Movement_Visualization_David.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "EiW8JGeODF1p"
      },
      "outputs": [
        {
          "ename": "RuntimeError",
          "evalue": "No ffmpeg exe could be found. Install ffmpeg on your system, or set the IMAGEIO_FFMPEG_EXE environment variable.",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[2], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mstruct\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmultiprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Pool\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01meditor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m VideoClip\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvideo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbindings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mplfig_to_npimage\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/moviepy/editor.py:36\u001b[0m\n\u001b[1;32m     33\u001b[0m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPYGAME_HIDE_SUPPORT_PROMPT\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;66;03m# Clips\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvideo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mVideoFileClip\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m VideoFileClip\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvideo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mImageSequenceClip\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ImageSequenceClip\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvideo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdownloader\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m download_webfile\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/moviepy/video/io/VideoFileClip.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01maudio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mAudioFileClip\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AudioFileClip\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mClip\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Clip\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvideo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mffmpeg_reader\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FFMPEG_VideoReader\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/moviepy/audio/io/AudioFileClip.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m division\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01maudio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mAudioClip\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AudioClip\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01maudio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mreaders\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FFMPEG_AudioReader\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mAudioFileClip\u001b[39;00m(AudioClip):\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/moviepy/audio/AudioClip.py:7\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mproglog\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtqdm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01maudio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mffmpeg_audiowriter\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ffmpeg_audiowrite\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mClip\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Clip\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecorators\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m requires_duration\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/moviepy/audio/io/ffmpeg_audiowriter.py:7\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mproglog\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DEVNULL\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_setting\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmoviepy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecorators\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m requires_duration\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mFFMPEG_AudioWriter\u001b[39;00m:\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/moviepy/config.py:36\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m FFMPEG_BINARY\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mffmpeg-imageio\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m     35\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mimageio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mplugins\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mffmpeg\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_exe\n\u001b[0;32m---> 36\u001b[0m     FFMPEG_BINARY \u001b[38;5;241m=\u001b[39m \u001b[43mget_exe\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m FFMPEG_BINARY\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauto-detect\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m     40\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m try_cmd([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mffmpeg\u001b[39m\u001b[38;5;124m'\u001b[39m])[\u001b[38;5;241m0\u001b[39m]:\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/imageio/plugins/ffmpeg.py:173\u001b[0m, in \u001b[0;36mget_exe\u001b[0;34m()\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_exe\u001b[39m():  \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Wrapper for imageio_ffmpeg.get_ffmpeg_exe()\"\"\"\u001b[39;00m\n\u001b[0;32m--> 173\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimageio_ffmpeg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_ffmpeg_exe\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/imageio_ffmpeg/_utils.py:34\u001b[0m, in \u001b[0;36mget_ffmpeg_exe\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m exe\n\u001b[1;32m     33\u001b[0m \u001b[38;5;66;03m# Nothing was found\u001b[39;00m\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m     35\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo ffmpeg exe could be found. Install ffmpeg on your system, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor set the IMAGEIO_FFMPEG_EXE environment variable.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     37\u001b[0m )\n",
            "\u001b[0;31mRuntimeError\u001b[0m: No ffmpeg exe could be found. Install ffmpeg on your system, or set the IMAGEIO_FFMPEG_EXE environment variable."
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import glob\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "import os\n",
        "import base64\n",
        "import struct\n",
        "from multiprocessing import Pool\n",
        "\n",
        "from moviepy.editor import VideoClip\n",
        "from moviepy.video.io.bindings import mplfig_to_npimage\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "vdv0VLFoD63s"
      },
      "outputs": [],
      "source": [
        "#ForDavid\n",
        "DataPath = \"./RawData/**/*.csv\"\n",
        "FilteredDataPath = \"./GeneratedData/\"\n",
        "AnimPath = \"./GeneratedVideos/\"\n",
        "FilteredDataPathPlayback = \"./GeneratedData/playback/\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0492hbbaF3yW"
      },
      "outputs": [],
      "source": [
        "def decode_position(encoded_position):\n",
        "    # Decode the Base64 string to bytes\n",
        "    bytes_array = base64.b64decode(encoded_position)\n",
        "\n",
        "    # Unpack the byte array to floats; 'f' denotes a 32-bit float, and '<' denotes little-endian byte order\n",
        "    x, y, z = struct.unpack('<fff', bytes_array)\n",
        "\n",
        "    return np.array([x, y, z])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "Oobrg9ABF8Sx"
      },
      "outputs": [],
      "source": [
        "def CreateAnimation(in_df, filename):\n",
        "  fig, ax = plt.subplots()\n",
        "  StartTime = in_df[\"ScenarioTime\"].min()\n",
        "  StopTime = in_df[\"ScenarioTime\"].max()\n",
        "  colums = [x for x in in_df if \"Pos\" in x]\n",
        "  scat = {}\n",
        "  xmin =5000\n",
        "  ymin=5000\n",
        "  xmax=-5000\n",
        "  ymax=-5000\n",
        "  padding =2\n",
        "  for col in colums:\n",
        "    scat[col] = ax.scatter(in_df[col].iloc[0][0], in_df[col].iloc[0][2],s=0.25)\n",
        "    if (in_df[col].apply(lambda pos: pos[0]).min()<xmin):\n",
        "      xmin = in_df[col].apply(lambda pos: pos[0]).min()\n",
        "    if (in_df[col].apply(lambda pos: pos[0]).max()>xmax):\n",
        "      xmax = in_df[col].apply(lambda pos: pos[0]).max()\n",
        "    if (in_df[col].apply(lambda pos: pos[2]).min()<ymin):\n",
        "      ymin = in_df[col].apply(lambda pos: pos[2]).min()\n",
        "    if (in_df[col].apply(lambda pos: pos[2]).max()>ymax):\n",
        "      ymax = in_df[col].apply(lambda pos: pos[2]).max()\n",
        "    \n",
        "    \n",
        "\n",
        "  ax.set(xlim=[xmin-padding, xmax+padding], ylim=[ymin-padding, ymax+padding], xlabel='x', ylabel='z')\n",
        "  ax.legend()\n",
        "\n",
        "\n",
        "\n",
        "  def update(t):\n",
        "    #print(t)\n",
        "    index_of_nearest = (np.abs(in_df['ScenarioTime'] - t)).argmin()\n",
        "    for col in colums:\n",
        "      data = np.stack([in_df[col].iloc[index_of_nearest][0],in_df[col].iloc[index_of_nearest][2]]).T\n",
        "      scat[col].set_offsets(data)\n",
        "    return mplfig_to_npimage(fig)\n",
        "\n",
        "  ani = VideoClip(update,duration=StopTime )\n",
        "  ani.write_videofile(os.path.join(AnimPath,filename+\".mp4\"),fps=24)\n",
        "  #ani.ipython_display(fps = 18, loop = True, autoplay = True)\n",
        "  #ani = animation.FuncAnimation(fig=fig, func=update, frames=in_df.shape[0], interval=18)\n",
        "  #ani.save(filename=os.path.join(AnimPath,filename+\".mp4\"), writer=\"ffmpeg\")\n",
        "  \n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [],
      "source": [
        "def process_csv_file(val):\n",
        "    Scenario = os.path.basename(val).split('-')[1].split('_')[0]\n",
        "    run = os.path.basename(val).split('-')[2].split('_')[0]\n",
        "    try:\n",
        "        df = pd.read_csv(val, sep=';')\n",
        "    except Exception as e:\n",
        "        print(f\"An error occurred while reading {Scenario} at {run}: {e}\")\n",
        "        return  # Exit the function on error\n",
        "    df_out = pd.DataFrame()\n",
        "    columns = [x for x in df if \"Pos\" in x]\n",
        "    for col in columns:\n",
        "       # print(col)\n",
        "        expanded_cols = df[col].apply(lambda x: decode_position(x))\n",
        "        df_out = pd.concat([df_out, expanded_cols], axis=1)\n",
        "    df_out = pd.concat([df_out, df[\"ScenarioTime\"]], axis=1)\n",
        "    #print(\"Making The Video for:\", Scenario, run)\n",
        "    CreateAnimation(df_out, Scenario + \"-\" + run)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "pd.options.mode.chained_assignment = None\n",
        "CSVFiles = glob.glob(DataPath, recursive=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [17:20<197:23:41, 1040.44s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-101.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-101.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [24:24<277:52:55, 1464.68s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-101.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [24:26<278:12:57, 1466.44s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-102.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-102.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [30:36<348:28:55, 1836.80s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-102.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [30:38<348:51:52, 1838.82s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-103.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-103.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [40:14<458:09:54, 2414.93s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-103.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [40:17<458:35:49, 2417.20s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-104.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-104.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [51:02<580:58:37, 3062.25s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-104.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [51:04<581:24:42, 3064.54s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-105.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-105.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [1:02:03<706:21:47, 3723.14s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-105.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [1:02:06<706:58:02, 3726.33s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-106.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-106.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [1:10:37<803:57:23, 4237.55s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-106.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [1:10:38<804:07:14, 4238.41s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-12.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-12.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [1:13:07<832:22:32, 4387.34s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-12.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [1:13:08<832:32:36, 4388.22s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-15.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-15.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [1:16:24<869:51:55, 4584.94s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-15.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [1:16:25<870:01:40, 4585.80s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-16.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-16.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [1:18:52<897:51:01, 4732.45s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-16.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [1:18:53<898:01:21, 4733.36s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-21.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-21.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [1:22:43<941:44:54, 4963.83s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-21.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [1:22:44<941:54:38, 4964.68s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-3.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-3.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [1:25:42<975:44:01, 5142.96s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-3.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [1:25:43<975:55:30, 5143.97s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Ped-7.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Ped-7.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [1:28:49<1011:04:13, 5329.21s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Ped-7.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
            "t:   0%|          | 1/684 [1:28:49<1011:10:30, 5329.77s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video ./GeneratedVideos/Practice-temp.mp4.\n",
            "Moviepy - Writing video ./GeneratedVideos/Practice-temp.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "t:   0%|          | 1/684 [1:29:04<1014:03:39, 5344.98s/it, now=None]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready ./GeneratedVideos/Practice-temp.mp4\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "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"
        },
        {
          "data": {
            "image/png": 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",
            "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"
        },
        {
          "data": {
            "image/png": 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Ztm2bI04A0E/09v275n9bDQDgUBJHAAAFcQQAUDisLsgGgGq675/uzcP/9r8zYX1XTmr/Zc5e+JcZNHZMtafF2+TIEQD0kR+82p53/fqNNG57f17bMT4v3XtvtafEAXDkCAD2YsvPfppH/sfdedeu0Wk/eVT+43/8/bf8DeVzhzbn4eNeyvBX/jXveXVTRl5w5SGaLX1JHAHAXjx6x99mdOexOXbIe/L6yp/nRyOW5OxPfyrp2Jz21ffkG6+Py7D/dV/e3XlSXn/Xz3PCjP+WT///F+TTb/N5dnV0Zsm3fpB1G/4l731xS2Zc/aUMPXHSQXlN9I7TagCwF89Pa0z7rmfS1vWL/GLE69m6aEX+8kt/lr+8+ku59d+68m+/uD1Tt3w8r498KlNffjL/+5H/cUDPs3jxiny7/tlMH70hOwa8kS1f+1ofvxLeLnEEwBFvyytb8q2138qWV7bk2Y7XcvMj69M+6Iw8+B+G5dUhg/OeLZ3Z8f6NeaVuZ17ZNThDn9qYsaPPyOqmJTlq6wlZPfzEHP978w7ouW94blM+3rEqDUN+mQ+OeTJj/vt/7+NXx9vltBoAR6xdHZ15eUV77h++KEteWJIk6Xz+k3nk58/mY+8bnY996L/k5JaheWX9N7PlJ4MyqGNMXhyQfHz2f87qgZUMf/35bJ9yXj534pcPeA5fPuN38vUfPp+Tdz2Z0b97sVNqNUAcAXDEenlFe179+Yv5/z5wajqPez2f/g+fTuV9b150/Z8+8u6MbRjy5sBjrs+Sl36eSsfzGTatKR856YS8u3Nn/m7YFfnP40e/ozl84rcm5RO/NSnJJe/w1dBXfLfaAfDdagCHh91HjoZ/pDkDG+r3O/bZjtfy9ys29owm+pXevn+LowMgjgCg//HFswAAB0AcAQAUxBEAQEEcAQAUxBEAQEEcAQAUxBEAQEEcAQAUxBEAQEEcAQAUxBEAQEEcAQAUxBEAQEEcAQAUxBEAVbPjxRfyo3v/Z3a8+EK1pwLdxBEAVfPTxT/Mv65ekZ8u/mG1pwLdjqr2BAA4cp30yZk9/oRaII4AqJqj3zU60y+YU+1pQA9OqwEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFMQRAEBBHAEAFPpNHLW2tmb69OkZNmxYRo4cudcxGzduzLnnnpvhw4enqakpl156abq6unqMWbduXU477bQMHTo0xx57bL7yla+kUqkcglcAAPQHR1V7Ar3V1dWVCy64INOmTcttt922x+27du3KrFmzcswxx2Tp0qV54YUXcuGFF6ZSqeSWW25JknR0dOSss87KGWeckZUrV2b9+vVpaWnJ8OHDc/nllx/qlwQA1KB+E0fXXnttkuSOO+7Y6+2LFi3KU089lba2towfPz5JcuONN6alpSWtra1paGjI3Xffnddeey133HFH6uvrM3ny5Kxfvz433XRTPv/5z6euru5QvRwAoEb1m9Nqb2XZsmWZPHlydxglycyZM9PZ2ZnVq1d3jznttNNSX1/fY8ymTZvyzDPP7POxOzs709HR0WMBAA5Ph00ctbe3Z+zYsT3WjRo1KoMHD057e/s+x+z+efeYvbn++uvT2NjYvUyYMKGPZw8A1IqqxtE111yTurq6/S6rVq3q9ePt7bRYpVLpsf43x+y+GHt/p9QWLFiQbdu2dS9tbW29nhMA0L9U9Zqj+fPnZ/bs2fsdM3HixF49VnNzc5YvX95j3datW7Nz587uo0PNzc17HCHasmVLkuxxRKlUX1/f41QcAHD4qmocNTU1pampqU8ea9q0aWltbc3mzZszbty4JG9epF1fX5+pU6d2j7nqqqvS1dWVwYMHd48ZP358ryMMADi89ZtrjjZu3Ji1a9dm48aN2bVrV9auXZu1a9dmx44dSZIZM2Zk0qRJmTt3btasWZPFixfniiuuyLx589LQ0JAkmTNnTurr69PS0pKf/exn+d73vpfrrrvOb6oBAN3qKv3kExBbWlpy55137rH+sccey+mnn57kzYC6+OKL8+ijj2bo0KGZM2dOFi5c2OOU2Lp163LJJZdkxYoVGTVqVC666KJ8+ctffltx1NHRkcbGxmzbtq07vACA2tbb9+9+E0e1RBwBQP/T2/fvfnNaDQDgUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHAAAFcQQAUBBHABwRdj67Jc9949bsfHZLr+/z8kudWXH/hrz8UudBnBm1RhwBcER46d57s+Oxx/LSvff2+j5PLt2UZ376fJ5cummvt295ZUu+tfZb2fJK74OL2ndUtScAAIfCyAsu6PFnb5z48fE9/vxN962/L0t+vSRJ8rkpn3tnE6Rm1FUqlUq1J9HfdHR0pLGxMdu2bUtDQ0O1pwPAIdLeuTO3Ll6SY9vvz2+959z8zrST8uATt+esp9en619HJ+87KxPO/90MbKiv9lTZi96+f/eb02qtra2ZPn16hg0blpEjR+51zGWXXZapU6emvr4+U6ZM2euYdevW5bTTTsvQoUNz7LHH5itf+Ur0IQC98Xd/+fmc9ItvZMKo5dn4y3szZtiY/O4vt+fhR4bnf23+RS56dkO+cdffVXuavEP9Jo66urpywQUX5HOf2/dhy0qlkj/6oz/K7//+7+/19o6Ojpx11lkZP358Vq5cmVtuuSULFy7MTTfddLCmDcBhZMyTS/Papt/OthcmZNBzE5MkP1rZmO2v16e9Y1BGtv1b7tvpipX+rt9swWuvvTZJcscdd+xzzNe//vUkyXPPPZef/vSne9x+991357XXXssdd9yR+vr6TJ48OevXr89NN92Uz3/+86mrq9vr43Z2dqaz8//9pkJHR8c7eCUA9FeDP/jxdD35RDoHnJ6Zf/iZJMn0/3p2Fn/ju2nPgLx4XHNmjxtU5VnyTvWbOOoLy5Yty2mnnZb6+v93LnjmzJlZsGBBnnnmmbz3ve/d6/2uv/767jgD4Mg1+4pb9lj33sm/k//67d+pwmw4WPrNabW+0N7enrFjx/ZYt/vn9vb2fd5vwYIF2bZtW/fS1tZ2UOcJAFRPVePommuuSV1d3X6XVatW9elz/uaps90XY+/rlFqS1NfXp6GhoccCAByeqnpabf78+Zk9e/Z+x0ycOLHPnq+5uXmPI0Rbtrz5wV2/eUQJADgyVTWOmpqa0tTUdMieb9q0abnqqqvS1dWVwYMHJ0kWLVqU8ePH92mEAQD9V7+55mjjxo1Zu3ZtNm7cmF27dmXt2rVZu3ZtduzY0T3m6aefztq1a9Pe3p5XX321e0xXV1eSZM6cOamvr09LS0t+9rOf5Xvf+16uu+66/f6mGgBwZOk3n5Dd0tKSO++8c4/1jz32WE4//fQkyemnn57HH398jzEbNmzoPjK0bt26XHLJJVmxYkVGjRqViy66KF/+8pffVhz5hGwA6H96+/7db+KologjAOh/DruvDwEAOBTEEQBAQRwBABTEEQBAQRwBABSOqC+e7Su7f8Gvo6OjyjMBAHpr9/v2W/2ivjg6ANu3b0+STJgwocozAQDeru3bt6exsXGft/ucowPwxhtvZNOmTRkxYoRP1j7IOjo6MmHChLS1tflMqRpj29Qu26Y22S7VV6lUsn379owfPz4DBuz7yiJHjg7AgAEDctxxx1V7GkeUhoYG/5jUKNumdtk2tcl2qa79HTHazQXZAAAFcQQAUBBH1LT6+vpcffXVqa+vr/ZU+A22Te2ybWqT7dJ/uCAbAKDgyBEAQEEcAQAUxBEAQEEcAQAUxBE1a+LEiamrq+uxfOELX+gx5jdvr6ury7e//e0qzfjI0Ztts3Hjxpx77rkZPnx4mpqacumll6arq6tKMz6ydHZ2ZsqUKamrq8vatWt73Gafqa79bRv7TO3wCdnUtK985SuZN29e989HH330HmNuv/32nH322d0/9+bTT3nn9rdtdu3alVmzZuWYY47J0qVL88ILL+TCCy9MpVLJLbfcUo3pHlH+9E//NOPHj88TTzyx19vtM9Wzr21jn6kt4oiaNmLEiDQ3N+93zMiRI99yDH1vf9tm0aJFeeqpp9LW1pbx48cnSW688ca0tLSktbXVVyccRA8++GAWLVqU++67Lw8++OBex9hnqmN/28Y+U1ucVqOmfe1rX8vo0aMzZcqUtLa27vUQ8/z589PU1JRTTjkl3/72t/PGG29UYaZHnv1tm2XLlmXy5Mnd/8gnycyZM9PZ2ZnVq1dXY7pHhGeffTbz5s3LXXfdlWHDhu1znH3m0HurbWOfqS2OHFGzLrvssnz4wx/OqFGjsmLFiixYsCAbNmzIX//1X3eP+fM///N88pOfzNChQ7N48eJcfvnlef755/PFL36xijM//L3Vtmlvb8/YsWN73GfUqFEZPHhw2tvbqzHlw16lUklLS0suuuiinHzyyXnmmWf2Os4+c+j1ZtvYZ2pMBQ6hq6++upJkv8vKlSv3et/vfve7lSSV559/fp+Pv3DhwkpDQ8PBmv5hrS+3zbx58yozZszYY9ygQYMqf//3f39QX8fhprfb5a/+6q8q06dPr7z++uuVSqVS2bBhQyVJZc2aNft9fPvMgevLbWOfqS2OHHFIzZ8/P7Nnz97vmIkTJ+51/cc+9rEkydNPP53Ro0fvc0xHR0eeffbZPf4vjP3ry23T3Nyc5cuX9xizdevW7Ny503Z5m3q7Xb761a/mxz/+8R7f23XyySfnD/7gD3LnnXfu9b72mQPXl9vGPlNbxBGHVFNTU5qamg7ovmvWrEmSjBs3br9jhgwZkpEjRx7QcxzJ+nLbTJs2La2trdm8eXP3ukWLFqW+vj5Tp07tmwkfIXq7Xb7+9a/nq1/9avfPmzZtysyZM/MP//AP+ehHP7rP+9lnDlxfbhv7TG0RR9SkZcuW5cc//nHOOOOMNDY2ZuXKlfmTP/mTfOpTn8q73/3uJMkPfvCDtLe3Z9q0aRk6dGgee+yx/Nmf/Vk++9nP+tbrg6g322bGjBmZNGlS5s6dmxtuuCEvvvhirrjiisybN89v3Rwku//ud9v90Qrvf//7c9xxxyWxz1RLb7aNfabGVPu8HuzN6tWrKx/96EcrjY2NlSFDhlR++7d/u3L11VdXXn755e4xDz74YGXKlCmVo48+ujJs2LDK5MmTKzfffHNl586dVZz54a8326ZSqVR+9atfVWbNmlUZOnRo5V3veldl/vz5lddee61Ksz7y7O26FvtMbdjX9WD2mdpRV6lUKtUONACAWuFzjgAACuIIAKAgjgAACuIIAKAgjgAACuIIAKAgjgAACuIIAKAgjgAACuIIAKAgjgAACuIIOOI999xzaW5uznXXXde9bvny5Rk8eHAWLVpUxZkB1eCLZwGS/NM//VPOP//8/OhHP8rxxx+fD33oQ5k1a1Zuvvnmak8NOMTEEcC/u+SSS/LII4/klFNOyRNPPJGVK1dmyJAh1Z4WcIiJI4B/9+qrr2by5Mlpa2vLqlWrctJJJ1V7SkAVuOYI4N/98pe/zKZNm/LGG2/kV7/6VbWnA1SJI0cASbq6uvKRj3wkU6ZMyfHHH5+bbrop69aty9ixY6s9NeAQE0cASa688sp897vfzRNPPJGjjz46Z5xxRkaMGJH777+/2lMDDjGn1YAj3pIlS3LzzTfnrrvuSkNDQwYMGJC77rorS5cuzbe+9a1qTw84xBw5AgAoOHIEAFAQRwAABXEEAFAQRwAABXEEAFAQRwAABXEEAFAQRwAABXEEAFAQRwAABXEEAFD4v3mFVKJqtKRYAAAAAElFTkSuQmCC",
            "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"
        },
        {
          "data": {
            "image/png": 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",
            "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"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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eeUTLly8P7bN48WItXbpUeXl5Ki4ulsfj0ahRo1RVVWXj5AAAIJJEdPC8//77uuaaazR27Fj16tVLN9xwg7KysrR582ZJh8/uLFu2TH/84x913XXXqV+/fnrmmWd08OBBPf/88zZPDwAAIkVEB8+ll16qt99+W19++aUk6aOPPtLGjRt11VVXSZJKSkrk8/mUlZUVeo3T6dTw4cNVVFTU5PsGg0EFAoEGDwAAYK6Odg9wPHPmzJHf71efPn0UFRWluro6LViwQL/85S8lST6fT5LkdrsbvM7tdmvXrl1Nvu/ChQv1wAMPtN7gAAAgokT0GZ6XXnpJa9as0fPPP69///vfeuaZZ7RkyRI988wzDfZzOBwNvrYsq9G2o+Xk5Mjv94cepaWlrTI/AACIDBF9hufee+/V3LlzNXHiRElS//79tWvXLi1cuFBTpkyRx+ORdPhMT3Jycuh1FRUVjc76HM3pdMrpdLbu8AAAIGJE9BmegwcPqkOHhiNGRUWFfi09LS1NHo9HBQUFoedra2tVWFiozMzMNp0VAABErog+wzN+/HgtWLBAqamp6tu3r7Zu3aqlS5fq5ptvlnT4o6zs7Gzl5uYqPT1d6enpys3NVWxsrCZNmmTz9AAAIFJEdPAsX75cf/rTnzRz5kxVVFTI6/Vq+vTpuv/++0P7zJ49WzU1NZo5c6YqKys1ePBg5efnKy4uzsbJAQBAJHFYlmXZPYTdAoGAXC6X/H6/4uPj7R4HAAA0Q0t+fkf0NTwAAADhQPAAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMF7EB0+vXr3kcDgaPW699VZJkmVZmj9/vrxer2JiYjRixAht377d5qkBAEAkifjgKS4u1t69e0OPgoICSdKECRMkSYsXL9bSpUuVl5en4uJieTwejRo1SlVVVXaODQAAIojDsizL7iFaIjs7W2+88Ya++uorSZLX61V2drbmzJkjSQoGg3K73Vq0aJGmT59+zPcIBoMKBoOhrwOBgFJSUuT3+xUfH9/6iwAAAKcsEAjI5XI16+d3xJ/hOVptba3WrFmjm2++WQ6HQyUlJfL5fMrKygrt43Q6NXz4cBUVFTX5PgsXLpTL5Qo9UlJS2mJ8AABgk3YVPK+88oq+//57/eY3v5Ek+Xw+SZLb7W6wn9vtDj13LDk5OfL7/aFHaWlpq80MAADs19HuAVriqaee0pgxY+T1ehtsdzgcDb62LKvRtqM5nU45nc5WmREAAESednOGZ9euXVq/fr1uueWW0DaPxyNJjc7mVFRUNDrrAwAATl/tJnhWrVqlHj16aOzYsaFtaWlp8ng8od/ckg5f51NYWKjMzEw7xgQAABGoXXykVV9fr1WrVmnKlCnq2PF/R3Y4HMrOzlZubq7S09OVnp6u3NxcxcbGatKkSTZODAAAIkm7CJ7169dr9+7duvnmmxs9N3v2bNXU1GjmzJmqrKzU4MGDlZ+fr7i4OBsmBQAAkajd3YenNbTk9/gBAEBkMPY+PAAAACeD4AEAAMYjeAAAgPEIHgAAYDyCBwAAGI/gAQAAxiN4AACA8QgeAABgPIIHAAAYj+ABAADGI3gAAIDxCB4AAGA8ggcAABiP4AEAAMYjeAAAgPEIHgAAYDyCBwAAGI/gAQAAxiN4AACA8QgeAABgPIIHAAAYj+ABAADGI3gAAIDxCB4AAGA8ggcAABiP4AEAAMYjeAAAgPEIHgAAYDyCBwAAGI/gAQAAxiN4AACA8QgeAABgPIIHAAAYj+ABAADGI3gAAIDxCB4AAGA8ggcAABiP4AEAAMYjeAAA7Ur1/v+q6B/Pq3r/f+0eBe0IwQMAaFc+fnudvt6ySR+/vc7uUdCOdLR7AAAAWuKCy0c3+BdoDoIHANCudE1IVOaESXaPgXaGj7QAAIDxIj54ysrK9Otf/1qJiYmKjY3VgAEDtGXLltDzlmVp/vz58nq9iomJ0YgRI7R9+3YbJwYAAJEmooOnsrJSw4YNU3R0tN566y199tlnevTRR9WtW7fQPosXL9bSpUuVl5en4uJieTwejRo1SlVVVfYNDgAAIorDsizL7iGaMnfuXP3rX//Se++9d8znLcuS1+tVdna25syZI0kKBoNyu91atGiRpk+f3qzvEwgE5HK55Pf7FR8fH7b5AQBA62nJz++IPsPz2muv6eKLL9aECRPUo0cPZWRk6G9/+1vo+ZKSEvl8PmVlZYW2OZ1ODR8+XEVFRU2+bzAYVCAQaPAAAADmiujg2blzp1asWKH09HStW7dOM2bM0B133KFnn31WkuTz+SRJbre7wevcbnfouWNZuHChXC5X6JGSktJ6iwAAALaL6OCpr6/XRRddpNzcXGVkZGj69OmaNm2aVqxY0WA/h8PR4GvLshptO1pOTo78fn/oUVpa2irzAwAgSQrslTb8+fC/sEVEB09ycrLOP//8BtvOO+887d69W5Lk8XgkqdHZnIqKikZnfY7mdDoVHx/f4AEAQKv59zPSF28d/he2iOjgGTZsmL744osG27788kv17NlTkpSWliaPx6OCgoLQ87W1tSosLFRmZmabzgoAQJMumiL1HnP4X9giou+0fNdddykzM1O5ubm68cYbtWnTJj355JN68sknJR3+KCs7O1u5ublKT09Xenq6cnNzFRsbq0mTuAsnACBCxCdLI+baPcVpLaKDZ9CgQVq7dq1ycnL04IMPKi0tTcuWLdOvfvWr0D6zZ89WTU2NZs6cqcrKSg0ePFj5+fmKi4uzcXIAABBJIvo+PG2F+/AAAND+GHMfntPBoW8rtC/vrzr0bYXdowAAYCyCx2bf/+Mfqn7nHX3/j3/YPQoAAMaK6Gt4TgfdJkxo8C8AAAg/zvDYrNbpUkmvq1TrdNk9CgAAxuIMTxv46O2ntHX1XxXbw6OEQb/UFRMmh57bvrFc33z8nSTpknFpdo0IAIDROMPTBj5+Pk8J+7rph479VfndG3qloEAVByu0YtsKuQd2Uq8LktT3Uq/dYwIAYCzO8LSBPUNu06tff6/hpdvlDvr0tf6p/3fGDm3Ys0GS9Ptxv7d3QAAADEfwtIF/7uuuCXuK9eOPtSr5tqeGJn6kM899QJJ0/bnX2zwdAADm4yOtNjBjUE9tOitdHaM7KS25VF2vXaIesT30+wG/V4/YHnaPBwCA8bjTsrjTMgAA7RF3WgYAADgKwQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADj8dfSAQBoob0f/0dvrnhcPx7opgtuHKSh48bbPRJOgDM8AAC00Ker1iiqW6Kiz/9K/3p3o93joBkIHgAAWqhHx0Sp23806IfP1C1xl93joBkIHgAAWih14gh1rjlHH8acr+q0TLvHQTNwDQ8AAC3UfWCGJg7MsHsMtADBAwDASXj0T9nqWhaj6jNrdM9Dy+weByfAR1oAAJyErmUxiqo7V13LYuweBc1A8AAAcBKqz6xRXdSXqj6zxu5R0AwOy7Isu4ewWyAQkMvlkt/vV3x8vN3jAADaqZfeeVWfbNgqV3cpyv+junc+qL0Heuqtrim6Ln6dbrziNp15Tn+7xzRGS35+c4YHAIAw2VFYpLNjvlDt9zWqP7RP39fGaa86aO7X5XJs66QNb+TaPeJpi4uWAQAIE2/nXbr4h8+1ubOlAz8kqkunKl2/x6UzYnop6odD+rzkR7tHPG0RPAAAhElqz/+jLbukmI7dNfa2OeqRlKofvi7XrlX5KrX2aMz/nWT3iKctruER1/AAANAecQ0PAADAUQgeAABgPIIHAAAYj+ABAADGI3gAAIDxCB4AAGA8ggcAABiP4AEAAMbjTsuSjtx7MRAI2DwJAABoriM/t5tzD2WCR1JVVZUkKSUlxeZJAABAS1VVVcnlch13H/60hKT6+nqVl5crLi5ODoejyf0CgYBSUlJUWlp6WvwJCtZrttNtvdLpt2bWa7bTbb1S4zVblqWqqip5vV516HD8q3Q4wyOpQ4cOOuuss5q9f3x8/GnzH5fEek13uq1XOv3WzHrNdrqtV2q45hOd2TmCi5YBAIDxCB4AAGA8gqcFnE6n5s2bJ6fTafcobYL1mu10W690+q2Z9ZrtdFuvdGpr5qJlAABgPM7wAAAA4xE8AADAeAQPAAAwHsEDAACMR/A0w9VXX63U1FR17txZycnJmjx5ssrLyxvs43A4Gj1Wrlxp08Snrjlr3r17t8aPH68uXbooKSlJd9xxh2pra22a+OR98803mjp1qtLS0hQTE6Ozzz5b8+bNa7QWU45xc9dryvGVpAULFigzM1OxsbHq1q3bMfcx5fge0Zw1m3SMf6pXr16NjufcuXPtHiusHn/8caWlpalz584aOHCg3nvvPbtHahXz589vdCw9Hk+L34c7LTfDyJEj9Yc//EHJyckqKyvTrFmzdMMNN6ioqKjBfqtWrdKVV14Z+rq5d3+MRCdac11dncaOHaszzjhDGzdu1H//+19NmTJFlmVp+fLlNk/fMv/5z39UX1+vJ554Quecc44+/fRTTZs2TQcOHNCSJUsa7GvCMW7Oek06vpJUW1urCRMmaOjQoXrqqaea3M+E43vEidZs2jE+lgcffFDTpk0Lfd21a1cbpwmvl156SdnZ2Xr88cc1bNgwPfHEExozZow+++wzpaam2j1e2PXt21fr168PfR0VFdXyN7HQYq+++qrlcDis2tra0DZJ1tq1a+0bqpX9dM1vvvmm1aFDB6usrCy0zwsvvGA5nU7L7/fbNWbYLF682EpLS2uwzeRj/NP1mnp8V61aZblcrmM+Z+rxbWrNph7jI3r27Gk99thjdo/Rai655BJrxowZDbb16dPHmjt3rk0TtZ558+ZZF1544Sm/Dx9ptdD+/fv13HPPKTMzU9HR0Q2eu+2225SUlKRBgwZp5cqVqq+vt2nK8DrWmt9//33169dPXq83tN/o0aMVDAa1ZcsWu0YNG7/fr4SEhEbbTT3GP12v6ce3KaYe32M5HY7xokWLlJiYqAEDBmjBggXGfFxXW1urLVu2KCsrq8H2rKysRp88mOKrr76S1+tVWlqaJk6cqJ07d7b4PfhIq5nmzJmjvLw8HTx4UEOGDNEbb7zR4PmHHnpIl19+uWJiYvT222/rnnvu0Xfffaf77rvPpolP3fHW7PP55Ha7G+zfvXt3derUST6fr61HDauvv/5ay5cv16OPPtpgu4nHWDr2ek0+vk0x9fg2xfRjfOedd+qiiy5S9+7dtWnTJuXk5KikpER///vf7R7tlH333Xeqq6trdPzcbrcRx+6nBg8erGeffVbnnnuuvv32Wz388MPKzMzU9u3blZiY2Pw3OvWTTe3TvHnzLEnHfRQXF4f237dvn/XFF19Y+fn51rBhw6yrrrrKqq+vb/L9lyxZYsXHx7fFUpotnGueNm2alZWV1eh7REdHWy+88EKbrel4Wrpey7KssrIy65xzzrGmTp16wvePtGMczvWaenyP95HWT0Xa8bWs8K65PRzjnzqZ9R/x8ssvW5Ks7777ro2nDr+ysjJLklVUVNRg+8MPP2z17t3bpqnaTnV1teV2u61HH320Ra87bc/w3HbbbZo4ceJx9+nVq1fofyclJSkpKUnnnnuuzjvvPKWkpOiDDz7Q0KFDj/naIUOGKBAI6Ntvv21U4XYJ55o9Ho8+/PDDBq+trKzUoUOH2u16y8vLNXLkSA0dOlRPPvnkCd8/0o5xONdr4vFtqUg7vlJ419wejvFPncr6hwwZIknasWNHy84KRKCkpCRFRUU1OptTUVERsccunLp06aL+/fvrq6++atHrTtvgOfLD/GRY//Pnx4LBYJP7bN26VZ07d27y10HtEM41Dx06VAsWLNDevXuVnJwsScrPz5fT6dTAgQPDM/Apasl6y8rKNHLkSA0cOFCrVq1Shw4nvrwt0o5xONdr2vE9GZF2fKXwrrk9HOOfOpX1b926VZJCa23POnXqpIEDB6qgoEC/+MUvQtsLCgp0zTXX2DhZ2wgGg/r888/185//vEWvO22Dp7k2bdqkTZs26dJLL1X37t21c+dO3X///Tr77LNDZ3def/11+Xw+DR06VDExMXrnnXf0xz/+Ub/73e/a5V+xbc6as7KydP7552vy5Ml65JFHtH//fs2aNUvTpk1TfHy8zStomfLyco0YMUKpqalasmSJ9u3bF3ruyL0eTDrGzVmvScdXOny/mf3792v37t2qq6vTtm3bJEnnnHOOunbtatTxPeJEazbtGB/t/fff1wcffKCRI0fK5XKpuLhYd911V+j+Yia4++67NXnyZF188cWhs7S7d+/WjBkz7B4t7GbNmqXx48crNTVVFRUVevjhhxUIBDRlypSWvVGrfMBmkI8//tgaOXKklZCQYDmdTqtXr17WjBkzrD179oT2eeutt6wBAwZYXbt2tWJjY61+/fpZy5Ytsw4dOmTj5CevOWu2LMvatWuXNXbsWCsmJsZKSEiwbrvtNuuHH36waeqTt2rVqiavBzjCpGPcnPValjnH17Isa8qUKcdc7zvvvGNZllnH94gTrdmyzDrGR9uyZYs1ePBgy+VyWZ07d7Z69+5tzZs3zzpw4IDdo4XVX//6V6tnz55Wp06drIsuusgqLCy0e6RWcdNNN1nJyclWdHS05fV6reuuu87avn17i9/HYVn/81kFAACAobgPDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPACMs2/fPnk8HuXm5oa2ffjhh+rUqZPy8/NtnAyAXfjjoQCM9Oabb+raa69VUVGR+vTpo4yMDI0dO1bLli2zezQANiB4ABjr1ltv1fr16zVo0CB99NFHKi4uVufOne0eC4ANCB4AxqqpqVG/fv1UWlqqzZs364ILLrB7JAA24RoeAMbauXOnysvLVV9fr127dtk9DgAbcYYHgJFqa2t1ySWXaMCAAerTp4+WLl2qTz75RG632+7RANiA4AFgpHvvvVcvv/yyPvroI3Xt2lUjR45UXFyc3njjDbtHA2ADPtICYJwNGzZo2bJlWr16teLj49WhQwetXr1aGzdu1IoVK+weD4ANOMMDAACMxxkeAABgPIIHAAAYj+ABAADGI3gAAIDxCB4AAGA8ggcAABiP4AEAAMYjeAAAgPEIHgAAYDyCBwAAGI/gAQAAxvv/JHkfXGN15qsAAAAASUVORK5CYII=",
            "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"
        },
        {
          "data": {
            "image/png": 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",
            "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": [
        "for file in CSVFiles:\n",
        "    process_csv_file(file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7OSOZypES4k",
        "outputId": "968bb5b5-d26f-4f4d-8eae-0cf35ead2968"
      },
      "outputs": [],
      "source": [
        "\n",
        "    \n",
        "    # Adjust the max_workers parameter as needed based on your system's capabilities\n",
        "with Pool(12) as p:\n",
        "  print(p.map(process_csv_file,CSVFiles))\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "test\n"
          ]
        }
      ],
      "source": [
        "print(\"test\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['./RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-101_Session-temp_2024-02-14-11-21-21.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-102_Session-temp_2024-02-14-11-10-25.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-103_Session-temp_2024-02-14-11-28-53.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-104_Session-temp_2024-02-14-11-32-04.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-105_Session-temp_2024-02-14-11-42-44.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-106_Session-temp_2024-02-14-11-33-31.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-12_Session-temp_2024-02-14-11-40-15.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-15_Session-temp_2024-02-14-11-37-13.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-16_Session-temp_2024-02-14-11-14-18.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-21_Session-temp_2024-02-14-11-05-22.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-3_Session-temp_2024-02-14-11-18-15.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Ped-7_Session-temp_2024-02-14-11-24-47.csv',\n",
              " './RawData\\\\Pilot2\\\\csv\\\\CSV_Scenario-Practice_Session-temp_2024-02-14-11-04-01.csv']"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "CSVFiles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}