raab/competition_experiments
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

forgot the commit and push on wednesday

Browse Source
This commit is contained in:
Till Raab 2023-06-09 07:58:56 +02:00
parent 07daf7457c
commit 47a1ac1058
3 changed files with 254 additions and 151 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

358
event_time_analysis.py
View File

@ -1,5 +1,6 @@
import os
import sys
import itertools
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
@ -48,8 +49,8 @@ def iei_analysis(event_times, win_sex, lose_sex, kernal_w, title=''):
ax[sp].plot(conv_y, kde_array, zorder=2, color=color, linestyle=linestyle, lw=2)
ax[0].set_xlim(conv_y[0], conv_y[-1])
ax[0].set_ylabel('event rate [Hz]', fontsize=12)
ax[2].set_ylabel('event rate [Hz]', fontsize=12)
ax[0].set_ylabel('KDE', fontsize=12)
ax[2].set_ylabel('KDE', fontsize=12)
ax[2].set_xlabel('time [s]', fontsize=12)
ax[3].set_xlabel('time [s]', fontsize=12)
fig.suptitle(title, fontsize=12)
@ -63,7 +64,9 @@ def iei_analysis(event_times, win_sex, lose_sex, kernal_w, title=''):
plt.setp(ax[0].get_xticklabels(), visible=False)
plt.setp(ax[1].get_xticklabels(), visible=False)
plt.show()
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{title}_iei.png'), dpi=300)
plt.close()
# plt.show()
# for iei, kernal_w in zip([ici_lose, ici_win, iri_lose, iri_win],
# [1, 1, 5, 50]):
@ -151,18 +154,26 @@ def relative_rate_progression(all_event_t, title=''):
ax.set_xlim(0, 3)
ax.set_ylim(0, 5)
x = np.hstack(all_snippeqt_ratio)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{title}_progression.png'), dpi=300)
plt.close()
# plt.show()
x = np.hstack(all_snippet_ratio)
y = np.hstack(np.tile(snippet_starts, (all_snippet_ratio.shape[0], 1)))
r, p = scp.pearsonr(x, y)
print(f'{title}: pearson-r={r:.2f} p={p:.3f}')
plt.show()
def main(base_path):
# ToDo: for chirp and rise analysis different datasets!!!
trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
trial_summary = trial_summary[chirp_notes['good'] == 1]
all_rise_times_lose = []
all_rise_times_win = []
@ -227,6 +238,9 @@ def main(base_path):
win_sex.append(trial['sex_win'])
lose_sex.append(trial['sex_lose'])
win_sex = np.array(win_sex)
lose_sex = np.array(lose_sex)
iei_analysis(all_chirp_times_lose, win_sex, lose_sex, kernal_w=1, title=r'chirps$_{lose}$')
iei_analysis(all_chirp_times_win, win_sex, lose_sex, kernal_w=1, title=r'chirps$_{win}$')
iei_analysis(all_rise_times_lose, win_sex, lose_sex, kernal_w=5, title=r'rises$_{lose}$')
@ -244,140 +258,208 @@ def main(base_path):
#############################################################################
all_pre_chase_event_mask = []
all_chase_event_mask = []
all_end_chase_event_mask = []
all_after_chase_event_mask = []
all_around_countact_event_mask = []
all_pre_chase_time = []
all_chase_time = []
all_end_chase_time = []
all_after_chase_time = []
all_around_countact_time = []
time_tol = 2
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):
if len(ag_on_t) == 0:
continue
pre_chase_event_mask = np.zeros_like(chirp_times_lose)
chase_event_mask = np.zeros_like(chirp_times_lose)
end_chase_event_mask = np.zeros_like(chirp_times_lose)
after_chase_event_mask = np.zeros_like(chirp_times_lose)
for chase_on_t, chase_off_t in zip(ag_on_t, ag_off_t):
pre_chase_event_mask[(chirp_times_lose >= chase_on_t - time_tol) & (chirp_times_lose < chase_on_t)] = 1
chase_event_mask[(chirp_times_lose >= chase_on_t) & (chirp_times_lose < chase_off_t - time_tol)] = 1
end_chase_event_mask[(chirp_times_lose >= chase_off_t - time_tol) & (chirp_times_lose < chase_off_t)] = 1
after_chase_event_mask[(chirp_times_lose >= chase_off_t) & (chirp_times_lose < chase_off_t + time_tol)] = 1
all_pre_chase_event_mask.append(pre_chase_event_mask)
all_chase_event_mask.append(chase_event_mask)
all_end_chase_event_mask.append(end_chase_event_mask)
all_after_chase_event_mask.append(after_chase_event_mask)
all_pre_chase_time.append(len(ag_on_t) * time_tol)
chasing_dur = (ag_off_t - ag_on_t) - time_tol
chasing_dur[chasing_dur < 0 ] = 0
all_chase_time.append(np.sum(chasing_dur))
all_end_chase_time.append(len(ag_on_t) * time_tol)
all_after_chase_time.append(len(ag_on_t) * time_tol)
around_countact_event_mask = np.zeros_like(chirp_times_lose)
for ct in contact_t:
around_countact_event_mask[(chirp_times_lose >= ct-time_tol) & (chirp_times_lose < ct+time_tol)] = 1
all_around_countact_event_mask.append(around_countact_event_mask)
all_around_countact_time.append(len(contact_t) * time_tol*2)
all_pre_chase_time = np.array(all_pre_chase_time)
all_chase_time = np.array(all_chase_time)
all_end_chase_time = np.array(all_end_chase_time)
all_after_chase_time = np.array(all_after_chase_time)
all_around_countact_time = np.array(all_around_countact_time)
all_pre_chase_time_ratio = all_pre_chase_time / (3*60*60)
all_chase_time_ratio = all_chase_time / (3*60*60)
all_end_chase_time_ratio = all_end_chase_time / (3*60*60)
all_after_chase_time_ratio = all_after_chase_time / (3*60*60)
all_around_countact_time_ratio = all_around_countact_time / (3*60*60)
all_pre_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_pre_chase_event_mask)))
all_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_chase_event_mask)))
all_end_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_end_chase_event_mask)))
all_after_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_after_chase_event_mask)))
all_around_countact_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_around_countact_event_mask)))
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 all_event_t, event_name in zip([all_chirp_times_lose, all_chirp_times_win, all_rise_times_lose, all_rise_times_win],
[r'chirps$_{lose}$', r'chirps$_{win}$', r'rises$_{lose}$', r'rises$_{win}$']):
print('')
all_pre_chase_event_mask = []
all_chase_event_mask = []
all_end_chase_event_mask = []
all_after_chase_event_mask = []
all_around_countact_event_mask = []
all_pre_chase_time = []
all_chase_time = []
all_end_chase_time = []
all_after_chase_time = []
all_around_countact_time = []
video_trial_win_sex = []
video_trial_lose_sex = []
time_tol = 5
for enu, contact_t, ag_on_t, ag_off_t, event_times in zip(
np.arange(len(all_contact_t)), all_contact_t, all_ag_on_t, all_ag_off_t, all_event_t):
if len(ag_on_t) == 0:
continue
pre_chase_event_mask = np.zeros_like(event_times)
chase_event_mask = np.zeros_like(event_times)
end_chase_event_mask = np.zeros_like(event_times)
after_chase_event_mask = np.zeros_like(event_times)
video_trial_win_sex.append(win_sex[enu])
video_trial_lose_sex.append(lose_sex[enu])
for chase_on_t, chase_off_t in zip(ag_on_t, ag_off_t):
pre_chase_event_mask[(event_times >= chase_on_t - time_tol) & (event_times < chase_on_t)] = 1
chase_event_mask[(event_times >= chase_on_t) & (event_times < chase_off_t - time_tol)] = 1
end_chase_event_mask[(event_times >= chase_off_t - time_tol) & (event_times < chase_off_t)] = 1
after_chase_event_mask[(event_times >= chase_off_t) & (event_times < chase_off_t + time_tol)] = 1
all_pre_chase_event_mask.append(pre_chase_event_mask)
all_chase_event_mask.append(chase_event_mask)
all_end_chase_event_mask.append(end_chase_event_mask)
all_after_chase_event_mask.append(after_chase_event_mask)
all_pre_chase_time.append(len(ag_on_t) * time_tol)
chasing_dur = (ag_off_t - ag_on_t) - time_tol
chasing_dur[chasing_dur < 0 ] = 0
all_chase_time.append(np.sum(chasing_dur))
all_end_chase_time.append(len(ag_on_t) * time_tol)
all_after_chase_time.append(len(ag_on_t) * time_tol)
around_countact_event_mask = np.zeros_like(event_times)
for ct in contact_t:
around_countact_event_mask[(event_times >= ct-time_tol) & (event_times < ct+time_tol)] = 1
all_around_countact_event_mask.append(around_countact_event_mask)
all_around_countact_time.append(len(contact_t) * time_tol*2)
all_pre_chase_time = np.array(all_pre_chase_time)
all_chase_time = np.array(all_chase_time)
all_end_chase_time = np.array(all_end_chase_time)
all_after_chase_time = np.array(all_after_chase_time)
all_around_countact_time = np.array(all_around_countact_time)
video_trial_win_sex = np.array(video_trial_win_sex)
video_trial_lose_sex = np.array(video_trial_lose_sex)
all_pre_chase_time_ratio = all_pre_chase_time / (3*60*60)
all_chase_time_ratio = all_chase_time / (3*60*60)
all_end_chase_time_ratio = all_end_chase_time / (3*60*60)
all_after_chase_time_ratio = all_after_chase_time / (3*60*60)
all_around_countact_time_ratio = all_around_countact_time / (3*60*60)
all_pre_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_pre_chase_event_mask)))
all_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_chase_event_mask)))
all_end_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_end_chase_event_mask)))
all_after_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_after_chase_event_mask)))
all_around_countact_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_around_countact_event_mask)))
for x, y, name in [[all_pre_chase_event_ratio, all_pre_chase_time_ratio, 'pre chase'],
[all_chase_event_ratio, all_chase_time_ratio, 'while chase'],
[all_end_chase_event_ratio, all_end_chase_time_ratio, 'end chase'],
[all_after_chase_event_ratio, all_after_chase_time_ratio, 'after chase'],
[all_around_countact_event_ratio, all_around_countact_time_ratio, 'aroung contact']]:
t, p = scp.ttest_rel(x, y)
print(f'{event_name} {name}: t={t:.2f} p={p:.3f}')
fig = plt.figure(figsize=(20/2.54, 12/2.54))
gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.15, right=0.95, top=0.9)
ax = fig.add_subplot(gs[0, 0])
ax_pie = fig.add_subplot(gs[0, 1])
ax.boxplot([all_pre_chase_event_ratio/all_pre_chase_time_ratio,
all_chase_event_ratio/all_chase_time_ratio,
all_end_chase_event_ratio/all_end_chase_time_ratio,
all_after_chase_event_ratio/all_after_chase_time_ratio,
all_around_countact_event_ratio/all_around_countact_time_ratio], positions=np.arange(5), sym='', zorder=2)
ylim = list(ax.get_ylim())
ylim[0] = -.1 if ylim[0] < -.1 else ylim[0]
ylim[1] = 1.1 if ylim[1] < 1.1 else ylim[1]
##############################################################################
for sex_w, sex_l in itertools.product(['m', 'f'], repeat=2):
mec = 'k' if sex_w == sex_l else 'None'
if 'lose' in event_name:
marker='o'
c = male_color if sex_l == 'm' else female_color
elif "win" in event_name:
marker='p'
c = male_color if sex_w == 'm' else female_color
else:
print('error')
embed()
quit()
values = np.array(all_pre_chase_event_ratio/all_pre_chase_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
ax.plot(np.ones_like(values) * 0, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1)
values = np.array(all_chase_event_ratio/all_chase_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
ax.plot(np.ones_like(values) * 1, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1)
values = np.array(all_end_chase_event_ratio/all_end_chase_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
ax.plot(np.ones_like(values) * 2, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1)
values = np.array(all_after_chase_event_ratio/all_after_chase_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
ax.plot(np.ones_like(values) * 3, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1)
values = np.array(all_around_countact_event_ratio/all_around_countact_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
ax.plot(np.ones_like(values) * 4, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1)
# ax.plot(np.ones_like(all_pre_chase_event_ratio) * 0, all_pre_chase_event_ratio/all_pre_chase_time_ratio, 'ok')
# ax.plot(np.ones_like(all_chase_event_ratio) * 1, all_chase_event_ratio/all_chase_time_ratio, 'ok')
# ax.plot(np.ones_like(all_end_chase_event_ratio) * 2, all_end_chase_event_ratio/all_end_chase_time_ratio, 'ok')
# ax.plot(np.ones_like(all_after_chase_event_ratio) * 3, all_after_chase_event_ratio/all_after_chase_time_ratio, 'ok')
# ax.plot(np.ones_like(all_around_countact_event_ratio) * 4, all_around_countact_event_ratio/all_around_countact_time_ratio, 'ok')
##############################################################################
ax.plot(np.arange(7)-1, np.ones(7), linestyle='dotted', lw=2, color='k')
ax.set_xlim(-0.5, 4.5)
ax.set_ylim(ylim[0], ylim[1])
ax.set_ylabel(r'rel. count$_{event}$ / rel. time$_{event}$', fontsize=12)
ax.set_xticks(np.arange(5))
ax.set_xticklabels([r'chase$_{before}$', r'chasing', r'chase$_{end}$', r'chase$_{after}$', 'contact'], rotation=45)
ax.tick_params(labelsize=10)
# ax.set_title(event_name)
fig.suptitle(f'{event_name}: n={len(np.hstack(all_event_t))}')
# plt.show()
###############################################
flat_pre_chase_event_mask = np.hstack(all_pre_chase_event_mask)
flat_chase_event_mask = np.hstack(all_chase_event_mask)
flat_end_chase_event_mask = np.hstack(all_end_chase_event_mask)
flat_after_chase_event_mask = np.hstack(all_after_chase_event_mask)
flat_around_countact_event_mask = np.hstack(all_around_countact_event_mask)
flat_pre_chase_event_mask[flat_around_countact_event_mask == 1] = 0
flat_chase_event_mask[flat_around_countact_event_mask == 1] = 0
flat_end_chase_event_mask[flat_around_countact_event_mask == 1] = 0
flat_after_chase_event_mask[flat_around_countact_event_mask == 1] = 0
event_context_values = [np.sum(flat_pre_chase_event_mask) / len(flat_pre_chase_event_mask),
np.sum(flat_chase_event_mask) / len(flat_chase_event_mask),
np.sum(flat_end_chase_event_mask) / len(flat_end_chase_event_mask),
np.sum(flat_after_chase_event_mask) / len(flat_after_chase_event_mask),
np.sum(flat_around_countact_event_mask) / len(flat_around_countact_event_mask)]
event_context_values.append(1 - np.sum(event_context_values))
time_context_values = [np.sum(all_pre_chase_time), np.sum(all_chase_time), np.sum(all_end_chase_time),
np.sum(all_after_chase_time), np.sum(all_around_countact_time)]
time_context_values.append(len(all_pre_chase_time) * 3*60*60 - np.sum(time_context_values))
time_context_values /= np.sum(time_context_values)
# fig, ax = plt.subplots(figsize=(12/2.54,12/2.54))
size = 0.3
outer_colors = ['tab:red', 'tab:orange', 'yellow', 'tab:green', 'k', 'tab:grey']
ax_pie.pie(event_context_values, radius=1, colors=outer_colors,
wedgeprops=dict(width=size, edgecolor='w'), startangle=90, center=(0, 1))
ax_pie.pie(time_context_values, radius=1-size, colors=outer_colors,
wedgeprops=dict(width=size, edgecolor='w', alpha=.6), startangle=90, center=(0, 1))
ax_pie.set_title(r'event context')
legend_elements = [Patch(facecolor='tab:red', edgecolor='w', label='%.1f' % (event_context_values[0] * 100) + '%'),
Patch(facecolor='tab:orange', edgecolor='w', label='%.1f' % (event_context_values[1] * 100) + '%'),
Patch(facecolor='yellow', edgecolor='w', label='%.1f' % (event_context_values[2] * 100) + '%'),
Patch(facecolor='tab:green', edgecolor='w', label='%.1f' % (event_context_values[3] * 100) + '%'),
Patch(facecolor='k', edgecolor='w', label='%.1f' % (event_context_values[4] * 100) + '%'),
Patch(facecolor='tab:red', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[0] * 100) + '%'),
Patch(facecolor='tab:orange', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[1] * 100) + '%'),
Patch(facecolor='yellow', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[2] * 100) + '%'),
Patch(facecolor='tab:green', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[3] * 100) + '%'),
Patch(facecolor='k', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[4] * 100) + '%')]
ax_pie.legend(handles=legend_elements, loc='lower right', ncol=2, bbox_to_anchor=(1.15, -0.25), frameon=False, fontsize=9)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{event_name}_categories.png'), dpi=300)
plt.close()
# plt.show()
ax.boxplot([all_pre_chase_event_ratio/all_pre_chase_time_ratio,
all_chase_event_ratio/all_chase_time_ratio,
all_end_chase_event_ratio/all_end_chase_time_ratio,
all_after_chase_event_ratio/all_after_chase_time_ratio,
all_around_countact_event_ratio/all_around_countact_time_ratio], positions=np.arange(5), sym='')
ax.plot(np.arange(7)-1, np.ones(7), linestyle='dotted', lw=2, color='k')
ax.set_xlim(-0.5, 4.5)
ax.set_ylabel(r'rel. chrips$_{event}$ / rel. time$_{event}$', fontsize=12)
ax.set_xticks(np.arange(5))
ax.set_xticklabels([r'chase$_{before}$', r'chasing', r'chase$_{end}$', r'chase$_{after}$', 'contact'])
ax.tick_params(labelsize=10)
plt.show()
###############################################
flat_pre_chase_event_mask = np.hstack(all_pre_chase_event_mask)
flat_chase_event_mask = np.hstack(all_chase_event_mask)
flat_end_chase_event_mask = np.hstack(all_end_chase_event_mask)
flat_after_chase_event_mask = np.hstack(all_after_chase_event_mask)
flat_around_countact_event_mask = np.hstack(all_around_countact_event_mask)
flat_pre_chase_event_mask[flat_around_countact_event_mask == 1] = 0
flat_chase_event_mask[flat_around_countact_event_mask == 1] = 0
flat_end_chase_event_mask[flat_around_countact_event_mask == 1] = 0
flat_after_chase_event_mask[flat_around_countact_event_mask == 1] = 0
event_context_values = [np.sum(flat_pre_chase_event_mask) / len(flat_pre_chase_event_mask),
np.sum(flat_chase_event_mask) / len(flat_chase_event_mask),
np.sum(flat_end_chase_event_mask) / len(flat_end_chase_event_mask),
np.sum(flat_after_chase_event_mask) / len(flat_after_chase_event_mask),
np.sum(flat_around_countact_event_mask) / len(flat_around_countact_event_mask)]
event_context_values.append(1 - np.sum(event_context_values))
time_context_values = [np.sum(all_pre_chase_time), np.sum(all_chase_time), np.sum(all_end_chase_time),
np.sum(all_after_chase_time), np.sum(all_around_countact_time)]
time_context_values.append(len(all_pre_chase_time) * 3*60*60 - np.sum(time_context_values))
time_context_values /= np.sum(time_context_values)
fig, ax = plt.subplots(figsize=(12/2.54,12/2.54))
size = 0.3
outer_colors = ['tab:red', 'tab:orange', 'yellow', 'tab:green', 'k', 'tab:grey']
ax.pie(event_context_values, radius=1, colors=outer_colors,
wedgeprops=dict(width=size, edgecolor='w'), startangle=90, center=(0, .5))
ax.pie(time_context_values, radius=1-size, colors=outer_colors,
wedgeprops=dict(width=size, edgecolor='w', alpha=.6), startangle=90, center=(0, .5))
ax.set_title(r'event context')
legend_elements = [Patch(facecolor='tab:red', edgecolor='w', label='%.1f' % (event_context_values[0] * 100) + '%'),
Patch(facecolor='tab:orange', edgecolor='w', label='%.1f' % (event_context_values[1] * 100) + '%'),
Patch(facecolor='yellow', edgecolor='w', label='%.1f' % (event_context_values[2] * 100) + '%'),
Patch(facecolor='tab:green', edgecolor='w', label='%.1f' % (event_context_values[3] * 100) + '%'),
Patch(facecolor='k', edgecolor='w', label='%.1f' % (event_context_values[4] * 100) + '%'),
Patch(facecolor='tab:red', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[0] * 100) + '%'),
Patch(facecolor='tab:orange', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[1] * 100) + '%'),
Patch(facecolor='yellow', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[2] * 100) + '%'),
Patch(facecolor='tab:green', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[3] * 100) + '%'),
Patch(facecolor='k', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[4] * 100) + '%')]
ax.legend(handles=legend_elements, loc='lower right', ncol=2, bbox_to_anchor=(1.1, -0.15), frameon=False, fontsize=9)
plt.show()
embed()
quit()
if __name__ == '__main__':

8
event_time_correlations.py
View File

@ -186,10 +186,13 @@ def jackknife_kde(event_dt, conv_t, repetitions = 2000, max_mem_use_GB = 2, jack
# max_jitter = 2*max_dt
select_event_dt = event_dt[np.abs(event_dt) <= float(cp.max(conv_t)) * 2]
if len(select_event_dt) == 0:
return np.zeros((repetitions, len(conv_t)))
# conv_t = cp.arange(-max_dt, max_dt, 1)
conv_tt = cp.reshape(conv_t, (len(conv_t), 1, 1))
chunk_size = int(np.floor(max_mem_use_GB / (select_event_dt.nbytes * jack_pct * conv_t.size / 1e9)))
chunk_collector =[]
for _ in range(repetitions // chunk_size):
@ -238,7 +241,10 @@ def single_kde(event_dt, conv_t, kernal_w = 1, kernal_h = 0.2):
return cp.asnumpy(single_kdes)
def main(base_path):
# ToDo: for chirp and rise analysis different datasets!!!
trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
trial_summary = trial_summary[chirp_notes['good'] == 1]
lose_chrips_centered_on_ag_off_t = []
lose_chrips_centered_on_ag_on_t = []

39
trial_summary_eval.py
View File

@ -36,10 +36,13 @@ def plot_rise_vs_chirp_count(trial_summary):
ax_rises.set_yticklabels(['Win', 'Lose'])
plt.setp(ax_rises.get_xticklabels(), visible=False)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'rise_vs_chirp_count.png'), dpi=300)
plt.close()
def plot_beh_count_per_pairing(trial_summary,
beh_key_win=None, beh_key_lose=None,
ylabel='y'):
ylabel='y', save_str='random_plot_title'):
mek = ['k', 'None', 'None', 'k']
markersize = 12
@ -73,11 +76,14 @@ def plot_beh_count_per_pairing(trial_summary,
ax.set_ylabel(ylabel, fontsize=12)
plt.tick_params(labelsize=10)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{save_str}.png'), dpi=300)
plt.close()
def plot_beh_count_vs_meta(trial_summary,
beh_key_win=None, beh_key_lose=None,
meta_key_win=None, meta_key_lose=None,
xlabel='x'):
xlabel='x', save_str='random_plot_title'):
mek = ['k', 'None', 'None', 'k']
markersize = 12
win_colors = [male_color, male_color, female_color, female_color]
@ -132,8 +138,12 @@ def plot_beh_count_vs_meta(trial_summary,
plt.setp(ax[3].get_yticklabels(), visible=False)
plt.tick_params(labelsize=10)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{save_str}.png'), dpi=300)
plt.close()
def plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_key_lose='chirps_lose', ylabel='chirps [n]'):
def plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_key_lose='chirps_lose',
ylabel='chirps [n]', save_str='random_plot_title'):
mek = ['k', 'None', 'None', 'k']
markersize = 10
win_colors = [male_color, male_color, female_color, female_color]
@ -185,9 +195,12 @@ def plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_ke
ax.set_ylabel(ylabel, fontsize=12)
ax.tick_params(labelsize=10)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{save_str}.png'), dpi=300)
plt.close()
def main(base_path):
def main(base_path):
# ToDo: for chirp and rise analysis different datasets!!!
trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
trial_summary = trial_summary[chirp_notes['good'] == 1]
@ -237,27 +250,27 @@ def main(base_path):
print(f'Risescount - female lose - male lose: MW-U={U:.2f} p={p:.3f}')
plot_beh_count_per_pairing(trial_summary,
beh_key_win='chirps_win', beh_key_lose='chirps_lose',
ylabel='chirps [n]')
ylabel='chirps [n]', save_str='chirps_per_pairing')
plot_beh_count_per_pairing(trial_summary,
beh_key_win='rises_win', beh_key_lose='rises_lose',
ylabel='rises [n]')
ylabel='rises [n]', save_str='rises_per_pairing')
plot_beh_count_vs_meta(trial_summary,
beh_key_win='chirps_win', beh_key_lose='chirps_lose',
meta_key_win="size_win", meta_key_lose='size_lose',
xlabel=u'$\Delta$size [cm]')
xlabel=u'$\Delta$size [cm]', save_str='chirps_vs_dSize')
plot_beh_count_vs_meta(trial_summary,
beh_key_win='rises_win', beh_key_lose='rises_lose',
meta_key_win="size_win", meta_key_lose='size_lose',
xlabel=u'$\Delta$size [cm]')
xlabel=u'$\Delta$size [cm]', save_str='rises_vs_dSize')
plot_beh_count_vs_meta(trial_summary,
beh_key_win='chirps_win', beh_key_lose='chirps_lose',
meta_key_win="EODf_win", meta_key_lose='EODf_lose',
xlabel=u'$\Delta$EODf [Hz]')
xlabel=u'$\Delta$EODf [Hz]', save_str='chirps_vs_dEODf')
plot_beh_count_vs_meta(trial_summary,
beh_key_win='rises_win', beh_key_lose='rises_lose',
meta_key_win="EODf_win", meta_key_lose='EODf_lose',
xlabel=u'$\Delta$EODf [Hz]')
xlabel=u'$\Delta$EODf [Hz]', save_str='rises_vs_dEODf')
if True:
### chirp count vs. dSize ###
for key in ['chirps_lose', 'chirps_win', 'rises_win', 'rises_lose']:
@ -297,8 +310,10 @@ def main(base_path):
print(f'(all) {key} - dSize: Pearson-r={r:.2f} p={p:.3f}')
plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_key_lose='chirps_lose', ylabel='chirps [n]')
plot_beh_conut_vs_experience(trial_summary, beh_key_win='rises_win', beh_key_lose='rises_lose', ylabel='rises [n]')
plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_key_lose='chirps_lose',
ylabel='chirps [n]', save_str='chirps_by_experince')
plot_beh_conut_vs_experience(trial_summary, beh_key_win='rises_win', beh_key_lose='rises_lose', ylabel='rises [n]',
save_str='chirps_by_experince')
if True:
for key in ['chirps_lose', 'chirps_win', 'rises_lose', 'rises_win']: