159 lines
5.7 KiB
Python
159 lines
5.7 KiB
Python
import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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import numpy as np
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import pandas as pd
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import os
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import sys
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import glob
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from IPython import embed
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def load_frame_times(trial_path):
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t_filepath = glob.glob(os.path.join(trial_path, '*.dat'))
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if len(t_filepath) == 0:
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return np.array([])
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else:
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t_filepath = t_filepath[0]
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f = open(t_filepath, 'r')
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frame_t = []
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for line in f.readlines():
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t = sum(x * float(t) for x, t in zip([3600, 60, 1], line.replace('\n', '').split(":")))
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frame_t.append(t)
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return np.array(frame_t)
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def load_and_converete_boris_events(trial_path, recording, sr, video_stated_FPS=25):
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def converte_video_frames_to_grid_idx(event_frames, led_frames, led_idx):
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event_idx_grid = (event_frames - led_frames[0]) / (led_frames[-1] - led_frames[0]) * (led_idx[-1] - led_idx[0]) + led_idx[0]
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return event_idx_grid
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# idx in grid-recording
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led_idx = pd.read_csv(os.path.join(trial_path, 'led_idxs.csv'), header=None).iloc[:, 0].to_numpy()
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# frames where LED gets switched on
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led_frames = np.load(os.path.join(trial_path, 'LED_frames.npy'))
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times, behavior, t_ag_on_off, t_contact, video_FPS = load_boris(trial_path, recording)
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contact_frame = np.array(np.round(t_contact * video_FPS), dtype=int)
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ag_on_off_frame = np.array(np.round(t_ag_on_off * video_FPS), dtype=int)
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# led_t_GRID = led_idx / sr
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contact_t_GRID = converte_video_frames_to_grid_idx(contact_frame, led_frames, led_idx) / sr
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ag_on_off_t_GRID = converte_video_frames_to_grid_idx(ag_on_off_frame, led_frames, led_idx) / sr
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return contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames
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def load_boris(trial_path, recording):
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boris_file = '-'.join(recording.split('-')[:3]) + '.csv'
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data = pd.read_csv(os.path.join(trial_path, boris_file))
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times = data['Start (s)']
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behavior = data['Behavior']
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t_ag_on = times[behavior == 0]
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t_ag_off = times[behavior == 1]
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t_ag_on_off = []
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for t in t_ag_on:
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t1 = np.array(t_ag_off)[t_ag_off > t]
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if len(t1) >= 1:
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t_ag_on_off.append(np.array([t, t1[0]]))
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t_contact = times[behavior == 2]
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return times, behavior, np.array(t_ag_on_off), t_contact.to_numpy(), data['FPS'][0]
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def main(data_folder=None):
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trials_meta = pd.read_csv('order_meta.csv')
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fish_meta = pd.read_csv('id_meta.csv')
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fish_meta['mean_w'] = np.nanmean(fish_meta.loc[:, ['w1', 'w2', 'w3']], axis=1)
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fish_meta['mean_l'] = np.nanmean(fish_meta.loc[:, ['l1', 'l2', 'l3']], axis=1)
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video_stated_FPS = 25 # cap.get(cv2.CAP_PROP_FPS)
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sr = 20_000
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for trial_idx in range(len(trials_meta)):
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print('')
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group = trials_meta['group'][trial_idx]
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recording = trials_meta['recording'][trial_idx][1:-1]
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rec_id1 = trials_meta['rec_id1'][trial_idx]
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rec_id2 = trials_meta['rec_id2'][trial_idx]
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f1_length = float(fish_meta['mean_l'][(fish_meta['group'] == trials_meta['group'][trial_idx]) &
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(fish_meta['fish'] == trials_meta['fish1'][trial_idx])])
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f2_length = float(fish_meta['mean_l'][(fish_meta['group'] == trials_meta['group'][trial_idx]) &
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(fish_meta['fish'] == trials_meta['fish2'][trial_idx])])
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if group < 3:
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continue
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trial_path = os.path.join(data_folder, recording)
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if not os.path.exists(trial_path):
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continue
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if not os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
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continue
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if not os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
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continue
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fund_v = np.load(os.path.join(trial_path, 'fund_v.npy'))
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ident_v = np.load(os.path.join(trial_path, 'ident_v.npy'))
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idx_v = np.load(os.path.join(trial_path, 'idx_v.npy'))
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times = np.load(os.path.join(trial_path, 'times.npy'))
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if len(uid:=np.unique(ident_v[~np.isnan(ident_v)])) >2:
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print(f'to many ids: {len(uid)}')
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print(f'ids in recording: {uid[0]:.0f} {uid[1]:.0f}')
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print(f'ids in meta: {rec_id1:.0f} {rec_id2:.0f}')
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meta_id_in_uid = list(map(lambda x: x in uid, [rec_id1, rec_id2]))
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if ~np.all(meta_id_in_uid):
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continue
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contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
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load_and_converete_boris_events(trial_path, recording, sr, video_stated_FPS=video_stated_FPS)
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embed()
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quit()
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###############################################################################
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fig = plt.figure(figsize=(30/2.54, 18/2.54))
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gs = gridspec.GridSpec(2, 1, left = 0.1, bottom = 0.1, right=0.95, top=0.95, height_ratios=[1, 3])
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ax = []
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ax.append(fig.add_subplot(gs[0, 0]))
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ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0]))
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for id in uid:
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ax[1].plot(times[idx_v[ident_v == id]] / 3600, fund_v[ident_v == id], marker='.')
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ax[0].plot(contact_t_GRID / 3600, np.ones_like(contact_t_GRID) , '|', markersize=20, color='k')
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ax[0].plot(ag_on_off_t_GRID[:, 0] / 3600, np.ones_like(ag_on_off_t_GRID[:, 0]) * 2, '|', markersize=20, color='red')
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min_f, max_f = np.min(fund_v[~np.isnan(ident_v)]), np.nanmax(fund_v[~np.isnan(ident_v)])
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ax[0].set_ylim(0, 3)
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ax[0].set_yticks([1, 2])
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ax[0].set_yticklabels(['contact', 'chase'])
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ax[1].set_ylim(min_f-50, max_f+50)
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ax[1].set_xlim(times[0]/3600, times[-1]/3600)
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plt.setp(ax[0].get_xticklabels(), visible=False)
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fig.suptitle(f'{recording}')
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plt.show()
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embed()
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quit()
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pass
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if __name__ == '__main__':
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# main("/home/raab/data/mount_data/")
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main("/home/raab/data/2020_competition_mount") |