raab/competition_experiments
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continued on analysis ... included the calculus of mean_l and w and detected all LED_frames

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Till Raab 2023-05-15 15:20:07 +02:00
parent c6254eac59
commit fdd5e1cc01
1 changed files with 53 additions and 14 deletions
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67
complete_analysis.py
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@ -1,4 +1,5 @@
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np
import pandas as pd
import os
@ -6,6 +7,7 @@ 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:
@ -19,6 +21,7 @@ def load_frame_times(trial_path):
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]
@ -29,10 +32,10 @@ def load_and_converete_boris_events(trial_path, recording, sr, video_stated_FPS=
# 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)
times, behavior, t_ag_on_off, t_contact, video_FPS = 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)
contact_frame = np.array(np.round(t_contact * video_FPS), dtype=int)
ag_on_off_frame = np.array(np.round(t_ag_on_off * video_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
@ -40,6 +43,7 @@ def load_and_converete_boris_events(trial_path, recording, sr, video_stated_FPS=
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'
@ -58,22 +62,34 @@ def load_boris(trial_path, recording):
t_contact = times[behavior == 2]
return times, behavior, np.array(t_ag_on_off), t_contact.to_numpy()
return times, behavior, np.array(t_ag_on_off), t_contact.to_numpy(), data['FPS'][0]
def main(data_folder=None):
def main(data_folder=None):
trials_meta = pd.read_csv('order_meta.csv')
video_stated_FPS = 25. # cap.get(cv2.CAP_PROP_FPS)
fish_meta = pd.read_csv('id_meta.csv')
fish_meta['mean_w'] = np.nanmean(fish_meta.loc[:, ['w1', 'w2', 'w3']], axis=1)
fish_meta['mean_l'] = np.nanmean(fish_meta.loc[:, ['l1', 'l2', 'l3']], axis=1)
video_stated_FPS = 25 # cap.get(cv2.CAP_PROP_FPS)
sr = 20_000
for trial_idx in range(len(trials_meta)):
print('')
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]
f1_length = float(fish_meta['mean_l'][(fish_meta['group'] == trials_meta['group'][trial_idx]) &
(fish_meta['fish'] == trials_meta['fish1'][trial_idx])])
f2_length = float(fish_meta['mean_l'][(fish_meta['group'] == trials_meta['group'][trial_idx]) &
(fish_meta['fish'] == trials_meta['fish2'][trial_idx])])
if group < 3:
continue
@ -87,9 +103,6 @@ def main(data_folder=None):
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'))
@ -100,13 +113,39 @@ def main(data_folder=None):
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))
meta_id_in_uid = list(map(lambda x: x in uid, [rec_id1, rec_id2]))
if ~np.all(meta_id_in_uid):
continue
contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
load_and_converete_boris_events(trial_path, recording, sr, video_stated_FPS=video_stated_FPS)
embed()
quit()
###############################################################################
fig = plt.figure(figsize=(30/2.54, 18/2.54))
gs = gridspec.GridSpec(2, 1, left = 0.1, bottom = 0.1, right=0.95, top=0.95, height_ratios=[1, 3])
ax = []
ax.append(fig.add_subplot(gs[0, 0]))
ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0]))
for id in uid:
ax.plot(times[idx_v[ident_v == id]] / 3600, fund_v[ident_v == id], marker='.')
ax[1].plot(times[idx_v[ident_v == id]] / 3600, fund_v[ident_v == id], marker='.')
ax[0].plot(contact_t_GRID / 3600, np.ones_like(contact_t_GRID) , '|', markersize=20, color='k')
ax[0].plot(ag_on_off_t_GRID[:, 0] / 3600, np.ones_like(ag_on_off_t_GRID[:, 0]) * 2, '|', markersize=20, color='red')
min_f, max_f = np.min(fund_v[~np.isnan(ident_v)]), np.nanmax(fund_v[~np.isnan(ident_v)])
ax[0].set_ylim(0, 3)
ax[0].set_yticks([1, 2])
ax[0].set_yticklabels(['contact', 'chase'])
ax[1].set_ylim(min_f-50, max_f+50)
ax[1].set_xlim(times[0]/3600, times[-1]/3600)
plt.setp(ax[0].get_xticklabels(), visible=False)
fig.suptitle(f'{recording}')
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()