From fdd5e1cc01801fe8536905d39bdd27ae8d1dbcd2 Mon Sep 17 00:00:00 2001 From: Till Raab Date: Mon, 15 May 2023 15:20:07 +0200 Subject: [PATCH] continued on analysis ... included the calculus of mean_l and w and detected all LED_frames --- complete_analysis.py | 67 +++++++++++++++++++++++++++++++++++--------- 1 file changed, 53 insertions(+), 14 deletions(-) diff --git a/complete_analysis.py b/complete_analysis.py index 6de31bd..0acb15e 100644 --- a/complete_analysis.py +++ b/complete_analysis.py @@ -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()