capture_strike_directory = os.path.join(experiment.subdirs['analysis'],
'capture_strikes')
# Import data
stimulus_map_directory = os.path.join(experiment.subdirs['analysis'],
'stimulus_maps')
random_histogram = np.load(
os.path.join(stimulus_map_directory, 'random_histogram.npy'))
random_average = np.load(
os.path.join(stimulus_map_directory, 'random_average.npy'))
random_histogram = random_histogram[100:200, 100:150].T
random_average = random_average[100:150, 100:200]
random_histogram = smoothed_histogram(random_histogram,
random_average,
sigma=1.0)
strike_sequence_directory = os.path.join(experiment.subdirs['analysis'],
'capture_strikes',
'strike_sequences')
attack_histograms = np.load(
os.path.join(strike_sequence_directory, 'attack_histogram.npy'))
attack_averages = np.load(
os.path.join(strike_sequence_directory, 'attack_average.npy'))
sstrike_histograms = np.load(
os.path.join(strike_sequence_directory, 'sstrike_histogram.npy'))
sstrike_averages = np.load(
os.path.join(strike_sequence_directory, 'sstrike_average.npy'))
import numpy as np
stimulus_map_directory = os.path.join(experiment.subdirs['analysis'], 'stimulus_maps')
# RANDOM MAPS FOR BLU
random_histogram = np.load(os.path.join(stimulus_map_directory, 'random_histogram.npy'))
random_average = np.load(os.path.join(stimulus_map_directory, 'random_average.npy'))[..., 1:251]
ymin, ymax = 50, 200
xmin, xmax = 60, 190
sigma = 2.0
random_histogram = random_histogram[ymin:ymax, xmin:xmax].T
random_average = random_average[xmin:xmax, ymin:ymax]
random = smoothed_histogram(random_histogram, random_average, threshold=150, sigma=sigma)
for condition in ('control', 'unilateral'):
averages = np.load(os.path.join(stimulus_map_directory, '{}_module_averages.npy'.format(condition)))[..., 1:251]
histograms = np.load(os.path.join(stimulus_map_directory, '{}_module_histograms.npy'.format(condition)))
for i in range(7):
fig, axes = plt.subplots(1, 3)
pre_histogram = histograms[i, 0, ymin:ymax, xmin:xmax].T
post_histogram = histograms[i, 1, ymin:ymax, xmin:xmax].T
pre_average = averages[i, 0, xmin:xmax, ymin:ymax]
attack_average = attack_average[:, 100:150, 100:200]
sstrike_histogram = np.load(
os.path.join(strike_sequence_directory, 'sstrike_histogram.npy'))
sstrike_average = np.load(
os.path.join(strike_sequence_directory, 'sstrike_average.npy'))
sstrike_histogram = sstrike_histogram[:, 100:200, 100:150]
sstrike_average = sstrike_average[:, 100:150, 100:200]
random_histogram = np.load(
os.path.join(stimulus_map_directory, 'random_histogram.npy'))
random_average = np.load(
os.path.join(stimulus_map_directory, 'random_average.npy'))
random_histogram = random_histogram[100:200, 100:150].T
random_average = random_average[100:150, 100:200]
random_histogram = smoothed_histogram(random_histogram, random_average)
attack = []
for cropped, average in zip(attack_histogram, attack_average):
smoothed = smoothed_histogram(cropped.T,
average,
threshold=20,
sigma=1.0)
diff = (smoothed - random_histogram).T[::-1]
attack.append(diff)
attack = np.array(attack)
sstrike = []
for cropped, average in zip(sstrike_histogram, sstrike_average):
smoothed = smoothed_histogram(cropped.T,
average,
control_average = np.load(os.path.join(hunting_sequence_directory, 'control_attack_average.npy'))
random_histogram = np.load(os.path.join(stimulus_map_directory, 'random_histogram.npy'))
random_average = np.load(os.path.join(stimulus_map_directory, 'random_average.npy'))
# Take the frame of strike onset and crop images
unilateral_histogram = unilateral_histogram[-50, 100:200, 100:150].T
unilateral_average = unilateral_average[-50, 100:150, 100:200]
control_histogram = control_histogram[-50, 100:200, 100:150].T
control_average = control_average[-50, 100:150, 100:200]
random_histogram = random_histogram[100:200, 100:150].T
random_average = random_average[100:150, 100:200]
# Mask out fish
unilateral_histogram = smoothed_histogram(unilateral_histogram, unilateral_average)
control_histogram = smoothed_histogram(control_histogram, control_average)
random_histogram = smoothed_histogram(random_histogram, random_average)
fig, axes = plt.subplots(1, 2, figsize=(1.4, 1.4), gridspec_kw=dict(bottom=0, top=1, left=0, right=1, wspace=0))
control = (control_histogram - random_histogram).T[::-1]
axes[0].imshow(control_average.T[::-1], cmap='binary_r', vmin=0, vmax=255, interpolation='bilinear')
axes[0].contourf(control, cmap='magma', levels=np.linspace(0.0001, 0.005, 100))
unilateral = (unilateral_histogram - random_histogram).T[::-1]
axes[1].imshow(unilateral_average.T[::-1], cmap='binary_r', vmin=0, vmax=255, interpolation='bilinear')
axes[1].contourf(unilateral, cmap='magma', levels=np.linspace(0.0001, 0.005, 100))
for ax in axes:
ax.axis('off')