LED_detect.py needs to be done for all recordings. next we need to check if trial_analysis.py does everything we need.

LED_detect.py

@@ -63,6 +63,7 @@ def main(file_path, check, x, y):
     LED_frames = np.arange(len(LED_val)-1)[(LED_val[:-1] < light_th) & (LED_val[1:] > light_th)]
 
     np.save(os.path.join(folder, 'LED_frames.npy'), LED_frames)
+
     fig, ax = plt.subplots()
     ax.plot(np.arange(len(LED_val)), LED_val, color='k')
     ax.plot(LED_frames, np.ones(len(LED_frames))*light_th, 'o', color='firebrick')
@@ -73,8 +74,8 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Detect frames of blinking LED in video recordings.')
     parser.add_argument('file', type=str, help='video file to be analyzed')
     parser.add_argument("-c", '--check', action="store_true", help="check if LED pos is correct")
-    parser.add_argument('-x', type=int, nargs=2, default=[1240, 1250], help='x-borders of LED detect area (in pixels)')
+    parser.add_argument('-x', type=int, nargs=2, default=[675, 695], help='x-borders of LED detect area (in pixels)')
-    parser.add_argument('-y', type=int, nargs=2, default=[1504, 1526], help='y-borders of LED area (in pixels)')
+    parser.add_argument('-y', type=int, nargs=2, default=[350, 360], help='y-borders of LED area (in pixels)')
     args = parser.parse_args()
     import glob
 

complete_analysis.py

@@ -1,15 +1,78 @@
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import os
 import sys
+import glob
 from IPython import embed
 
+def load_frame_times(trial_path):
+    t_filepath = glob.glob(os.path.join(trial_path, '*.dat'))
+    if len(t_filepath) == 0:
+        return np.array([])
+    else:
+        t_filepath = t_filepath[0]
+    f = open(t_filepath, 'r')
+    frame_t = []
+    for line in f.readlines():
+        t = sum(x * float(t) for x, t in zip([3600, 60, 1], line.replace('\n', '').split(":")))
+        frame_t.append(t)
+    return np.array(frame_t)
+
+def load_and_converete_boris_events(trial_path, recording, sr, video_stated_FPS=25):
+    def converte_video_frames_to_grid_idx(event_frames, led_frames, led_idx):
+        event_idx_grid = (event_frames - led_frames[0]) / (led_frames[-1] - led_frames[0]) * (led_idx[-1] - led_idx[0]) + led_idx[0]
+        return event_idx_grid
+
+    # idx in grid-recording
+    led_idx = pd.read_csv(os.path.join(trial_path, 'led_idxs.csv'), header=None).iloc[:, 0].to_numpy()
+    # frames where LED gets switched on
+    led_frames = np.load(os.path.join(trial_path, 'LED_frames.npy'))
+
+    times, behavior, t_ag_on_off, t_contact = load_boris(trial_path, recording)
+
+    contact_frame = np.array(np.round(t_contact * video_stated_FPS), dtype=int)
+    ag_on_off_frame = np.array(np.round(t_ag_on_off * video_stated_FPS), dtype=int)
+
+    # led_t_GRID = led_idx / sr
+    contact_t_GRID = converte_video_frames_to_grid_idx(contact_frame, led_frames, led_idx) / sr
+    ag_on_off_t_GRID = converte_video_frames_to_grid_idx(ag_on_off_frame, led_frames, led_idx) / sr
+
+    return contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames
+
+def load_boris(trial_path, recording):
+    boris_file = '-'.join(recording.split('-')[:3]) + '.csv'
+
+    data = pd.read_csv(os.path.join(trial_path, boris_file))
+    times = data['Start (s)']
+    behavior = data['Behavior']
+
+    t_ag_on = times[behavior == 0]
+    t_ag_off = times[behavior == 1]
+
+    t_ag_on_off = []
+    for t in t_ag_on:
+        t1 = np.array(t_ag_off)[t_ag_off > t]
+        if len(t1) >= 1:
+            t_ag_on_off.append(np.array([t, t1[0]]))
+
+    t_contact = times[behavior == 2]
+
+    return times, behavior, np.array(t_ag_on_off), t_contact.to_numpy()
+
 def main(data_folder=None):
+
+
     trials_meta = pd.read_csv('order_meta.csv')
+    video_stated_FPS = 25.  # cap.get(cv2.CAP_PROP_FPS)
+
+    sr = 20_000
 
     for trial_idx in range(len(trials_meta)):
         group = trials_meta['group'][trial_idx]
         recording = trials_meta['recording'][trial_idx][1:-1]
+        rec_id1 = trials_meta['rec_id1'][trial_idx]
+        rec_id2 = trials_meta['rec_id2'][trial_idx]
 
         if group < 3:
             continue
@@ -21,18 +84,37 @@ def main(data_folder=None):
         if not os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
             continue
 
-        print(group, recording)
+        if not os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
+            continue
+
+        contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
+            load_and_converete_boris_events(trial_path, recording, sr)
+
+        fund_v = np.load(os.path.join(trial_path, 'fund_v.npy'))
+        ident_v = np.load(os.path.join(trial_path, 'ident_v.npy'))
+        idx_v = np.load(os.path.join(trial_path, 'idx_v.npy'))
+        times = np.load(os.path.join(trial_path, 'times.npy'))
+
+        if len(uid:=np.unique(ident_v[~np.isnan(ident_v)])) >2:
+            print(f'to many ids: {len(uid)}')
+        print(f'ids in recording: {uid[0]:.0f} {uid[1]:.0f}')
+        print(f'ids in meta: {rec_id1:.0f} {rec_id2:.0f}')
+
+        fig, ax = plt.subplots(figsize=(30/2.54, 18/2.54))
+        for id in uid:
+            ax.plot(times[idx_v[ident_v == id]] / 3600, fund_v[ident_v == id], marker='.')
+
+        ax.plot(contact_t_GRID / 3600, np.ones_like(contact_t_GRID) * 1050, '|', markersize=20, color='k')
+        ax.plot(ag_on_off_t_GRID[:, 0] / 3600, np.ones_like(ag_on_off_t_GRID[:, 0]) * 1150, '|', markersize=20, color='red')
+        ax.set_ylim(400, 1200)
+        plt.show()
 
-        LED_on_idx_DF = pd.read_csv(os.path.join(trial_path, 'led_idxs.csv'))
-        i0 = np.array([int(LED_on_idx_DF.keys()[0])])
-        LED_on_idx_DATA = np.concatenate((i0, np.array(LED_on_idx_DF).T[0]))
-        LED_on_time_BORIS = np.load(os.path.join(trial_path, 'LED_on_time.npy'), allow_pickle=True)
 
-        print(len(LED_on_idx_DATA), len(LED_on_time_BORIS))
 
     embed()
     quit()
     pass
 
 if __name__ == '__main__':
-    main("/home/raab/data/mount_data/")
+    # main("/home/raab/data/mount_data/")
+    main("/home/raab/data/2020_competition_mount")