import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import itertools from event_time_correlations import load_and_converete_boris_events from tqdm import tqdm 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, video_FPS = load_boris(trial_path, recording) # # 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 # 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(), data['FPS'][0] def get_baseline_freq(fund_v, idx_v, times, ident_v, idents = None, binwidth = 300): if not hasattr(idents, '__len__'): idents = np.unique(ident_v[~np.isnan(ident_v)]) base_freqs = [] for id in idents: f = fund_v[ident_v == id] t = times[idx_v[ident_v == id]] bins = np.arange(-binwidth/2, times[-1] + binwidth/2, binwidth) base_f = np.full(len(bins)-1, np.nan) for i in range(len(bins)-1): Cf = f[(t > bins[i]) & (t <= bins[i+1])] if len(Cf) == 0: continue else: base_f[i] = np.percentile(Cf, 5) base_freqs.append(base_f) return np.array(base_freqs), np.array(bins[:-1] + (bins[1] - bins[0])/2) def q10(f1, f2, t1, t2): return(f2/f1)**(10/(t2 - t1)) def frequency_q10_compensation(baseline_freqs : np.ndarray, baseline_freq_times : np.ndarray, temp : np.ndarray, temp_t : np.ndarray, light_start_sec : float): """ Compute baseline frequency at 25 degree Celsius using Q10 formula. Q10 values are computed between each frequency- temperature pair after light_start_sec (since frequency modulations can be assumed minimal during light). Q10- compensated baseline freqs are computed for all values in baseline_freqs using the median q10 value computed previously. Parameters ---------- baseline_freqs: 2D-array: For each fish and each time in baseline_freq_times a correpsonding frequency in Hz. baseline_freq_times: 1D-array: Time stamps corresponding to baseline_freq. temp: 1D-array: temperature values detected at timespamps temp_t. temp_t: 1D-array: corresponding time stamps light_start_sec: time when light is switched on and frequency modulations can be assumed to be minimal. Q10 values only calculated for timestamps after light_start_sec Returns ------- """ q10_lit = 1.56 q10_comp_freq = [] q10_vals = [] for bf in baseline_freqs: Cbf = np.copy(bf) Ctemp = [] for base_line_time in baseline_freq_times: Ctemp.append(temp[np.argmin(np.abs(temp_t - base_line_time))]) Ctemp = np.array(Ctemp) q10s = [] for i, j in itertools.combinations(range(len(Cbf)), r=2): if Cbf[i] == Cbf[j] or Ctemp[i] == Ctemp[j]: # q10 with same values is useless continue if baseline_freq_times[i] < light_start_sec or baseline_freq_times[j] < light_start_sec: # too much frequency changes due to rises in first part of rec !!! continue # if np.abs(Ctemp[i] - Ctemp[j]) < 0.5: # continue Cq10 = q10(Cbf[i], Cbf[j], Ctemp[i], Ctemp[j]) q10s.append(Cq10) # q10_comp_freq.append(Cbf * np.median(q10s) ** ((25 - Ctemp) / 10)) q10_comp_freq.append(Cbf * q10_lit ** ((25 - Ctemp) / 10)) q10_vals.append(np.median(q10s)) print(f'Q10-values: {q10_vals[0]:.2f} {q10_vals[1]:.2f}') return q10_comp_freq, q10_vals def get_temperature(folder_path): temp_file = pd.read_csv(os.path.join(folder_path, 'temperatures.csv'), sep=';') temp_t = temp_file[temp_file.keys()[0]] temp = temp_file[temp_file.keys()[1]] temp_t = np.array(temp_t) temp = np.array(temp) if type(temp[-1]).__name__== 'str': temp = np.array(temp[:-1], dtype=float) temp_t = np.array(temp_t[:-1], dtype=int) return np.array(temp_t), np.array(temp) def main(base_path=None): colors = ['#BA2D22', '#53379B', '#F47F17', '#3673A4', '#AAB71B', '#DC143C', '#1E90FF'] female_color, male_color = '#e74c3c', '#3498db' Wc, Lc = 'darkgreen', '#3673A4' if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures')): os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures')) # trials_meta = pd.read_csv('order_meta.csv') trials_meta = pd.read_csv(os.path.join(base_path, 'order_meta.csv')) # fish_meta = pd.read_csv('id_meta.csv') fish_meta = pd.read_csv(os.path.join(base_path, '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 light_start_sec = 3*60*60 trial_summary = pd.DataFrame(columns=['recording', 'group', 'win_fish', 'lose_fish', 'win_ID', 'lose_ID', 'sex_win', 'sex_lose', 'size_win', 'size_lose', 'dsize', 'EODf_win', 'EODf_lose', 'dEODf', 'exp_win', 'exp_lose', 'chirps_win', 'chirps_lose', 'rises_win', 'rises_lose', 'chase_count', 'contact_count', 'med_chase_dur', 'comp_dur0', 'comp_dur1', 'draw']) trial_summary_row = {f'{s}':None for s in trial_summary.keys()} for trial_idx in tqdm(np.arange(len(trials_meta)), desc='Trials'): video_eval = True group = trials_meta['group'][trial_idx] recording = trials_meta['recording'][trial_idx][1:-1] print('') print(recording) rec_id1 = trials_meta['rec_id1'][trial_idx] rec_id2 = trials_meta['rec_id2'][trial_idx] if group < 3: continue trial_path = os.path.join(base_path, recording) if not os.path.exists(trial_path): continue if group < 5: video_eval = False if not os.path.exists(os.path.join(trial_path, 'led_idxs.csv')): video_eval = False if not os.path.exists(os.path.join(trial_path, 'LED_frames.npy')): video_eval = False ############################################################################################################# ### meta collect if (winner_fish := trials_meta['winner'][trial_idx]) == -1: pass elif np.isnan(winner_fish): continue elif winner_fish != trials_meta['fish1'][trial_idx] and winner_fish != trials_meta['fish2'][trial_idx]: embed() quit() print(f'not participating winner in {recording}!!!') continue win_id = rec_id1 if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else rec_id2 lose_id = rec_id2 if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else rec_id1 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])]) win_l = f1_length if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else f2_length lose_l = f2_length if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else f1_length win_exp = trials_meta['exp1'][trial_idx] if trials_meta['winner'][trial_idx] == trials_meta['fish1'][trial_idx] else trials_meta['exp2'][trial_idx] lose_exp = trials_meta['exp2'][trial_idx] if trials_meta['winner'][trial_idx] == trials_meta['fish1'][trial_idx] else trials_meta['exp1'][trial_idx] ############################################################################################################# 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}') meta_id_in_uid = list(map(lambda x: x in uid, [rec_id1, rec_id2])) if ~np.all(meta_id_in_uid): continue ids = np.load(os.path.join(trial_path, 'analysis', 'ids.npy')) sorter = -1 if win_id != ids[0] else 1 temp_t, temp = get_temperature(trial_path) baseline_freqs = np.load(os.path.join(trial_path, 'analysis', 'baseline_freqs.npy'))[::sorter] baseline_freq_times = np.load(os.path.join(trial_path, 'analysis', 'baseline_freq_times.npy')) q10_comp_freq, q10_vals = frequency_q10_compensation(baseline_freqs, baseline_freq_times, temp, temp_t, light_start_sec=light_start_sec) ############################################################################################################# ### communication got_chirps = False if os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')): chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy')) chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy')) chirp_times = [chirp_t[chirp_ids == win_id], chirp_t[chirp_ids == lose_id]] got_chirps = True rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter] rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))] ############################################################################################################# ### physical behavior med_chase_dur = contact_n = chase_n = comp_dur0 = comp_dur1 = -1 if video_eval: contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = load_and_converete_boris_events(trial_path, recording, sr) only_contact_mask = np.ones_like(contact_t_GRID, dtype=bool) for enu, ct in enumerate(contact_t_GRID): for Cag_on_off_t in ag_on_off_t_GRID: if Cag_on_off_t[0] <= ct <= Cag_on_off_t[1]: only_contact_mask[enu] = 0 break elif ct < Cag_on_off_t[0]: break contact_t_solely = contact_t_GRID[only_contact_mask] ag_offs = np.concatenate((contact_t_GRID, ag_on_off_t_GRID[:, 1])) ag_offs = ag_offs[np.argsort(ag_offs)] med_chase_dur = np.median(ag_on_off_t_GRID[:,1] - ag_on_off_t_GRID[:,0]) contact_n = len(contact_t_GRID) chase_n = len(ag_on_off_t_GRID) comp_dur0 = ag_offs[2] comp_dur1 = ag_offs[2] - ag_offs[0] win_fish_no = trials_meta['fish1'][trial_idx] if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else trials_meta['fish2'][trial_idx] lose_fish_no = trials_meta['fish2'][trial_idx] if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else trials_meta['fish1'][trial_idx] trial_summary.loc[len(trial_summary)] = trial_summary_row trial_summary.iloc[-1] = {'recording': recording, 'group': trials_meta['group'][trial_idx], 'win_fish': win_fish_no, 'lose_fish': lose_fish_no, 'win_ID': win_id, 'lose_ID': lose_id, 'sex_win': 'n', 'sex_lose': 'n', 'size_win': win_l, 'size_lose': lose_l, 'dsize': win_l - lose_l, 'EODf_win': np.nanmedian(q10_comp_freq[0]), 'EODf_lose': np.nanmedian(q10_comp_freq[1]), 'dEODf': np.nanmedian(q10_comp_freq[0]) - np.nanmedian(q10_comp_freq[1]), 'exp_win': win_exp, 'exp_lose': lose_exp, 'chirps_win': len(chirp_times[0]), 'chirps_lose': len(chirp_times[1]), 'rises_win': len(rise_idx_int[0]), 'rises_lose': len(rise_idx_int[1]), 'draw': 1 if trials_meta['winner'][trial_idx] == -1 else 0, 'chase_count': chase_n, 'contact_count': contact_n, 'med_chase_dur': med_chase_dur, 'comp_dur0': comp_dur0, 'comp_dur1': comp_dur1 } # embed() ############################################################################### 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], hspace=0) ax = [] ax.append(fig.add_subplot(gs[0, 0])) ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0])) #################################################### ### traces ax[1].plot(times[idx_v[ident_v == win_id]] / 3600, fund_v[ident_v == win_id], color=Wc, label=f'ID {win_id} {np.nanmedian(q10_comp_freq[0]):.2f}Hz') ax[1].plot(times[idx_v[ident_v == lose_id]] / 3600, fund_v[ident_v == lose_id], color=Lc, label=f'ID {lose_id} {np.nanmedian(q10_comp_freq[1]):.2f}Hz') # ax[1].plot(baseline_freq_times / 3600, q10_comp_freq[0], '--', color=Wc, lw=1) # ax[1].plot(baseline_freq_times / 3600, q10_comp_freq[1], '--', color=Lc, lw=1) # ax[1].plot(times[idx_v[ident_v == lose_id]] / 3600, fund_v[ident_v == lose_id], color=Lc) min_f, max_f = np.min(fund_v[~np.isnan(ident_v)]), np.nanmax(fund_v[~np.isnan(ident_v)]) 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) ax_m = ax[1].twinx() ax_m.plot(temp_t/3600, temp, '--', lw=2, color='tab:red') ylim0, ylim1 = ax[1].get_ylim() ax_m.set_ylim(np.nanmedian(temp) - (ylim1-ylim0) / 40 / 2, np.nanmedian(temp) + (ylim1-ylim0) / 40 / 2) ax[1].legend(loc='upper right', bbox_to_anchor=(1, 1), title=r'EODf$_{25}$') #################################################### ### behavior if video_eval: ax[0].plot(contact_t_GRID / 3600, np.ones_like(contact_t_GRID) , '|', markersize=10, color='k') ax[0].plot(ag_on_off_t_GRID[:, 0] / 3600, np.ones_like(ag_on_off_t_GRID[:, 0]) * 2, '|', markersize=10, color='firebrick') ax[0].plot(times[rise_idx_int[0]] / 3600, np.ones_like(rise_idx_int[0]) * 4, '|', markersize=10, color=Wc) ax[0].plot(times[rise_idx_int[1]] / 3600, np.ones_like(rise_idx_int[1]) * 5, '|', markersize=10, color=Lc) if got_chirps: ax[0].plot(chirp_times[0] / 3600, np.ones_like(chirp_times[0]) * 7, '|', markersize=10, color=Wc) ax[0].plot(chirp_times[1] / 3600, np.ones_like(chirp_times[1]) * 8, '|', markersize=10, color=Lc) ax[0].set_ylim(0, 9) ax[0].set_yticks([1, 2, 4, 5, 7, 8]) ax[0].set_yticklabels(['contact', 'chase', r'rise$_{win}$', r'rise$_{lose}$', r'chirp$_{win}$', r'chirp$_{lose}$']) fig.suptitle(f'{recording}') plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{recording}.png'), dpi=300) # plt.savefig(os.path.join(os.path.join(os.path.split(__file__)[0], 'figures', f'{recording}.png')), dpi=300) plt.close() for g in pd.unique(trial_summary['group']): fish_no = np.unique(np.concatenate((trial_summary['win_fish'][trial_summary['group'] == g], trial_summary['lose_fish'][trial_summary['group'] == g]))) for f in fish_no: fish_EODf25 = np.concatenate((trial_summary['EODf_lose'][(trial_summary['group'] == g) & (trial_summary['lose_fish'] == f)], trial_summary['EODf_win'][(trial_summary['group'] == g) & (trial_summary['win_fish'] == f)])) if np.nanmedian(fish_EODf25) < 730: sex = 'f' else: sex = 'm' trial_summary['sex_win'][(trial_summary['group'] == g) & (trial_summary['win_fish'] == f)] = sex trial_summary['sex_lose'][(trial_summary['group'] == g) & (trial_summary['lose_fish'] == f)] = sex trial_summary.to_csv(os.path.join(base_path, 'trial_summary.csv')) pass if __name__ == '__main__': # main("/home/raab/data/mount_data/") main("/home/raab/data/2020_competition_mount")