experiment_bouts = experiment_bouts.map(eigenfish, whiten=True, mean=mean_tail, std=std_tail)
# Compute distance matrices
print_heading('CALCULATING DISTANCE MATRICES')
distances = {}
analysis_times = []
timer = Timer()
timer.start()
for ID in experiment_bouts.metadata['ID'].unique():
output_path, path_exists = create_filepath(output_directory, ID, '.npy', True)
if path_exists:
distances[ID] = np.load(output_path)
if not path_exists:
print ID + '...',
queries = experiment_bouts.list_bouts(IDs=[ID], values=True, ndims=n_dims)
fish_distances = calculate_distance_matrix_templates(queries, exemplars, fs=frame_rate)
distances[ID] = fish_distances
time_taken = timer.lap()
analysis_times.append(time_taken)
print timer.convert_time(time_taken)
np.save(output_path, fish_distances)
if len(analysis_times) > 0:
print 'Average time: {}'.format(timer.convert_time(timer.average))
# Assign exemplars
mapped_bouts = experiment_bouts.metadata.copy()
mapped_bouts['exemplar'] = None
for ID, fish_distances in distances.iteritems():
bout_idxs = mapped_bouts[mapped_bouts['ID'] == ID].index
nearest_exemplar = np.argmin(fish_distances, axis=1)
mapped_bouts.loc[bout_idxs, 'exemplar'] = nearest_exemplar
def map_bouts(self, n_dims, frame_rate):
# Import data from mapping experiment
mapping_space_directory = os.path.join(self.parent.subdirs['analysis'],
'behaviour_space')
eigenfish = np.load(
os.path.join(mapping_space_directory, 'eigenfish.npy'))
mean_tail, std_tail = np.load(
os.path.join(mapping_space_directory, 'tail_statistics.npy'))
exemplar_info = pd.read_csv(os.path.join(
self.parent.subdirs['analysis'], 'exemplars.csv'),
index_col='bout_index',
dtype={
'ID': str,
'video_code': str
})
exemplar_info = exemplar_info[exemplar_info['clean']]
exemplars = BoutData.from_directory(exemplar_info,
self.parent.subdirs['kinematics'],
check_tail_lengths=False,
tail_columns_only=True)
exemplars = exemplars.map(eigenfish,
whiten=True,
mean=mean_tail,
std=std_tail)
exemplars = exemplars.list_bouts(values=True, ndims=n_dims)
# Set paths
output_directory = create_folder(self.subdirs['analysis'],
'distance_matrices')
# Import experiment bouts
experiment_bouts = import_bouts(self.directory)
experiment_bouts = experiment_bouts.map(eigenfish,
whiten=True,
mean=mean_tail,
std=std_tail)
# Compute distance matrices
print_heading('CALCULATING DISTANCE MATRICES')
distances = {}
analysis_times = []
timer = Timer()
timer.start()
for ID in experiment_bouts.metadata['ID'].unique():
output_path, path_exists = create_filepath(output_directory, ID,
'.npy', True)
if path_exists:
distances[ID] = np.load(output_path)
if not path_exists:
print ID + '...',
queries = experiment_bouts.list_bouts(IDs=[ID],
values=True,
ndims=n_dims)
fish_distances = calculate_distance_matrix_templates(
queries, exemplars, fs=frame_rate)
distances[ID] = fish_distances
time_taken = timer.lap()
analysis_times.append(time_taken)
print timer.convert_time(time_taken)
np.save(output_path, fish_distances)
if len(analysis_times) > 0:
print 'Average time: {}'.format(timer.convert_time(timer.average))
# Assign exemplars
mapped_bouts = experiment_bouts.metadata.copy()
mapped_bouts['exemplar'] = None
for ID, fish_distances in distances.iteritems():
bout_idxs = mapped_bouts[mapped_bouts['ID'] == ID].index
nearest_exemplar = np.argmin(fish_distances, axis=1)
mapped_bouts.loc[bout_idxs, 'exemplar'] = nearest_exemplar
mapped_bouts.to_csv(
os.path.join(self.subdirs['analysis'], 'mapped_bouts.csv'))