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()