import os
import sys
import argparse
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np
import pandas as pd
from thunderfish.eventdetection import detect_peaks
from IPython import embed
class Trial(object):
def __init__(self, folder, base_path, meta, fish_count):
self._isValid = False
self.base_path = base_path
self.folder = folder
self.meta = meta
self.fish_count = fish_count
self.light_sec = 3 * 60 * 60
self.ids = None
self.fish_freq = None
self.fish_freq_interp = None
self.fish_freq_val = None
self.baseline_freq_times = None
self.baseline_freqs = None
self.rise_idxs = []
self.rise_size = []
self.fish_sign = None
self.fish_sign_interp = None
self.winner = None
self.loser = None
self.mean_shelter_power = None
if os.path.exists(os.path.join(self.base_path, self.folder, 'fund_v.npy')):
self.load()
def __repr__(self):
return f'Trial(Date={self.folder}, winner={self.winner})'
# return self.folder
def load(self):
self.fund_v = np.load(os.path.join(self.base_path, self.folder, 'fund_v.npy'))
self.idx_v = np.load(os.path.join(self.base_path, self.folder, 'idx_v.npy'))
self.times = np.load(os.path.join(self.base_path, self.folder, 'times.npy'))
self.ident_v = np.load(os.path.join(self.base_path, self.folder, 'ident_v.npy'))
self.sign_v = np.load(os.path.join(self.base_path, self.folder, 'sign_v.npy'))
self.ids = np.unique(self.ident_v[~np.isnan(self.ident_v)])
if len(self.ids) == self.fish_count:
self.isValid = True
def reshape_and_interpolate(self):
self.fish_freq = np.full((self.fish_count, len(self.times)), np.nan)
self.fish_sign = np.full((self.fish_count, len(self.times), self.sign_v.shape[1]), np.nan)
for enu, id in enumerate(self.ids):
self.fish_freq[enu][self.idx_v[self.ident_v == id]] = self.fund_v[self.ident_v == id]
self.fish_sign[enu][self.idx_v[self.ident_v == id]] = self.sign_v[self.ident_v == id]
self.fish_freq_interp = np.full(self.fish_freq.shape, np.nan)
self.fish_sign_interp = np.full(self.fish_sign.shape, np.nan)
for enu, id in enumerate(self.ids):
i0, i1 = self.idx_v[self.ident_v == id][0], self.idx_v[self.ident_v == id][-1]
# self.fish_freq_interp[enu, i0:i1+1] = np.interp(self.times[i0:i1+1],
# self.times[self.idx_v[self.ident_v == id]],
# self.fish_freq[enu][~np.isnan(self.fish_freq[enu])])
self.fish_freq_interp[enu, i0:i1+1] = np.interp(self.times[i0:i1+1],
self.times[self.idx_v[self.ident_v == id]],
self.fund_v[self.ident_v == id])
# help_sign_v = list(map(lambda x: np.interp(self.times[i0:i1+1], self.times[self.idx_v[self.ident_v == id]], x),
# self.fish_sign[enu][~np.isnan(self.fish_freq[enu])].T))
help_sign_v = list(map(lambda x: np.interp(self.times[i0:i1+1], self.times[self.idx_v[self.ident_v == id]], x),
self.sign_v[self.ident_v == id].T))
self.fish_sign_interp[enu, i0:i1+1] = np.array(help_sign_v).T
def baseline_freq(self, bw = 300):
bins = np.arange(-bw / 2, self.times[-1] + bw / 2, bw)
self.baseline_freq_times = np.array(bins[:-1] + (bins[1] - bins[0])/2)
self.baseline_freqs = np.full((2, len(self.baseline_freq_times)), np.nan)
self.pct95_freqs = np.full((2, len(self.baseline_freq_times)), np.nan)
for enu, id in enumerate(self.ids):
for i in range(len(bins) - 1):
Cf = self.fish_freq[enu][(self.times > bins[i]) & (self.times <= bins[i + 1])]
if len(Cf) == 0:
continue
else:
self.baseline_freqs[enu][i] = np.nanpercentile(Cf, 5)
self.pct95_freqs[enu][i] = np.nanpercentile(Cf, 75)
self.fish_freq_val = [np.nanmean(x[self.baseline_freq_times > self.light_sec]) for x in self.baseline_freqs]
def winner_detection(self):
day_mask = self.times > self.light_sec
day_idxs = np.arange(len(self.times))[day_mask]
shelter_power = np.empty((2, len(day_idxs)))
for enu, id in enumerate(self.ids):
shelter_power[enu] = self.fish_sign_interp[enu][day_idxs, -1]
self.mean_shelter_power = np.nanmean(shelter_power, axis=1)
self.winner = 1 if self.mean_shelter_power[1] > self.mean_shelter_power[0] else 0
self.loser = 0 if self.winner == 1 else 1
def rise_detection(self, rise_th):
def check_rises_size(peak):
peak_f = self.fish_freq[i][peak]
peak_t = self.times[peak]
closest_baseline_idx = list(map(lambda x: np.argmin(np.abs(self.baseline_freq_times - x)), peak_t))
closest_baseline_freq = self.baseline_freqs[i][closest_baseline_idx]
rise_size = peak_f - closest_baseline_freq
return rise_size
def correct_rise_idx(rise_peak_idx):
rise_dt = np.diff(self.times[rise_peak_idx])
rise_dt[rise_dt >= 10] = 10
rise_dt[rise_dt < 10] = rise_dt[rise_dt < 10] - 1
rise_dt = np.append(np.array([10]), rise_dt)
freq_slope = np.full(np.shape(self.fish_freq)[1], np.nan)
non_nan_idx = np.arange(len(freq_slope))[~np.isnan(self.fish_freq[i])]
freq_slope[non_nan_idx[1:]] = np.diff(self.fish_freq[i][~np.isnan(self.fish_freq[i])])
corrected_rise_idxs = []
for enu, r_idx in enumerate(rise_peak_idx):
mask = np.arange(len(freq_slope))[(self.times <= self.times[r_idx]) &
(self.times > self.times[r_idx] - rise_dt[enu]) &
(~np.isnan(freq_slope))]
if len(mask) == 0:
corrected_rise_idxs.append(np.nan)
else:
corrected_rise_idxs.append(mask[np.argmax(freq_slope[mask])])
corrected_rise_idxs = np.array(corrected_rise_idxs)
return corrected_rise_idxs
for i in range(len(self.fish_freq)):
rise_peak_idx, trough = detect_peaks(self.fish_freq[i][~np.isnan(self.fish_freq[i])], rise_th)
non_nan_idx = np.arange(len(self.fish_freq[i]))[~np.isnan(self.fish_freq[i])]
rise_peak_idx, trough = non_nan_idx[rise_peak_idx], non_nan_idx[trough]
rise_size = check_rises_size(rise_peak_idx)
rise_idx = correct_rise_idx(rise_peak_idx)
# print(np.min(np.diff(self.times[rise_peak_idx])))
self.rise_idxs.append(np.array(rise_idx[(rise_size >= rise_th) & (~np.isnan(rise_idx))], dtype=int))
self.rise_size.append(rise_size[(rise_size >= rise_th) & (~np.isnan(rise_idx))])
def update_meta(self):
entries = self.meta.index.tolist()
if self. folder not in entries:
self.meta.loc[self.folder] = ['' for _ in self.meta.columns]
self.meta.loc[self.folder, 'Win_ID'] = self.ids[self.winner]
self.meta.loc[self.folder, 'Lose_ID'] = self.ids[self.loser]
self.meta.loc[self.folder, 'Win_EODf'] = self.fish_freq_val[self.winner]
self.meta.loc[self.folder, 'Lose_EODf'] = self.fish_freq_val[self.loser]
self.meta.loc[self.folder, 'Win_rise_c'] = len(self.rise_idxs[self.winner])
self.meta.loc[self.folder, 'Lose_rise_c'] = len(self.rise_idxs[self.loser])
self.meta.loc[self.folder, 'light_sec'] = self.light_sec
self.meta.to_csv(os.path.join(self.base_path, 'meta.csv'), sep =',')
def ilustrate(self):
fig = plt.figure(figsize=(20/2.54, 12/2.54))
gs = gridspec.GridSpec(1, 1, left = 0.1, bottom = 0.1, right = 0.95, top = 0.95)
ax = fig.add_subplot(gs[0, 0])
for enu, id in enumerate(self.ids):
c = 'firebrick' if self.winner == enu else 'forestgreen'
ax.plot(self.times/3600, self.fish_freq[enu], marker='.', color=c, zorder=1, label=f'{self.mean_shelter_power[enu]:.2f}dB')
ax.plot(self.times[np.isnan(self.fish_freq[enu])]/3600, self.fish_freq_interp[enu][np.isnan(self.fish_freq[enu])], '.', zorder=1, color=c, alpha=0.25)
ax.plot(self.baseline_freq_times/3600, self.baseline_freqs[enu], '--', color='k', zorder=2)
ax.plot(self.baseline_freq_times/3600, self.pct95_freqs[enu], '--', color='k', zorder=2)
ax.plot(self.times[self.rise_idxs[enu]]/3600, self.fish_freq_interp[enu][self.rise_idxs[enu]], 'o', color='k')
win_str = '(W)' if self.winner == enu else ''
ax.text(self.times[-1]/3600, self.fish_freq_val[enu]-10, '%.0f' % id + win_str, va ='center', ha='right')
ax.set_xlim(0, self.times[-1]/3600)
freq_range = (np.nanmin(self.fish_freq), np.nanmax(self.fish_freq))
ax.set_ylim(freq_range[0] - 20, freq_range[1] + 10)
ax.legend(loc = 'upper right', bbox_to_anchor=(1, 1))
ax.set_title(self.folder)
plt.show()
def save(self):
saveorder = -1 if self.winner == 1 else 1
if not os.path.exists(os.path.join(self.base_path, self.folder, 'analysis')):
os.mkdir(os.path.join(self.base_path, self.folder, 'analysis'))
np.save(os.path.join(self.base_path, self.folder, 'analysis', 'ids.npy'), self.ids[::saveorder])
np.save(os.path.join(self.base_path, self.folder, 'analysis', 'fish_freq.npy'), self.fish_freq[::saveorder])
np.save(os.path.join(self.base_path, self.folder, 'analysis', 'fish_freq_interp.npy'), self.fish_freq_interp[::saveorder])
np.save(os.path.join(self.base_path, self.folder, 'analysis', 'baseline_freqs.npy'), self.baseline_freqs[::saveorder])
np.save(os.path.join(self.base_path, self.folder, 'analysis', 'baseline_freq_times.npy'), self.baseline_freq_times[::saveorder])
help_lens = [len(x) for x in self.rise_idxs]
rise_idxs_s = np.full((self.fish_count, np.max(help_lens)), np.nan)
rise_size_s = np.full((self.fish_count, np.max(help_lens)), np.nan)
for i in range(self.fish_count):
rise_idxs_s[i][:len(self.rise_idxs[i])] = self.rise_idxs[i]
rise_size_s[i][:len(self.rise_size[i])] = self.rise_size[i]
np.save(os.path.join(self.base_path, self.folder, 'analysis', 'rise_idx.npy'), rise_idxs_s[::saveorder])
np.save(os.path.join(self.base_path, self.folder, 'analysis', 'rise_size.npy'), rise_size_s[::saveorder])
@property
def isValid(self):
return self._isValid
@isValid.setter
def isValid(self, value):
print('Trial (%s) is valid' % (self.folder))
self._isValid = value
def frame_to_idx(self, event_frames):
self.sr = 20000
LED_idx = pd.read_csv(os.path.join(self.folder, 'led_idxs.csv'), sep=',', encoding = "utf-7")
led_idx = np.array(LED_idx).T[0]
led_frame = np.load(os.path.join(self.folder, 'LED_frames.npy'))
led_idx_span = led_idx[-1] - led_idx[0]
led_frame_span = led_frame[-1] - led_frame[0]
frames_to_idx = ((event_frames - led_frame[0]) / led_frame_span) * led_idx_span + led_idx[0]
event_times = frames_to_idx / self.sr
return event_times
def main():
parser = argparse.ArgumentParser(description='Evaluated electrode array recordings with multiple fish.')
parser.add_argument('file', type=str, help='single recording analysis', default='')
parser.add_argument('-d', "--dev", action="store_true", help="developer mode; no data saved")
# parser.add_argument('-x', type=int, nargs=2, default=[1272, 1282], help='x-borders of LED detect area (in pixels)')
# parser.add_argument('-y', type=int, nargs=2, default=[1500, 1516], help='y-borders of LED area (in pixels)')
args = parser.parse_args()
base_path = None
folders = []
for root, dirs, files in os.walk(args.file):
for file in files:
if file.endswith('.raw'):
root = os.path.normpath(root)
print(root, file)
print(os.path.join(root, file))
folders.append(os.path.split(root)[-1])
if not base_path:
base_path = os.path.split(root)[0]
folders = sorted(folders)
if os.path.exists(os.path.join(base_path, 'meta.csv')) and not args.dev:
meta = pd.read_csv(os.path.join(base_path, 'meta.csv'), sep=',', index_col=0, encoding = "utf-7")
else:
meta = None
# embed()
# if args.f == '':
# folders = os.listdir(args.f)
# folders = [x for x in folders if not '.' in x]
# else:
# folders= [os.path.split(os.path.normpath(args.f))[-1]]
# folders = sorted(folders)
trials = []
for folder in folders:
trial = Trial(folder, base_path, meta, fish_count=2)
if not trial.isValid:
continue
trial.reshape_and_interpolate()
trial.winner_detection()
trial.baseline_freq(bw=300)
# ToDo: q10 corrected EODfs
trial.rise_detection(rise_th=5)
if meta is not None:
if not args.dev:
trial.update_meta()
if not args.dev:
trial.save()
trial.ilustrate()
trials.append(trial)
# meta.loc[folder, 'Fish1_ID'] = 1
# meta.to_csv('')
if __name__ == '__main__':
main()