ystep1=2)) for condition in ('het', 'mut'): transition_directory = os.path.join(experiment.subdirs['analysis'], 'transitions') USVs = np.load( os.path.join(transition_directory, condition, 'USVs.npy')) USVa = np.load( os.path.join(transition_directory, condition, 'USVa.npy')) # ===================== # Plot transition modes # ===================== fig1 = transition_mode_plot(USVs[2], USVa[2]) # ==================== # Plot singular values # ==================== fig2, axes = plt.subplots(1, 2, figsize=(2, 1)) # Symmetric axes[0].plot(np.arange(10), np.diag(USVs[1])[:10], c=mutant_colors['blu_s257'][condition], zorder=0) axes[0].scatter(np.arange(10), np.diag(USVs[1])[:10], s=10, c='w', edgecolor=mutant_colors['blu_s257'][condition],
from plotting import * from plotting.plots import transition_mode_plot from datasets.lensectomy import experiment import numpy as np import os if __name__ == "__main__": transition_directory = os.path.join(experiment.subdirs['analysis'], 'transitions') for condition in ('control', 'unilateral', 'bilateral'): Us, Ss, Vs = np.load(os.path.join(transition_directory, condition, 'USVs.npy')) Ua, Sa, Va = np.load(os.path.join(transition_directory, condition, 'USVa.npy')) if condition == 'control': Va[:, :2] *= (-1, 1) fig = transition_mode_plot(Vs, Va) save_fig(fig, 'figure7', '{}_transition_modes'.format(condition)) plt.close(fig) # plt.show()