if __name__ == "__main__":
mapped_bouts = pd.read_csv(os.path.join(experiment.subdirs['analysis'],
'mapped_bouts.csv'),
index_col=0,
dtype={
'ID': str,
'video_code': str
})
# Re-weighting in isomap space
isomap = np.load(
os.path.join(experiment.parent.subdirs['analysis'],
'isomap.npy'))[:, :3]
W = generate_weights(isomap)
# Create paths for saving
transition_directory = create_folder(experiment.subdirs['analysis'],
'transitions')
# Compute the transition matrix for each fish
T = fish_transition_matrices(mapped_bouts,
state_col='exemplar',
n_states=len(isomap),
shuffle=False)
np.save(os.path.join(transition_directory, 'transition_matrices.npy'), T)
print T.shape
# Redistribute transitions
WTW = redistribute_transitions(T, W)
np.save(
os.path.join(transition_directory, 'smoothed_transition_matrices.npy'),
experiment.data.loc[experiment.data[experiment.data['condition'].
isin(['left', 'right'])].index,
'condition'] = 'unilateral'
mapped_bouts = pd.read_csv(os.path.join(experiment.subdirs['analysis'],
'mapped_bouts.csv'),
index_col=0,
dtype={
'ID': str,
'video_code': str
})
# Re-weighting in isomap space
isomap = np.load(
os.path.join(experiment.parent.subdirs['analysis'],
'isomap.npy'))[:, :3]
W = generate_weights(isomap)
# Create paths for saving
transition_directory = create_folder(experiment.subdirs['analysis'],
'transitions')
for condition, fish_info in experiment.data.groupby('condition'):
print condition
condition_directory = create_folder(transition_directory, condition)
condition_bouts = mapped_bouts[mapped_bouts['ID'].isin(
fish_info['ID'])]
# Compute the transition matrix for each fish
T = fish_transition_matrices(condition_bouts,
state_col='exemplar',
n_states=len(isomap),
shuffle=False)
np.save(os.path.join(condition_directory, 'transition_matrices.npy'),
def redistribute_transitions(self, bandwidth):
self.W = generate_weights(self.points, bandwidth=bandwidth)
self.T_ = np.dot(np.dot(self.W, self.T), self.W.T)