2_process_video_files.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import and functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip\n",
    "import shutil\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "def process_camera_data(Camera_ts_raw):\n",
    "\n",
    "    Camera_ts = convert_uncycle_Timestamps(Camera_ts_raw)\n",
    "\n",
    "    #check for dropped frames:\n",
    "    check_timestamps(Camera_ts, Frame_rate = 60)\n",
    "    # Find triggers:\n",
    "    Camera_trig_states = find_trigger_states(Camera_ts_raw)\n",
    "    #check if triggers are working:\n",
    "    result = np.max(Camera_trig_states) == np.min(Camera_trig_states)\n",
    "    \n",
    "    # #pull out video name\n",
    "    # video_name = [TimeStampPath.split(\"\\\\\")[-1].split(\".\")[0] + '-camera-timestamp-data']\n",
    "\n",
    "    if not result:\n",
    "\n",
    "        # make camera dataframe:\n",
    "        Camera_dataframe = pd.DataFrame(\n",
    "            {'Time Stamps': Camera_ts,\n",
    "            'Trigger State': Camera_trig_states,\n",
    "            # 'DataPath': ([TimeStampPath] * len(Camera_ts))\n",
    "            })\n",
    "        \n",
    "    return Camera_dataframe\n",
    "\n",
    "def converttime(time):\n",
    "    #offset = time & 0xFFF\n",
    "    cycle1 = (time >> 12) & 0x1FFF\n",
    "    cycle2 = (time >> 25) & 0x7F\n",
    "    seconds = cycle2 + cycle1 / 8000.\n",
    "    return seconds\n",
    "\n",
    "def uncycle(time):\n",
    "    cycles = np.insert(np.diff(time) < 0, 0, False)\n",
    "    cycleindex = np.cumsum(cycles)\n",
    "    return time + cycleindex * 128\n",
    "\n",
    "def convert_uncycle_Timestamps(Camera_ts):\n",
    "    ##################   Convert the timestamps into seconds and uncycle them:\n",
    "    t_stamps = {}  \n",
    "    stamps_s = []\n",
    "    for indx, row in Camera_ts.iterrows():\n",
    "        if row.trigger > 0: \n",
    "            timestamp_new = converttime(int(row.timestamps))\n",
    "            stamps_s.append(timestamp_new)\n",
    "        # else:    \n",
    "        #     raise ValueError('Timestamps are broken')\n",
    "    t_stamps = uncycle(stamps_s)\n",
    "    t_stamps = t_stamps - t_stamps[0] # make first timestamp 0 and the others relative to this \n",
    "    return(t_stamps)\n",
    "\n",
    "def check_timestamps(t_stamps, Frame_rate):\n",
    "    # plot 1/(diff between time stamps). This tells you roughly the frequency and if you've droppped frames.\n",
    "    Frame_gaps = 1/np.diff(t_stamps)\n",
    "    Frames_dropped = 0\n",
    "    for gaps in Frame_gaps:\n",
    "        if gaps < (Frame_rate-5) or gaps > (Frame_rate+5):\n",
    "            Frames_dropped = Frames_dropped + 1\n",
    "    print('Frames dropped = ' + str(Frames_dropped))\n",
    "\n",
    "    plt.suptitle('Frame rate = ' + str(Frame_rate) + 'fps', color = 'red')\n",
    "    frame_gaps = plt.hist(Frame_gaps, bins=100)\n",
    "    plt.xlabel('Frequency')\n",
    "    plt.ylabel('Number of frames')\n",
    "    \n",
    "def find_trigger_states(Camera_ts_raw):\n",
    "    triggers = Camera_timestamps.trigger.values[np.where(Camera_timestamps.loc[:,'trigger']>0)]\n",
    "    down_state = list(triggers)[0]\n",
    "    down_state_times = np.where(triggers == down_state)\n",
    "    Triggers_temp = np.ones(len(triggers))\n",
    "    for index in down_state_times:\n",
    "        Triggers_temp[index] = 0\n",
    "    trigger_state = Triggers_temp\n",
    "    return trigger_state\n",
    "\n",
    "def load_video(path):\n",
    "    return cv2.VideoCapture(path)\n",
    "\n",
    "def get_total_frames(cap):\n",
    "    return int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "\n",
    "def calculate_interval(total_frames, num_frames):\n",
    "    return total_frames // num_frames\n",
    "\n",
    "def process_frame(frame):\n",
    "    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "    gray_frame = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2GRAY)\n",
    "    blurred_frame = cv2.GaussianBlur(gray_frame, (15, 15), 0)\n",
    "    circles = cv2.HoughCircles(blurred_frame, cv2.HOUGH_GRADIENT, dp=1.2, minDist=50, param1=50, param2=30, minRadius=25, maxRadius=40)\n",
    "    return frame_rgb, circles\n",
    "\n",
    "def draw_circles(frame_rgb, circles):\n",
    "    if circles is not None:\n",
    "        circles = np.round(circles[0, :]).astype(\"int\")\n",
    "        for (x, y, r) in circles:\n",
    "            cv2.circle(frame_rgb, (x, y), r, (0, 255, 0), 4)\n",
    "            cv2.rectangle(frame_rgb, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)\n",
    "        return len(circles)\n",
    "    return 0\n",
    "\n",
    "def analyze_video_type(cam_path, start_time, end_time, num_frames=20):\n",
    "    cap = load_video(cam_path)\n",
    "    total_frames = get_total_frames(cap)\n",
    "    fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "    \n",
    "    start_frame = int(start_time * fps)\n",
    "    end_frame = int(end_time * fps)\n",
    "    interval = (end_frame - start_frame) // num_frames\n",
    "    \n",
    "    fig, axes = plt.subplots(2, int(np.ceil(num_frames / 2)), figsize=(15, 6))\n",
    "    circles_count = []\n",
    "\n",
    "    for i in range(num_frames):\n",
    "        frame_number = start_frame + i * interval\n",
    "        if frame_number > end_frame:\n",
    "            break\n",
    "        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)\n",
    "        ret, frame = cap.read()\n",
    "        if ret:\n",
    "            frame_rgb, circles = process_frame(frame)\n",
    "            count = draw_circles(frame_rgb, circles)\n",
    "            circles_count.append(count)\n",
    "            \n",
    "            ax = axes[i // int(np.ceil(num_frames / 2)), i % int(np.ceil(num_frames / 2))]\n",
    "            ax.imshow(frame_rgb)\n",
    "            ax.set_title(f'Frame {frame_number}')\n",
    "            ax.axis('off')\n",
    "        else:\n",
    "            print(f\"Error: Could not read frame at position {frame_number}.\")\n",
    "            circles_count.append(0)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.plot(range(num_frames), circles_count, marker='o')\n",
    "    plt.xlabel('Frame Index')\n",
    "    plt.ylabel('Number of Circles Detected')\n",
    "    plt.title('Number of Circles Detected in Each Frame')\n",
    "    plt.show()\n",
    "    return circles_count\n",
    "\n",
    "def convert_seconds(total_seconds):\n",
    "    hours = total_seconds // 3600\n",
    "    minutes = (total_seconds % 3600) // 60\n",
    "    seconds = total_seconds % 60\n",
    "    return hours, minutes, seconds\n",
    "\n",
    "\n",
    "def check_time_dropped(times,Frame_rate = 60):\n",
    "    \n",
    "    # Calculate the expected time difference between frames\n",
    "    expected_diff = 1 / Frame_rate\n",
    "\n",
    "    # Calculate the actual time differences between frames\n",
    "    actual_diffs = np.diff(times)\n",
    "\n",
    "    # Identify where frames have been dropped\n",
    "    dropped_frames = np.where((actual_diffs < expected_diff - 0.001) | (actual_diffs > expected_diff + 0.001))[0]\n",
    "\n",
    "    # Calculate the total time dropped\n",
    "    total_time_dropped = np.sum(actual_diffs[dropped_frames] - expected_diff)\n",
    "\n",
    "    print(f\"Total frames dropped: {len(dropped_frames)}\")\n",
    "    print(f\"Total time dropped is approximatley: {total_time_dropped} seconds\")\n",
    "\n",
    "    # Example usage\n",
    "    total_seconds = total_time_dropped\n",
    "    hours, minutes, seconds = convert_seconds(total_seconds)\n",
    "    print(f\"{total_seconds} seconds is equal to {hours} hours, {minutes} minutes, and {seconds} seconds.\")\n",
    "    \n",
    "\n",
    "\n",
    "def convert_date_format(date_str):\n",
    "    date_obj = datetime.strptime(date_str, '%Y%m%d')\n",
    "    formatted_date = date_obj.strftime('%d-%m-%Y')\n",
    "    return formatted_date\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pull in all paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ap5lr_2024-11-13_09-43-32\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_3_LR\\AP5_3_LR__20241113\n",
      "ap5lr_2024-11-13_09-43-32\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_3_LR\\AP5_3_LR__20241113\n",
      "ap5lr_2024-11-14_10-06-19\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_3_LR\\AP5_3_LR__20241114\n",
      "ap5lr_2024-11-14_10-06-19\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_3_LR\\AP5_3_LR__20241114\n",
      "ap5lr_2024-11-22_09-32-10\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_3_LR\\AP5_3_LR__20241122\n",
      "ap5lr_2024-11-22_09-32-10\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_3_LR\\AP5_3_LR__20241122\n",
      "seq006_2024-11-14_09-24-46\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\seq006\\seq006__20241114\n",
      "seq006_2024-11-14_09-24-46\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\seq006\\seq006__20241114\n"
     ]
    }
   ],
   "source": [
    "base_path = r\"Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\\"\n",
    "save_path_base = r\"Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\\"\n",
    "\n",
    "files_to_process = []\n",
    "full_files_to_process = []\n",
    "for mouse_file in os.listdir(base_path):\n",
    "    mouse_base = os.path.join(base_path, mouse_file)\n",
    "    for recording in os.listdir(mouse_base):\n",
    "        full_file = os.path.join(mouse_base, recording)\n",
    "        for file in os.listdir(full_file):\n",
    "            if '.csv' in file:\n",
    "                full_files_to_process += [full_file]\n",
    "                files_to_process+=[file]\n",
    "                \n",
    "                mouse_file = full_file.split('\\\\')[-2]\n",
    "                mouse_file = mouse_file.lower()\n",
    "                if 'ap5' in mouse_file:\n",
    "                    mouse_file = mouse_file.split('_')[0] + mouse_file.split('_')[-1]\n",
    "                \n",
    "                ## handle weird differences in data naming\n",
    "                if not mouse_file in file:\n",
    "                    if 'camera' in file:\n",
    "                        date = file.split('_')[1][0:-3]\n",
    "                    else:\n",
    "                        date = file.split('_')[1][1:11]\n",
    "                else:\n",
    "                    date = convert_date_format(file.split('__')[1])\n",
    "                    date_obj = datetime.strptime(date, '%d-%m-%Y')\n",
    "                    date = date_obj.strftime('%Y-%m-%d')\n",
    "                    \n",
    "                for forbidden_file in os.listdir(r\"Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\ephys\\other_sessions\\\\\"):\n",
    "                    if mouse_file in forbidden_file and date in forbidden_file:\n",
    "                        print(forbidden_file)\n",
    "                        print(full_file)\n",
    "                        #remove this item \n",
    "                        full_files_to_process.pop()\n",
    "                        files_to_process.pop()\n",
    "                        break\n",
    "                    \n",
    "                    \n",
    "                \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# create directories and split out paths that have ephys data and havent been processed already"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_1_L\\AP5_1_L__20241118\\camera1_2024-11-18T14_00_31.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5L_implant1\\recording1_18-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_1_L_18-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_1_L_18-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_1_L\\AP5_1_L__20241118\\camera2_2024-11-18T14_00_31.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5L_implant1\\recording1_18-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_1_L_18-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_1_L_18-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_1_L\\AP5_1_L__20241119\\camera1_2024-11-19T14_18_45.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5L_implant1\\recording2_19-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_1_L_19-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_1_L_19-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_1_L\\AP5_1_L__20241119\\camera2_2024-11-19T14_18_45.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5L_implant1\\recording2_19-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_1_L_19-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_1_L_19-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_2_R\\AP5_2_R__20241116\\camera1_2024-11-16T12_24_50.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5R_implant1\\recording1_16-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_2_R_16-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_2_R_16-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_2_R\\AP5_2_R__20241116\\camera2_2024-11-16T12_24_50.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5R_implant1\\recording1_16-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_2_R_16-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_2_R_16-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_2_R\\AP5_2_R__20241118\\camera1_2024-11-18T08_35_50.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5R_implant1\\recording2_18-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_2_R_18-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_2_R_18-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_2_R\\AP5_2_R__20241118\\camera2_2024-11-18T08_35_50.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5R_implant1\\recording2_18-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_2_R_18-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_2_R_18-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_2_R\\AP5_2_R__20241119\\camera1_2024-11-19T09_30_15.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5R_implant1\\recording3_19-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_2_R_19-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_2_R_19-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_2_R\\AP5_2_R__20241119\\camera2_2024-11-19T09_30_15.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5R_implant1\\recording3_19-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_2_R_19-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_2_R_19-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_2_R\\AP5_2_R__20241120\\camera1_2024-11-20T09_00_51.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5R_implant1\\recording4_20-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_2_R_20-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_2_R_20-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_2_R\\AP5_2_R__20241120\\camera2_2024-11-20T09_00_51.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5R_implant1\\recording4_20-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_2_R_20-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_2_R_20-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_2_R\\AP5_2_R__20241121\\camera1_2024-11-21T09_22_24.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5R_implant1\\recording5_21-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_2_R_21-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_2_R_21-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\AP5_2_R\\AP5_2_R__20241121\\camera2_2024-11-21T09_22_24.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\ap5R_implant1\\recording5_21-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_AP5_2_R_21-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_AP5_2_R_21-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
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      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\seq007\\seq007__20241129\\seq007__20241129__timestamp_22024-11-29T10_31_34.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\seq007_implant1\\recording4_29-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_seq007_29-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_seq007_29-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\seq008\\seq008__20241111\\timestamp_02024-11-11T15_02_12.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\seq008_implant1\\recording1_11-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_seq008_11-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_seq008_11-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\seq008\\seq008__20241111\\timestamp_22024-11-11T15_02_12.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\seq008_implant1\\recording1_11-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_seq008_11-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_seq008_11-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\seq008\\seq008__20241112\\timestamp_02024-11-12T12_58_41.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\seq008_implant1\\recording2_12-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_seq008_12-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_seq008_12-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\seq008\\seq008__20241112\\timestamp_22024-11-12T12_58_41.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\seq008_implant1\\recording2_12-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_seq008_12-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_seq008_12-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\seq008\\seq008__20241113\\seq008__20241113__timestamp_02024-11-13T09_12_52.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\seq008_implant1\\recording3_13-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_seq008_13-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_seq008_13-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\seq008\\seq008__20241113\\seq008__20241113__timestamp_22024-11-13T09_12_52.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\seq008_implant1\\recording3_13-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_seq008_13-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_seq008_13-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\seq008\\seq008__20241115\\seq008__20241115__timestamp_02024-11-15T14_07_23.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\seq008_implant1\\recording4_15-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_seq008_15-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_seq008_15-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\seq008\\seq008__20241115\\seq008__20241115__timestamp_22024-11-15T14_07_23.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\seq008_implant1\\recording4_15-11-2024\\video\\videos\n",
      "----------------\n",
      "ABOVE_CAM_seq008_15-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "BACK_CAM_seq008_15-11-2024.avi\n",
      "ALREADY EXISTS\n",
      "MAKING FILE DIR\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241016\\SP156_20241016_timestamp_02024-10-16T13_53_26.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording1_16-10-2024\\video\\videos\n",
      "----------------\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241016\\SP156_20241016_timestamp_22024-10-16T13_53_26.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording1_16-10-2024\\video\\videos\n",
      "----------------\n",
      "MAKING FILE DIR\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241017\\SP156_20241017_timestamp_02024-10-17T15_37_12.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording2_17-10-2024\\video\\videos\n",
      "----------------\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241017\\SP156_20241017_timestamp_22024-10-17T15_37_12.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording2_17-10-2024\\video\\videos\n",
      "----------------\n",
      "MAKING FILE DIR\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241022\\SP156_20241022_timestamp_02024-10-22T16_01_56.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording3_22-10-2024\\video\\videos\n",
      "----------------\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241022\\SP156_20241022_timestamp_22024-10-22T16_01_56.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording3_22-10-2024\\video\\videos\n",
      "----------------\n",
      "MAKING FILE DIR\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241023\\SP156_20241023_timestamp_02024-10-23T15_55_58.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording4_23-10-2024\\video\\videos\n",
      "----------------\n",
      "----------------\n",
      "raw video\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241023\\SP156_20241023_timestamp_22024-10-23T15_55_58.csv\n",
      "output dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording4_23-10-2024\\video\\videos\n",
      "----------------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "final_files_to_process = []\n",
    "final_full_files_to_process = []\n",
    "final_out_paths = []\n",
    "\n",
    "# loop across every single camera file\n",
    "for i in range(len(files_to_process)):\n",
    "    file = files_to_process[i]\n",
    "    full_file = full_files_to_process[i]\n",
    "    \n",
    "    # find save path\n",
    "    full_cam_save_path = 'Error'\n",
    "    current_amimal = full_file.split('\\\\')[-1].split('__')[0]\n",
    "    \n",
    "    # for all the animal folders in the save path\n",
    "    for f_path in os.listdir(save_path_base):\n",
    "        if current_amimal in f_path:\n",
    "            full_cam_save_path = os.path.join(save_path_base, f_path)\n",
    "        elif current_amimal.split('_')[0].lower() + current_amimal.split('_')[-1]+'_' in f_path:\n",
    "            full_cam_save_path = os.path.join(save_path_base, f_path)\n",
    "        elif current_amimal.split('_')[0].lower() + current_amimal.split('_')[-1].lower()+'_' in f_path:\n",
    "            full_cam_save_path = os.path.join(save_path_base, f_path)\n",
    "\n",
    "\n",
    "    date = full_file.split('\\\\')[-1].split('__')[-1]\n",
    "    if '_' in date:\n",
    "        date = date.split('_')[0]\n",
    "    formatted_date = convert_date_format(date)\n",
    "\n",
    "    full_cam_save_out_path = None\n",
    "    process = False\n",
    "    for fi_path in os.listdir(full_cam_save_path):\n",
    "        if formatted_date in fi_path:\n",
    "            full_cam_save_out_path = os.path.join(full_cam_save_path, fi_path,'video','videos')\n",
    "            process = True\n",
    "            break\n",
    "            \n",
    "            \n",
    "    # # temporary to remove all files\n",
    "    # print(full_cam_save_path, fi_path,'video')\n",
    "    # if os.path.isdir(os.path.join(full_cam_save_path, fi_path,'video')):\n",
    "    #     print('deleting')\n",
    "    #     shutil.rmtree(os.path.join(full_cam_save_path, fi_path,'video'))\n",
    "            \n",
    "            \n",
    "    if process == True:      \n",
    "        #if not existing already, make a new dir\n",
    "        if not os.path.isdir(full_cam_save_out_path):\n",
    "            print('MAKING FILE DIR')\n",
    "            os.makedirs(full_cam_save_out_path)\n",
    "        \n",
    "        print('----------------')\n",
    "        print('raw video')\n",
    "        print(os.path.join(full_file,file))\n",
    "        print('output dir')\n",
    "        print(full_cam_save_out_path)\n",
    "        print('----------------')\n",
    "        \n",
    "        already_exists = False\n",
    "        for final_file in os.listdir(full_cam_save_out_path):\n",
    "            if '.avi' in final_file:\n",
    "                print(final_file)\n",
    "                print('ALREADY EXISTS')\n",
    "                already_exists = True\n",
    "        \n",
    "        # ### JUST FOR TESTING     \n",
    "        # already_exists = False\n",
    "            \n",
    "        if already_exists == False:\n",
    "            final_files_to_process += [file]\n",
    "            final_full_files_to_process += [full_file]\n",
    "            final_out_paths += [full_cam_save_out_path]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# processing loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------NEXT DATA: --------------\n",
      "**in data\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241016\\SP156_20241016_timestamp_02024-10-16T13_53_26.csv\n",
      "SP156\n",
      "16-10-2024\n",
      "**out dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording1_16-10-2024\\video\\videos\n",
      "**************** PROCESSING ****************\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241016\\SP156_20241016_timestamp_02024-10-16T13_53_26.csv\n",
      "Total frames dropped: 0\n",
      "Total time dropped is approximatley: 0.0 seconds\n",
      "0.0 seconds is equal to 0.0 hours, 0.0 minutes, and 0.0 seconds.\n",
      "[(0, 171), (481, 802)]\n",
      "False\n",
      "done\n",
      "----------------NEXT DATA: --------------\n",
      "**in data\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241016\\SP156_20241016_timestamp_22024-10-16T13_53_26.csv\n",
      "SP156\n",
      "16-10-2024\n",
      "**out dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording1_16-10-2024\\video\\videos\n",
      "**************** PROCESSING ****************\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241016\\SP156_20241016_timestamp_22024-10-16T13_53_26.csv\n",
      "Total frames dropped: 0\n",
      "Total time dropped is approximatley: 0.0 seconds\n",
      "0.0 seconds is equal to 0.0 hours, 0.0 minutes, and 0.0 seconds.\n",
      "[(0, 171), (481, 802)]\n",
      "True\n",
      "done\n",
      "----------------NEXT DATA: --------------\n",
      "**in data\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241017\\SP156_20241017_timestamp_02024-10-17T15_37_12.csv\n",
      "SP156\n",
      "17-10-2024\n",
      "**out dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording2_17-10-2024\\video\\videos\n",
      "**************** PROCESSING ****************\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241017\\SP156_20241017_timestamp_02024-10-17T15_37_12.csv\n",
      "Total frames dropped: 0\n",
      "Total time dropped is approximatley: 0.0 seconds\n",
      "0.0 seconds is equal to 0.0 hours, 0.0 minutes, and 0.0 seconds.\n",
      "[(0, 173), (565, 730)]\n",
      "False\n",
      "done\n",
      "----------------NEXT DATA: --------------\n",
      "**in data\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241017\\SP156_20241017_timestamp_22024-10-17T15_37_12.csv\n",
      "SP156\n",
      "17-10-2024\n",
      "**out dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording2_17-10-2024\\video\\videos\n",
      "**************** PROCESSING ****************\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241017\\SP156_20241017_timestamp_22024-10-17T15_37_12.csv\n",
      "Total frames dropped: 0\n",
      "Total time dropped is approximatley: 0.0 seconds\n",
      "0.0 seconds is equal to 0.0 hours, 0.0 minutes, and 0.0 seconds.\n",
      "[(0, 173), (566, 731)]\n",
      "True\n",
      "done\n",
      "----------------NEXT DATA: --------------\n",
      "**in data\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241022\\SP156_20241022_timestamp_02024-10-22T16_01_56.csv\n",
      "SP156\n",
      "22-10-2024\n",
      "**out dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording3_22-10-2024\\video\\videos\n",
      "**************** PROCESSING ****************\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241022\\SP156_20241022_timestamp_02024-10-22T16_01_56.csv\n",
      "Total frames dropped: 0\n",
      "Total time dropped is approximatley: 0.0 seconds\n",
      "0.0 seconds is equal to 0.0 hours, 0.0 minutes, and 0.0 seconds.\n",
      "[(0, 128), (495, 768)]\n",
      "False\n",
      "done\n",
      "----------------NEXT DATA: --------------\n",
      "**in data\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241022\\SP156_20241022_timestamp_22024-10-22T16_01_56.csv\n",
      "SP156\n",
      "22-10-2024\n",
      "**out dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording3_22-10-2024\\video\\videos\n",
      "**************** PROCESSING ****************\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241022\\SP156_20241022_timestamp_22024-10-22T16_01_56.csv\n",
      "Total frames dropped: 0\n",
      "Total time dropped is approximatley: 0.0 seconds\n",
      "0.0 seconds is equal to 0.0 hours, 0.0 minutes, and 0.0 seconds.\n",
      "[(0, 128), (495, 768)]\n",
      "True\n",
      "done\n",
      "----------------NEXT DATA: --------------\n",
      "**in data\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241023\\SP156_20241023_timestamp_02024-10-23T15_55_58.csv\n",
      "SP156\n",
      "23-10-2024\n",
      "**out dir\n",
      "Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\SP156_implant1\\recording4_23-10-2024\\video\\videos\n",
      "**************** PROCESSING ****************\n",
      "Z:\\projects\\sequence_squad\\revision_data\\lars_recordings\\video\\\\SP156\\SP156__20241023\\SP156_20241023_timestamp_02024-10-23T15_55_58.csv\n",
      "Total frames dropped: 0\n",
      "Total time dropped is approximatley: 0.0 seconds\n",
      "0.0 seconds is equal to 0.0 hours, 0.0 minutes, and 0.0 seconds.\n",
      "[(0, 110), (632, 856)]\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "for run_index in range(len(final_files_to_process)):\n",
    "    \n",
    "    print('----------------NEXT DATA: --------------')\n",
    "    file = final_files_to_process[run_index]\n",
    "    full_file = final_full_files_to_process[run_index]\n",
    "    \n",
    "    current_amimal = full_file.split('\\\\')[-1].split('__')[0]\n",
    "    \n",
    "    date = full_file.split('\\\\')[-1].split('__')[-1]\n",
    "    if '_' in date:\n",
    "        date = date.split('_')[0]\n",
    "    formatted_date = convert_date_format(date)\n",
    "    \n",
    "    full_cam_save_path = final_out_paths[run_index]\n",
    "    \n",
    "    \n",
    "    # # TEMP - JUST FOR GETTING RID OF THE OLD FILES\n",
    "    # for file in os.listdir(final_out_paths[run_index]):\n",
    "    #     if file.split('_')[0] == 'camera':\n",
    "    #         #delete file \n",
    "    #         os.remove(os.path.join(final_out_paths[run_index],file))\n",
    "    # ##################################################\n",
    "    \n",
    "    print('**in data')\n",
    "    print(os.path.join(full_file, file))\n",
    "\n",
    "    print(current_amimal)\n",
    "    print(formatted_date)\n",
    "    \n",
    "    print('**out dir')\n",
    "    print(full_cam_save_path)\n",
    "    \n",
    "    ### PROCESS \n",
    "    \n",
    "    print(\"**************** PROCESSING ****************\")\n",
    "    print(os.path.join(full_file, file))\n",
    "    \n",
    "    Camera_timestamps = pd.read_csv(os.path.join(full_file, file))\n",
    "    \n",
    "    if len(Camera_timestamps.columns) > 1:\n",
    "        Camera_timestamps.columns = ['trigger', 'timestamps', 'blank']\n",
    "        Camera_timestamps.trigger = Camera_timestamps.trigger.astype(float)\n",
    "    else:\n",
    "        Camera_timestamps[['trigger', 'timestamps', 'blank']] = Camera_timestamps.iloc[:, 0].str.split(expand=True)\n",
    "        Camera_timestamps.drop(Camera_timestamps.columns[0], axis=1, inplace=True)\n",
    "        Camera_timestamps.trigger = Camera_timestamps.trigger.astype(float)\n",
    "        \n",
    "    #check if timestmaps are reversed\n",
    "    if int(Camera_timestamps.timestamps.astype(float)[1] - Camera_timestamps.timestamps.astype(float)[0]) == 1:\n",
    "        Camera_timestamps = Camera_timestamps.rename(columns={'timestamps': 'blank', 'blank': 'timestamps'})\n",
    "        \n",
    "        # Convert timestamps to datetime\n",
    "        Camera_timestamps['datetime'] = pd.to_datetime(Camera_timestamps['timestamps'], unit='ns')\n",
    "    \n",
    "        Camera_dataframe = pd.DataFrame({})\n",
    "        Camera_dataframe['Trigger State'] = find_trigger_states(Camera_timestamps)\n",
    "        Camera_dataframe['Time Stamps'] = (Camera_timestamps.datetime - Camera_timestamps.datetime[0]).dt.total_seconds()\n",
    "        check_time_dropped(Camera_dataframe['Time Stamps'].values, Frame_rate = 60)\n",
    "\n",
    "    else:\n",
    "        Camera_dataframe = process_camera_data(Camera_timestamps)\n",
    "        check_time_dropped(Camera_dataframe['Time Stamps'].values, Frame_rate = 60)\n",
    "        \n",
    "    ## split into 3 sperates epochs based on triggers\n",
    "    # find trigger onset times\n",
    "    trigger_changes = np.where(np.diff(Camera_dataframe['Trigger State'].values) == 1)[0]\n",
    "    trigger_onset_times = Camera_dataframe['Time Stamps'].values[trigger_changes]\n",
    "\n",
    "    np.diff(trigger_onset_times)\n",
    "    sleep_trigger_inds = np.where((np.diff(trigger_onset_times) > 28.5) & (np.diff(trigger_onset_times) < 31.5))\n",
    "\n",
    "    plt.plot(np.diff(trigger_onset_times))\n",
    "    plt.plot(np.array(sleep_trigger_inds)[0],np.diff(trigger_onset_times)[sleep_trigger_inds],'o')\n",
    "\n",
    "    consecutive_chunks = []\n",
    "    start = None\n",
    "    for i in range(len(sleep_trigger_inds[0]) - 1):\n",
    "        if sleep_trigger_inds[0][i + 1] - sleep_trigger_inds[0][i] == 1:\n",
    "            if start is None:\n",
    "                start = sleep_trigger_inds[0][i]\n",
    "        else:\n",
    "            if start is not None:\n",
    "                end = sleep_trigger_inds[0][i]\n",
    "                consecutive_chunks.append((start, end))\n",
    "                start = None\n",
    "    if start is not None:\n",
    "        consecutive_chunks.append((start, sleep_trigger_inds[0][-1]))\n",
    "\n",
    "    # Remove any chunks that aren't at least 100 long\n",
    "    consecutive_chunks = [(start, end) for start, end in consecutive_chunks if end - start >= 100]\n",
    "\n",
    "    print(consecutive_chunks)\n",
    "\n",
    "    ### determine video type:\n",
    "    cam_path = None\n",
    "    for cam_file in os.listdir(full_file):\n",
    "        if '.avi' in cam_file:\n",
    "            try:\n",
    "                if file.split('.csv')[0].split('timestamp')[1] in cam_file:\n",
    "                    cam_path = os.path.join(full_file,cam_file)\n",
    "            except:\n",
    "                if file.split('.csv')[0].split('_')[0] in cam_file:\n",
    "                    cam_path = os.path.join(full_file,cam_file)\n",
    "                    \n",
    "\n",
    "    \n",
    "    # # NICER but for one animal slightly error prone WAY BASED ON COUNTING CIRCLES IN FRAMES \n",
    "               \n",
    "    # analyze the video based on cicles detected in frames \n",
    "    # start = trigger_onset_times[consecutive_chunks[0][-1]]\n",
    "    # end = trigger_onset_times[consecutive_chunks[-1][0]]\n",
    "    # circles_count = analyze_video_type(cam_path,start,end)\n",
    "\n",
    "\n",
    "    # consecutive_threshold = 4\n",
    "    # count_threshold = 5\n",
    "    # consecutive_count = 0\n",
    "    # BACK_CAM = False\n",
    "\n",
    "    # for count in circles_count:\n",
    "    #     if count > count_threshold:\n",
    "    #         consecutive_count += 1\n",
    "    #         if consecutive_count >= consecutive_threshold:\n",
    "    #             BACK_CAM = True\n",
    "    #             break\n",
    "    #     else:\n",
    "    #         consecutive_count = 0\n",
    "    \n",
    "    \n",
    "    # temporary fix because for one animal the above auto thing doesnt work  \n",
    "    BACK_CAM = False\n",
    "    if 'camera2' in file:\n",
    "        BACK_CAM = True\n",
    "    if 'timestamp_2' in file:\n",
    "        BACK_CAM = True\n",
    "\n",
    "    print(BACK_CAM)\n",
    "    \n",
    "    # plot the splits\n",
    "    if BACK_CAM == True:\n",
    "        plt.plot(np.diff(trigger_onset_times))  \n",
    "        s = consecutive_chunks[0][-1]\n",
    "        e = consecutive_chunks[-1][0]\n",
    "        plt.fill_between(range(s, e), max(np.diff(trigger_onset_times)), np.diff(trigger_onset_times)[s:e], color='red', alpha=0.4)\n",
    "    else:\n",
    "        s = 0\n",
    "        e = consecutive_chunks[0][-1]\n",
    "        plt.fill_between(range(s, e), max(np.diff(trigger_onset_times)), np.diff(trigger_onset_times)[s:e], color='green', alpha=0.4)\n",
    "\n",
    "        s = consecutive_chunks[-1][0]\n",
    "        e = consecutive_chunks[-1][-1]\n",
    "        plt.fill_between(range(s, e), max(np.diff(trigger_onset_times)), np.diff(trigger_onset_times)[s:e], color='yellow', alpha=0.4)\n",
    "        \n",
    "        \n",
    "    # create dump file for processing the videos with DLC \n",
    "    DLC_dump_path_above = r\"Z:\\projects\\sequence_squad\\revision_data\\emmett_revisions\\DLC_video_dump\\ABOVE\\\\\"\n",
    "    if not os.path.exists(DLC_dump_path_above):\n",
    "        os.makedirs(DLC_dump_path_above)\n",
    "    DLC_dump_path_back = r\"Z:\\projects\\sequence_squad\\revision_data\\emmett_revisions\\DLC_video_dump\\BACK\\\\\"\n",
    "    if not os.path.exists(DLC_dump_path_back):\n",
    "        os.makedirs(DLC_dump_path_back)\n",
    "        \n",
    "\n",
    "    ## save out cam dataframe: \n",
    "    trigger_onset_inds = Camera_dataframe.index.values[trigger_changes]\n",
    "    behaviour_start = trigger_onset_inds[consecutive_chunks[0][-1]]\n",
    "    behaviour_end = trigger_onset_inds[consecutive_chunks[-1][0]]\n",
    "    # add epoch label column\n",
    "    Camera_dataframe['epoch'] = ['presleep'] * behaviour_start + ['task'] * (behaviour_end - behaviour_start) + ['postsleep'] * (len(Camera_dataframe) - behaviour_end)\n",
    "    # save this csv out unless it already exists:\n",
    "    # if file already exists:\n",
    "    if BACK_CAM == True:\n",
    "        save_out_file = full_cam_save_path + \"\\BACK_camera_timestamp_df_\" + current_amimal + '_' +formatted_date + '.csv'\n",
    "    else:\n",
    "        save_out_file = full_cam_save_path + \"\\ABOVE_camera_timestamp_df_\" + current_amimal + '_' +formatted_date + '.csv'\n",
    "    if not os.path.exists(save_out_file):\n",
    "        Camera_dataframe.to_csv(save_out_file)\n",
    "    else:\n",
    "        print('file exists')\n",
    "        print(save_out_file)\n",
    "\n",
    "    #### copy the video to the correct location:\n",
    "    if BACK_CAM == True:\n",
    "        # Copy the video to organised folder\n",
    "        new_file = full_cam_save_path + \"\\BACK_CAM_\" + current_amimal + '_' +formatted_date + '.avi'\n",
    "        shutil.copy(cam_path, new_file) \n",
    "        # copy to DLC dump location\n",
    "        new_file = DLC_dump_path_back + \"BACK_CAM_\" + current_amimal + '_' +formatted_date + '.avi'\n",
    "        shutil.copy(cam_path, new_file) \n",
    "\n",
    "    else:\n",
    "        # Copy the video to organised folder\n",
    "        new_file = full_cam_save_path + \"\\ABOVE_CAM_\" + current_amimal + '_' +formatted_date + '.avi'\n",
    "        shutil.copy(cam_path, new_file) \n",
    "        # copy to DLC dump location\n",
    "        new_file = DLC_dump_path_above + \"ABOVE_CAM_\" + current_amimal + '_' +formatted_date + '.avi'\n",
    "        shutil.copy(cam_path, new_file) \n",
    "\n",
    "    print('done')\n",
    "        \n",
    "print('ALL DONE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Camera_timestamps = pd.read_csv(os.path.join(full_file, file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Z:\\\\projects\\\\sequence_squad\\\\revision_data\\\\lars_recordings\\\\video\\\\\\\\seq006\\\\seq006__20241118\\\\seq006__20241118__timestamp_02024-11-18T13_11_58.csv'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.join(full_file, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "[2606464 rows x 3 columns]"
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Camera_timestamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Camera_dataframe' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[17], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mCamera_dataframe\u001b[49m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Camera_dataframe' is not defined"
     ]
    }
   ],
   "source": [
    "Camera_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# testing area: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### OLD METHOD OF SPLITTING INTO 3 FILES (DOESNT WORK PROPERLY BECAUSE FFMPEG DOESNT NEATLY CUT THE VIDEO, IT CUTS IT AT THE CLOSEST FRAME AND THIS MESSES UP THE TIMINGS)\n",
    "   \n",
    "    if BACK_CAM == True:\n",
    "        # save out task\n",
    "        start = trigger_onset_times[consecutive_chunks[0][-1]]\n",
    "        end = trigger_onset_times[consecutive_chunks[-1][0]]\n",
    "        print(start,end)\n",
    "        ffmpeg_extract_subclip(cam_path, start, end, full_cam_save_path_task + \"\\BACK_task_\" + current_amimal + '_' +formatted_date + '_subclip_' + str(start) + '-' + str(end) + '.avi')\n",
    "        # Copy the saved video to another location\n",
    "        shutil.copy(full_cam_save_path_task + \"\\BACK_task_\" + current_amimal + '_' +formatted_date + '_subclip_' + str(start) + '-' + str(end) + '.avi', DLC_dump_path_back) \n",
    "        # save out csv\n",
    "        task_camera_dataframe.to_csv(full_cam_save_path_task + \"\\BACK_task_\" + current_amimal + '_' +formatted_date + '_subclip_' + str(start) + '-' + str(end) + '.csv')\n",
    "        \n",
    "    else:\n",
    "        ## save out sleep1\n",
    "        start = 0 \n",
    "        end = trigger_onset_times[consecutive_chunks[0][-1]]\n",
    "        print(start,end)\n",
    "        ffmpeg_extract_subclip(cam_path, start, end, full_cam_save_path_pre + \"\\ABOVE_presleep_\" + current_amimal + '_' +formatted_date + '_subclip_' + str(start) + '-' + str(end) + '.avi')\n",
    "        # Copy the saved video to another location\n",
    "        shutil.copy(full_cam_save_path_pre + \"\\ABOVE_presleep_\" + current_amimal + '_' +formatted_date + '_subclip_' + str(start) + '-' + str(end) + '.avi', DLC_dump_path_above)\n",
    "        # save out csv\n",
    "        presleep_camera_dataframe.to_csv(full_cam_save_path_pre + \"\\ABOVE_presleep_\" + current_amimal + '_' +formatted_date + '_subclip_' + str(start) + '-' + str(end) + '.csv')\n",
    "\n",
    "        \n",
    "        # save out postsleep\n",
    "        start = trigger_onset_times[consecutive_chunks[-1][0]]\n",
    "        end = trigger_onset_times[consecutive_chunks[-1][-1]]\n",
    "        print(start,end)\n",
    "        ffmpeg_extract_subclip(cam_path, start, end, full_cam_save_path_post + \"\\ABOVE_postleep_\" + current_amimal + '_' +formatted_date + '_subclip_' + str(start) + '-' + str(end) + '.avi')\n",
    "        # Copy the saved video to another location\n",
    "        shutil.copy(full_cam_save_path_post + \"\\ABOVE_postleep_\" + current_amimal + '_' +formatted_date + '_subclip_' + str(start) + '-' + str(end) + '.avi', DLC_dump_path_above)\n",
    "        # save out csv\n",
    "        postsleep_camera_dataframe.to_csv(full_cam_save_path_post + \"\\ABOVE_postleep_\" + current_amimal + '_' +formatted_date + '_subclip_' + str(start) + '-' + str(end) + '.csv')\n",
    "        \n",
    "    print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frames dropped = 64\n",
      "Total frames dropped: 64\n",
      "Total time dropped is approximatley: 60.920958333332415 seconds\n",
      "60.920958333332415 seconds is equal to 0.0 hours, 1.0 minutes, and 0.9209583333324147 seconds.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_path_base = r\"Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\\"\n",
    "# full_file = r\"D:\\video_testing\\seq006__20241114\\\\\"\n",
    "full_file = r\"D:\\video_testing\\AP5_1_L__20241119\\\\\"\n",
    "\n",
    "# file = \"seq006__20241114__timestamp_02024-11-14T09_26_38.csv\"\n",
    "# file = \"seq006__20241114__timestamp_22024-11-14T09_26_38.csv\"\n",
    "\n",
    "file = \"camera2_2024-11-19T14_18_45.csv\"\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Camera_timestamps = pd.read_csv(os.path.join(full_file, file))\n",
    "\n",
    "# Camera_timestamps = pd.read_csv(os.path.join(r\"D:\\video_testing\\AP5_1_L__20241119\\camera1_2024-11-19T14_18_45.csv\"))\n",
    "\n",
    "if len(Camera_timestamps.columns) > 1:\n",
    "    Camera_timestamps.columns = ['trigger', 'timestamps', 'blank']\n",
    "    Camera_timestamps.trigger = Camera_timestamps.trigger.astype(float)\n",
    "else:\n",
    "    Camera_timestamps[['trigger', 'timestamps', 'blank']] = Camera_timestamps.iloc[:, 0].str.split(expand=True)\n",
    "    Camera_timestamps.drop(Camera_timestamps.columns[0], axis=1, inplace=True)\n",
    "    Camera_timestamps.trigger = Camera_timestamps.trigger.astype(float)\n",
    "    \n",
    "#check if timestmaps are reversed\n",
    "if int(Camera_timestamps.timestamps.astype(float)[1] - Camera_timestamps.timestamps.astype(float)[0]) == 1:\n",
    "    Camera_timestamps = Camera_timestamps.rename(columns={'timestamps': 'blank', 'blank': 'timestamps'})\n",
    "    \n",
    "    # Convert timestamps to datetime\n",
    "    Camera_timestamps['datetime'] = pd.to_datetime(Camera_timestamps['timestamps'], unit='ns')\n",
    "\n",
    "    Camera_dataframe = pd.DataFrame({})\n",
    "    Camera_dataframe['Trigger State'] = find_trigger_states(Camera_timestamps)\n",
    "    Camera_dataframe['Time Stamps'] = (Camera_timestamps.datetime - Camera_timestamps.datetime[0]).dt.total_seconds()\n",
    "    check_time_dropped(Camera_dataframe['Time Stamps'].values, Frame_rate = 60)\n",
    "\n",
    "else:\n",
    "    Camera_dataframe = process_camera_data(Camera_timestamps)\n",
    "    check_time_dropped(Camera_dataframe['Time Stamps'].values, Frame_rate = 60)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}