raab/competition_experiments
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adapted code to compute statistics correctly: trial_mask. done for trial_summary_eval.py. The rest is missing. DO IT. Next and last script: marcov model

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Till Raab 2023-06-12 15:28:32 +02:00
parent 3122cb10dc
commit 50c762ab8d
3 changed files with 181 additions and 122 deletions
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14
complete_analysis.py
View File

@ -161,7 +161,7 @@ def get_temperature(folder_path):
return np.array(temp_t), np.array(temp)
def main(data_folder=None):
def main(base_path=None):
colors = ['#BA2D22', '#53379B', '#F47F17', '#3673A4', '#AAB71B', '#DC143C', '#1E90FF']
female_color, male_color = '#e74c3c', '#3498db'
Wc, Lc = 'darkgreen', '#3673A4'
@ -170,9 +170,9 @@ def main(data_folder=None):
os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures'))
# trials_meta = pd.read_csv('order_meta.csv')
trials_meta = pd.read_csv(os.path.join(data_folder, 'order_meta.csv'))
trials_meta = pd.read_csv(os.path.join(base_path, 'order_meta.csv'))
# fish_meta = pd.read_csv('id_meta.csv')
fish_meta = pd.read_csv(os.path.join(data_folder, 'id_meta.csv'))
fish_meta = pd.read_csv(os.path.join(base_path, 'id_meta.csv'))
fish_meta['mean_w'] = np.nanmean(fish_meta.loc[:, ['w1', 'w2', 'w3']], axis=1)
fish_meta['mean_l'] = np.nanmean(fish_meta.loc[:, ['l1', 'l2', 'l3']], axis=1)
@ -181,7 +181,7 @@ def main(data_folder=None):
light_start_sec = 3*60*60
trial_summary = pd.DataFrame(columns=['recording', 'group', 'win_fish', 'lose_fish', 'win_ID', 'lose_ID',
'sex_win', 'sex_lose', 'size_win', 'size_lose', 'EODf_win', 'EODf_lose',
'sex_win', 'sex_lose', 'size_win', 'size_lose', 'dsize', 'EODf_win', 'EODf_lose', 'dEODf',
'exp_win', 'exp_lose', 'chirps_win', 'chirps_lose', 'rises_win', 'rises_lose',
'chase_count', 'contact_count', 'med_chase_dur', 'comp_dur0', 'comp_dur1',
'draw'])
@ -201,7 +201,7 @@ def main(data_folder=None):
if group < 3:
continue
trial_path = os.path.join(data_folder, recording)
trial_path = os.path.join(base_path, recording)
if not os.path.exists(trial_path):
continue
@ -317,8 +317,10 @@ def main(data_folder=None):
'sex_lose': 'n',
'size_win': win_l,
'size_lose': lose_l,
'dsize': win_l - lose_l,
'EODf_win': np.nanmedian(q10_comp_freq[0]),
'EODf_lose': np.nanmedian(q10_comp_freq[1]),
'dEODf': np.nanmedian(q10_comp_freq[0]) - np.nanmedian(q10_comp_freq[1]),
'exp_win': win_exp,
'exp_lose': lose_exp,
'chirps_win': len(chirp_times[0]),
@ -401,7 +403,7 @@ def main(data_folder=None):
sex = 'm'
trial_summary['sex_win'][(trial_summary['group'] == g) & (trial_summary['win_fish'] == f)] = sex
trial_summary['sex_lose'][(trial_summary['group'] == g) & (trial_summary['lose_fish'] == f)] = sex
trial_summary.to_csv('trial_summary.csv')
trial_summary.to_csv(os.path.join(base_path, 'trial_summary.csv'))
pass
if __name__ == '__main__':

3
event_time_analysis.py
View File

@ -171,8 +171,9 @@ def relative_rate_progression(all_event_t, title=''):
def main(base_path):
# ToDo: for chirp and rise analysis different datasets!!!
trial_summary = pd.read_csv('trial_summary.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)
good_chirp_trial_idx = np.arange(len(chirp_notes))[chirp_notes['good'] == 1]
trial_summary = trial_summary[chirp_notes['good'] == 1]
all_rise_times_lose = []

286
trial_summary_eval.py
View File

@ -13,25 +13,29 @@ female_color, male_color = '#e74c3c', '#3498db'
Wc, Lc = 'darkgreen', '#3673A4'
def plot_rise_vs_chirp_count(trial_summary):
def plot_rise_vs_chirp_count(trial_summary, trial_mask):
fig = plt.figure(figsize=(20/2.54, 20/2.54))
gs = gridspec.GridSpec(2, 2, left=0.1, bottom=0.1, right=0.95, top=0.95, height_ratios=[1, 3], width_ratios=[3, 1])
ax = fig.add_subplot(gs[1, 0])
ax.plot(trial_summary['rises_win'], trial_summary['chirps_win'], 'o', color=Wc, label='winner')
ax.plot(trial_summary['rises_lose'], trial_summary['chirps_lose'], 'o', color=Lc, label='loster')
ax.plot(trial_summary['rises_win'][(trial_summary["draw"] == 0) & trial_mask],
trial_summary['chirps_win'][(trial_summary["draw"] == 0) & trial_mask], 'o', color=Wc, label='winner')
ax.plot(trial_summary['rises_lose'][(trial_summary["draw"] == 0) & trial_mask],
trial_summary['chirps_lose'][(trial_summary["draw"] == 0) & trial_mask], 'o', color=Lc, label='loster')
ax.set_xlabel('rises [n]', fontsize=12)
ax.set_ylabel('chirps [n]', fontsize=12)
ax.tick_params(labelsize=10)
ax_chirps = fig.add_subplot(gs[1, 1], sharey=ax)
ax_chirps.boxplot([trial_summary['chirps_win'], trial_summary['chirps_lose']], widths = .5, positions = [1, 2])
ax_chirps.boxplot([trial_summary['chirps_win'][(trial_summary["draw"] == 0) & trial_mask],
trial_summary['chirps_lose'][(trial_summary["draw"] == 0) & trial_mask]], widths = .5, positions = [1, 2])
ax_chirps.set_xticks([1, 2])
ax_chirps.set_xticklabels(['Win', 'Lose'])
plt.setp(ax_chirps.get_yticklabels(), visible=False)
ax_rises = fig.add_subplot(gs[0, 0], sharex=ax)
ax_rises.boxplot([trial_summary['rises_win'], trial_summary['rises_lose']], widths = .5, positions = [1, 2], vert=False)
ax_rises.boxplot([trial_summary['rises_win'][(trial_summary["draw"] == 0) & trial_mask],
trial_summary['rises_lose'][(trial_summary["draw"] == 0) & trial_mask]], widths = .5, positions = [1, 2], vert=False)
ax_rises.set_yticks([1, 2])
ax_rises.set_yticklabels(['Win', 'Lose'])
plt.setp(ax_rises.get_xticklabels(), visible=False)
@ -40,7 +44,7 @@ def plot_rise_vs_chirp_count(trial_summary):
plt.close()
def plot_beh_count_per_pairing(trial_summary,
def plot_beh_count_per_pairing(trial_summary, trial_mask=None,
beh_key_win=None, beh_key_lose=None,
ylabel='y', save_str='random_plot_title'):
@ -49,16 +53,18 @@ def plot_beh_count_per_pairing(trial_summary,
win_colors = [male_color, male_color, female_color, female_color]
lose_colors = [male_color, female_color, male_color, female_color]
if not hasattr(trial_mask, '__len__'):
trial_mask = np.ones(len(trial_summary))
win_count = []
lose_count = []
for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
win_count.append(trial_summary[beh_key_win][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
(trial_summary["draw"] == 0) & trial_mask].to_numpy())
lose_count.append(trial_summary[beh_key_lose][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
(trial_summary["draw"] == 0) & trial_mask].to_numpy())
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)
@ -80,31 +86,28 @@ def plot_beh_count_per_pairing(trial_summary,
plt.close()
def plot_meta_correlation(trial_summary, key1, key2, key1_name, key2_name, save_str='random_plot_title'):
def plot_meta_correlation(trial_summary, trial_mask, key1, key2, key1_name, key2_name, save_str='random_plot_title'):
mek = ['k', 'None', 'None', 'k']
markersize = 12
win_colors = [male_color, male_color, female_color, female_color]
lose_colors = [male_color, female_color, male_color, female_color]
if 'lose' in key2_name:
colors = lose_colors
marker = 'o'
elif 'win' in key2_name:
colors = win_colors
marker = 'd'
else:
colors = win_colors
marker = 's'
key1_collect = []
key2_collect = []
if 'chirp' in key1 or 'chirp' in key2:
pass
else:
trial_mask = np.ones(len(trial_summary))
for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
k1 = trial_summary[key1][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy()
(trial_summary["draw"] == 0) & trial_mask].to_numpy()
k2 = trial_summary[key2][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy()
(trial_summary["draw"] == 0) & trial_mask].to_numpy()
mask = np.ones_like(k1, dtype=bool)
mask[(k1 == -1) | (k2 == -1)] = 0
k1 = k1[mask]
@ -112,23 +115,55 @@ def plot_meta_correlation(trial_summary, key1, key2, key1_name, key2_name, save_
key1_collect.append(k1)
key2_collect.append(k2)
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])
fig = plt.figure(figsize=(20/2.54, 20/2.54))
gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.95, top=0.95)
ax = []
ax.append(fig.add_subplot(gs[0, 0]))
ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0]))
for i in range(len(key1_collect)):
ax.plot(key1_collect[i], key2_collect[i], marker = marker, color=colors[i], markeredgecolor=mek[i],
ax[0].plot(key1_collect[i], key2_collect[i], marker = 'p', color=win_colors[i], markeredgecolor=mek[i],
markersize=markersize, markeredgewidth=2, linestyle='None')
ax[1].plot(key1_collect[i], key2_collect[i], marker = 'o', color=lose_colors[i], markeredgecolor=mek[i],
markersize=markersize, markeredgewidth=2, linestyle='None')
ax[1].set_xlabel(f'{key1_name}', fontsize=12)
ax[0].set_ylabel(f'{key2_name}', fontsize=12)
ax[1].set_ylabel(f'{key2_name}', fontsize=12)
ax.set_xlabel(f'{key1_name}', fontsize=12)
ax.set_ylabel(f'{key2_name}', fontsize=12)
plt.tick_params(labelsize=10)
plt.show()
embed()
quit()
if True:
r_coll = []
p_coll = []
print(f'\n{key1_name} - {key2_name}')
for win_lose_key, sex in itertools.product(['sex_win', 'sex_lose'], ['m', 'f']):
k1 = trial_summary[key1][(trial_summary[win_lose_key] == sex) & (trial_summary["draw"] == 0) & trial_mask].to_numpy()
k2 = trial_summary[key2][(trial_summary[win_lose_key] == sex) & (trial_summary["draw"] == 0) & trial_mask].to_numpy()
mask = np.ones_like(k1, dtype=bool)
mask[np.isnan(k1) | np.isnan(k2)] = 0
r, p = scp.spearmanr(k1[mask], k2[mask])
r_coll.append(r)
p_coll.append(p)
print(f'{win_lose_key}: {sex} --> spearman-r={r:.2f} p={p:.3f}')
k1 = trial_summary[key1][(trial_summary["draw"] == 0) & trial_mask].to_numpy()
k2 = trial_summary[key2][(trial_summary["draw"] == 0) & trial_mask].to_numpy()
mask = np.ones_like(k1, dtype=bool)
mask[np.isnan(k1) | np.isnan(k2)] = 0
r, p = scp.spearmanr(k1[mask], k2[mask])
def plot_beh_count_vs_dmeta(trial_summary,
ax[0].text(1, 1, f'male win: spaerman-r = {r_coll[0]:.2f} p={p_coll[0]:.3f}\n'
f'female win: spaerman-r = {r_coll[1]:.2f} p={p_coll[1]:.3f}', ha='right', va='bottom', transform = ax[0].transAxes)
ax[1].text(1, 1, f'male lose: spaerman-r = {r_coll[2]:.2f} p={p_coll[2]:.3f}\n'
f'female lose: spaerman-r = {r_coll[3]:.2f} p={p_coll[3]:.3f}', ha='right', va='bottom', transform = ax[1].transAxes)
ax[1].text(1, -.1, f'all: spaerman-r = {r:.2f} p={p:.3f}', ha='right', va='top', transform = ax[1].transAxes)
print(f'all --> spearman-r={r:.2f} p={p:.3f}')
plt.setp(ax[0].get_xticklabels(), visible=False)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'correlations_{key1}_{key2}.png'), dpi=300)
plt.close()
def plot_beh_count_vs_dmeta(trial_summary, trial_mask=None,
beh_key_win=None, beh_key_lose=None,
meta_key_win=None, meta_key_lose=None,
xlabel='x', save_str='random_plot_title'):
@ -137,6 +172,8 @@ def plot_beh_count_vs_dmeta(trial_summary,
win_colors = [male_color, male_color, female_color, female_color]
lose_colors = [male_color, female_color, male_color, female_color]
if not hasattr(trial_mask, '__len__'):
trial_mask = np.ones(len(trial_summary))
win_count = []
lose_count = []
@ -146,18 +183,18 @@ def plot_beh_count_vs_dmeta(trial_summary,
for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
win_count.append(trial_summary[beh_key_win][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0) & trial_mask].to_numpy())
lose_count.append(trial_summary[beh_key_lose][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0) & trial_mask].to_numpy())
win_meta.append(trial_summary[meta_key_win][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0) & trial_mask].to_numpy())
lose_meta.append(trial_summary[meta_key_lose][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0) & trial_mask].to_numpy())
fig = plt.figure(figsize=(20/2.54, 20/2.54))
gs = gridspec.GridSpec(2, 2, left=0.1, bottom=0.1, right=0.95, top=0.95, hspace=0.1, wspace=0.1)
@ -190,22 +227,25 @@ def plot_beh_count_vs_dmeta(trial_summary,
plt.close()
def plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_key_lose='chirps_lose',
def plot_beh_conut_vs_experience(trial_summary, trial_mask = None, 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]
lose_colors = [male_color, female_color, male_color, female_color]
if not hasattr(trial_mask, '__len__'):
trial_mask = np.ones(len(trial_summary))
lose_beh_per_exp = []
win_beh_per_exp = []
for i in np.unique(trial_summary['exp_lose']):
lose_beh_per_exp.append(trial_summary[beh_key_lose][(trial_summary['exp_lose'] == i) &
(trial_summary["draw"] == 0)].to_numpy())
(trial_summary["draw"] == 0) & trial_mask].to_numpy())
win_beh_per_exp.append(trial_summary[beh_key_win][(trial_summary['exp_lose'] == i) &
(trial_summary["draw"] == 0)].to_numpy())
(trial_summary["draw"] == 0) & trial_mask].to_numpy())
fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
@ -218,18 +258,18 @@ def plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_ke
for enu, (win_sex, lose_sex) in enumerate(itertools.product(['m', 'f'], repeat=2)):
lose_beh_count = trial_summary[beh_key_lose][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy()
(trial_summary["draw"] == 0) & trial_mask].to_numpy()
win_beh_count = trial_summary[beh_key_win][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy()
(trial_summary["draw"] == 0) & trial_mask].to_numpy()
lose_exp = trial_summary['exp_lose'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy()
(trial_summary["draw"] == 0) & trial_mask].to_numpy()
win_exp = trial_summary['exp_win'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy()
(trial_summary["draw"] == 0) & trial_mask].to_numpy()
ax.plot(lose_exp-0.15, lose_beh_count, 'o', color=lose_colors[enu], markeredgecolor=mek[enu],
markersize=markersize, markeredgewidth=2)
@ -250,38 +290,39 @@ def plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_ke
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)
trial_summary = pd.read_csv('trial_summary.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]
trial_mask = 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)]))
rc = np.concatenate((trial_summary['rises_win'][(trial_summary["draw"] == 0) & trial_mask],
trial_summary['rises_lose'][(trial_summary["draw"] == 0) & trial_mask]))
cc = np.concatenate((trial_summary['chirps_win'][(trial_summary["draw"] == 0) & trial_mask],
trial_summary['chirps_lose'][(trial_summary["draw"] == 0) & trial_mask]))
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)])
r, p = scp.spearmanr(trial_summary['rises_win'][(trial_summary["draw"] == 0) & trial_mask],
trial_summary['chirps_win'][(trial_summary["draw"] == 0) & trial_mask])
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)])
r, p = scp.spearmanr(trial_summary['rises_lose'][(trial_summary["draw"] == 0) & trial_mask],
trial_summary['chirps_lose'][(trial_summary["draw"] == 0) & trial_mask])
print(f'Risescount - Chirpscount - lose: Pearson-r={r:.2f} p={p:.3f}')
plot_rise_vs_chirp_count(trial_summary)
plot_rise_vs_chirp_count(trial_summary, trial_mask)
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)]
chirps_lose_female_win = trial_summary['chirps_lose'][(trial_summary['sex_win'] == 'f') & (trial_summary["draw"] == 0) & trial_mask]
chirps_lose_male_win = trial_summary['chirps_lose'][(trial_summary['sex_win'] == 'm') & (trial_summary["draw"] == 0) & trial_mask]
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)]
chirps_lose_female_lose = trial_summary['chirps_lose'][(trial_summary['sex_lose'] == 'f') & (trial_summary["draw"] == 0) & trial_mask]
chirps_lose_male_lose = trial_summary['chirps_lose'][(trial_summary['sex_lose'] == 'm') & (trial_summary["draw"] == 0) & trial_mask]
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}')
@ -297,87 +338,102 @@ def main(base_path):
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,
plot_beh_count_per_pairing(trial_summary, trial_mask,
beh_key_win='chirps_win', beh_key_lose='chirps_lose',
ylabel='chirps [n]', save_str='chirps_per_pairing')
plot_beh_count_per_pairing(trial_summary,
plot_beh_count_per_pairing(trial_summary, trial_mask=None,
beh_key_win='rises_win', beh_key_lose='rises_lose',
ylabel='rises [n]', save_str='rises_per_pairing')
plot_beh_count_vs_dmeta(trial_summary,
plot_beh_count_vs_dmeta(trial_summary, trial_mask,
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]', save_str='chirps_vs_dSize')
plot_beh_count_vs_dmeta(trial_summary,
plot_beh_count_vs_dmeta(trial_summary, trial_mask=None,
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]', save_str='rises_vs_dSize')
plot_beh_count_vs_dmeta(trial_summary,
plot_beh_count_vs_dmeta(trial_summary, trial_mask,
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]', save_str='chirps_vs_dEODf')
plot_beh_count_vs_dmeta(trial_summary,
plot_beh_count_vs_dmeta(trial_summary, trial_mask=None,
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]', save_str='rises_vs_dEODf')
plot_meta_correlation(trial_summary, key1='med_chase_dur', key2='chirps_lose',
key1_name=r'chase duration$_{median}$ [s]', key2_name=r'chirps$_{lose}$')
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_size_male_win - win_size_male_win)*-1, lose_chirps_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',
keys = ['dsize', 'dEODf', 'chirps_win', 'chirps_lose', 'rises_win', 'rises_lose', 'chase_count', 'contact_count', 'med_chase_dur', 'comp_dur0', 'comp_dur1']
keys_names = [r'$\Delta$size$_{win}$', r'$\Delta$EODf$_{win}$', r'chirps$_{win}$', r'chirps$_{lose}$', 'rises$_{win}$', 'rises$_{lose}$', 'chase$_{n}$', 'contact$_{n}$', 'med_chase_dur', 'comp_dur0', 'comp_dur1']
# for key1, key2 in itertools.combinations(keys, r = 2):
for i, j in itertools.combinations(np.arange(len(keys)), r = 2):
plot_meta_correlation(trial_summary, trial_mask, key1=keys[i], key2=keys[j],
key1_name=keys_names[i], key2_name=keys_names[j])
# plot_meta_correlation(trial_summary, key1='med_chase_dur', key2='chirps_lose',
# key1_name=r'chase duration$_{median}$ [s]', key2_name=r'chirps$_{lose}$')
# plot_meta_correlation(trial_summary, key1='med_chase_dur', key2='rises_lose',
# key1_name=r'chase duration$_{median}$ [s]', key2_name=r'rises$_{lose}$')
# 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_size_male_win - win_size_male_win)*-1, lose_chirps_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, trial_mask, 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]',
plot_beh_conut_vs_experience(trial_summary, trial_mask=None, beh_key_win='rises_win', beh_key_lose='rises_lose', ylabel='rises [n]',
save_str='rises_by_experince')
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)
if 'chirps' in key:
lose_events = trial_summary[key][(trial_summary["draw"] == 0) & trial_mask]
lose_exp = trial_summary['exp_lose'][(trial_summary["draw"] == 0) & trial_mask]
win_exp = trial_summary['exp_win'][(trial_summary["draw"] == 0) & trial_mask]
else:
lose_events = 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_events, lose_exp)
print(f'(all) {key} - lose exp: Pearson-r={r:.2f} p={p:.3f}')
r, p = scp.pearsonr(lose_chirps, win_exp)
r, p = scp.pearsonr(lose_events, win_exp)
print(f'(all) {key} - win exp: Pearson-r={r:.2f} p={p:.3f}')
plt.show()