156 lines
5.2 KiB
Python
156 lines
5.2 KiB
Python
import os
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import sys
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import argparse
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import numpy as np
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import cupy as cp
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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import pandas as pd
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from IPython import embed
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def load_and_converete_boris_events(trial_path, recording, sr):
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def converte_video_frames_to_grid_idx(event_frames, led_frames, led_idx):
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event_idx_grid = (event_frames - led_frames[0]) / (led_frames[-1] - led_frames[0]) * (led_idx[-1] - led_idx[0]) + led_idx[0]
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return event_idx_grid
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# idx in grid-recording
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led_idx = pd.read_csv(os.path.join(trial_path, 'led_idxs.csv'), header=None).iloc[:, 0].to_numpy()
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# frames where LED gets switched on
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led_frames = np.load(os.path.join(trial_path, 'LED_frames.npy'))
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times, behavior, t_ag_on_off, t_contact, video_FPS = load_boris(trial_path, recording)
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contact_frame = np.array(np.round(t_contact * video_FPS), dtype=int)
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ag_on_off_frame = np.array(np.round(t_ag_on_off * video_FPS), dtype=int)
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# led_t_GRID = led_idx / sr
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contact_t_GRID = converte_video_frames_to_grid_idx(contact_frame, led_frames, led_idx) / sr
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ag_on_off_t_GRID = converte_video_frames_to_grid_idx(ag_on_off_frame, led_frames, led_idx) / sr
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return contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames
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def load_boris(trial_path, recording):
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boris_file = '-'.join(recording.split('-')[:3]) + '.csv'
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data = pd.read_csv(os.path.join(trial_path, boris_file))
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times = data['Start (s)']
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behavior = data['Behavior']
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t_ag_on = times[behavior == 0]
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t_ag_off = times[behavior == 1]
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t_ag_on_off = []
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for t in t_ag_on:
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t1 = np.array(t_ag_off)[t_ag_off > t]
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if len(t1) >= 1:
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t_ag_on_off.append(np.array([t, t1[0]]))
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t_contact = times[behavior == 2]
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return times, behavior, np.array(t_ag_on_off), t_contact.to_numpy(), data['FPS'][0]
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def gauss(t, shift, sigma, size, norm = False):
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if not hasattr(shift, '__len__'):
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g = np.exp(-((t - shift) / sigma) ** 2 / 2) * size
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if norm:
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g /= np.sum(g)
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return g
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else:
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t = np.array([t, ] * len(shift))
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res = np.exp(-((t.transpose() - shift).transpose() / sigma) ** 2 / 2) * size
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return res
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def event_centered_times(centered_event_times, surrounding_event_times, max_dt = 60):
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event_dt = []
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for Cevent_t in centered_event_times:
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Cdt = np.array(surrounding_event_times - Cevent_t)
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event_dt.extend(Cdt[np.abs(Cdt) <= max_dt])
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return np.array(event_dt)
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def kde(event_dt, max_dt = 60):
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kernal_w = 1
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kernal_h = 0.2
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conv_t = np.arange(-max_dt, max_dt, 1)
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conv_array = np.zeros(len(conv_t))
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for e in event_dt:
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conv_array += gauss(conv_t, e, kernal_w, kernal_h, norm=True)
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plt.plot(conv_t, conv_array)
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def permulation_kde(event_dt, repetitions = 100, max_dt = 60):
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embed()
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quit()
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kernal_w = 1
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kernal_h = 0.2
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conv_t = cp.arange(-max_dt, max_dt, 1)
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conv_tt = cp.reshape(conv_t, (len(conv_t), 1, 1))
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# array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
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event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
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# conv_t_perm = cp.tile(conv_tt, (1, repetitions, len(event_dt)))
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gauss_3d = cp.exp(-((conv_tt - event_dt_perm) / kernal_w) ** 2 / 2) * kernal_h
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kde_3d = cp.sum(gauss_3d, axis = 2).transpose()
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kde_3d_numpy = cp.asnumpy(kde_3d)
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def main(base_path):
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trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
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lose_chrips_centered_on_ag_off_t = []
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for index, trial in trial_summary.iterrows():
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trial_path = os.path.join(base_path, trial['recording'])
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if trial['group'] < 5:
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continue
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if not os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
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continue
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if not os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
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continue
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ids = np.load(os.path.join(trial_path, 'analysis', 'ids.npy'))
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times = np.load(os.path.join(trial_path, 'times.npy'))
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sorter = -1 if trial['win_ID'] != ids[0] else 1
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### event times --> BORIS behavior
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contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
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load_and_converete_boris_events(trial_path, trial['recording'], sr=20_000)
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### communication
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got_chirps = False
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if os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
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chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy'))
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chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy'))
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chirp_times = [chirp_t[chirp_ids == trial['win_ID']], chirp_t[chirp_ids == trial['lose_ID']]]
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got_chirps = True
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rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter]
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rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))]
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rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]]
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lose_chrips_centered_on_ag_off_t.append(event_centered_times(ag_on_off_t_GRID[:, 1], chirp_times[1]))
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kde(np.hstack(lose_chrips_centered_on_ag_off_t))
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permulation_kde(np.hstack(lose_chrips_centered_on_ag_off_t))
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embed()
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quit()
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pass
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if __name__ == '__main__':
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main(sys.argv[1])
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