314 lines
12 KiB
Python
314 lines
12 KiB
Python
import os
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from matplotlib.patches import Patch
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import pandas as pd
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from IPython import embed
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from event_time_correlations import load_and_converete_boris_events, kde, gauss
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female_color, male_color = '#e74c3c', '#3498db'
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def iei_analysis(all_chirp_times_lose, all_chirp_times_win, all_rise_times_lose, all_rise_times_win, win_sex, lose_sex):
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ici_lose = []
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ici_win = []
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iri_lose = []
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iri_win = []
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for i in range(len(all_chirp_times_lose)):
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ici_lose.append(np.diff(all_chirp_times_lose[i]))
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ici_win.append(np.diff(all_chirp_times_win[i]))
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iri_lose.append(np.diff(all_rise_times_lose[i]))
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iri_win.append(np.diff(all_rise_times_win[i]))
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for iei, kernal_w in zip([ici_lose, ici_win, iri_lose, iri_win],
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[1, 1, 5, 50]):
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fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
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gs = gridspec.GridSpec(2, 2, left=0.1, bottom=0.1, right=0.95, top=0.95)
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ax = []
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ax.append(fig.add_subplot(gs[0, 0]))
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ax.append(fig.add_subplot(gs[0, 1], sharey=ax[0], sharex=ax[0]))
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ax.append(fig.add_subplot(gs[1, 0], sharey=ax[0], sharex=ax[0]))
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ax.append(fig.add_subplot(gs[1, 1], sharey=ax[0], sharex=ax[0]))
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for i in range(len(iei)):
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if win_sex[i] == 'm':
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if lose_sex[i] == 'm':
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color, linestyle = male_color, '-'
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sp = 0
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else:
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color, linestyle = male_color, '--'
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sp = 1
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else:
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if lose_sex[i] == 'm':
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color, linestyle = female_color, '--'
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sp = 2
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else:
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color, linestyle = female_color, '-'
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sp = 3
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conv_y_chirp_lose = np.arange(0, np.percentile(np.hstack(iei), 90), .5)
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kde_array = kde(iei[i], conv_y_chirp_lose, kernal_w=kernal_w, kernal_h=1)
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# kde_array /= np.sum(kde_array)
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ax[sp].plot(conv_y_chirp_lose, kde_array, zorder=2, color=color, linestyle=linestyle, lw=2)
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plt.setp(ax[1].get_yticklabels(), visible=False)
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plt.setp(ax[3].get_yticklabels(), visible=False)
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plt.setp(ax[0].get_xticklabels(), visible=False)
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plt.setp(ax[1].get_xticklabels(), visible=False)
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plt.show()
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def relative_rate_progression(all_event_t, title=''):
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stop_t = 3*60*60
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snippet_len = 15*60
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snippet_starts = np.arange(0, stop_t, snippet_len)
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all_snippet_ratio = []
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for event_t in all_event_t:
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if len(event_t) == 0:
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continue
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expected_snippet_count = len(event_t[event_t <= stop_t]) / (stop_t / snippet_len)
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snippet_ratio = []
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for s0 in snippet_starts:
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snippet_count = len(event_t[(event_t >= s0) & (event_t < s0 + snippet_len)])
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snippet_ratio.append(snippet_count/expected_snippet_count)
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all_snippet_ratio.append(snippet_ratio)
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all_snippet_ratio = np.array(all_snippet_ratio)
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fig = plt.figure(figsize=(20/2.54, 12/2.54))
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gs = gridspec.GridSpec(1, 1, left=.1, bottom=.1, right=0.95, top=0.95)
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ax = fig.add_subplot(gs[0, 0])
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plot_t = np.repeat(snippet_starts, 2)
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plot_t[1::2] += snippet_len
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for event_ratios in all_snippet_ratio:
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plot_ratios = np.repeat(event_ratios, 2)
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ax.plot(plot_t / 3600, plot_ratios, color='grey', lw=1, alpha=0.5)
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# ax.plot(snippet_starts + snippet_len/2, event_ratios)
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mean_ratio = np.median(all_snippet_ratio, axis=0)
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plot_mean_ratio = np.repeat(mean_ratio, 2)
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ax.plot(plot_t / 3600, plot_mean_ratio, color='k', lw=3)
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ax.plot(plot_t / 3600, np.ones_like(plot_t), linestyle='dotted', lw=2, color='k')
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ax.set_xlabel('time [h]', fontsize=12)
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ax.set_ylabel('norm. event rate', fontsize=12)
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ax.set_title(title)
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ax.tick_params(labelsize=10)
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ax.set_xlim(0, 3)
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ax.set_ylim(0, 5)
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plt.show()
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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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all_rise_times_lose = []
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all_rise_times_win = []
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all_chirp_times_lose = []
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all_chirp_times_win = []
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win_sex = []
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lose_sex = []
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all_contact_t = []
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all_ag_on_t = []
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all_ag_off_t = []
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for index, trial in trial_summary.iterrows():
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print(index, len(trial_summary))
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got_boris = False
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trial_path = os.path.join(base_path, trial['recording'])
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if trial['group'] < 3:
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continue
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if trial['draw'] == 1:
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continue
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if os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
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got_boris = True
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if os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
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got_boris = True
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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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if got_boris:
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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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all_contact_t.append(contact_t_GRID)
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all_ag_on_t.append(ag_on_off_t_GRID[:, 0])
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all_ag_off_t.append(ag_on_off_t_GRID[:, 1])
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else:
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all_contact_t.append(np.array([]))
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all_ag_on_t.append(np.array([]))
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all_ag_off_t.append(np.array([]))
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### communication
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if not os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
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continue
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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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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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# iri = np.diff(rise_times[1])
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all_rise_times_lose.append(rise_times[1])
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all_rise_times_win.append(rise_times[0])
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all_chirp_times_lose.append(chirp_times[1])
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all_chirp_times_win.append(chirp_times[0])
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win_sex.append(trial['sex_win'])
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lose_sex.append(trial['sex_lose'])
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embed()
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quit()
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iei_analysis(all_chirp_times_lose, all_chirp_times_win, all_rise_times_lose, all_rise_times_win, win_sex, lose_sex)
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relative_rate_progression(all_chirp_times_lose, title=r'chirp$_{lose}$')
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relative_rate_progression(all_chirp_times_win, title=r'chirp$_{win}$')
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relative_rate_progression(all_rise_times_lose, title=r'rise$_{lose}$')
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relative_rate_progression(all_rise_times_win, title=r'rise$_{win}$')
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relative_rate_progression(all_contact_t, title=r'contact')
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relative_rate_progression(all_ag_on_t, title=r'chasing')
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all_chase_chirp_mask = []
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all_chasing_t = []
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all_chase_off_chirp_mask = []
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all_chase_off_t = []
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all_contact_chirp_mask = []
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all_physical_t = []
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time_tol = 2
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for contact_t, ag_on_t, ag_off_t, chirp_times_lose in zip(all_contact_t, all_ag_on_t, all_ag_off_t, all_chirp_times_lose):
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if len(contact_t) == 0:
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continue
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# ToDo: the 5 seconds are a little dirty... sometimes 5s is longer than chasing dur
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chase_chirp_mask = np.zeros_like(chirp_times_lose)
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chase_off_chirp_mask = np.zeros_like(chirp_times_lose)
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for chase_on_t, chase_off_t in zip(ag_on_t, ag_off_t):
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chase_chirp_mask[(chirp_times_lose >= chase_on_t) & (chirp_times_lose < chase_off_t-time_tol)] = 1
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chase_off_chirp_mask[(chirp_times_lose >= chase_off_t-time_tol) & (chirp_times_lose < chase_off_t+time_tol)] = 1
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all_chase_chirp_mask.append(chase_chirp_mask)
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all_chase_off_chirp_mask.append(chase_off_chirp_mask)
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chasing_dur = (ag_off_t - ag_on_t) - time_tol
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chasing_dur[chasing_dur < 0] = 0
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chasing_t = np.sum(chasing_dur)
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all_chasing_t.append(chasing_t)
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all_chase_off_t.append(len(ag_off_t) * time_tol*2)
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contact_chirp_mask = np.zeros_like(chirp_times_lose)
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for ct in contact_t:
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contact_chirp_mask[(chirp_times_lose >= ct-time_tol) & (chirp_times_lose < ct+time_tol)] = 1
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all_contact_chirp_mask.append(contact_chirp_mask)
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all_physical_t.append(len(contact_t) * time_tol*2)
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all_physical_t = np.array(all_physical_t)
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all_chasing_t = np.array(all_chasing_t)
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all_chase_off_t = np.array(all_chase_off_t)
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physical_t_ratio = all_physical_t / (3*60*60)
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chase_t_ratio = all_chasing_t / (3*60*60)
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chase_off_t_ratio = all_chase_off_t / (3*60*60)
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contact_chirp_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_contact_chirp_mask)))
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chase_chirp_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_chase_chirp_mask)))
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chase_off_chirp_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_chase_off_chirp_mask)))
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fig = plt.figure(figsize=(20/2.54, 12/2.54))
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gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.95, top=0.95)
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ax = fig.add_subplot(gs[0, 0])
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ax.boxplot([chase_chirp_ratio/chase_t_ratio,
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contact_chirp_ratio/physical_t_ratio,
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chase_off_chirp_ratio/chase_off_t_ratio], positions=np.arange(3), sym='')
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ax.plot(np.arange(5)-1, np.ones(5), linestyle='dotted', lw=2, color='k')
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ax.set_xlim(-0.5, 2.5)
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ax.set_ylabel(r'rel. chrips$_{event}$ / rel. time$_{event}$', fontsize=12)
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ax.set_xticks(np.arange(3))
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ax.set_xticklabels(['chasing', 'contact', r'chase$_{off}$'])
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ax.tick_params(labelsize=10)
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plt.show()
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flat_contact_chirp_mask = np.hstack(all_contact_chirp_mask)
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flat_chase_chirp_mask = np.hstack(all_chase_chirp_mask)
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flat_chase_off_chirp_mask = np.hstack(all_chase_off_chirp_mask)
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flat_chase_chirp_mask[flat_contact_chirp_mask == 1] = 0
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flat_chase_off_chirp_mask[flat_contact_chirp_mask == 1] = 0
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flat_chase_chirp_mask[flat_chase_off_chirp_mask == 1] = 0
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chirps_context_values = [np.sum(flat_contact_chirp_mask) / len(flat_contact_chirp_mask),
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np.sum(flat_chase_chirp_mask) / len(flat_chase_chirp_mask),
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np.sum(flat_chase_off_chirp_mask) / len(flat_chase_off_chirp_mask)]
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chirps_context_values.append(1 - np.sum(chirps_context_values))
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time_context_values = [np.sum(all_physical_t), np.sum(all_chasing_t), np.sum(all_chase_off_t)]
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time_context_values.append(len(all_chasing_t) * 3*60*60 - np.sum(time_context_values))
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time_context_values /= np.sum(time_context_values)
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fig, ax = plt.subplots(figsize=(12/2.54,12/2.54))
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size = 0.3
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outer_colors = ['tab:red', 'tab:orange', 'tab:green', 'tab:grey']
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ax.pie(chirps_context_values, radius=1, colors=outer_colors,
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wedgeprops=dict(width=size, edgecolor='w'), startangle=90, center=(0, .5))
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ax.pie(time_context_values, radius=1-size, colors=outer_colors,
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wedgeprops=dict(width=size, edgecolor='w', alpha=.6), startangle=90, center=(0, .5))
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ax.set_title(r'chirp$_{lose}$ context')
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legend_elements = [Patch(facecolor='tab:red', edgecolor='w', label='%.1f' % (chirps_context_values[0] * 100) + '%'),
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Patch(facecolor='tab:orange', edgecolor='w', label='%.1f' % (chirps_context_values[1] * 100) + '%'),
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Patch(facecolor='tab:green', edgecolor='w', label='%.1f' % (chirps_context_values[2] * 100) + '%'),
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Patch(facecolor='tab:red', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[0] * 100) + '%'),
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Patch(facecolor='tab:orange', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[1] * 100) + '%'),
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Patch(facecolor='tab:green', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[2] * 100) + '%')]
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# ax.text(-0.65, -1.4, 'chirps', fontsize=10, va='center', ha='center')
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# ax.text(0.75, -1.4, 'time', fontsize=10, va='center', ha='center')
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ax.legend(handles=legend_elements, loc='lower right', ncol=2, bbox_to_anchor=(1.1, -0.15), frameon=False, fontsize=9)
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plt.show()
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embed()
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quit()
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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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