alot of statistics
This commit is contained in:
parent
bb45677bd1
commit
07daf7457c
@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
|
||||
import matplotlib.gridspec as gridspec
|
||||
from matplotlib.patches import Patch
|
||||
import pandas as pd
|
||||
import scipy.stats as scp
|
||||
from IPython import embed
|
||||
from event_time_correlations import load_and_converete_boris_events, kde, gauss
|
||||
|
||||
@ -150,6 +151,13 @@ def relative_rate_progression(all_event_t, title=''):
|
||||
ax.set_xlim(0, 3)
|
||||
ax.set_ylim(0, 5)
|
||||
|
||||
x = np.hstack(all_snippeqt_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()
|
||||
|
||||
|
||||
@ -210,7 +218,6 @@ def main(base_path):
|
||||
rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))]
|
||||
rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]]
|
||||
|
||||
# iri = np.diff(rise_times[1])
|
||||
all_rise_times_lose.append(rise_times[1])
|
||||
all_rise_times_win.append(rise_times[0])
|
||||
|
||||
@ -225,6 +232,7 @@ def main(base_path):
|
||||
iei_analysis(all_rise_times_lose, win_sex, lose_sex, kernal_w=5, title=r'rises$_{lose}$')
|
||||
iei_analysis(all_rise_times_win, win_sex, lose_sex, kernal_w=50, title=r'rises$_{win}$')
|
||||
|
||||
print('')
|
||||
relative_rate_progression(all_chirp_times_lose, title=r'chirp$_{lose}$')
|
||||
relative_rate_progression(all_chirp_times_win, title=r'chirp$_{win}$')
|
||||
relative_rate_progression(all_rise_times_lose, title=r'rises$_{lose}$')
|
||||
@ -233,15 +241,8 @@ def main(base_path):
|
||||
relative_rate_progression(all_contact_t, title=r'contact')
|
||||
relative_rate_progression(all_ag_on_t, title=r'chasing')
|
||||
|
||||
#####################################################################################################
|
||||
# all_chase_chirp_mask = []
|
||||
# all_chasing_t = []
|
||||
#
|
||||
# all_chase_off_chirp_mask = []
|
||||
# all_chase_off_t = []
|
||||
#
|
||||
# all_contact_chirp_mask = []
|
||||
# all_physical_t = []
|
||||
|
||||
#############################################################################
|
||||
|
||||
all_pre_chase_event_mask = []
|
||||
all_chase_event_mask = []
|
||||
@ -266,20 +267,12 @@ def main(base_path):
|
||||
end_chase_event_mask = np.zeros_like(chirp_times_lose)
|
||||
after_chase_event_mask = np.zeros_like(chirp_times_lose)
|
||||
|
||||
# chase_chirp_mask = np.zeros_like(chirp_times_lose)
|
||||
# chase_off_chirp_mask = np.zeros_like(chirp_times_lose)
|
||||
for chase_on_t, chase_off_t in zip(ag_on_t, ag_off_t):
|
||||
# chase_chirp_mask[(chirp_times_lose >= chase_on_t) & (chirp_times_lose < chase_off_t-time_tol)] = 1
|
||||
# chase_off_chirp_mask[(chirp_times_lose >= chase_off_t-time_tol) & (chirp_times_lose < chase_off_t+time_tol)] = 1
|
||||
|
||||
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_chase_chirp_mask.append(chase_chirp_mask)
|
||||
# all_chase_off_chirp_mask.append(chase_off_chirp_mask)
|
||||
|
||||
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)
|
||||
@ -292,21 +285,12 @@ def main(base_path):
|
||||
all_end_chase_time.append(len(ag_on_t) * time_tol)
|
||||
all_after_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
|
||||
# chasing_t = np.sum(chasing_dur)
|
||||
# all_chasing_t.append(chasing_t)
|
||||
# all_chase_off_t.append(len(ag_off_t) * time_tol*2)
|
||||
|
||||
# contact_chirp_mask = np.zeros_like(chirp_times_lose)
|
||||
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_physical_t.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)
|
||||
@ -325,27 +309,10 @@ def main(base_path):
|
||||
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)))
|
||||
|
||||
# all_physical_t = np.array(all_physical_t)
|
||||
# all_chasing_t = np.array(all_chasing_t)
|
||||
# all_chase_off_t = np.array(all_chase_off_t)
|
||||
|
||||
# physical_t_ratio = all_physical_t / (3*60*60)
|
||||
# chase_t_ratio = all_chasing_t / (3*60*60)
|
||||
# chase_off_t_ratio = all_chase_off_t / (3*60*60)
|
||||
|
||||
# contact_chirp_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_contact_chirp_mask)))
|
||||
# chase_chirp_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_chase_chirp_mask)))
|
||||
# chase_off_chirp_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_chase_off_chirp_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])
|
||||
|
||||
# ax.boxplot([chase_chirp_ratio/chase_t_ratio,
|
||||
# contact_chirp_ratio/physical_t_ratio,
|
||||
# chase_off_chirp_ratio/chase_off_t_ratio], positions=np.arange(3), sym='')
|
||||
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,
|
||||
@ -372,14 +339,6 @@ def main(base_path):
|
||||
flat_end_chase_event_mask[flat_around_countact_event_mask == 1] = 0
|
||||
flat_after_chase_event_mask[flat_around_countact_event_mask == 1] = 0
|
||||
|
||||
# flat_contact_chirp_mask = np.hstack(all_contact_chirp_mask)
|
||||
# flat_chase_chirp_mask = np.hstack(all_chase_chirp_mask)
|
||||
# flat_chase_off_chirp_mask = np.hstack(all_chase_off_chirp_mask)
|
||||
|
||||
# flat_chase_chirp_mask[flat_contact_chirp_mask == 1] = 0
|
||||
# flat_chase_off_chirp_mask[flat_contact_chirp_mask == 1] = 0
|
||||
# flat_chase_chirp_mask[flat_chase_off_chirp_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),
|
||||
@ -394,7 +353,6 @@ def main(base_path):
|
||||
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']
|
||||
@ -415,18 +373,11 @@ def main(base_path):
|
||||
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.text(-0.65, -1.4, 'chirps', fontsize=10, va='center', ha='center')
|
||||
# ax.text(0.75, -1.4, 'time', fontsize=10, va='center', ha='center')
|
||||
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()
|
||||
# embed()
|
||||
# quit()
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@ -1,3 +1,6 @@
|
||||
import sys
|
||||
import os
|
||||
import scipy.stats as scp
|
||||
import numpy as np
|
||||
import itertools
|
||||
import pandas as pd
|
||||
@ -183,23 +186,62 @@ def plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_ke
|
||||
ax.tick_params(labelsize=10)
|
||||
|
||||
|
||||
def main():
|
||||
def main(base_path):
|
||||
|
||||
trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
|
||||
chirp_notes = pd.read_csv('chirp_notes.csv', index_col=0)
|
||||
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]
|
||||
|
||||
if True:
|
||||
print('')
|
||||
rc = np.concatenate((trial_summary['rises_win'][(trial_summary["draw"] == 0)].to_numpy(),
|
||||
trial_summary['rises_lose'][(trial_summary["draw"] == 0)]))
|
||||
cc = np.concatenate((trial_summary['chirps_win'][(trial_summary["draw"] == 0)].to_numpy(),
|
||||
trial_summary['chirps_lose'][(trial_summary["draw"] == 0)]))
|
||||
r, p = scp.spearmanr(rc, cc)
|
||||
print(f'Risescount - Chirpscount - all: Pearson-r={r:.2f} p={p:.3f}')
|
||||
|
||||
r, p = scp.spearmanr(trial_summary['rises_win'][(trial_summary["draw"] == 0)],
|
||||
trial_summary['chirps_win'][(trial_summary["draw"] == 0)])
|
||||
print(f'Risescount - Chirpscount - win: Pearson-r={r:.2f} p={p:.3f}')
|
||||
|
||||
r, p = scp.spearmanr(trial_summary['rises_lose'][(trial_summary["draw"] == 0)],
|
||||
trial_summary['chirps_lose'][(trial_summary["draw"] == 0)])
|
||||
print(f'Risescount - Chirpscount - lose: Pearson-r={r:.2f} p={p:.3f}')
|
||||
plot_rise_vs_chirp_count(trial_summary)
|
||||
|
||||
if True:
|
||||
print('')
|
||||
chirps_lose_female_win = trial_summary['chirps_lose'][(trial_summary['sex_win'] == 'f') & (trial_summary["draw"] == 0)]
|
||||
chirps_lose_male_win = trial_summary['chirps_lose'][(trial_summary['sex_win'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
|
||||
U, p = scp.mannwhitneyu(chirps_lose_female_win, chirps_lose_male_win)
|
||||
print(f'Chirpscount - female win - male win: MW-U={U:.2f} p={p:.3f}')
|
||||
|
||||
chirps_lose_female_lose = trial_summary['chirps_lose'][(trial_summary['sex_lose'] == 'f') & (trial_summary["draw"] == 0)]
|
||||
chirps_lose_male_lose = trial_summary['chirps_lose'][(trial_summary['sex_lose'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
|
||||
U, p = scp.mannwhitneyu(chirps_lose_female_lose, chirps_lose_male_lose)
|
||||
print(f'Chirpscount - female lose - male lose: MW-U={U:.2f} p={p:.3f}')
|
||||
###################################################################################
|
||||
rises_lose_female_win = trial_summary['rises_lose'][(trial_summary['sex_win'] == 'f') & (trial_summary["draw"] == 0)]
|
||||
rises_lose_male_win = trial_summary['rises_lose'][(trial_summary['sex_win'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
|
||||
U, p = scp.mannwhitneyu(rises_lose_female_win, rises_lose_male_win)
|
||||
print(f'Risescount - female win - male win: MW-U={U:.2f} p={p:.3f}')
|
||||
|
||||
rises_lose_female_lose = trial_summary['rises_lose'][(trial_summary['sex_lose'] == 'f') & (trial_summary["draw"] == 0)]
|
||||
rises_lose_male_lose = trial_summary['rises_lose'][(trial_summary['sex_lose'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
|
||||
U, p = scp.mannwhitneyu(rises_lose_female_lose, rises_lose_male_lose)
|
||||
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]')
|
||||
plot_beh_count_per_pairing(trial_summary,
|
||||
plot_beh_count_per_pairing(trial_summary,
|
||||
beh_key_win='rises_win', beh_key_lose='rises_lose',
|
||||
ylabel='rises [n]')
|
||||
|
||||
|
||||
|
||||
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',
|
||||
@ -216,13 +258,63 @@ def main():
|
||||
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]')
|
||||
if True:
|
||||
### chirp count vs. dSize ###
|
||||
for key in ['chirps_lose', 'chirps_win', 'rises_win', 'rises_lose']:
|
||||
print('')
|
||||
lose_chirps_male_win = trial_summary[key][(trial_summary['sex_win'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
lose_size_male_win = trial_summary['size_lose'][(trial_summary['sex_win'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
win_size_male_win = trial_summary['size_win'][(trial_summary['sex_win'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
|
||||
r, p = scp.pearsonr(lose_chirps_male_win, lose_size_male_win - win_size_male_win)
|
||||
print(f'(Male win) {key} - dSize: Pearson-r={r:.2f} p={p:.3f}')
|
||||
|
||||
lose_chirps_female_win = trial_summary[key][(trial_summary['sex_win'] == 'f') & (trial_summary["draw"] == 0)]
|
||||
lose_size_female_win = trial_summary['size_lose'][(trial_summary['sex_win'] == 'f') & (trial_summary["draw"] == 0)]
|
||||
win_size_female_win = trial_summary['size_win'][(trial_summary['sex_win'] == 'f') & (trial_summary["draw"] == 0)]
|
||||
|
||||
r, p = scp.pearsonr(lose_chirps_female_win, lose_size_female_win - win_size_female_win)
|
||||
print(f'(Female win) {key} - dSize: Pearson-r={r:.2f} p={p:.3f}')
|
||||
|
||||
lose_chirps_male_lose = trial_summary[key][(trial_summary['sex_lose'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
lose_size_male_lose = trial_summary['size_lose'][(trial_summary['sex_lose'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
win_size_male_lose = trial_summary['size_win'][(trial_summary['sex_lose'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
|
||||
r, p = scp.pearsonr(lose_chirps_male_lose, lose_size_male_lose - win_size_male_lose)
|
||||
print(f'(Male lose) {key} - dSize: Pearson-r={r:.2f} p={p:.3f}')
|
||||
|
||||
lose_chirps_female_lose = trial_summary[key][(trial_summary['sex_lose'] == 'f') & (trial_summary["draw"] == 0)]
|
||||
lose_size_female_lose = trial_summary['size_lose'][(trial_summary['sex_lose'] == 'f') & (trial_summary["draw"] == 0)]
|
||||
win_size_female_lose = trial_summary['size_win'][(trial_summary['sex_lose'] == 'f') & (trial_summary["draw"] == 0)]
|
||||
|
||||
r, p = scp.pearsonr(lose_chirps_female_lose, lose_size_female_lose - win_size_female_lose)
|
||||
print(f'(Female lose) {key} - dSize: Pearson-r={r:.2f} p={p:.3f}')
|
||||
|
||||
all_lose_chrips = trial_summary[key][(trial_summary["draw"] == 0)]
|
||||
all_lose_size = trial_summary['size_lose'][(trial_summary["draw"] == 0)]
|
||||
all_win_size = trial_summary['size_win'][(trial_summary["draw"] == 0)]
|
||||
r, p = scp.pearsonr(all_lose_chrips, all_lose_size - all_win_size)
|
||||
|
||||
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]')
|
||||
|
||||
if True:
|
||||
for key in ['chirps_lose', 'chirps_win', 'rises_lose', 'rises_win']:
|
||||
print('')
|
||||
lose_chirps = trial_summary[key][(trial_summary["draw"] == 0)]
|
||||
lose_exp = trial_summary['exp_lose'][(trial_summary["draw"] == 0)]
|
||||
win_exp = trial_summary['exp_win'][(trial_summary["draw"] == 0)]
|
||||
|
||||
r, p = scp.pearsonr(lose_chirps, lose_exp)
|
||||
print(f'(all) {key} - lose exp: Pearson-r={r:.2f} p={p:.3f}')
|
||||
r, p = scp.pearsonr(lose_chirps, win_exp)
|
||||
print(f'(all) {key} - win exp: Pearson-r={r:.2f} p={p:.3f}')
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
main(sys.argv[1])
|
||||
Loading…
Reference in New Issue
Block a user