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'),
T)
print T.shape
# Redistribute transitions
W = generate_weights(isomap, bandwidth=40.)
WTW = redistribute_transitions(T, W)
np.save(
os.path.join(condition_directory,
'smoothed_transition_matrices.npy'), WTW)
# Sum transitions over all fish
T_all = T.sum(axis=0)
WTW_all = redistribute_transitions(T_all, W)
np.save(os.path.join(condition_directory, 'T.npy'), T_all)
np.save(os.path.join(condition_directory, 'WTW.npy'), WTW_all)
from scipy import stats as ss
import os
modelling_directory = os.path.join(experiment.subdirs['analysis'], 'modelling')
if __name__ == "__main__":
mapped_bouts = pd.read_csv(os.path.join(experiment.subdirs['analysis'], 'mapped_bouts.csv'),
index_col='transition_index', dtype={'ID': str, 'video_code': str})
n_clusters = len(mapped_bouts['module'].unique())
S = []
for shuffle in np.arange(1000):
if shuffle % 10 == 0:
print shuffle
shuffled = fish_transition_matrices(mapped_bouts, state_col='module', shuffle=True, verbose=False)
S.append(shuffled.sum(axis=0))
S = np.array(S)
T = fish_transition_matrices(mapped_bouts, state_col='module', shuffle=False)
T = T.sum(axis=0)
model_params = np.array([S.mean(axis=0), S.std(axis=0)])
p_values_decrease = np.zeros((n_clusters, n_clusters))
for i in np.arange(n_clusters):
for j in np.arange(n_clusters):
norm = ss.norm(loc=model_params[0, i, j], scale=model_params[1, i, j])
p_values_decrease[i, j] = norm.cdf(T[i, j])
p_values_increase = 1 - p_values_decrease
if __name__ == "__main__":
# Import data
bouts = pd.read_csv(os.path.join(experiment.subdirs['analysis'],
'mapped_bouts.csv'),
index_col='transition_index',
dtype={
'ID': str,
'video_code': str
})
isomap = np.load(os.path.join(experiment.subdirs['analysis'],
'isomap.npy'))[:, :3]
# Compute the transition matrix for each fish
T = fish_transition_matrices(bouts)
np.save(os.path.join(transition_directory, 'transition_matrices.npy'), T)
print T.shape
# Redistribute transitions
W = generate_weights(isomap, bandwidth=40.)
WTW = redistribute_transitions(T, W)
np.save(
os.path.join(transition_directory, 'smoothed_transition_matrices.npy'),
WTW)
# Sum transitions over all fish
T_all = T.sum(axis=0)
WTW_all = redistribute_transitions(T_all, W)
np.save(os.path.join(transition_directory, 'T.npy'), T_all)
np.save(os.path.join(transition_directory, 'WTW.npy'), WTW_all)
'video_code': str
})
isomap = np.load(os.path.join(experiment.subdirs['analysis'],
'isomap.npy'))[:, :3]
weights = generate_weights(isomap)
n_fish = len(bouts['ID'].unique())
timer = Timer()
timer.start()
# Compute the average of 100 shuffled transition matrices for each fish
print 'Generating shuffled matrices...',
n_shuffles = 100
S = np.zeros((n_fish, len(isomap), len(isomap)))
for shuffle in range(n_shuffles):
S += fish_transition_matrices(bouts, shuffle=True, verbose=False)
S /= n_shuffles
S = redistribute_transitions(S, weights)
np.save(
os.path.join(transition_directory, 'shuffled_transition_matrices.npy'),
S)
print timer.convert_time(timer.lap())
S = np.load(
os.path.join(transition_directory, 'shuffled_transition_matrices.npy'))
T = np.load(
os.path.join(transition_directory, 'smoothed_transition_matrices.npy'))
# Generate permuted matrices
print 'Generating permutations...',
S_permuted = np.empty((n_permutations, S.shape[1], S.shape[2]))