worked on categorization of agonistic interactions
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dca11a7860
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159
ethogram.py
159
ethogram.py
@ -98,6 +98,16 @@ def main(base_path):
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all_marcov_matrix = []
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all_event_counts = []
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all_agonistic_categorie = []
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all_chase_durs = []
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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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got_examples = [False, False, False, False]
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example_skips = [3, 4, 1, 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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@ -174,7 +184,107 @@ def main(base_path):
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# plot_transition_matrix(marcov_matrix, loop_labels)
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# plot_transition_diagram(marcov_matrix, loop_labels, node_size=event_counts)
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# plot_transition_diagram(marcov_matrix / event_counts.reshape(len(event_counts), 1) * 100, loop_labels, node_size=event_counts)
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# plot_transition_diagram(marcov_matrix / event_counts.reshape(len(event_counts), 1) * 100, loop_labels, node_size=event_counts)+
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agonitic_categorie = np.zeros(len(ag_on_off_t_GRID))
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chase_durs = np.zeros_like(agonitic_categorie)
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all_agonistic_categorie.append(agonitic_categorie)
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all_chase_durs.append(chase_durs)
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### plotting
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for enu, (chase_on_time, chase_off_time) in enumerate(ag_on_off_t_GRID):
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chase_dur = chase_off_time - chase_on_time
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chase_durs[enu] = chase_dur
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chirp_dt = chase_dur if chase_dur < 5 else 5
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max_dt = 5
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rise_before, chirp_arround_end = False, False
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if np.any(((chase_on_time - rise_times[1]) > 0) & ((chase_on_time - rise_times[1]) < max_dt)):
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rise_times_oi = rise_times[1][((chase_on_time - rise_times[1]) > 0) & ((chase_on_time - rise_times[1]) < max_dt)]
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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_arround_end = True
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if rise_before:
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if chirp_arround_end:
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agonitic_categorie[enu] = 1
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else:
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agonitic_categorie[enu] = 2
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else:
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if chirp_arround_end:
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agonitic_categorie[enu] = 3
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else:
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agonitic_categorie[enu] = 4
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if agonitic_categorie[enu] == 1 and not got_examples[0]:
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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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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[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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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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plt.show()
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embed()
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quit()
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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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@ -220,6 +330,53 @@ def main(base_path):
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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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