403 lines
22 KiB
Python
403 lines
22 KiB
Python
import sys
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import os
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import scipy.stats as scp
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import numpy as np
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import itertools
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from IPython import embed
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colors = ['#BA2D22', '#53379B', '#F47F17', '#3673A4', '#AAB71B', '#DC143C', '#1E90FF']
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female_color, male_color = '#e74c3c', '#3498db'
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Wc, Lc = 'darkgreen', '#3673A4'
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def plot_rise_vs_chirp_count(trial_summary, trial_mask):
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fig = plt.figure(figsize=(20/2.54, 20/2.54))
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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])
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ax = fig.add_subplot(gs[1, 0])
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ax.plot(trial_summary['rises_win'][(trial_summary["draw"] == 0) & trial_mask],
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trial_summary['chirps_win'][(trial_summary["draw"] == 0) & trial_mask], 'o', color=Wc, label='winner')
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ax.plot(trial_summary['rises_lose'][(trial_summary["draw"] == 0) & trial_mask],
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trial_summary['chirps_lose'][(trial_summary["draw"] == 0) & trial_mask], 'o', color=Lc, label='loster')
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ax.set_xlabel('rises [n]', fontsize=12)
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ax.set_ylabel('chirps [n]', fontsize=12)
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ax.tick_params(labelsize=10)
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ax_chirps = fig.add_subplot(gs[1, 1], sharey=ax)
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ax_chirps.boxplot([trial_summary['chirps_win'][(trial_summary["draw"] == 0) & trial_mask],
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trial_summary['chirps_lose'][(trial_summary["draw"] == 0) & trial_mask]], widths = .5, positions = [1, 2])
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ax_chirps.set_xticks([1, 2])
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ax_chirps.set_xticklabels(['Win', 'Lose'])
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plt.setp(ax_chirps.get_yticklabels(), visible=False)
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ax_rises = fig.add_subplot(gs[0, 0], sharex=ax)
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ax_rises.boxplot([trial_summary['rises_win'][(trial_summary["draw"] == 0) & trial_mask],
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trial_summary['rises_lose'][(trial_summary["draw"] == 0) & trial_mask]], widths = .5, positions = [1, 2], vert=False)
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ax_rises.set_yticks([1, 2])
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ax_rises.set_yticklabels(['Win', 'Lose'])
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plt.setp(ax_rises.get_xticklabels(), visible=False)
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'rise_vs_chirp_count.png'), dpi=300)
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plt.close()
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def plot_beh_count_per_pairing(trial_summary, trial_mask=None,
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beh_key_win=None, beh_key_lose=None,
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ylabel='y', save_str='random_plot_title'):
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mek = ['k', 'None', 'None', 'k']
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markersize = 12
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win_colors = [male_color, male_color, female_color, female_color]
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lose_colors = [male_color, female_color, male_color, female_color]
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if not hasattr(trial_mask, '__len__'):
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trial_mask = np.ones(len(trial_summary))
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win_count = []
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lose_count = []
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for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
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win_count.append(trial_summary[beh_key_win][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy())
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lose_count.append(trial_summary[beh_key_lose][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy())
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fig = plt.figure(figsize=(20/2.54, 12/2.54))
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gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.95, top=0.95)
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ax = fig.add_subplot(gs[0, 0])
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ax.boxplot(win_count, positions=np.arange(len(win_count))-0.15, widths= .2, sym='')
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ax.boxplot(lose_count, positions=np.arange(len(lose_count))+0.15, widths= .2, sym='')
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ax.set_xticks(np.arange(len(win_count)))
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ax.set_xticklabels([u'\u2642\u2642', u'\u2642\u2640', u'\u2640\u2642', u'\u2640\u2640'])
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# ax.set_xticklabels(['mm', 'mf', 'fm', 'ff'])
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y0, y1 = ax.get_ylim()
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for i in range(len(win_count)):
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ax.text(i, y1, f'n={len(win_count[i]):.0f}', fontsize=10, ha='center', va='bottom')
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ax.set_ylim(top = y1*1.1)
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ax.set_ylabel(ylabel, fontsize=12)
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plt.tick_params(labelsize=10)
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{save_str}.png'), dpi=300)
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plt.close()
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def plot_meta_correlation(trial_summary, trial_mask, key1, key2, key1_name, key2_name, save_str='random_plot_title'):
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mek = ['k', 'None', 'None', 'k']
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markersize = 12
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win_colors = [male_color, male_color, female_color, female_color]
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lose_colors = [male_color, female_color, male_color, female_color]
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key1_collect = []
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key2_collect = []
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if 'chirp' in key1 or 'chirp' in key2:
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pass
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else:
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trial_mask = np.ones(len(trial_summary))
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for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
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k1 = trial_summary[key1][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy()
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k2 = trial_summary[key2][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy()
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mask = np.ones_like(k1, dtype=bool)
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mask[(k1 == -1) | (k2 == -1)] = 0
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k1 = k1[mask]
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k2 = k2[mask]
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key1_collect.append(k1)
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key2_collect.append(k2)
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fig = plt.figure(figsize=(20/2.54, 20/2.54))
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gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.95, top=0.95)
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ax = []
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ax.append(fig.add_subplot(gs[0, 0]))
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ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0]))
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for i in range(len(key1_collect)):
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ax[0].plot(key1_collect[i], key2_collect[i], marker = 'p', color=win_colors[i], markeredgecolor=mek[i],
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markersize=markersize, markeredgewidth=2, linestyle='None')
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ax[1].plot(key1_collect[i], key2_collect[i], marker = 'o', color=lose_colors[i], markeredgecolor=mek[i],
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markersize=markersize, markeredgewidth=2, linestyle='None')
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ax[1].set_xlabel(f'{key1_name}', fontsize=12)
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ax[0].set_ylabel(f'{key2_name}', fontsize=12)
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ax[1].set_ylabel(f'{key2_name}', fontsize=12)
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plt.tick_params(labelsize=10)
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if True:
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r_coll = []
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p_coll = []
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# print(f'\n{key1_name} - {key2_name}')
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for win_lose_key, sex in itertools.product(['sex_win', 'sex_lose'], ['m', 'f']):
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print(win_lose_key, sex)
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k1 = trial_summary[key1][(trial_summary[win_lose_key] == sex) & (trial_summary["draw"] == 0) & trial_mask].to_numpy()
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k2 = trial_summary[key2][(trial_summary[win_lose_key] == sex) & (trial_summary["draw"] == 0) & trial_mask].to_numpy()
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mask = np.ones_like(k1, dtype=bool)
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mask[np.isnan(k1) | np.isnan(k2)] = 0
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r, p = scp.pearsonr(k1[mask], k2[mask])
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r_coll.append(r)
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p_coll.append(p)
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# print(f'{win_lose_key}: {sex} --> spearman-r={r:.2f} p={p:.3f}')
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k1 = trial_summary[key1][(trial_summary["draw"] == 0) & trial_mask].to_numpy()
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k2 = trial_summary[key2][(trial_summary["draw"] == 0) & trial_mask].to_numpy()
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mask = np.ones_like(k1, dtype=bool)
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mask[np.isnan(k1) | np.isnan(k2)] = 0
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r, p = scp.pearsonr(k1[mask], k2[mask])
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ax[0].text(1, 1, f'male win: pearson-r = {r_coll[0]:.2f} p={p_coll[0]:.3f}\n'
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f'female win: pearson-r = {r_coll[1]:.2f} p={p_coll[1]:.3f}', ha='right', va='bottom', transform = ax[0].transAxes)
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ax[1].text(1, 1, f'male lose: pearson-r = {r_coll[2]:.2f} p={p_coll[2]:.3f}\n'
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f'female lose: pearson-r = {r_coll[3]:.2f} p={p_coll[3]:.3f}', ha='right', va='bottom', transform = ax[1].transAxes)
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ax[1].text(1, -.1, f'all: pearson-r = {r:.2f} p={p:.3f}', ha='right', va='top', transform = ax[1].transAxes)
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# print(f'all --> spearman-r={r:.2f} p={p:.3f}')
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plt.setp(ax[0].get_xticklabels(), visible=False)
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'meta_correlations', f'corr_{key1}_{key2}.png'), dpi=300)
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plt.close()
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def plot_beh_count_vs_dmeta(trial_summary, trial_mask=None,
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beh_key_win=None, beh_key_lose=None,
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meta_key_win=None, meta_key_lose=None,
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xlabel='x', save_str='random_plot_title'):
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mek = ['k', 'None', 'None', 'k']
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markersize = 12
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win_colors = [male_color, male_color, female_color, female_color]
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lose_colors = [male_color, female_color, male_color, female_color]
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if not hasattr(trial_mask, '__len__'):
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trial_mask = np.ones(len(trial_summary))
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win_count = []
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lose_count = []
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win_meta = []
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lose_meta = []
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for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
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win_count.append(trial_summary[beh_key_win][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy())
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lose_count.append(trial_summary[beh_key_lose][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy())
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win_meta.append(trial_summary[meta_key_win][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy())
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lose_meta.append(trial_summary[meta_key_lose][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy())
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fig = plt.figure(figsize=(20/2.54, 20/2.54))
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gs = gridspec.GridSpec(2, 2, left=0.1, bottom=0.1, right=0.95, top=0.95, hspace=0.1, wspace=0.1)
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ax = []
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ax.append(fig.add_subplot(gs[0, 0]))
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ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0]))
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ax.append(fig.add_subplot(gs[0, 1], sharey=ax[0]))
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ax.append(fig.add_subplot(gs[1, 1], sharex=ax[2], sharey=ax[1]))
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for i in range(len(win_count)):
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ax[0].plot(win_meta[i]-lose_meta[i], win_count[i], 'p', color=win_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2)
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ax[1].plot(win_meta[i]-lose_meta[i], lose_count[i], 'p', color=win_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2)
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ax[2].plot((win_meta[i]-lose_meta[i])*-1, win_count[i], 'o', color=lose_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2)
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ax[3].plot((win_meta[i]-lose_meta[i])*-1, lose_count[i], 'o', color=lose_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2 )
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ax[0].set_ylabel(f'{beh_key_win} [n]', fontsize=12)
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ax[1].set_ylabel(f'{beh_key_lose} [n]', fontsize=12)
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ax[1].set_xlabel(f'{xlabel}', fontsize=12)
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ax[3].set_xlabel(f'{xlabel}', fontsize=12)
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plt.setp(ax[0].get_xticklabels(), visible=False)
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plt.setp(ax[2].get_xticklabels(), visible=False)
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plt.setp(ax[2].get_yticklabels(), visible=False)
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plt.setp(ax[3].get_yticklabels(), visible=False)
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plt.tick_params(labelsize=10)
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'meta_correlations', f'{save_str}.png'), dpi=300)
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plt.close()
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def plot_beh_conut_vs_experience(trial_summary, trial_mask = None, beh_key_win='chirps_win', beh_key_lose='chirps_lose',
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ylabel='chirps [n]', save_str='random_plot_title'):
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mek = ['k', 'None', 'None', 'k']
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markersize = 10
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win_colors = [male_color, male_color, female_color, female_color]
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lose_colors = [male_color, female_color, male_color, female_color]
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if not hasattr(trial_mask, '__len__'):
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trial_mask = np.ones(len(trial_summary))
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lose_beh_per_exp = []
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win_beh_per_exp = []
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for i in np.unique(trial_summary['exp_lose']):
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lose_beh_per_exp.append(trial_summary[beh_key_lose][(trial_summary['exp_lose'] == i) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy())
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win_beh_per_exp.append(trial_summary[beh_key_win][(trial_summary['exp_lose'] == i) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy())
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fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
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gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.15, right=0.95, top=0.95, hspace=0.1, wspace=0.1)
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ax = fig.add_subplot(gs[0, 0])
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ax.boxplot(lose_beh_per_exp, positions = np.unique(trial_summary['exp_lose'])-0.15, widths=0.2)
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ax.boxplot(win_beh_per_exp, positions = np.unique(trial_summary['exp_lose'])+0.15, widths=0.2)
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for enu, (win_sex, lose_sex) in enumerate(itertools.product(['m', 'f'], repeat=2)):
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lose_beh_count = trial_summary[beh_key_lose][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy()
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win_beh_count = trial_summary[beh_key_win][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy()
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lose_exp = trial_summary['exp_lose'][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy()
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win_exp = trial_summary['exp_win'][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0) & trial_mask].to_numpy()
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ax.plot(lose_exp-0.15, lose_beh_count, 'o', color=lose_colors[enu], markeredgecolor=mek[enu],
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markersize=markersize, markeredgewidth=2)
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ax.plot(win_exp+0.15, win_beh_count, 'p', color=win_colors[enu], markeredgecolor=mek[enu],
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markersize=markersize, markeredgewidth=2)
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ax.set_xticks(np.unique(trial_summary['exp_lose']))
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ax.set_xticklabels(np.unique(trial_summary['exp_lose']))
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ax.set_xlabel('experience [trials]', fontsize=12)
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ax.set_ylabel(ylabel, fontsize=12)
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ax.tick_params(labelsize=10)
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', f'{save_str}.png'), dpi=300)
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plt.close()
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def main(base_path):
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if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'meta_correlations')):
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os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'meta_correlations'))
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# trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
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trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
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chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
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# trial_summary = trial_summary[chirp_notes['good'] == 1]
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trial_mask = chirp_notes['good'] == 1
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if True:
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print('')
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rc = np.concatenate((trial_summary['rises_win'][(trial_summary["draw"] == 0) & trial_mask],
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trial_summary['rises_lose'][(trial_summary["draw"] == 0) & trial_mask]))
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cc = np.concatenate((trial_summary['chirps_win'][(trial_summary["draw"] == 0) & trial_mask],
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trial_summary['chirps_lose'][(trial_summary["draw"] == 0) & trial_mask]))
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r, p = scp.pearsonr(rc, cc)
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print(f'Risescount - Chirpscount - all: Pearson-r={r:.2f} p={p:.3f}')
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r, p = scp.pearsonr(trial_summary['rises_win'][(trial_summary["draw"] == 0) & trial_mask],
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trial_summary['chirps_win'][(trial_summary["draw"] == 0) & trial_mask])
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print(f'Risescount - Chirpscount - win: Pearson-r={r:.2f} p={p:.3f}')
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r, p = scp.pearsonr(trial_summary['rises_lose'][(trial_summary["draw"] == 0) & trial_mask],
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trial_summary['chirps_lose'][(trial_summary["draw"] == 0) & trial_mask])
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print(f'Risescount - Chirpscount - lose: Pearson-r={r:.2f} p={p:.3f}')
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plot_rise_vs_chirp_count(trial_summary, trial_mask)
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if True:
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print('')
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chirps_lose_female_win = trial_summary['chirps_lose'][(trial_summary['sex_win'] == 'f') & (trial_summary["draw"] == 0) & trial_mask]
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chirps_lose_male_win = trial_summary['chirps_lose'][(trial_summary['sex_win'] == 'm') & (trial_summary["draw"] == 0) & trial_mask]
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U, p = scp.mannwhitneyu(chirps_lose_female_win, chirps_lose_male_win)
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print(f'Chirpscount - female win - male win: MW-U={U:.2f} p={p:.3f}')
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chirps_lose_female_lose = trial_summary['chirps_lose'][(trial_summary['sex_lose'] == 'f') & (trial_summary["draw"] == 0) & trial_mask]
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chirps_lose_male_lose = trial_summary['chirps_lose'][(trial_summary['sex_lose'] == 'm') & (trial_summary["draw"] == 0) & trial_mask]
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U, p = scp.mannwhitneyu(chirps_lose_female_lose, chirps_lose_male_lose)
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print(f'Chirpscount - female lose - male lose: MW-U={U:.2f} p={p:.3f}')
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###################################################################################
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rises_lose_female_win = trial_summary['rises_lose'][(trial_summary['sex_win'] == 'f') & (trial_summary["draw"] == 0)]
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rises_lose_male_win = trial_summary['rises_lose'][(trial_summary['sex_win'] == 'm') & (trial_summary["draw"] == 0)]
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U, p = scp.mannwhitneyu(rises_lose_female_win, rises_lose_male_win)
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print(f'Risescount - female win - male win: MW-U={U:.2f} p={p:.3f}')
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rises_lose_female_lose = trial_summary['rises_lose'][(trial_summary['sex_lose'] == 'f') & (trial_summary["draw"] == 0)]
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rises_lose_male_lose = trial_summary['rises_lose'][(trial_summary['sex_lose'] == 'm') & (trial_summary["draw"] == 0)]
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U, p = scp.mannwhitneyu(rises_lose_female_lose, rises_lose_male_lose)
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print(f'Risescount - female lose - male lose: MW-U={U:.2f} p={p:.3f}')
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plot_beh_count_per_pairing(trial_summary, trial_mask,
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beh_key_win='chirps_win', beh_key_lose='chirps_lose',
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ylabel='chirps [n]', save_str='chirps_per_pairing')
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plot_beh_count_per_pairing(trial_summary, trial_mask=None,
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beh_key_win='rises_win', beh_key_lose='rises_lose',
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ylabel='rises [n]', save_str='rises_per_pairing')
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plot_beh_count_vs_dmeta(trial_summary, trial_mask,
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beh_key_win='chirps_win', beh_key_lose='chirps_lose',
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meta_key_win="size_win", meta_key_lose='size_lose',
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xlabel=u'$\Delta$size [cm]', save_str='chirps_vs_dSize')
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plot_beh_count_vs_dmeta(trial_summary, trial_mask=None,
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beh_key_win='rises_win', beh_key_lose='rises_lose',
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meta_key_win="size_win", meta_key_lose='size_lose',
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xlabel=u'$\Delta$size [cm]', save_str='rises_vs_dSize')
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plot_beh_count_vs_dmeta(trial_summary, trial_mask,
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beh_key_win='chirps_win', beh_key_lose='chirps_lose',
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meta_key_win="EODf_win", meta_key_lose='EODf_lose',
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xlabel=u'$\Delta$EODf [Hz]', save_str='chirps_vs_dEODf')
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plot_beh_count_vs_dmeta(trial_summary, trial_mask=None,
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beh_key_win='rises_win', beh_key_lose='rises_lose',
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meta_key_win="EODf_win", meta_key_lose='EODf_lose',
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xlabel=u'$\Delta$EODf [Hz]', save_str='rises_vs_dEODf')
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keys = ['dsize', 'dEODf', 'chirps_win', 'chirps_lose', 'rises_win', 'rises_lose', 'chase_count', 'contact_count', 'med_chase_dur', 'comp_dur0', 'comp_dur1']
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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']
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# for key1, key2 in itertools.combinations(keys, r = 2):
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for i, j in itertools.combinations(np.arange(len(keys)), r = 2):
|
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plot_meta_correlation(trial_summary, trial_mask, key1=keys[i], key2=keys[j],
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key1_name=keys_names[i], key2_name=keys_names[j])
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|
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plot_beh_conut_vs_experience(trial_summary, trial_mask, beh_key_win='chirps_win', beh_key_lose='chirps_lose',
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ylabel='chirps [n]', save_str='chirps_by_experince')
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plot_beh_conut_vs_experience(trial_summary, trial_mask=None, beh_key_win='rises_win', beh_key_lose='rises_lose', ylabel='rises [n]',
|
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save_str='rises_by_experince')
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|
|
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if True:
|
|
for key in ['chirps_lose', 'chirps_win', 'rises_lose', 'rises_win']:
|
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print('')
|
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if 'chirps' in key:
|
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lose_events = trial_summary[key][(trial_summary["draw"] == 0) & trial_mask]
|
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lose_exp = trial_summary['exp_lose'][(trial_summary["draw"] == 0) & trial_mask]
|
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win_exp = trial_summary['exp_win'][(trial_summary["draw"] == 0) & trial_mask]
|
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else:
|
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lose_events = trial_summary[key][(trial_summary["draw"] == 0)]
|
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lose_exp = trial_summary['exp_lose'][(trial_summary["draw"] == 0)]
|
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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_events, win_exp)
|
|
print(f'(all) {key} - win exp: Pearson-r={r:.2f} p={p:.3f}')
|
|
|
|
plt.show()
|
|
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|
|
if __name__ == '__main__':
|
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main(sys.argv[1]) |