09_Directional_Traffic_Flow_Map.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9a942ad5",
   "metadata": {},
   "source": [
    "# Directional Flow Map\n",
    "## Notebook 9\n",
    "\n",
    "## Gabriel del Valle\n",
    "## 10/25/24\n",
    "## NYC DATA SCIENCE ACADEMY\n",
    "\n",
    "### For any questions about this project or to request full map videos or datasets, please feel free to reach out on Linkedin: \n",
    "\n",
    "   www.linkedin.com/in/gabriel-del-valle-147616152\n",
    "\n",
    "\n",
    "In this notebook I've revisited the Animated NYC Congestion Zone Traffic Map Analysis to portray the to street and from street relationships in the data and the directional exchanges of volume they represent. My goal was to portray these directional volume relationships using markers systemically placed along the length of each relevant street. These markers both clarify where directional volume flow has been recorded in the Automated Traffic Counts dataset and allow me to use a color gradient to portray the direction of the traffic.\n",
    "\n",
    "Exchanges of volume between two streets are corresponded to a point on a map (an intersection) by comparing the toStreet and fromStreet values of each intersecting street. An intersection marker is generated for each intersection recorded in the data by comparing geometric intersections and the names of streets where geometric intersections occur in the base_map.geojson dataset.\n",
    "\n",
    "- In all rows of the Automated Traffic Counts dataset, the flow of traffic volume exchanged between intersections is uni-directional.\n",
    "\n",
    "- The grey-scale street markers are placed between intersections that have an exchange of volume recorded between them in the Automated Traffic Counts dataset.\n",
    "\n",
    "\n",
    "The data for connections between intersection markers and street markers, as well as the index of the street markers are generated by markerkey_connections_map2(). \n",
    "\n",
    "Street index values indicate the direction of flow: Index values towards 0 connect to \"gain_streets\" whereas index values at the end of the list connect to \"lose_streets\".\n",
    "\n",
    "Greyscale colors are assigned to street markers, normalized to the number of markers between two intersections.\n",
    "\n",
    "The greyscale colors do not imply volume quantities, as there is not data to support the assignment of specific volumes to the street markers between known intersections. Rather, the greyscale colors are purely meant to indicate the directional flow of the traffic.\n",
    "\n",
    "While intersection markers could be colored, as they have known to_volume, from_volume, net_volume, absolute_volume, and congestion values per datetime, I chose not to color intersection markers, since intersection markers are most often very near the WktGeom marker that is the source of its data. Thus, allowing the WktGeom markers alone to reflect the volume / congestion values gives clarity and precision to where recorded volumes correspond.\n",
    "\n",
    "\n",
    "To further develop what I have started here, I could do more to communicate the relationship between intersection markers and WktGeom markers, such as:\n",
    "\n",
    "- coloring intersection markers (with a nonclashing colormap like blue to purple), to portray the volumes gained or lost at each intersection\n",
    "\n",
    "- connecting streets between intersection markers and WktGeom markers with street markers if there is a corresponding flow of data between them. Despite that WktGeom markers are rarely perfectly aligned with an intersection (which helps to generate and correspond street markers), a WktGeom marker could be replaced by the nearest intersection marker on the map. This method has been proven to work but not integrated into the current output of this project.\n",
    "\n",
    "\n",
    "Overall, this new system has advanced my goal of portraying the maximum amount of traffic flow information available in the Automated Traffic Counts dataset in an animated form. Potentially, the system for systemically generating and coordinating markers could be combined with more information on traffic movement patterns to portray a simulation of traffic flow.\n",
    "\n",
    "---\n",
    " \n",
    "Note that opposed to the previous mapping notebooks in this project, the data is not aggregated. Previously data was aggregated to account for multiple rows per street per datetime, representing multiple segments per street. Each data recording nodes accounts for these segments.\n",
    "\n",
    "Nodes are colored with a congestion value, like in previous maps, however this is calculated with:\n",
    "\n",
    "vol per street / max_vol per street\n",
    "\n",
    "rather than:\n",
    "\n",
    "avg_vol per street / max_vol per street\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "## sorted_intersection_dates:\n",
    "\n",
    "Stores all the information which is needed to track the known exchanges of volumes between streets in the OTMD dataset, and correspond these to intersections on the map\n",
    "\n",
    "Created in the notebook 08_Intersections\n",
    "\n",
    "Columns:\n",
    "\n",
    "- datetime\n",
    "- to_vol \t\n",
    "- from_vol \t\n",
    "- total_vol  (really net_volume)\t\n",
    "- street1 \t\n",
    "- SegmentID1 \t\n",
    "- s1_dir \t\n",
    "- s1_from \t\n",
    "- s1_to \t\n",
    "- street2 \t\n",
    "- SegmentID2 \t\n",
    "- s2_dir \t\n",
    "- s2_from\n",
    "- s2_to \t\n",
    "- marker  (coordinates)\n",
    "- intersection_id \t\n",
    "- gain_street \t\n",
    "- lose_street\n",
    "\n",
    "\n",
    "\n",
    "## Functions:\n",
    "\n",
    "\n",
    "### generate_markers( )\n",
    "\n",
    "    Given a street geometry and a marker spacing generates markers for along the street geometry, with the number of markers determined by the street's vertical length.\n",
    "    \n",
    "    \n",
    "### process_street_markers( )\n",
    "\n",
    "    Given a street name and a marker spacing, calls generate_markers() to generate markers for each geometry value for the given street.\n",
    "    \n",
    "    \n",
    "### markerkey_connection_map2( )\n",
    "\n",
    "    Given a datetime and a marker spacing, generates marker_data_df, recording connections between intersection markers and street markers, as well as the index of the street markers are generated. Street marker indexes are assigned based on the direction of volume exchanges between streets. Uses process_street_markers to generate street markers for all streets for a given datetime that connect 2 or more intersections.\n",
    "\n",
    "### map_all_flow_direction2( )\n",
    "\n",
    "    Plots street markers, intersection markers, and wktGeom markers for a given datetime, coloring street markers based on their directional flow and WktGeom markers based on congestion values. Calls markerkey_connection_map2 to create marker network.\n",
    "    \n",
    "### animate_all_flow()\n",
    "\n",
    "    Given a start row and an end row, querries datetime values shared by both congestion_intersection_dates and OTMD. Calls map_all_flow_direction2() to generate and save maps per the given datetimes.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1563822b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.wkt import loads\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from shapely.geometry import MultiLineString, LineString\n",
    "import re\n",
    "import os\n",
    "import cv2\n",
    "from matplotlib.colors import Normalize\n",
    "from matplotlib.cm import get_cmap\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import numpy as np\n",
    "from shapely import wkt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3e974e",
   "metadata": {},
   "source": [
    "#### Prepare basic data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60e7a4c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_map = gpd.read_file('base_map.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be504463",
   "metadata": {},
   "outputs": [],
   "source": [
    "czone_October = pd.read_csv(\"czone_October.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e961c142",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recreate the congestion_streets dataframe with full columns\n",
    "# And no aggregation\n",
    "czone_October['datetime'] = pd.to_datetime(czone_October[['year', 'month', 'day', 'hour', 'minute']])\n",
    "\n",
    "noNA_October = czone_October.dropna(subset=['Vol'])\n",
    "\n",
    "maxStreetVol_Oct = noNA_October.groupby('street')['Vol'].max().reset_index()\n",
    "maxStreetVol_Oct.columns = ['street', 'max_volume']\n",
    "OTMD = pd.merge(noNA_October, maxStreetVol_Oct, on='street')\n",
    "\n",
    "OTMD['congestion'] = OTMD['Vol'] / OTMD['max_volume']\n",
    "OTMDdates = OTMD['datetime'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1729fcf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_intersection_dates = pd.read_csv(\"sorted_intersection_dates.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32411df4",
   "metadata": {},
   "source": [
    "inix markers are unused in this project but they store all of the intersections of the street geometries in base_map as well as what the corresponding streets are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ef9dfdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from ast import literal_eval  # To safely evaluate string tuples\n",
    "\n",
    "with open('inix.json', 'r') as json_file:\n",
    "    inix_str_keys = json.load(json_file)\n",
    "    \n",
    "# Convert string keys back to tuples\n",
    "inix = {literal_eval(key): value for key, value in inix_str_keys.items()}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e562e0",
   "metadata": {},
   "source": [
    "Map all wktGeom points in OTMD:\n",
    "\n",
    "- This plotting reveals that there are WktGeom points beyond the congestion zone influencing the volume counts for streets inside the congestion zone. \n",
    "\n",
    "- They could be removed by using the matplotlib axis to select markers based on geometric boundaries. These markers can then be corresponded to rows that can be excluded by OTMD\n",
    "\n",
    "- It would be best to do this retroactively for the previous notebooks as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4670429e",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "OTMD2 = OTMD.copy()\n",
    "\n",
    "OTMD2['geometry'] = OTMD2['WktGeom'].apply(wkt.loads)\n",
    "\n",
    "# Create a GeoDataFrame from OTMD with WktGeom geometries\n",
    "wkt_gdf = gpd.GeoDataFrame(OTMD2, geometry='geometry')\n",
    "\n",
    "wkt_gdf.crs = \"EPSG:2263\"  # Assuming New York State Plane\n",
    "wkt_gdf = wkt_gdf.to_crs(epsg=4326)  # Convert to WGS84\n",
    "\n",
    "# Step 3: Plot the basemap and overlay the WktGeom points\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "# Plot the basemap\n",
    "base_map.plot(ax=ax, color='lightgray', edgecolor='black')\n",
    "\n",
    "wkt_gdf.plot(ax=ax, color = 'blue', edgecolor='black', alpha=1.0, legend=False, zorder=2, markersize=10)\n",
    "\n",
    "ax.set_xlim([-74.04, -73.94])\n",
    "ax.set_ylim([40.68, 40.79])\n",
    "\n",
    "\n",
    "# Customize plot\n",
    "plt.axis('off')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "400e0375",
   "metadata": {},
   "source": [
    "A custom color spectrum can be used to avoid the middle values (yellow) being dimmer and more difficult to read than high or low values in the matplotlib RgYlGn_r color map:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5c3e9e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [\"green\", \"yellow\", \"red\"]  # Simple green to yellow to red gradient\n",
    "RGcmap = LinearSegmentedColormap.from_list(\"custom_green_red\", colors, N=256)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00d4ac9e",
   "metadata": {},
   "source": [
    "Assign an abs_vol column to sorted_intersection_dates (reflecting a true total volume, since the total_vol column actually reflects net volume). The maximum absolute volume of a given intersection marker can be used to calculate a congestion value. While potentially useful in the future, this aspect is currently unused:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0784820c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>to_vol</th>\n",
       "      <th>from_vol</th>\n",
       "      <th>total_vol</th>\n",
       "      <th>street1</th>\n",
       "      <th>SegmentID1</th>\n",
       "      <th>s1_dir</th>\n",
       "      <th>s1_from</th>\n",
       "      <th>s1_to</th>\n",
       "      <th>street2</th>\n",
       "      <th>SegmentID2</th>\n",
       "      <th>s2_dir</th>\n",
       "      <th>s2_from</th>\n",
       "      <th>s2_to</th>\n",
       "      <th>marker</th>\n",
       "      <th>intersection_id</th>\n",
       "      <th>gain_street</th>\n",
       "      <th>lose_street</th>\n",
       "      <th>abs_vol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13728</th>\n",
       "      <td>2017-10-13 00:00:00</td>\n",
       "      <td>553.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>36375.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 61 STREET</td>\n",
       "      <td>ASTORIA LINE</td>\n",
       "      <td>EAST 58 STREET</td>\n",
       "      <td>165548.0</td>\n",
       "      <td>EB</td>\n",
       "      <td>3 AVENUE</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>POINT (-73.96480079595024 40.759982138903275)</td>\n",
       "      <td>36375.0-165548.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>EAST 58 STREET</td>\n",
       "      <td>793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13735</th>\n",
       "      <td>2017-10-13 00:15:00</td>\n",
       "      <td>499.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>36375.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 61 STREET</td>\n",
       "      <td>ASTORIA LINE</td>\n",
       "      <td>EAST 58 STREET</td>\n",
       "      <td>165548.0</td>\n",
       "      <td>EB</td>\n",
       "      <td>3 AVENUE</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>POINT (-73.96480079595024 40.759982138903275)</td>\n",
       "      <td>36375.0-165548.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>EAST 58 STREET</td>\n",
       "      <td>713.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13742</th>\n",
       "      <td>2017-10-13 00:30:00</td>\n",
       "      <td>505.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>36375.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 61 STREET</td>\n",
       "      <td>ASTORIA LINE</td>\n",
       "      <td>EAST 58 STREET</td>\n",
       "      <td>165548.0</td>\n",
       "      <td>EB</td>\n",
       "      <td>3 AVENUE</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>POINT (-73.96480079595024 40.759982138903275)</td>\n",
       "      <td>36375.0-165548.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>EAST 58 STREET</td>\n",
       "      <td>717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13749</th>\n",
       "      <td>2017-10-13 00:45:00</td>\n",
       "      <td>496.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>36375.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 61 STREET</td>\n",
       "      <td>ASTORIA LINE</td>\n",
       "      <td>EAST 58 STREET</td>\n",
       "      <td>165548.0</td>\n",
       "      <td>EB</td>\n",
       "      <td>3 AVENUE</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>POINT (-73.96480079595024 40.759982138903275)</td>\n",
       "      <td>36375.0-165548.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>EAST 58 STREET</td>\n",
       "      <td>709.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13756</th>\n",
       "      <td>2017-10-13 01:00:00</td>\n",
       "      <td>487.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>36375.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 61 STREET</td>\n",
       "      <td>ASTORIA LINE</td>\n",
       "      <td>EAST 58 STREET</td>\n",
       "      <td>165548.0</td>\n",
       "      <td>EB</td>\n",
       "      <td>3 AVENUE</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>POINT (-73.96480079595024 40.759982138903275)</td>\n",
       "      <td>36375.0-165548.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>EAST 58 STREET</td>\n",
       "      <td>665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110550</th>\n",
       "      <td>2019-10-29 23:30:00</td>\n",
       "      <td>234.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>35944.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 43 STREET</td>\n",
       "      <td>EAST 42 STREET</td>\n",
       "      <td>EAST 42 STREET</td>\n",
       "      <td>35945.0</td>\n",
       "      <td>WB</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>FLUSHING LINE</td>\n",
       "      <td>POINT (-73.9722037214914 40.74982300502161)</td>\n",
       "      <td>35944.0-35945.0</td>\n",
       "      <td>EAST 42 STREET</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110551</th>\n",
       "      <td>2019-10-29 23:30:00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>-278.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>36116.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 48 STREET</td>\n",
       "      <td>EAST 49 STREET</td>\n",
       "      <td>EAST 49 STREET</td>\n",
       "      <td>36127.0</td>\n",
       "      <td>WB</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>1 AVENUE</td>\n",
       "      <td>POINT (-73.96897803741389 40.75425053544146)</td>\n",
       "      <td>36116.0-36127.0</td>\n",
       "      <td>EAST 49 STREET</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110575</th>\n",
       "      <td>2019-10-29 23:45:00</td>\n",
       "      <td>156.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>-75.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>33290.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 24 STREET</td>\n",
       "      <td>EAST 23 STREET</td>\n",
       "      <td>EAST 23 STREET</td>\n",
       "      <td>33291.0</td>\n",
       "      <td>EB</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>1 AVENUE</td>\n",
       "      <td>POINT (-73.98090534080625 40.73788616382121)</td>\n",
       "      <td>33290.0-33291.0</td>\n",
       "      <td>EAST 23 STREET</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110578</th>\n",
       "      <td>2019-10-29 23:45:00</td>\n",
       "      <td>218.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>35944.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 43 STREET</td>\n",
       "      <td>EAST 42 STREET</td>\n",
       "      <td>EAST 42 STREET</td>\n",
       "      <td>35945.0</td>\n",
       "      <td>WB</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>FLUSHING LINE</td>\n",
       "      <td>POINT (-73.9722037214914 40.74982300502161)</td>\n",
       "      <td>35944.0-35945.0</td>\n",
       "      <td>EAST 42 STREET</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110579</th>\n",
       "      <td>2019-10-29 23:45:00</td>\n",
       "      <td>95.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>-259.0</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>36116.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 48 STREET</td>\n",
       "      <td>EAST 49 STREET</td>\n",
       "      <td>EAST 49 STREET</td>\n",
       "      <td>36127.0</td>\n",
       "      <td>WB</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>1 AVENUE</td>\n",
       "      <td>POINT (-73.96897803741389 40.75425053544146)</td>\n",
       "      <td>36116.0-36127.0</td>\n",
       "      <td>EAST 49 STREET</td>\n",
       "      <td>2 AVENUE</td>\n",
       "      <td>449.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7872 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   datetime  to_vol  from_vol  total_vol   street1  \\\n",
       "13728   2017-10-13 00:00:00   553.0     240.0      313.0  2 AVENUE   \n",
       "13735   2017-10-13 00:15:00   499.0     214.0      285.0  2 AVENUE   \n",
       "13742   2017-10-13 00:30:00   505.0     212.0      293.0  2 AVENUE   \n",
       "13749   2017-10-13 00:45:00   496.0     213.0      283.0  2 AVENUE   \n",
       "13756   2017-10-13 01:00:00   487.0     178.0      309.0  2 AVENUE   \n",
       "...                     ...     ...       ...        ...       ...   \n",
       "110550  2019-10-29 23:30:00   234.0     226.0        8.0  2 AVENUE   \n",
       "110551  2019-10-29 23:30:00    85.0     363.0     -278.0  2 AVENUE   \n",
       "110575  2019-10-29 23:45:00   156.0     231.0      -75.0  2 AVENUE   \n",
       "110578  2019-10-29 23:45:00   218.0     223.0       -5.0  2 AVENUE   \n",
       "110579  2019-10-29 23:45:00    95.0     354.0     -259.0  2 AVENUE   \n",
       "\n",
       "        SegmentID1 s1_dir         s1_from           s1_to         street2  \\\n",
       "13728      36375.0     SB  EAST 61 STREET    ASTORIA LINE  EAST 58 STREET   \n",
       "13735      36375.0     SB  EAST 61 STREET    ASTORIA LINE  EAST 58 STREET   \n",
       "13742      36375.0     SB  EAST 61 STREET    ASTORIA LINE  EAST 58 STREET   \n",
       "13749      36375.0     SB  EAST 61 STREET    ASTORIA LINE  EAST 58 STREET   \n",
       "13756      36375.0     SB  EAST 61 STREET    ASTORIA LINE  EAST 58 STREET   \n",
       "...            ...    ...             ...             ...             ...   \n",
       "110550     35944.0     SB  EAST 43 STREET  EAST 42 STREET  EAST 42 STREET   \n",
       "110551     36116.0     SB  EAST 48 STREET  EAST 49 STREET  EAST 49 STREET   \n",
       "110575     33290.0     SB  EAST 24 STREET  EAST 23 STREET  EAST 23 STREET   \n",
       "110578     35944.0     SB  EAST 43 STREET  EAST 42 STREET  EAST 42 STREET   \n",
       "110579     36116.0     SB  EAST 48 STREET  EAST 49 STREET  EAST 49 STREET   \n",
       "\n",
       "        SegmentID2 s2_dir   s2_from          s2_to  \\\n",
       "13728     165548.0     EB  3 AVENUE       2 AVENUE   \n",
       "13735     165548.0     EB  3 AVENUE       2 AVENUE   \n",
       "13742     165548.0     EB  3 AVENUE       2 AVENUE   \n",
       "13749     165548.0     EB  3 AVENUE       2 AVENUE   \n",
       "13756     165548.0     EB  3 AVENUE       2 AVENUE   \n",
       "...            ...    ...       ...            ...   \n",
       "110550     35945.0     WB  2 AVENUE  FLUSHING LINE   \n",
       "110551     36127.0     WB  2 AVENUE       1 AVENUE   \n",
       "110575     33291.0     EB  2 AVENUE       1 AVENUE   \n",
       "110578     35945.0     WB  2 AVENUE  FLUSHING LINE   \n",
       "110579     36127.0     WB  2 AVENUE       1 AVENUE   \n",
       "\n",
       "                                               marker   intersection_id  \\\n",
       "13728   POINT (-73.96480079595024 40.759982138903275)  36375.0-165548.0   \n",
       "13735   POINT (-73.96480079595024 40.759982138903275)  36375.0-165548.0   \n",
       "13742   POINT (-73.96480079595024 40.759982138903275)  36375.0-165548.0   \n",
       "13749   POINT (-73.96480079595024 40.759982138903275)  36375.0-165548.0   \n",
       "13756   POINT (-73.96480079595024 40.759982138903275)  36375.0-165548.0   \n",
       "...                                               ...               ...   \n",
       "110550    POINT (-73.9722037214914 40.74982300502161)   35944.0-35945.0   \n",
       "110551   POINT (-73.96897803741389 40.75425053544146)   36116.0-36127.0   \n",
       "110575   POINT (-73.98090534080625 40.73788616382121)   33290.0-33291.0   \n",
       "110578    POINT (-73.9722037214914 40.74982300502161)   35944.0-35945.0   \n",
       "110579   POINT (-73.96897803741389 40.75425053544146)   36116.0-36127.0   \n",
       "\n",
       "           gain_street     lose_street  abs_vol  \n",
       "13728         2 AVENUE  EAST 58 STREET    793.0  \n",
       "13735         2 AVENUE  EAST 58 STREET    713.0  \n",
       "13742         2 AVENUE  EAST 58 STREET    717.0  \n",
       "13749         2 AVENUE  EAST 58 STREET    709.0  \n",
       "13756         2 AVENUE  EAST 58 STREET    665.0  \n",
       "...                ...             ...      ...  \n",
       "110550  EAST 42 STREET        2 AVENUE    460.0  \n",
       "110551  EAST 49 STREET        2 AVENUE    448.0  \n",
       "110575  EAST 23 STREET        2 AVENUE    387.0  \n",
       "110578  EAST 42 STREET        2 AVENUE    441.0  \n",
       "110579  EAST 49 STREET        2 AVENUE    449.0  \n",
       "\n",
       "[7872 rows x 19 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_intersection_dates['abs_vol'] = sorted_intersection_dates['from_vol'] + sorted_intersection_dates['to_vol']\n",
    "\n",
    "sorted_intersection_dates[(sorted_intersection_dates['from_vol'] > 0) & (sorted_intersection_dates['to_vol'] > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3c002a44",
   "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>datetime</th>\n",
       "      <th>to_vol</th>\n",
       "      <th>from_vol</th>\n",
       "      <th>total_vol</th>\n",
       "      <th>street1</th>\n",
       "      <th>SegmentID1</th>\n",
       "      <th>s1_dir</th>\n",
       "      <th>s1_from</th>\n",
       "      <th>s1_to</th>\n",
       "      <th>street2</th>\n",
       "      <th>...</th>\n",
       "      <th>s2_dir</th>\n",
       "      <th>s2_from</th>\n",
       "      <th>s2_to</th>\n",
       "      <th>marker</th>\n",
       "      <th>intersection_id</th>\n",
       "      <th>gain_street</th>\n",
       "      <th>lose_street</th>\n",
       "      <th>abs_vol</th>\n",
       "      <th>max_volume</th>\n",
       "      <th>congestion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-01 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>-69.0</td>\n",
       "      <td>CENTRE STREET</td>\n",
       "      <td>32337.0</td>\n",
       "      <td>NB</td>\n",
       "      <td>HOWARD STREET</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>...</td>\n",
       "      <td>EB</td>\n",
       "      <td>BROADWAY</td>\n",
       "      <td>CROSBY STREET</td>\n",
       "      <td>POINT (-73.9985232734708 40.719785146159396)</td>\n",
       "      <td>32337.0-32327.0</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>CENTRE STREET</td>\n",
       "      <td>69.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.352041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-01 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>-92.0</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>32327.0</td>\n",
       "      <td>EB</td>\n",
       "      <td>BROADWAY</td>\n",
       "      <td>CROSBY STREET</td>\n",
       "      <td>LAFAYETTE STREET</td>\n",
       "      <td>...</td>\n",
       "      <td>SB</td>\n",
       "      <td>HOWARD STREET</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>POINT (-73.99906145014921 40.72004430035703)</td>\n",
       "      <td>32327.0-32332.0</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>LAFAYETTE STREET</td>\n",
       "      <td>92.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.661871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-01 00:15:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>CENTRE STREET</td>\n",
       "      <td>32337.0</td>\n",
       "      <td>NB</td>\n",
       "      <td>HOWARD STREET</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>...</td>\n",
       "      <td>EB</td>\n",
       "      <td>BROADWAY</td>\n",
       "      <td>CROSBY STREET</td>\n",
       "      <td>POINT (-73.9985232734708 40.719785146159396)</td>\n",
       "      <td>32337.0-32327.0</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>CENTRE STREET</td>\n",
       "      <td>74.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.377551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-01 00:15:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-81.0</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>32327.0</td>\n",
       "      <td>EB</td>\n",
       "      <td>BROADWAY</td>\n",
       "      <td>CROSBY STREET</td>\n",
       "      <td>LAFAYETTE STREET</td>\n",
       "      <td>...</td>\n",
       "      <td>SB</td>\n",
       "      <td>HOWARD STREET</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>POINT (-73.99906145014921 40.72004430035703)</td>\n",
       "      <td>32327.0-32332.0</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>LAFAYETTE STREET</td>\n",
       "      <td>81.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.582734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-01 00:30:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-77.0</td>\n",
       "      <td>CENTRE STREET</td>\n",
       "      <td>32337.0</td>\n",
       "      <td>NB</td>\n",
       "      <td>HOWARD STREET</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>...</td>\n",
       "      <td>EB</td>\n",
       "      <td>BROADWAY</td>\n",
       "      <td>CROSBY STREET</td>\n",
       "      <td>POINT (-73.9985232734708 40.719785146159396)</td>\n",
       "      <td>32337.0-32327.0</td>\n",
       "      <td>GRAND STREET</td>\n",
       "      <td>CENTRE STREET</td>\n",
       "      <td>77.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.392857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111829</th>\n",
       "      <td>2019-10-31 04:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>WEST 18 STREET</td>\n",
       "      <td>32463.0</td>\n",
       "      <td>EB</td>\n",
       "      <td>8 AVENUE</td>\n",
       "      <td>9 AVENUE</td>\n",
       "      <td>9 AVENUE</td>\n",
       "      <td>...</td>\n",
       "      <td>SB</td>\n",
       "      <td>WEST 40 STREET</td>\n",
       "      <td>WEST 39 STREET</td>\n",
       "      <td>POINT (-74.00354201925023 40.74344827908281)</td>\n",
       "      <td>32463.0-33699.0</td>\n",
       "      <td>9 AVENUE</td>\n",
       "      <td>WEST 18 STREET</td>\n",
       "      <td>10.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111830</th>\n",
       "      <td>2019-10-31 04:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-72.0</td>\n",
       "      <td>5 AVENUE</td>\n",
       "      <td>32813.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 17 STREET</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>...</td>\n",
       "      <td>EB</td>\n",
       "      <td>BROADWAY</td>\n",
       "      <td>PARK AVENUE SOUTH</td>\n",
       "      <td>POINT (-73.99179768837975 40.73850365378348)</td>\n",
       "      <td>32813.0-164329.0</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>5 AVENUE</td>\n",
       "      <td>72.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>0.189474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111831</th>\n",
       "      <td>2019-10-31 04:15:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>-54.0</td>\n",
       "      <td>5 AVENUE</td>\n",
       "      <td>32813.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 17 STREET</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>...</td>\n",
       "      <td>EB</td>\n",
       "      <td>BROADWAY</td>\n",
       "      <td>PARK AVENUE SOUTH</td>\n",
       "      <td>POINT (-73.99179768837975 40.73850365378348)</td>\n",
       "      <td>32813.0-164329.0</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>5 AVENUE</td>\n",
       "      <td>54.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>0.142105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111832</th>\n",
       "      <td>2019-10-31 04:30:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-50.0</td>\n",
       "      <td>5 AVENUE</td>\n",
       "      <td>32813.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 17 STREET</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>...</td>\n",
       "      <td>EB</td>\n",
       "      <td>BROADWAY</td>\n",
       "      <td>PARK AVENUE SOUTH</td>\n",
       "      <td>POINT (-73.99179768837975 40.73850365378348)</td>\n",
       "      <td>32813.0-164329.0</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>5 AVENUE</td>\n",
       "      <td>50.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>0.131579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111833</th>\n",
       "      <td>2019-10-31 04:45:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>5 AVENUE</td>\n",
       "      <td>32813.0</td>\n",
       "      <td>SB</td>\n",
       "      <td>EAST 17 STREET</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>...</td>\n",
       "      <td>EB</td>\n",
       "      <td>BROADWAY</td>\n",
       "      <td>PARK AVENUE SOUTH</td>\n",
       "      <td>POINT (-73.99179768837975 40.73850365378348)</td>\n",
       "      <td>32813.0-164329.0</td>\n",
       "      <td>EAST 18 STREET</td>\n",
       "      <td>5 AVENUE</td>\n",
       "      <td>20.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111834 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   datetime  to_vol  from_vol  total_vol         street1  \\\n",
       "0       2016-10-01 00:00:00     0.0      69.0      -69.0   CENTRE STREET   \n",
       "1       2016-10-01 00:00:00     0.0      92.0      -92.0    GRAND STREET   \n",
       "2       2016-10-01 00:15:00     0.0      74.0      -74.0   CENTRE STREET   \n",
       "3       2016-10-01 00:15:00     0.0      81.0      -81.0    GRAND STREET   \n",
       "4       2016-10-01 00:30:00     0.0      77.0      -77.0   CENTRE STREET   \n",
       "...                     ...     ...       ...        ...             ...   \n",
       "111829  2019-10-31 04:00:00     0.0      10.0      -10.0  WEST 18 STREET   \n",
       "111830  2019-10-31 04:00:00     0.0      72.0      -72.0        5 AVENUE   \n",
       "111831  2019-10-31 04:15:00     0.0      54.0      -54.0        5 AVENUE   \n",
       "111832  2019-10-31 04:30:00     0.0      50.0      -50.0        5 AVENUE   \n",
       "111833  2019-10-31 04:45:00     0.0      20.0      -20.0        5 AVENUE   \n",
       "\n",
       "        SegmentID1 s1_dir         s1_from           s1_to           street2  \\\n",
       "0          32337.0     NB   HOWARD STREET    GRAND STREET      GRAND STREET   \n",
       "1          32327.0     EB        BROADWAY   CROSBY STREET  LAFAYETTE STREET   \n",
       "2          32337.0     NB   HOWARD STREET    GRAND STREET      GRAND STREET   \n",
       "3          32327.0     EB        BROADWAY   CROSBY STREET  LAFAYETTE STREET   \n",
       "4          32337.0     NB   HOWARD STREET    GRAND STREET      GRAND STREET   \n",
       "...            ...    ...             ...             ...               ...   \n",
       "111829     32463.0     EB        8 AVENUE        9 AVENUE          9 AVENUE   \n",
       "111830     32813.0     SB  EAST 17 STREET  EAST 18 STREET    EAST 18 STREET   \n",
       "111831     32813.0     SB  EAST 17 STREET  EAST 18 STREET    EAST 18 STREET   \n",
       "111832     32813.0     SB  EAST 17 STREET  EAST 18 STREET    EAST 18 STREET   \n",
       "111833     32813.0     SB  EAST 17 STREET  EAST 18 STREET    EAST 18 STREET   \n",
       "\n",
       "        ...  s2_dir         s2_from              s2_to  \\\n",
       "0       ...      EB        BROADWAY      CROSBY STREET   \n",
       "1       ...      SB   HOWARD STREET       GRAND STREET   \n",
       "2       ...      EB        BROADWAY      CROSBY STREET   \n",
       "3       ...      SB   HOWARD STREET       GRAND STREET   \n",
       "4       ...      EB        BROADWAY      CROSBY STREET   \n",
       "...     ...     ...             ...                ...   \n",
       "111829  ...      SB  WEST 40 STREET     WEST 39 STREET   \n",
       "111830  ...      EB        BROADWAY  PARK AVENUE SOUTH   \n",
       "111831  ...      EB        BROADWAY  PARK AVENUE SOUTH   \n",
       "111832  ...      EB        BROADWAY  PARK AVENUE SOUTH   \n",
       "111833  ...      EB        BROADWAY  PARK AVENUE SOUTH   \n",
       "\n",
       "                                              marker   intersection_id  \\\n",
       "0       POINT (-73.9985232734708 40.719785146159396)   32337.0-32327.0   \n",
       "1       POINT (-73.99906145014921 40.72004430035703)   32327.0-32332.0   \n",
       "2       POINT (-73.9985232734708 40.719785146159396)   32337.0-32327.0   \n",
       "3       POINT (-73.99906145014921 40.72004430035703)   32327.0-32332.0   \n",
       "4       POINT (-73.9985232734708 40.719785146159396)   32337.0-32327.0   \n",
       "...                                              ...               ...   \n",
       "111829  POINT (-74.00354201925023 40.74344827908281)   32463.0-33699.0   \n",
       "111830  POINT (-73.99179768837975 40.73850365378348)  32813.0-164329.0   \n",
       "111831  POINT (-73.99179768837975 40.73850365378348)  32813.0-164329.0   \n",
       "111832  POINT (-73.99179768837975 40.73850365378348)  32813.0-164329.0   \n",
       "111833  POINT (-73.99179768837975 40.73850365378348)  32813.0-164329.0   \n",
       "\n",
       "           gain_street       lose_street abs_vol  max_volume  congestion  \n",
       "0         GRAND STREET     CENTRE STREET    69.0       196.0    0.352041  \n",
       "1         GRAND STREET  LAFAYETTE STREET    92.0       139.0    0.661871  \n",
       "2         GRAND STREET     CENTRE STREET    74.0       196.0    0.377551  \n",
       "3         GRAND STREET  LAFAYETTE STREET    81.0       139.0    0.582734  \n",
       "4         GRAND STREET     CENTRE STREET    77.0       196.0    0.392857  \n",
       "...                ...               ...     ...         ...         ...  \n",
       "111829        9 AVENUE    WEST 18 STREET    10.0       120.0    0.083333  \n",
       "111830  EAST 18 STREET          5 AVENUE    72.0       380.0    0.189474  \n",
       "111831  EAST 18 STREET          5 AVENUE    54.0       380.0    0.142105  \n",
       "111832  EAST 18 STREET          5 AVENUE    50.0       380.0    0.131579  \n",
       "111833  EAST 18 STREET          5 AVENUE    20.0       380.0    0.052632  \n",
       "\n",
       "[111834 rows x 21 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxStreetVol_int = sorted_intersection_dates.groupby('intersection_id')['abs_vol'].max().reset_index()\n",
    "maxStreetVol_int.columns = ['intersection_id', 'max_volume']\n",
    "congestion_intersection_dates = pd.merge(sorted_intersection_dates, maxStreetVol_int, on='intersection_id')\n",
    "\n",
    "congestion_intersection_dates['congestion'] = congestion_intersection_dates['abs_vol'] / congestion_intersection_dates['max_volume']\n",
    "\n",
    "congestion_intersection_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "463eab2e",
   "metadata": {},
   "source": [
    "# The following is where the Directional Flow animation is created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "0eaddd57",
   "metadata": {},
   "outputs": [],
   "source": [
    "congestion_intersection_dates['marker'] = congestion_intersection_dates['marker'].apply(loads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "cf16ad65",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_markers(geometry, marker_spacing=10):\n",
    "    \"\"\"\n",
    "    Divide a LineString or MultiLineString geometry into evenly spaced markers based on street length.\n",
    "    \n",
    "    :param geometry: LineString or MultiLineString geometry of the street\n",
    "    :param marker_spacing: Distance between each marker (in units consistent with the geometry's CRS)\n",
    "    :return: List of Point geometries for each marker\n",
    "    \"\"\"\n",
    "    markers = []\n",
    "    \n",
    "    if isinstance(geometry, MultiLineString):\n",
    "        for line in geometry.geoms:  # Iterate over each LineString in the MultiLineString\n",
    "            markers.extend(generate_markers(line, marker_spacing))\n",
    "    \n",
    "    # If the geometry is a LineString, process it normally\n",
    "    elif isinstance(geometry, LineString):\n",
    "        length = geometry.length\n",
    "        num_markers = max(int(length // marker_spacing) + 1, 2)  # Ensure at least 2 markers (start and end)\n",
    "\n",
    "        distances = [i / (num_markers - 1) * length for i in range(num_markers)]\n",
    "        markers.extend([geometry.interpolate(distance) for distance in distances])\n",
    "\n",
    "    else:\n",
    "        raise TypeError(f\"Expected LineString or MultiLineString geometry, but got {type(geometry)}\")\n",
    "\n",
    "    return markers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "b8e05c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_street_markers(street_name, marker_spacing=10):\n",
    "    \"\"\"\n",
    "    Generate markers for a street based on its geometry.\n",
    "    \n",
    "    :param street_name: The name of the street for which to generate markers\n",
    "    :param marker_spacing: The distance between each marker\n",
    "    :return: List of generated markers for the street\n",
    "    \"\"\"\n",
    "    street_map = base_map[base_map['st_name'] == street_name]\n",
    "    street_markers = []\n",
    "\n",
    "    # Loop through each geometry for the street\n",
    "    for geom in street_map['geometry'].unique():\n",
    "        try:\n",
    "            # Generate markers for each unique geometry segment\n",
    "            markers = generate_markers(geom, marker_spacing)\n",
    "\n",
    "            # Add markers to the list\n",
    "            street_markers.extend(markers)\n",
    "        except Exception as e:\n",
    "            print(f\"Error processing geometry for {street_name}: {e}\")\n",
    "    \n",
    "    return street_markers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d275a57",
   "metadata": {},
   "source": [
    "### For a given datetime:\n",
    "### Generate street markers between intersections that share the same street\n",
    "### Create an index identifying the order of the markers between two known intersections, indicating direction of traffic flow (towards gain street or lose street)\n",
    "### Index connections between street markers and intersections\n",
    "### Used by map_all_flow_directions and map_all_flow_directions2 to plot street markers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "a1f55d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "def masterkey_connections_map2(datetime, marker_spacing=10):\n",
    "    \"\"\"\n",
    "    Generate and plot all street markers for intersection markers, assigning `street_index` \n",
    "    based on flow direction from gain_street to lose_street.\n",
    "    \"\"\"\n",
    "    \n",
    "    time_slice = congestion_intersection_dates[congestion_intersection_dates['datetime'] == datetime]\n",
    "    \n",
    "    marker_data = []\n",
    "    \n",
    "    processed_streets = set()\n",
    "\n",
    "    street_markers_info = {}\n",
    "\n",
    "    # Step 2: Loop through each intersection marker and check if it shares a street with another intersection marker\n",
    "    for i, marker_data_row in time_slice.iterrows():\n",
    "        marker1 = marker_data_row['marker']\n",
    "\n",
    "        for j, compare_marker_data_row in time_slice.iterrows():\n",
    "            if i == j:\n",
    "                continue  # Skip the same marker\n",
    "\n",
    "            marker2 = compare_marker_data_row['marker']\n",
    "            shared_street = None\n",
    "\n",
    "            # Identify the shared street\n",
    "            if marker_data_row['street1'] == compare_marker_data_row['street1'] or \\\n",
    "               marker_data_row['street1'] == compare_marker_data_row['street2']:\n",
    "                shared_street = marker_data_row['street1']\n",
    "            elif marker_data_row['street2'] == compare_marker_data_row['street1'] or \\\n",
    "                 marker_data_row['street2'] == compare_marker_data_row['street2']:\n",
    "                shared_street = marker_data_row['street2']\n",
    "\n",
    "            # Generate street markers if shared street not processed\n",
    "            if shared_street and shared_street not in processed_streets:\n",
    "                street_markers = process_street_markers(shared_street, marker_spacing)\n",
    "                processed_streets.add(shared_street)\n",
    "\n",
    "                # Determine the flow direction for `street_index` ordering\n",
    "                if marker_data_row['gain_street'] == shared_street:\n",
    "                    ordered_markers = enumerate(street_markers)  # Normal order\n",
    "                else:\n",
    "                    ordered_markers = enumerate(reversed(street_markers))  # Reversed order\n",
    "\n",
    "                # Step 3: Store the street markers and information about their connections in master key\n",
    "                for idx, marker in ordered_markers:\n",
    "                    marker_key = f\"{shared_street}_{idx}\"\n",
    "\n",
    "                    # Initialize details for each marker in master key\n",
    "                    if marker_key not in street_markers_info:\n",
    "                        street_markers_info[marker_key] = {\n",
    "                            'street': shared_street,\n",
    "                            'marker_geom': marker,\n",
    "                            'connections': [],\n",
    "                            'is_intersection': False\n",
    "                        }\n",
    "\n",
    "                    # Add connection info based on the position of the marker\n",
    "                    if any(matching_marker.marker.equals(marker) for matching_marker in time_slice.itertuples()):\n",
    "                        street_markers_info[marker_key]['is_intersection'] = True\n",
    "                    if idx == 0:\n",
    "                        street_markers_info[marker_key]['connections'].append(marker1)\n",
    "                    elif idx == len(street_markers) - 1:\n",
    "                        street_markers_info[marker_key]['connections'].append(marker2)\n",
    "                    else:\n",
    "                        street_markers_info[marker_key]['connections'].append((marker1, marker2))\n",
    "    \n",
    "    # Organize markers and connections\n",
    "    for key, value in street_markers_info.items():\n",
    "        marker_data.append({\n",
    "            'street': value['street'],\n",
    "            'marker_geom': value['marker_geom'],\n",
    "            'connections': value['connections'],\n",
    "            'street_index': int(key.split('_')[-1]),  # `street_index` based on adjusted direction\n",
    "            'is_intersection': value['is_intersection']\n",
    "        })\n",
    "\n",
    "    marker_data_df = pd.DataFrame(marker_data)\n",
    "    return marker_data_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "174a8f00",
   "metadata": {},
   "source": [
    "### Identify datetimes shared by both congestion_intersection_dates and OTMD (intersection data and WktGeom data) for querring relevant datetimes in the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "8953de4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8276"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shared_datetimes = [date for date in cid_dates if date in OTMDdates]\n",
    "\n",
    "\n",
    "\n",
    "len(shared_datetimes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6c3ac81",
   "metadata": {},
   "source": [
    "### Plot a Direction Traffic_Flow_Map in the notebook without saving it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "19a43638",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import Normalize\n",
    "\n",
    "def map_all_flow_direction(datetime, marker_spacing=10):\n",
    "    \"\"\"\n",
    "    Plot street markers for intersections, emphasizing directional flow with grayscale and prioritizing intersection markers.\n",
    "    \"\"\"\n",
    "    # Generate the markers and connections using the modified masterkey_connections_map\n",
    "    marker_data_df = masterkey_connections_map2(datetime, marker_spacing)\n",
    "    \n",
    "    cid_data = congestion_intersection_dates[congestion_intersection_dates['datetime'] == datetime]\n",
    "    \n",
    "    plotted_wkts = []\n",
    "    \n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10, 10))\n",
    "    base_map.plot(ax=ax, color='lightgray', edgecolor='none')  # Plot base map\n",
    "    \n",
    "    \n",
    "    cmap = cm.binary\n",
    "    # Loop through each street's markers\n",
    "    if len(marker_data_df) > 0:\n",
    "        for street, markers_group in marker_data_df.groupby('street'):\n",
    "            markers_group = markers_group.sort_values('street_index')\n",
    "            num_markers = len(markers_group)\n",
    "\n",
    "\n",
    "            # Normalize grayscale based on position along the street segment\n",
    "            norm = Normalize(vmin=0, vmax=num_markers - 1)\n",
    "            for i, (_, row) in enumerate(markers_group.iterrows()):\n",
    "                # Darker (close to 1) near gain street, lighter (close to 0) near lose street\n",
    "                color = cmap(norm(i)) \n",
    "                ax.scatter(row['marker_geom'].x, row['marker_geom'].y, color=color, \n",
    "                           edgecolor='black', linewidth = 0.2, s=15, zorder=2, alpha=1)\n",
    "                \n",
    "    \n",
    "    OTMDdata = OTMD[OTMD['datetime'] == datetime]\n",
    "\n",
    "    # Create a GeoDataFrame from OTMD with WktGeom geometries\n",
    "    wkt_gdf = gpd.GeoDataFrame(OTMDdata, geometry='geometry')\n",
    "\n",
    "    wkt_gdf.crs = \"EPSG:2263\"  # Assuming New York State Plane\n",
    "    wkt_gdf = wkt_gdf.to_crs(epsg=4326)  # Convert to WGS84\n",
    "    \n",
    "    wkt_gdf.plot(ax=ax, column='congestion', \n",
    "                 cmap=RGcmap, edgecolor='black', linewidth = 0.2, alpha=1.0, legend=False, zorder=3, markersize=15)\n",
    "            \n",
    "            \n",
    "                \n",
    "    \n",
    "    \n",
    "    # Customize plot\n",
    "    ax.set_axis_off()\n",
    "    ax.set_xlim([-74.04, -73.94])  # Adjust x-limits for NYC\n",
    "    ax.set_ylim([40.68, 40.79])     # Adjust y-limits for NYC\n",
    "    ax.set_title(f\"Traffic Flow for {datetime}\")\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "6d7eb1e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example usage\n",
    "datetime = shared_datetimes[8000]\n",
    "map_all_flow_direction(datetime)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00b182a",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c6fed1ea",
   "metadata": {},
   "source": [
    "### Saves the map to the given output_directory rather than printing it inline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "91942cd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_all_flow_direction2(datetime, frame_num, output_dir, marker_spacing=10):\n",
    "    \"\"\"\n",
    "    Plot street markers for intersections, emphasizing directional flow with grayscale and prioritizing intersection markers.\n",
    "    \"\"\"\n",
    "    # Generate the markers and connections using the modified masterkey_connections_map\n",
    "    marker_data_df = masterkey_connections_map2(datetime, marker_spacing)\n",
    "    \n",
    "    cid_data = congestion_intersection_dates[congestion_intersection_dates['datetime'] == datetime]\n",
    "    \n",
    "    plotted_wkts = []\n",
    "    \n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10, 10))\n",
    "    base_map.plot(ax=ax, color='lightgray', edgecolor='none')  # Plot base map\n",
    "    \n",
    "    \n",
    "    cmap = cm.binary\n",
    "    # Loop through each street's markers\n",
    "    if len(marker_data_df) > 0:\n",
    "        for street, markers_group in marker_data_df.groupby('street'):\n",
    "            markers_group = markers_group.sort_values('street_index')\n",
    "            num_markers = len(markers_group)\n",
    "\n",
    "\n",
    "            # Normalize grayscale based on position along the street segment\n",
    "            norm = Normalize(vmin=0, vmax=num_markers - 1)\n",
    "            for i, (_, row) in enumerate(markers_group.iterrows()):\n",
    "                # Darker (close to 1) near gain street, lighter (close to 0) near lose street\n",
    "                color = cmap(norm(i)) # if not row['is_intersection'] else 'blue'\n",
    "                ax.scatter(row['marker_geom'].x, row['marker_geom'].y, color=color, \n",
    "                           edgecolor='black', linewidth = 0.2, s=15, zorder=2, alpha=1)\n",
    "                \n",
    "    \n",
    "    OTMDdata = OTMD[OTMD['datetime'] == datetime]\n",
    "\n",
    "    # Create a GeoDataFrame from OTMD with WktGeom geometries\n",
    "    wkt_gdf = gpd.GeoDataFrame(OTMDdata, geometry='geometry')\n",
    "\n",
    "    wkt_gdf.crs = \"EPSG:2263\"  # Assuming New York State Plane\n",
    "    wkt_gdf = wkt_gdf.to_crs(epsg=4326)  # Convert to WGS84\n",
    "    \n",
    "    wkt_gdf.plot(ax=ax, column='congestion', \n",
    "                 cmap=RGcmap, edgecolor='black', linewidth = 0.2, alpha=1.0, legend=False, zorder=3, markersize=15)\n",
    "            \n",
    "            \n",
    "                \n",
    "    \n",
    "    \n",
    "    # Customize plot\n",
    "    ax.set_axis_off()\n",
    "    ax.set_xlim([-74.04, -73.94])  # Adjust x-limits for NYC\n",
    "    ax.set_ylim([40.68, 40.79])     # Adjust y-limits for NYC\n",
    "    ax.set_title(f\"{datetime}\")\n",
    "    \n",
    "    # Save the frame as an image\n",
    "    frame_filename = f\"{datetime}_{frame_num:03d}.png\"\n",
    "    plt.savefig(os.path.join(output_dir, frame_filename), bbox_inches='tight', pad_inches=0.1, dpi=300)\n",
    "    plt.close(fig)  # Close the figure to free up memory\n",
    "    \n",
    "    print(f\"Saved frame {frame_num + 1}: {frame_filename}\")\n",
    "    \n",
    "    return frame_filename\n",
    "    #plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78961802",
   "metadata": {},
   "source": [
    "### Querry datetimes using index of a list of datetimes\n",
    "\n",
    "### Use datetimes to plot Direction Flow Maps\n",
    "\n",
    "### Save all created filenames for further processing into video:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "395f5d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "def animate_all_flow(start_datetime_index, end_datetime_index, datetime_list, output_dir):\n",
    "\n",
    "    file_names = []\n",
    "    \n",
    "    counter = start_datetime_index\n",
    "    \n",
    "\n",
    "    # Ensure start and end indices are within bounds\n",
    "    if start_datetime_index < 0 or end_datetime_index >= len(datetime_list):\n",
    "        raise ValueError(\"Datetime indices are out of bounds.\")\n",
    "    \n",
    "    # Get the list of datetimes to generate frames for\n",
    "    datetime_range = datetime_list[start_datetime_index:end_datetime_index + 1]\n",
    "\n",
    "    # Step 2: Create the output directory if it doesn't exist\n",
    "    if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "        \n",
    "    \n",
    "\n",
    "    # Step 3: Loop through each datetime and generate frames\n",
    "    for date in datetime_range:\n",
    "\n",
    "        # Plot the traffic flow using simulate_traffic_flow\n",
    "        file_name = map_all_flow_direction2(date, counter, output_dir)\n",
    "        \n",
    "        counter += 1\n",
    "\n",
    "        \n",
    "        file_names.append(file_name)\n",
    "        \n",
    "\n",
    "    print(f\"All frames saved in directory: {output_dir}\")\n",
    "    return file_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "996724c5",
   "metadata": {},
   "source": [
    "### Segment processing of all dates into 1000 querries at a time (or less depending on your computer) to avoid overwhelming the kernel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "fb2d1fae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
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     ]
    }
   ],
   "source": [
    "allflow1000 = animate_all_flow(0, 1000, shared_datetimes, 'all_flow3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "8a427af0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
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      "All frames saved in directory: all_flow3\n"
     ]
    }
   ],
   "source": [
    "allflow2000 = animate_all_flow(1001, 2000, shared_datetimes, 'all_flow3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "1c6653c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "All frames saved in directory: all_flow3\n"
     ]
    }
   ],
   "source": [
    "allflow3000 = animate_all_flow(2001, 3000, shared_datetimes, 'all_flow3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "565323ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
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      "All frames saved in directory: all_flow3\n"
     ]
    }
   ],
   "source": [
    "allflow4000 = animate_all_flow(3001, 4000, shared_datetimes, 'all_flow3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "0d4a345e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "All frames saved in directory: all_flow3\n"
     ]
    }
   ],
   "source": [
    "allflow5000 = animate_all_flow(4001, 5000, shared_datetimes, 'all_flow3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "5e124574",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
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      "All frames saved in directory: all_flow3\n"
     ]
    }
   ],
   "source": [
    "allflow6000 = animate_all_flow(5001, 6000, shared_datetimes, 'all_flow3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "026d3b58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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      "All frames saved in directory: all_flow3\n"
     ]
    }
   ],
   "source": [
    "allflow7000 = animate_all_flow(6001, 7000, shared_datetimes, 'all_flow3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "869a33f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "All frames saved in directory: all_flow3\n"
     ]
    }
   ],
   "source": [
    "allflow8000 = animate_all_flow(7001, 8000, shared_datetimes, 'all_flow3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "a3a1ac44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
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      "All frames saved in directory: all_flow3\n"
     ]
    }
   ],
   "source": [
    "allflow8276 = animate_all_flow(8001, 8275, shared_datetimes, 'all_flow3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c601b4",
   "metadata": {},
   "source": [
    "#### Combine lists of filenames for video processing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "id": "6a005938",
   "metadata": {},
   "outputs": [],
   "source": [
    "allflow_all = allflow1000 + allflow2000\n",
    "\n",
    "allflow_all = allflow_all + allflow3000\n",
    "\n",
    "allflow_all = allflow_all + allflow4000\n",
    "\n",
    "allflow_all = allflow_all + allflow5000\n",
    "\n",
    "allflow_all = allflow_all + allflow6000\n",
    "\n",
    "allflow_all = allflow_all + allflow7000\n",
    "\n",
    "allflow_all = allflow_all + allflow8000\n",
    "\n",
    "allflow_all = allflow_all + allflow8276"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7cccc4",
   "metadata": {},
   "source": [
    "### In past notebooks I used a custom function to duplicate each map image 12 times so that each map could appear for half a second in a 24 fps video with cv2\n",
    "\n",
    "### I have just now realized this is a ridiculous approach when each frame can be added to the video writer with cv2 much more easily using a nested loop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f311151",
   "metadata": {},
   "outputs": [],
   "source": [
    "fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
    "\n",
    "# Open the first image to get its dimensions\n",
    "first_image_path = f'all_flow3/{allflow_all[0]}'\n",
    "first_image = cv2.imread(first_image_path)\n",
    "frame_width = first_image.shape[1]\n",
    "frame_height = first_image.shape[0]\n",
    "\n",
    "\n",
    "#aspect_ratio = frame_width / frame_height\n",
    "\n",
    "# Initialize the VideoWriter object with the calculated dimensions\n",
    "output_video = cv2.VideoWriter('all_flow2.mp4', fourcc, 24, (frame_width, frame_height))\n",
    "\n",
    "# Iterate over each image and add it to the video\n",
    "for image in allflow_all:\n",
    "    image_path = f'all_flow3/{image}'\n",
    "    img = cv2.imread(image_path)\n",
    "    for i in range(12):\n",
    "        \n",
    "        output_video.write(img)\n",
    "\n",
    "# Release the VideoWriter object\n",
    "output_video.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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