqoyRo4caURFRRm+vr5GZGSkkZKSYmzbts1lG5xXjTtWnFf/97v9Yhffdm8YLXte2QzDMJosXQEAALRBjCECAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACW9/8BQo93XjgHkegAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distribution of length for normal_period_ddf and drought_period_ddf\n",
    "fig, ax = plt.subplots()\n",
    "normal_period_df['length'].plot.hist(ax=ax, bins=50, alpha=0.5, label='Normal period')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "drought_period_df['length'].plot.hist(ax=ax, bins=50, alpha=0.5, label='Drought period')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "drought_period_df['cog'].plot.hist()\n",
    "plt.title('Distribution of COG')\n",
    "plt.xlabel('COG')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "index                                                    841\n",
       "sog                                                      5.1\n",
       "cog                                                74.400002\n",
       "rot                                                      0.0\n",
       "longitude                                           5.871003\n",
       "latitude                                           51.850986\n",
       "maneuver                                                   0\n",
       "seconds                                                 33.0\n",
       "name                                          testschip-1001\n",
       "draughtInland                                            0.4\n",
       "length                                                 110.0\n",
       "width                                                   12.0\n",
       "datetime                           2022-06-24 05:08:32+00:00\n",
       "time_diff                                                NaN\n",
       "traj_id                                                    4\n",
       "distance                                           45.374069\n",
       "speed                                              16.334665\n",
       "acceleration                                        0.025127\n",
       "geometry         POINT (5.871003150939941 51.85098648071289)\n",
       "lateral_pos                                        51.765461\n",
       "upstream                                                True\n",
       "Name: 5, dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot one point with the polygon and orientation line to validate the lateral position calculation\n",
    "i = 5\n",
    "fig, ax = plt.subplots()\n",
    "gpd.GeoSeries([polygon]).plot(ax=ax, color='red')\n",
    "gpd.GeoSeries([new_line]).plot(ax=ax)\n",
    "gpd.GeoSeries([Point(normal_period_df['longitude'].iloc[i], normal_period_df['latitude'].iloc[i])]).plot(ax=ax, color='blue')\n",
    "plt.show()\n",
    "normal_period_df.iloc[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_period_df['datetime'].dt.date.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drought_period_df['datetime'].dt.date.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing most severe outliers\n",
    "normal_period_df = normal_period_df[normal_period_df['sog'] < normal_period_df['sog'].quantile(0.99)]\n",
    "normal_period_df = normal_period_df[normal_period_df['sog'] > normal_period_df['sog'].quantile(0.01)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "drought_period_df = drought_period_df[drought_period_df['sog'] < drought_period_df['sog'].quantile(0.99)]\n",
    "drought_period_df = drought_period_df[drought_period_df['sog'] > drought_period_df['sog'].quantile(0.01)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "    <style>\n",
       "        .bk-notebook-logo {\n",
       "            display: block;\n",
       "            width: 20px;\n",
       "            height: 20px;\n",
       "            background-image: url(data:image/png;base64,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);\n",
       "        }\n",
       "    </style>\n",
       "    <div>\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
       "        <span id=\"b7a19886-a850-4c95-afa5-ef8ed929b23a\">Loading BokehJS ...</span>\n",
       "    </div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  const force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "const JS_MIME_TYPE = 'application/javascript';\n",
       "  const HTML_MIME_TYPE = 'text/html';\n",
       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    const script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    function drop(id) {\n",
       "      const view = Bokeh.index.get_by_id(id)\n",
       "      if (view != null) {\n",
       "        view.model.document.clear()\n",
       "        Bokeh.index.delete(view)\n",
       "      }\n",
       "    }\n",
       "\n",
       "    const cell = handle.cell;\n",
       "\n",
       "    const id = cell.output_area._bokeh_element_id;\n",
       "    const server_id = cell.output_area._bokeh_server_id;\n",
       "\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null) {\n",
       "      drop(id)\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd_clean, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            const id = msg.content.text.trim()\n",
       "            drop(id)\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd_destroy);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    const output_area = handle.output_area;\n",
       "    const output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      const bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      const script_attrs = bk_div.children[0].attributes;\n",
       "      for (let i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      const toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    const events = require('base/js/events');\n",
       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    const el = document.getElementById(\"b7a19886-a850-4c95-afa5-ef8ed929b23a\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error(url) {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < css_urls.length; i++) {\n",
       "      const url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
       "  const css_urls = [];\n",
       "\n",
       "  const inline_js = [    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "          for (let i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      const cell = $(document.getElementById(\"b7a19886-a850-4c95-afa5-ef8ed929b23a\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"b7a19886-a850-4c95-afa5-ef8ed929b23a\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"b7a19886-a850-4c95-afa5-ef8ed929b23a\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
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       "  const render_items = [{\"docid\":\"d3c9644d-a0c5-42be-87ab-4ce074040a05\",\"roots\":{\"p1493\":\"f8df4c46-421d-4c53-be47-a8898a552855\"},\"root_ids\":[\"p1493\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
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    "\n",
    "# Ensure output is rendered correctly in your notebook\n",
    "output_notebook()\n",
    "\n",
    "def plot_lateral_distribution_and_speeds(df, title, observation_days):\n",
    "    # Data preparation\n",
    "    lateral_positions_upstream = df[df['upstream']]['lateral_pos']\n",
    "    lateral_positions_downstream = df[~df['upstream']]['lateral_pos']\n",
    "\n",
    "    # Define the common range and bin edges for lateral positions\n",
    "    bin_edges = np.linspace(-125, 125, 51)  # 50 bins between -125 and 125\n",
    "\n",
    "    # Compute histograms and mean speeds\n",
    "    hist_up, edges_up = np.histogram(lateral_positions_upstream, bins=bin_edges)\n",
    "    hist_down, edges_down = np.histogram(lateral_positions_downstream, bins=bin_edges)\n",
    "\n",
    "    # Normalize histograms to crossings per day\n",
    "    hist_up_per_day = hist_up / observation_days\n",
    "    hist_down_per_day = hist_down / observation_days\n",
    "\n",
    "    # Calculate bin widths\n",
    "    bin_widths_up = np.diff(edges_up)\n",
    "    bin_widths_down = np.diff(edges_down)\n",
    "\n",
    "    # Normalize histograms to unit area\n",
    "    hist_up_normalized = hist_up_per_day / (hist_up_per_day * bin_widths_up).sum()\n",
    "    hist_down_normalized = hist_down_per_day / (hist_down_per_day * bin_widths_down).sum()\n",
    "\n",
    "    # Prepare histogram data for plotting\n",
    "    hist_df_up = pd.DataFrame({'hist': hist_up_normalized, 'left': edges_up[:-1], 'right': edges_up[1:]})\n",
    "    hist_df_down = pd.DataFrame({'hist': hist_down_normalized, 'left': edges_down[:-1], 'right': edges_down[1:]})\n",
    "\n",
    "    # Calculate mean speeds\n",
    "    mean_speeds_up = [df.loc[(df['upstream']) & (df['lateral_pos'] >= edges_up[i]) & (df['lateral_pos'] < edges_up[i+1]), 'sog'].mean()*1.852 for i in range(len(edges_up)-1)]\n",
    "    mean_speeds_down = [df.loc[(~df['upstream']) & (df['lateral_pos'] >= edges_down[i]) & (df['lateral_pos'] < edges_down[i+1]), 'sog'].mean()*1.852 for i in range(len(edges_down)-1)]\n",
    "\n",
    "       # Create a new plot\n",
    "    p = figure(title=title, x_axis_label='Lateral Position from Center', y_axis_label='Normalized Crossing Frequency')\n",
    "    \n",
    "    # Calculate appropriate y-ranges for histogram to make sure it's visible\n",
    "    max_hist_density = max(hist_up_normalized.max(), hist_down_normalized.max()) * 1.1  # 10% padding\n",
    "    p.y_range = Range1d(0, max_hist_density)  # Adjust the primary y-range to suit histogram values\n",
    "    \n",
    "    # Add histograms with transparency\n",
    "    p.quad(bottom=0, top='hist', left='left', right='right', source=ColumnDataSource(hist_df_up), fill_color='blue', line_color='black', legend_label=\"Upstream\", alpha=0.6)\n",
    "    p.quad(bottom=0, top='hist', left='left', right='right', source=ColumnDataSource(hist_df_down), fill_color='red', line_color='black', legend_label=\"Downstream\", alpha=0.6)\n",
    "\n",
    "\n",
    "    # Define a secondary range for plotting speeds below the primary histogram\n",
    "    max_hist = max(hist_up_normalized.max(), hist_down_normalized.max())\n",
    "    p.extra_y_ranges = {\"speed\": Range1d(start=0, end=30)}\n",
    "    p.add_layout(LinearAxis(y_range_name=\"speed\", axis_label=\"Mean Speed (km/h)\"), 'right')\n",
    "\n",
    "    # Add mean speeds using line with inverted y-axis\n",
    "    p.line(x=edges_up[:-1], y=mean_speeds_up, y_range_name=\"speed\", color='darkblue', legend_label=\"Mean Speed Upstream\", line_width=2)\n",
    "    p.line(x=edges_down[:-1], y=mean_speeds_down, y_range_name=\"speed\", color='darkred', legend_label=\"Mean Speed Downstream\", line_width=2)\n",
    "\n",
    "    # Customizations\n",
    "    p.grid.grid_line_alpha = 0.3\n",
    "    p.title.align = 'center'\n",
    "    p.legend.label_text_font_size = '10pt'  # Change '8pt' to any other appropriate size\n",
    "    p.legend.padding = 5  # Reduce padding; default is 10\n",
    "    p.legend.margin = 5   # Reduce margin around the legend\n",
    "\n",
    "    p.line(x=[0, 0], y=[0, max_hist], line_width=2, color='red', line_dash='dotted')\n",
    "    \n",
    "    p.line(x=[-110, -110], y=[0, max_hist], line_width=2, color='black')\n",
    "    p.line(x=[110, 110], y=[0, max_hist], line_width=2, color='black')\n",
    "    # Title font\n",
    "    p.title.text_font_size = '11pt'\n",
    "    p.title.text_font = \"Arial\"\n",
    "    p.title.text_color = \"black\"\n",
    "    \n",
    "    # Axis labels\n",
    "    p.xaxis.axis_label_text_font_size = '12pt'\n",
    "    p.xaxis.axis_label_text_font_style = 'bold'\n",
    "    p.yaxis.axis_label_text_font_size = '12pt'\n",
    "    p.yaxis.axis_label_text_font_style = 'bold'\n",
    "    \n",
    "    # Axis major labels (ticks)\n",
    "    p.xaxis.major_label_text_font_size = '10pt'\n",
    "    p.yaxis.major_label_text_font_size = '10pt'\n",
    "    \n",
    "    # Legend font\n",
    "    p.legend.label_text_font_size = '10pt'\n",
    "    p.legend.label_text_font = \"Arial\"\n",
    "\n",
    "    p.toolbar_location = None \n",
    "\n",
    "\n",
    "    # Display the plot\n",
    "    show(p)\n",
    "    return(p)\n",
    "    \n",
    "\n",
    "# Example usage, assuming you know the number of days of each dataset\n",
    "p = plot_lateral_distribution_and_speeds(df=normal_period_df, title='Distribution of Ship Crossings with Speeds - Normal Period (May-June 2022)', observation_days=61)\n",
    "#save(p, filename=\"distrib_spoor_normal.html\")\n",
    "\n",
    "p = plot_lateral_distribution_and_speeds(df=drought_period_df, title='Distribution of Ship Crossings with Speeds Drought Period (July-August 2022)  ', observation_days=61)\n",
    "\n",
    "#save(p, filename=\"distrib_spoor_drought.html\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "### G test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G-test statistic: 831.7850524212653\n",
      "p-value: 3.285123495596507e-116\n",
      "Degrees of freedom: 49\n",
      "Observed frequencies:\n",
      "[[  0   0   1   1   2   5   8  20  21  46  75  82 104 145 124 169 149  75\n",
      "  118  90  75  66  61  47  38  37  29  25  27  29  27  38  31  57  91  84\n",
      "  122 208 200 259 282 244 261 171  91  76  30   5   2   0]\n",
      " [  1   1   0   1   4   9  21  26  44 101 132 119 165 202 162 181 151 103\n",
      "  147  96  66  58  45  27  38  17  18  21   7  11   8  12  18  25  31  30\n",
      "   50  55  66  78 118 124 149 146  90  78  46  19  11   3]]\n",
      "Expected frequencies:\n",
      "[[  1.67311767   1.67311767   1.67311767   3.90394123   8.92329425\n",
      "   16.73117672  23.9813533   45.73188303  61.34764797 107.07953101\n",
      "  120.46447238 136.63794321 167.86947309 180.69670857 171.77341432\n",
      "  153.92682582 138.31106088 107.6372369  127.71464896  94.25229552\n",
      "   67.48241277  60.23223619  51.30894194  32.90464755  41.8279418\n",
      "   27.32758864  20.07741206  25.65447097  20.07741206  17.8465885\n",
      "   22.30823563  25.09676508  30.1161181   42.38564769  63.57847154\n",
      "   63.57847154  90.90606018 115.44511937 142.77270801 173.446532\n",
      "  201.33182653 196.31247351 232.00565052 189.62000283 129.38776663\n",
      "   94.25229552  59.6745303   26.21217686  10.03870603   2.78852945]\n",
      " [  1.32688233   1.32688233   1.32688233   3.09605877   7.07670575\n",
      "   13.26882328  19.0186467   36.26811697  48.65235203  84.92046899\n",
      "   95.53552762 108.36205679 133.13052691 143.30329143 136.22658568\n",
      "  122.07317418 109.68893912  85.3627631  101.28535104  74.74770448\n",
      "   53.51758723  47.76776381  40.69105806  26.09535245  33.1720582\n",
      "   21.67241136  15.92258794  20.34552903  15.92258794  14.1534115\n",
      "   17.69176437  19.90323492  23.8838819   33.61435231  50.42152846\n",
      "   50.42152846  72.09393982  91.55488063 113.22729199 137.553468\n",
      "  159.66817347 155.68752649 183.99434948 150.37999717 102.61223337\n",
      "   74.74770448  47.3254697   20.78782314   7.96129397   2.21147055]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import power_divergence\n",
    "\n",
    "# Assuming drought_period_df and normal_period_df are already defined\n",
    "upstream_drought = drought_period_df.loc[drought_period_df['upstream'] == True]\n",
    "upstream_normal = normal_period_df.loc[normal_period_df['upstream'] == True]\n",
    "\n",
    "def prepare_histogram_data(df, bins=50):\n",
    "    hist, bin_edges = np.histogram(df['lateral_pos'], bins=bins)\n",
    "    return hist, bin_edges\n",
    "\n",
    "# Generate histograms for drought and normal periods\n",
    "hist_drought, bin_edges_drought = prepare_histogram_data(upstream_drought, bins=50)\n",
    "hist_normal, bin_edges_normal = prepare_histogram_data(upstream_normal, bins=50)\n",
    "\n",
    "bin_edges = np.linspace(min(bin_edges_drought[0], bin_edges_normal[0]), \n",
    "                        max(bin_edges_drought[-1], bin_edges_normal[-1]), \n",
    "                        len(bin_edges_drought))\n",
    "hist_drought, _ = np.histogram(upstream_drought['lateral_pos'], bins=bin_edges)\n",
    "hist_normal, _ = np.histogram(upstream_normal['lateral_pos'], bins=bin_edges)\n",
    "\n",
    "# Combine the histograms into a contingency table\n",
    "observed = np.array([hist_drought, hist_normal])\n",
    "\n",
    "# Calculate the expected frequencies assuming no difference between periods\n",
    "total_drought = hist_drought.sum()\n",
    "total_normal = hist_normal.sum()\n",
    "total = total_drought + total_normal\n",
    "\n",
    "# Expected frequencies based on the null hypothesis\n",
    "expected_drought = (hist_drought + hist_normal) * (total_drought / total)\n",
    "expected_normal = (hist_drought + hist_normal) * (total_normal / total)\n",
    "\n",
    "# Combine expected frequencies into the same structure as observed\n",
    "expected = np.where(expected == 0, 1e-10, expected)\n",
    "\n",
    "# Check for zero expected frequencies\n",
    "if np.any(expected == 0):\n",
    "    print(\"Warning: Zero expected frequencies found!\")\n",
    "    expected = np.where(expected == 0, 1e-10, expected)  # Avoid zero expected frequencies\n",
    "\n",
    "# Perform the G-test on the contingency table\n",
    "g_stat, p_value = power_divergence(f_obs=observed.flatten(), f_exp=expected.flatten(), lambda_='log-likelihood')\n",
    "\n",
    "# Calculate degrees of freedom\n",
    "dof = (observed.shape[0] - 1) * (observed.shape[1] - 1)\n",
    "\n",
    "print(f'G-test statistic: {g_stat}')\n",
    "print(f'p-value: {p_value}')\n",
    "print(f'Degrees of freedom: {dof}')\n",
    "print(f'Observed frequencies:\\n{observed}')\n",
    "print(f'Expected frequencies:\\n{expected}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Bathymetry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Possibility to add bathymetry values but not used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import rasterio\n",
    "from rasterio.plot import show\n",
    "\n",
    "# Load the GeoTIFF file\n",
    "bathymetry_path = 'bathy_right.tif'\n",
    "bathymetry = rasterio.open(bathymetry_path)\n",
    "\n",
    "# Show the bathymetry map (optional)\n",
    "show(bathymetry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normal and drought df to gdf\n",
    "def to_geodataframe(df):\n",
    "    return gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.longitude, df.latitude))\n",
    "normal_gdf = to_geodataframe(normal_period_df)\n",
    "drought_gdf = to_geodataframe(drought_period_df)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "bathymetry_crs = bathymetry.crs\n",
    "# give AIS crs their WGS84\n",
    "\n",
    "normal_gdf.crs = 'EPSG:4326'\n",
    "drought_gdf.crs = 'EPSG:4326'\n",
    "\n",
    "# Change to bathymetry crs\n",
    "normal_gdf = normal_gdf.to_crs(bathymetry_crs)\n",
    "drought_gdf = drought_gdf.to_crs(bathymetry_crs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_bathymetry_values(raster, gdf):\n",
    "    coords = [(geom.x, geom.y) for geom in gdf.geometry]\n",
    "    bathymetry_values = []\n",
    "\n",
    "    for coord in coords:\n",
    "        row, col = raster.index(coord[0], coord[1])\n",
    "        bathymetry_values.append(raster.read(1)[row, col])\n",
    "\n",
    "    return bathymetry_values\n",
    "\n",
    "# Extract bathymetry values for AIS points\n",
    "drought_gdf['bathymetry'] = extract_bathymetry_values(bathymetry, drought_gdf)\n",
    "normal_gdf['bathymetry'] = extract_bathymetry_values(bathymetry, normal_gdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the drought and normal gdf\n",
    "drought_gdf.to_file('drought_gdf.geojson', driver='GeoJSON')\n",
    "normal_gdf.to_file('normal_gdf.geojson', driver='GeoJSON')\n",
    "# plot the bathymetry column using the geometry column as well\n",
    "fig, ax = plt.subplots()\n",
    "drought_gdf.plot(column='bathymetry', ax=ax, legend=True)\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "normal_gdf.plot(column='bathymetry', ax=ax, legend=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Discharge Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of instances with value .999999E+38: 199\n"
     ]
    }
   ],
   "source": [
    "# Debiet / Current\n",
    "debiet_xlsx = pd.read_excel('debiet.xlsx')\n",
    "\n",
    "debiet = debiet_xlsx[['WAARNEMINGDATUM', 'WAARNEMINGTIJD (MET/CET)', 'LOCATIE_CODE', 'ALFANUMERIEKEWAARDE']]\n",
    "\n",
    "debiet.loc[:, 'datetime'] = pd.to_datetime(debiet['WAARNEMINGDATUM'].astype(str) + ' ' + debiet['WAARNEMINGTIJD (MET/CET)'].astype(str))\n",
    "\n",
    "# Drop the original separate date and time columns\n",
    "debiet.drop(columns=['WAARNEMINGDATUM', 'WAARNEMINGTIJD (MET/CET)'], inplace=True)\n",
    "\n",
    "count = (debiet['ALFANUMERIEKEWAARDE'] == .999999E+38).sum()\n",
    "\n",
    "print(\"Number of instances with value .999999E+38:\", count)\n",
    "\n",
    "debiet = debiet[(debiet['ALFANUMERIEKEWAARDE'] != .999999E+38) & (debiet['ALFANUMERIEKEWAARDE'] != 0) & (debiet['LOCATIE_CODE']== \"LOBH\")]\n",
    "\n",
    "# Convert to Dask DataFrame\n",
    "#debiet = dd.from_pandas(debiet, npartitions=10)\n",
    "\n",
    "# Convert the 'datetime' column to datetime using the specified format\n",
    "debiet['datetime'] = pd.to_datetime(debiet['datetime'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a plot of the debiet (ALFANUMERIEKEWAARDE) over time\n",
    "debiet = debiet.set_index('datetime')\n",
    "\n",
    "debiet = debiet.reset_index()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "debiet.plot(x='datetime', y='ALFANUMERIEKEWAARDE', ax=ax,  label='Discharge (m3/s)')\n",
    "ax.set_title('Discharge Lobith 2022')\n",
    "ax.set_ylabel('Discharge (m3/s)')\n",
    "ax.set_xlabel('Date')\n",
    "ax.axhline(y=1020, color='r', linestyle='--', label='Agreed Low River Discharge (ALR)')\n",
    "ax.axvline(x=pd.to_datetime('2022-07'), color='g', linestyle='--', label='Normal - Extreme drought split')\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlim( left=pd.to_datetime('2022-05-01'), right=pd.to_datetime('2022-09'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example of setting 'datetime' as the index and ensuring it is sorted\n",
    "debiet['datetime'] = dd.to_datetime(debiet['datetime'])\n",
    "\n",
    "# Set datetime as the index and sort\n",
    "ddf = ddf.set_index('datetime')\n",
    "debiet = debiet.set_index('datetime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Possibility to add Discharge data to dask dataframes\n",
    "def add_debiet_to_ais(ais_df, debiet_df):\n",
    "    # Perform an asof merge using the indices\n",
    "    merged_df = dd.merge_asof(ais_df, debiet_df, left_index=True, right_index=True, direction='nearest')\n",
    "\n",
    "    # Rename and clean up\n",
    "    merged_df = merged_df.rename(columns={'ALFANUMERIEKEWAARDE': 'discharge'})\n",
    "\n",
    "    return merged_df\n",
    "\n",
    "# Assuming ais_gdf and debiet are Dask DataFrames\n",
    "#df['datetime'] = df['datetime'].dt.tz_localize(None)\n",
    "\n",
    "ddf_dis = add_debiet_to_ais(ddf, debiet)\n",
    "ddf_dis = ddf_dis.persist()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}