examples are nice... make a function, clean it up
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4c3bd818b9
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166
ethogram.py
166
ethogram.py
@ -2,6 +2,8 @@ import os
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import sys
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from matplotlib.patches import Patch
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from matplotlib.lines import Line2D
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from mpl_toolkits.axes_grid1 import make_axes_locatable
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import numpy as np
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import pandas as pd
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@ -87,6 +89,7 @@ def plot_transition_diagram(matrix, labels, node_size, ax, threshold=5,
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ax.set_xlim(-1.3, 1.3)
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ax.set_ylim(-1.3, 1.3)
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ax.set_title(title, fontsize=12)
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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', 'markov')):
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os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'markov'))
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@ -103,10 +106,20 @@ def main(base_path):
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# agonistic categorie plot
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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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gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.9, top=0.95, height_ratios=[1, 4], hspace=0)
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ax = fig.add_subplot(gs[1, 0])
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ax_spec = fig.add_subplot(gs[0, 0], sharex=ax)
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plt.setp(ax_spec.get_xticklabels(), visible=False)
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for i in range(1, 5):
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ax.fill_between([0, 4], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
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ax.fill_between([5, 10], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
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fill_dots = np.arange(4, 5.1, 0.125)
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ax.plot(fill_dots, np.ones_like(fill_dots)*i, '.', color='tab:grey', markersize=3)
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got_examples = [False, False, False, False]
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example_skips = [3, 4, 1, 0]
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example_skips = [3, 4, 3, 0]
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for index, trial in trial_summary.iterrows():
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trial_path = os.path.join(base_path, trial['recording'])
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@ -209,7 +222,8 @@ def main(base_path):
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rise_before = True
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if np.any( ((chase_off_time - chirp_times[1]) < chirp_dt) & ((chirp_times[1] - chase_off_time) < max_dt)):
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chirp_time_oi = chirp_times[1][((chase_off_time - chirp_times[1]) < chirp_dt) & ((chirp_times[1] - chase_off_time) < max_dt)]
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# chirp_time_oi = chirp_times[1][((chase_off_time - chirp_times[1]) < chirp_dt) & ((chirp_times[1] - chase_off_time) < max_dt)]
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chirp_time_oi = chirp_times[1][((chase_off_time - chirp_times[1]) < chase_dur) & ((chirp_times[1] - chase_off_time) < max_dt)]
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chirp_arround_end = True
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if rise_before:
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@ -227,9 +241,6 @@ def main(base_path):
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if chase_dur > 10:
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if np.any((chirp_time_oi - chase_off_time) < 0) and np.any((chirp_time_oi - chase_off_time) > 0):
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if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
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ax.fill_between([0, 10],
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np.array([-.2, -.2]) + agonitic_categorie[enu],
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np.array([.2, .2]) + agonitic_categorie[enu], color='tab:grey')
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for ct in chirp_time_oi:
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ax.plot([ct - chase_off_time + 10, ct - chase_off_time + 10],
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[agonitic_categorie[enu] - .2, agonitic_categorie[enu] + .2], color='k', lw=2)
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@ -239,73 +250,109 @@ def main(base_path):
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got_examples[0] = True
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else:
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example_skips[int(agonitic_categorie[enu] - 1)] -= 1
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elif agonitic_categorie[enu] == 2 and not got_examples[1]:
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if chase_dur > 10:
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if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
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ax.fill_between([0, 10],
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np.array([-.2, -.2]) + agonitic_categorie[enu],
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np.array([.2, .2]) + agonitic_categorie[enu], color='tab:grey')
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for rt in rise_times_oi:
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ax.plot([rt - chase_on_time, rt - chase_on_time],
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[agonitic_categorie[enu] - .2, agonitic_categorie[enu] + .2], color='firebrick', lw=2)
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got_examples[1] = True
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else:
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example_skips[int(agonitic_categorie[enu] - 1)] -= 1
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elif agonitic_categorie[enu] == 3 and not got_examples[2]:
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if chase_dur > 10:
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if np.any((chirp_time_oi - chase_off_time) < 0) and np.any((chirp_time_oi - chase_off_time) > 0):
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if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
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ax.fill_between([0, 10],
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np.array([-.2, -.2]) + agonitic_categorie[enu],
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np.array([.2, .2]) + agonitic_categorie[enu], color='tab:grey')
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for ct in chirp_time_oi:
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ax.plot([ct - chase_off_time + 10, ct - chase_off_time + 10],
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[agonitic_categorie[enu] - .2, agonitic_categorie[enu] + .2], color='k', lw=2)
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got_examples[2] = True
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else:
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example_skips[int(agonitic_categorie[enu] - 1)] -= 1
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elif agonitic_categorie[enu] == 4 and not got_examples[3]:
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if chase_dur > 10:
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ax.fill_between([0, 10],
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np.array([-.2, -.2]) + agonitic_categorie[enu],
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np.array([.2, .2]) + agonitic_categorie[enu], color='tab:grey')
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got_examples[3] = True
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else:
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pass
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for i in range(4):
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### agonistic categories
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stacked_agonistic_categories = np.hstack(all_agonistic_categorie)
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stacked_all_chase_durs = np.hstack(all_chase_durs)
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ax.plot([0, 0], [0, 5], '--', color='k', lw=1)
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ax.plot([10, 10], [0, 5], '--', color='k', lw=1)
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ax.set_ylim(0.5, 4.5)
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pct_each_categorie = np.zeros(4)
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for enu, cat in enumerate(range(1, 5)):
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pct_each_categorie[enu] = len(stacked_agonistic_categories[stacked_agonistic_categories == cat]) / len(stacked_agonistic_categories)
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# example plot
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for enu, cat_pct in enumerate(pct_each_categorie):
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ax.text(15.2, enu+1, f'{cat_pct*100:.1f}' + ' $\%$', clip_on=False, fontsize=14, ha='left', va='center')
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ax.plot([0, 0], [0.8, 5], '--', color='k', lw=1)
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ax.plot([10, 10], [0.8, 5], '--', color='k', lw=1)
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ax.set_ylim(0.25, 4.5)
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ax.set_xlim(-5, 15)
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ax.set_yticks([1, 2, 3, 4])
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# ax.set_yticklabels([r'rise$_{pre}$ $&$ chirp$_{end}$', r'only rise$_{pre}$', r'only chirp$_{end}$', 'no communication'])
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ax.set_yticklabels(['A ', 'B ', 'C ', 'D '])
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ax.invert_yaxis()
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ax.set_xlabel('time [s]', fontsize=12)
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ax.tick_params(axis='y', labelsize=20)
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ax.tick_params(axis = 'x', labelsize=10)
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legend_elements = [Line2D([0], [0], color='firebrick', lw=2, label=r'rise$_{lose}$'),
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Line2D([0], [0], color='k', lw=2, label=r'chirp$_{lose}$'),
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Patch(facecolor='tab:grey', edgecolor='w', label= 'chase event')]
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ax.legend(handles=legend_elements, loc='upper right', ncol=3, bbox_to_anchor=(1, 1), frameon=False, fontsize=10, facecolor='white')
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ax.spines[['right', 'top']].set_visible(False)
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plt.show()
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embed()
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quit()
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# bar plot
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fig, ax = plt.subplots(figsize=(20/2.54, 12/2.54))
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ax.bar(np.arange(4),
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[len(stacked_agonistic_categories[stacked_agonistic_categories == 1]),
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len(stacked_agonistic_categories[stacked_agonistic_categories == 2]),
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len(stacked_agonistic_categories[stacked_agonistic_categories == 3]),
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len(stacked_agonistic_categories[stacked_agonistic_categories == 4])])
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ax.set_xticks(np.arange(4))
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ax.set_xticklabels([r'rise$_{pre}$ + chirp$_{end}$', r'rise$_{pre}$ + _', r'_ + chirp$_{end}$', '_ + _'])
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plt.show()
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# pct
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pct_agon_categorie = np.zeros(shape=(len(all_agonistic_categorie), 4))
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for enu, agonitic_categorie in enumerate(all_agonistic_categorie):
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for cat in np.arange(4):
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pct_agon_categorie[enu, cat] = len(agonitic_categorie[agonitic_categorie == cat+1]) / len(agonitic_categorie)
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fig, ax = plt.subplots(figsize=(20 / 2.54, 12 / 2.54))
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ax.bar(np.arange(4), pct_agon_categorie.mean(0))
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ax.errorbar(np.arange(4), pct_agon_categorie.mean(0), yerr=pct_agon_categorie.std(0), fmt='', color='k', linestyle='None')
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ax.set_xticks(np.arange(4))
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ax.set_xticklabels([r'rise$_{pre}$ + chirp$_{end}$', r'rise$_{pre}$ + _', r'_ + chirp$_{end}$', '_ + _'])
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plt.show()
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### marcov models
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all_marcov_matrix = np.array(all_marcov_matrix)
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all_event_counts = np.array(all_event_counts)
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collective_marcov_matrix = np.sum(all_marcov_matrix, axis=0)
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collective_event_counts = np.sum(all_event_counts, axis=0)
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plot_transition_matrix(collective_marcov_matrix, loop_labels)
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fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
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fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
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plot_transition_diagram(collective_marcov_matrix / collective_event_counts.reshape(len(collective_event_counts), 1) * 100,
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loop_labels, collective_event_counts, ax, threshold=5, color_by_origin=True, title='origin triggers target [%]')
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plot_transition_diagram(
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collective_marcov_matrix / collective_event_counts.reshape(len(collective_event_counts), 1) * 100,
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loop_labels, collective_event_counts, ax, threshold=5, color_by_origin=True, title='origin triggers target [%]')
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_destination' + '.png'), dpi=300)
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plt.close()
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fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
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fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
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plot_transition_diagram(collective_marcov_matrix / collective_event_counts * 100,
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loop_labels, collective_event_counts, ax, threshold=5, color_by_target=True, title='target triggered by origin [%]')
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loop_labels, collective_event_counts, ax, threshold=5, color_by_target=True,
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title='target triggered by origin [%]')
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_origin' + '.png'), dpi=300)
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plt.close()
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@ -317,7 +364,8 @@ def main(base_path):
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marcov_matrix / event_counts.reshape(len(event_counts), 1) * 100,
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loop_labels, event_counts, ax, threshold=5, color_by_origin=True,
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title='origin triggers target [%]')
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_destination' + '.png'), dpi=300)
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_destination' + '.png'),
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dpi=300)
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plt.close()
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fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
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@ -325,58 +373,12 @@ def main(base_path):
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plot_transition_diagram(marcov_matrix / event_counts * 100,
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loop_labels, event_counts, ax, threshold=5, color_by_target=True,
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title='target triggered by origin [%]')
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_origin' + '.png'), dpi=300)
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_origin' + '.png'),
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dpi=300)
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plt.close()
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embed()
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quit()
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### agonistic categories
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# stacked
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stacked_agonistic_categories = np.hstack(all_agonistic_categorie)
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stacked_all_chase_durs = np.hstack(all_chase_durs)
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# idx_cat_4 = np.where(stacked_agonistic_categories == 4)[0]
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# idx_cat_4 = idx_cat_4[np.argsort(stacked_all_chase_durs[idx_cat_4])]
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# idx_cat_3 = np.where(stacked_agonistic_categories == 3)[0]
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# idx_cat_3 = idx_cat_3[np.argsort(stacked_all_chase_durs[idx_cat_3])]
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# idx_cat_2 = np.where(stacked_agonistic_categories == 2)[0]
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# idx_cat_2 = idx_cat_2[np.argsort(stacked_all_chase_durs[idx_cat_2])]
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# idx_cat_1 = np.where(stacked_agonistic_categories == 1)[0]
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# idx_cat_1 = idx_cat_1[np.argsort(stacked_all_chase_durs[idx_cat_1])]
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#
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# fig, ax = plt.subplots(figsize=(20/2.54, 12/2.54))
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# ax.plot(10 - stacked_all_chase_durs[idx_cat_4], np.arange(len(idx_cat_4)))
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# ax.plot(10 - stacked_all_chase_durs[idx_cat_3], np.arange(len(idx_cat_4), len(idx_cat_3) + len(idx_cat_4)))
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# ax.plot(10 - stacked_all_chase_durs[idx_cat_2], np.arange(len(idx_cat_3) + len(idx_cat_4), len(idx_cat_2) + len(idx_cat_3) + len(idx_cat_4)))
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# ax.plot(10 - stacked_all_chase_durs[idx_cat_1], np.arange(len(idx_cat_2) + len(idx_cat_3) + len(idx_cat_4), len(idx_cat_1) + len(idx_cat_2) + len(idx_cat_3) + len(idx_cat_4)))
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# ax.set_xlim(0, 10)
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# plt.show()
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fig, ax = plt.subplots(figsize=(20/2.54, 12/2.54))
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ax.bar(np.arange(4),
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[len(stacked_agonistic_categories[stacked_agonistic_categories == 1]),
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len(stacked_agonistic_categories[stacked_agonistic_categories == 2]),
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len(stacked_agonistic_categories[stacked_agonistic_categories == 3]),
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len(stacked_agonistic_categories[stacked_agonistic_categories == 4])])
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ax.set_xticks(np.arange(4))
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ax.set_xticklabels([r'rise$_{pre}$ + chirp$_{end}$', r'rise$_{pre}$ + _', r'_ + chirp$_{end}$', '_ + _'])
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plt.show()
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# pct
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pct_agon_categorie = np.zeros(shape=(len(all_agonistic_categorie), 4))
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for enu, agonitic_categorie in enumerate(all_agonistic_categorie):
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for cat in np.arange(4):
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pct_agon_categorie[enu, cat] = len(agonitic_categorie[agonitic_categorie == cat+1]) / len(agonitic_categorie)
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fig, ax = plt.subplots(figsize=(20 / 2.54, 12 / 2.54))
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ax.bar(np.arange(4), pct_agon_categorie.mean(0))
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ax.errorbar(np.arange(4), pct_agon_categorie.mean(0), yerr=pct_agon_categorie.std(0), fmt='', color='k', linestyle='None')
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ax.set_xticks(np.arange(4))
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ax.set_xticklabels([r'rise$_{pre}$ + chirp$_{end}$', r'rise$_{pre}$ + _', r'_ + chirp$_{end}$', '_ + _'])
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plt.show()
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pass
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