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', 'example_trials')):
os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'example_trials'))
# 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', 'example_trials', 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")