From 96cb4b607c3a245367b4cc7608e2ae773b2afd3c Mon Sep 17 00:00:00 2001 From: Till Raab Date: Tue, 13 Jun 2023 14:28:04 +0200 Subject: [PATCH] dada --- complete_analysis.py | 7 ++-- event_time_analysis.py | 85 +++++++++++++++++++++++++------------- event_time_correlations.py | 10 ++--- trial_summary_eval.py | 79 +++++++++-------------------------- 4 files changed, 83 insertions(+), 98 deletions(-) diff --git a/complete_analysis.py b/complete_analysis.py index 4c72ab1..974c6bc 100644 --- a/complete_analysis.py +++ b/complete_analysis.py @@ -154,7 +154,6 @@ def get_temperature(folder_path): temp_t = np.array(temp_t) temp = np.array(temp) - if type(temp[-1]).__name__== 'str': temp = np.array(temp[:-1], dtype=float) temp_t = np.array(temp_t[:-1], dtype=int) @@ -166,8 +165,8 @@ def main(base_path=None): female_color, male_color = '#e74c3c', '#3498db' Wc, Lc = 'darkgreen', '#3673A4' - if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures')): - os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures')) + if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'example_trials')): + os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'example_trials')) # trials_meta = pd.read_csv('order_meta.csv') trials_meta = pd.read_csv(os.path.join(base_path, 'order_meta.csv')) @@ -385,7 +384,7 @@ def main(base_path=None): fig.suptitle(f'{recording}') - plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{recording}.png'), dpi=300) + plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'example_trials', f'{recording}.png'), dpi=300) # plt.savefig(os.path.join(os.path.join(os.path.split(__file__)[0], 'figures', f'{recording}.png')), dpi=300) plt.close() diff --git a/event_time_analysis.py b/event_time_analysis.py index 6e519bb..4cf2595 100644 --- a/event_time_analysis.py +++ b/event_time_analysis.py @@ -64,7 +64,7 @@ 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.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{title}_iei.png'), dpi=300) + plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta', f'{title}_iei.png'), dpi=300) plt.close() # plt.show() @@ -154,7 +154,7 @@ def relative_rate_progression(all_event_t, title=''): ax.set_xlim(0, 3) ax.set_ylim(0, 5) - plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{title}_progression.png'), dpi=300) + plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta', f'{title}_progression.png'), dpi=300) plt.close() # plt.show() @@ -163,7 +163,7 @@ def relative_rate_progression(all_event_t, title=''): r, p = scp.pearsonr(x, y) - print(f'{title}: pearson-r={r:.2f} p={p:.3f}') + print(f'Progression {title}: pearson-r={r:.2f} p={p:.3f}') @@ -171,6 +171,14 @@ def relative_rate_progression(all_event_t, title=''): def main(base_path): # ToDo: for chirp and rise analysis different datasets!!! + if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta')): + os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta')) + + if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr')): + os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr')) + + + 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_mask = chirp_notes['good'] == 1 @@ -269,13 +277,15 @@ def main(base_path): all_chase_event_mask = [] all_end_chase_event_mask = [] all_after_chase_event_mask = [] - all_around_countact_event_mask = [] + all_before_contact_event_mask = [] + all_after_contact_event_mask = [] all_pre_chase_time = [] all_chase_time = [] all_end_chase_time = [] all_after_chase_time = [] - all_around_countact_time = [] + all_before_contact_time = [] + all_after_contact_time = [] video_trial_win_sex = [] video_trial_lose_sex = [] @@ -317,17 +327,23 @@ 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) - around_countact_event_mask = np.zeros_like(event_times) + before_countact_event_mask = np.zeros_like(event_times) + after_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) + before_countact_event_mask[(event_times >= ct-time_tol) & (event_times < ct)] = 1 + after_countact_event_mask[(event_times >= ct) & (event_times < ct+time_tol)] = 1 + all_before_contact_event_mask.append(before_countact_event_mask) + all_after_contact_event_mask.append(after_countact_event_mask) + + all_before_contact_time.append(len(contact_t) * time_tol) + all_after_contact_time.append(len(contact_t) * time_tol) 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_before_contact_time = np.array(all_before_contact_time) + all_after_contact_time = np.array(all_after_contact_time) video_trial_win_sex = np.array(video_trial_win_sex) video_trial_lose_sex = np.array(video_trial_lose_sex) @@ -336,19 +352,22 @@ def main(base_path): 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_before_countact_time_ratio = all_before_contact_time / (3*60*60) + all_after_countact_time_ratio = all_after_contact_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))) + all_before_countact_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_before_contact_event_mask))) + all_after_countact_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_after_contact_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']]: + [all_before_countact_event_ratio, all_before_countact_time_ratio, 'pre contact'], + [all_after_countact_event_ratio, all_after_countact_time_ratio, 'post contact']]: t, p = scp.ttest_rel(x, y) print(f'{event_name} {name}: t={t:.2f} p={p:.3f}') @@ -362,7 +381,8 @@ def main(base_path): 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) + all_before_countact_event_ratio/all_before_countact_time_ratio, + all_after_countact_event_ratio/all_after_countact_time_ratio], positions=np.arange(6), sym='', zorder=2) ylim = list(ax.get_ylim()) ylim[0] = -.1 if ylim[0] < -.1 else ylim[0] @@ -392,24 +412,27 @@ def main(base_path): 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)] + values = np.array(all_before_countact_event_ratio/all_before_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) + values = np.array(all_after_countact_event_ratio/all_after_countact_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] + ax.plot(np.ones_like(values) * 5, 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.ones_like(all_before_countact_event_ratio) * 4, all_before_countact_event_ratio/all_before_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_xlim(-0.5, 5.5) 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.set_xticks(np.arange(6)) + ax.set_xticklabels([r'chase$_{before}$', r'chasing', r'chase$_{end}$', r'chase$_{after}$', 'contact$_{before}$', 'contact$_{after}$'], rotation=45) ax.tick_params(labelsize=10) # ax.set_title(event_name) fig.suptitle(f'{event_name}: n={len(np.hstack(all_event_t))}') @@ -420,30 +443,32 @@ def main(base_path): 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_before_countact_event_mask = np.hstack(all_before_contact_event_mask) + flat_after_countact_event_mask = np.hstack(all_after_contact_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 + flat_pre_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0 + flat_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0 + flat_end_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0 + flat_after_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_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)] + np.sum(flat_before_countact_event_mask) / len(flat_before_countact_event_mask), + np.sum(flat_after_countact_event_mask) / len(flat_after_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)] + np.sum(all_after_chase_time), np.sum(all_before_contact_time), np.sum(all_after_contact_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'] + outer_colors = ['tab:red', 'tab:orange', 'yellow', 'tab:green', 'k','tab:brown', '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, @@ -455,15 +480,17 @@ def main(base_path): 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:brown', edgecolor='w', label='%.1f' % (event_context_values[5] * 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) + '%')] + Patch(facecolor='k', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[4] * 100) + '%'), + Patch(facecolor='tab:brown', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[5] * 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.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{event_name}_categories.png'), dpi=300) plt.close() # plt.show() diff --git a/event_time_correlations.py b/event_time_correlations.py index dad1e19..9c7404b 100644 --- a/event_time_correlations.py +++ b/event_time_correlations.py @@ -241,7 +241,9 @@ 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!!! + if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr')): + os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr')) + 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] @@ -381,8 +383,6 @@ def main(base_path): [win_rises_centered_on_contact_t, win_rises_count, r'rise$_{win}$ on contact'], [win_rises_centered_on_lose_chirps, win_rises_count, r'rise$_{win}$ on chirp$_{lose}$']]: - if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures')): - os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures')) save_str = title.replace('$', '').replace('{', '').replace('}', '').replace(' ', '_') ########################################################################################################### @@ -451,7 +451,7 @@ def main(base_path): ax[2].set_ylabel('event rate [Hz]', fontsize=12) fig.suptitle(title) - plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{save_str}_by_sexes.png'), dpi=300) + plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{save_str}_by_sexes.png'), dpi=300) plt.close() ########################################################################################################### @@ -490,7 +490,7 @@ def main(base_path): ax.set_xlim(-max_dt, max_dt) ax.tick_params(labelsize=10) - plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{save_str}.png'), dpi=300) + plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{save_str}.png'), dpi=300) plt.close() diff --git a/trial_summary_eval.py b/trial_summary_eval.py index 38aeb73..d667f03 100644 --- a/trial_summary_eval.py +++ b/trial_summary_eval.py @@ -136,31 +136,32 @@ def plot_meta_correlation(trial_summary, trial_mask, key1, key2, key1_name, key2 if True: r_coll = [] p_coll = [] - print(f'\n{key1_name} - {key2_name}') + # print(f'\n{key1_name} - {key2_name}') for win_lose_key, sex in itertools.product(['sex_win', 'sex_lose'], ['m', 'f']): + print(win_lose_key, sex) 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, p = scp.pearsonr(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}') + # 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]) + r, p = scp.pearsonr(k1[mask], k2[mask]) - 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}') + ax[0].text(1, 1, f'male win: pearson-r = {r_coll[0]:.2f} p={p_coll[0]:.3f}\n' + f'female win: pearson-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: pearson-r = {r_coll[2]:.2f} p={p_coll[2]:.3f}\n' + f'female lose: pearson-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: pearson-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.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'meta_correlations', f'corr_{key1}_{key2}.png'), dpi=300) plt.close() def plot_beh_count_vs_dmeta(trial_summary, trial_mask=None, @@ -223,7 +224,7 @@ def plot_beh_count_vs_dmeta(trial_summary, trial_mask=None, 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.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'meta_correlations', f'{save_str}.png'), dpi=300) plt.close() @@ -288,7 +289,9 @@ def plot_beh_conut_vs_experience(trial_summary, trial_mask = None, beh_key_win=' def main(base_path): - # ToDo: for chirp and rise analysis different datasets!!! + if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'meta_correlations')): + os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'meta_correlations')) + # trial_summary = pd.read_csv(os.path.join(base_path, '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) @@ -301,14 +304,14 @@ def main(base_path): 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) + r, p = scp.pearsonr(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_mask], + r, p = scp.pearsonr(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_mask], + r, p = scp.pearsonr(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, trial_mask) @@ -370,50 +373,6 @@ def main(base_path): 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, trial_mask=None, beh_key_win='rises_win', beh_key_lose='rises_lose', ylabel='rises [n]',