def cortex_distance_matrix(lm_dir=LINEAR_MODEL_DIRECTORY,
cent_dir=STRUCTURE_DIRECTORY, in_mm=True):
"""Compute distance matrix from centroid data.
Args:
lm_dir: Directory containing linear model data
cent_dir: Directory containing centroid data
in_mm: Set to true to return dist matrix in mm instead of 100um units
Returns:
distance matrix, centroid matrix"""
# Get labels
_, _, labels = aux.load_W_and_P(data_dir=lm_dir)
# Get mask of areas in cortex
mask = aux.mask_specific_structures(labels, ['CTX'])
# Get new labels
labels = list(np.array(labels)[mask])
# Load centroids
centroids = aux.load_centroids(labels, data_dir=cent_dir, in_mm=in_mm)
# Compute distance matrix
dist_mat = aux_tools.dist_mat(centroids)
return dist_mat, labels
def distance_matrix(lm_dir=DATA_DIRECTORY, cent_dir=DATA_DIRECTORY, in_mm=True):
"""Compute distance matrix from centroid data.
Args:
lm_dir: Directory containing linear model data
cent_dir: Directory containing centroid data
in_mm: Set to true to return dist matrix in mm instead of 100um units
Returns:
distance matrix, centroid matrix"""
# Get labels
_, _, labels = aux.load_W_and_P(data_dir=lm_dir)
# Load centroids
centroids = aux.load_centroids(labels, data_dir=cent_dir, in_mm=in_mm)
# Compute distance matrix
dist_mat = aux.dist_mat(centroids)
return dist_mat, centroids
def distance_matrix(cent_dir=DATA_DIRECTORY, in_mm=True):
"""Compute distance matrix from centroid data.
Args:
lm_dir: Directory containing linear model data
cent_dir: Directory containing centroid data
in_mm: Set to true to return dist matrix in mm instead of 100um units
Returns:
distance matrix, centroid matrix"""
# Get labels
_, _, labels = aux.load_W_and_P()
# Load centroids
centroids = aux.load_centroids(labels, data_dir=cent_dir, in_mm=in_mm)
# Compute distance matrix
dist_mat = aux.dist_mat(centroids)
return dist_mat, centroids
def weighted_undirected(p_th=.01, w_th=0, data_dir=DATA_DIRECTORY):
"""Load brain as binary undirected graph.
Returns:
NetworkX graph, weight matrix, row labels & column labels"""
# Load weights & p-values
W, P, labels = aux.load_W_and_P(data_dir=data_dir)
# Threshold weights via weights & p-values
W[(W < w_th)] = 0.
W[(P > p_th)] = 0.
# Symmetrize W by summing reciprocal weights
W = W + W.T
# Set self-weights to zero
np.fill_diagonal(W, 0.)
# Create graph from weight matrix
G = nx.from_numpy_matrix(W)
return G, W, labels
def binary_directed(p_th=.01, w_th=0, data_dir=DATA_DIRECTORY):
"""Load brain as binary directed graph.
Returns:
NetworkX graph, adjacency matrix, row labels & column labels"""
# Load weights & p-values
W, P, labels = aux.load_W_and_P(data_dir)
# Threshold weights via weights & p-values
W[(W < w_th)] = 0.
W[(P > p_th)] = 0.
# Set self-weights to zero
np.fill_diagonal(W, 0.)
# Create adjacency matrix
A = (W > 0).astype(int)
# Create graph from adjacency matrix
G = nx.from_numpy_matrix(A, create_using=nx.DiGraph())
return G, A, labels
def weighted_undirected(p_th=.01, w_th=0):
"""Load brain as binary undirected graph.
Returns:
NetworkX graph, weight matrix, row labels & column labels"""
# Load weights & p-values
W, P, labels = aux.load_W_and_P()
# Threshold weights via weights & p-values
W[(W < w_th)] = 0.
W[(P > p_th)] = 0.
# Symmetrize W by summing reciprocal weights
W = W + W.T
# Set self-weights to zero
np.fill_diagonal(W, 0.)
# Create graph from weight matrix
G = nx.from_numpy_matrix(W)
return G, W, labels
def binary_directed(p_th=.01, w_th=0):
"""Load brain as binary directed graph.
Returns:
NetworkX graph, adjacency matrix, row labels & column labels"""
# Load weights & p-values
W, P, labels = aux.load_W_and_P()
# Threshold weights via weights & p-values
W[(W < w_th)] = 0.
W[(P > p_th)] = 0.
# Set self-weights to zero
np.fill_diagonal(W, 0.)
# Create adjacency matrix
A = (W > 0).astype(int)
# Create graph from adjacency matrix
G = nx.from_numpy_matrix(A, create_using=nx.DiGraph())
return G, A, labels
def binary_undirected(p_th=.01, w_th=0):
"""Load brain as binary undirected graph.
Returns:
NetworkX graph, adjacency matrix, row labels & column labels"""
# Load weights & p-values
W, P, labels = aux.load_W_and_P()
# Threshold weights via weights & p-values
W[(W < w_th)] = 0.
W[(P > p_th)] = 0.
# Symmetrize W by summing reciprocal weights
W = W + W.T
# Set self-weights to zero
np.fill_diagonal(W, 0.)
# Create adjacency matrix
A = (W > 0).astype(int)
# Create graph from adjacency matrix
G = nx.from_numpy_matrix(A)
return G, A, labels
