def random_simple_deg_seq(sequence, brain_size=[7., 7., 7.], seed=None,
tries=10):
'''Wrapper function to get a SIMPLE (no parallel or self-loop edges) graph
that has a given degree sequence.
This graph is used conventionally as a control because it yields a random
graph that accounts for degree distribution.
Parameters:
sequence: list of int
Degree of each node to be added to the graph.
brain_size: list of 3 floats
Size of the brain to use when distributing node locations.
Added for convenience, but does not affect connectivity pattern.
seed: hashable object for random seed
Seed for the random number generator.
tries: int
Number of attempts at creating graph (function will retry if
self-loops exist.
Returns:
Networkx graph object, adjacency matrix, and random distances'''
G = nx.random_degree_sequence_graph(sequence=sequence, seed=seed,
tries=tries)
A = nx.adjacency_matrix(G)
N = len(sequence)
centroids = np.random.uniform([0, 0, 0], brain_size, (N, 3))
D = aux_tools.dist_mat(centroids)
return G, A, D
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 ER_distance(N=bc.num_brain_nodes, p=bc.num_brain_edges_directed, brain_size=[7., 7., 7.]):
"""Create an directed Erdos-Renyi random graph in which each node is
assigned a position in space, so that relative positions are represented
by a distance matrix."""
# Make graph & get adjacency matrix
G = nx.erdos_renyi_graph(N, p, directed=True)
A = nx.adjacency_matrix(G)
# Randomly distribute nodes in space & compute distance matrix
centroids = np.random.uniform([0, 0, 0], brain_size, (N, 3))
D = aux_tools.dist_mat(centroids)
return G, A.todense(), D
def ER_distance(N=426, p=.086, brain_size=[7., 7., 7.]):
"""Create an Erdos-Renyi random graph in which each node is assigned a
position in space, so that relative positions are represented by a distance
matrix."""
# Make graph & get adjacency matrix
G = nx.erdos_renyi_graph(N, p)
A = nx.adjacency_matrix(G)
# Randomly distribute nodes in space & compute distance matrix
centroids = np.random.uniform([0, 0, 0], brain_size, (N, 3))
D = aux_tools.dist_mat(centroids)
return G, A, D
def random_directed_deg_seq(in_sequence,
out_sequence,
simplify,
brain_size=[7., 7., 7.],
create_using=None,
seed=None):
'''Wrapper function to get a MULTIGRAPH (parallel or self-loop edges may
exist) given degree sequence. Chance of parallel/multiedges diminish
as graph has more nodes.
This graph is used conventionally as a control because it yields a random
graph that accounts for degree distribution.
Parameters:
-----------
in_sequence: list of int
In degree of each node to be added to the graph.
out_sequence: list of int
Out degree of each node to be added to the graph.
simplify: bool
Whether or not to remove self-loops and parallel edges. Will
change degree sequence slightly, but effect diminishes with
increasing size of graphs.
brain_size: list of 3 floats
Size of the brain to use when distributing node locations.
create_using: graph, optional
Return graph of this type. The instance will be cleared.
seed: hashable object for random seed
Seed for the random number generator.
Returns:
--------
Networkx graph object, adjacency matrix, and random distances'''
# Create configuration model using specified properties
G = nx.directed_configuration_model(in_sequence,
out_sequence,
create_using=create_using,
seed=seed)
if simplify:
G = nx.DiGraph(G) # Remove parallel edges
G.remove_edges_from(G.selfloop_edges()) # Remove self loops
# Get adjacency info and create random spatial locations
A = nx.adjacency_matrix(G)
N = len(in_sequence)
centroids = np.random.uniform([0, 0, 0], brain_size, (N, 3))
D = aux_tools.dist_mat(centroids)
return G, A, D
def random_directed_deg_seq(in_sequence, out_sequence, simplify,
brain_size=[7., 7., 7.], create_using=None,
seed=None):
'''Wrapper function to get a MULTIGRAPH (parallel or self-loop edges may
exist) given degree sequence. Chance of parallel/multiedges diminish
as graph has more nodes.
This graph is used conventionally as a control because it yields a random
graph that accounts for degree distribution.
Parameters:
-----------
in_sequence: list of int
In degree of each node to be added to the graph.
out_sequence: list of int
Out degree of each node to be added to the graph.
simplify: bool
Whether or not to remove self-loops and parallel edges. Will
change degree sequence slightly, but effect diminishes with
increasing size of graphs.
brain_size: list of 3 floats
Size of the brain to use when distributing node locations.
create_using: graph, optional
Return graph of this type. The instance will be cleared.
seed: hashable object for random seed
Seed for the random number generator.
Returns:
--------
Networkx graph object, adjacency matrix, and random distances'''
# Create configuration model using specified properties
G = nx.directed_configuration_model(in_sequence, out_sequence,
create_using=create_using, seed=seed)
if simplify:
G = nx.DiGraph(G) # Remove parallel edges
G.remove_edges_from(G.selfloop_edges()) # Remove self loops
# Get adjacency info and create random spatial locations
A = nx.adjacency_matrix(G)
N = len(in_sequence)
centroids = np.random.uniform([0, 0, 0], brain_size, (N, 3))
D = aux_tools.dist_mat(centroids)
return G, A, D
def biophysical_reverse(N=bc.num_brain_nodes, N_edges=bc.num_brain_edges_directed,
L=np.inf, gamma=1.75, brain_size=[7., 7., 7.]):
"""Create a biophysically inspired graph. Connection probabilities depend
on distance & degree.
Args:
N: how many nodes
N_edges: how many edges
L: length constant
gamma: power to raise degree to
brain_size: size of space in which nodes are randomly placed
Returns:
Networkx graph object, adjacency matrix, distance matrix"""
# Pick node positions & calculate distance matrix
centroids = np.random.uniform([0, 0, 0], brain_size, (N, 3))
# Calculate distance matrix and distance decay matrix
D = aux_tools.dist_mat(centroids)
D_decay = np.exp(-D / L)
# Initialize diagonal adjacency matrix
A = np.eye(N, dtype=float)
# Make graph object
G = nx.DiGraph()
G.add_nodes_from(np.arange(N))
# Randomly add edges
edge_ctr = 0
while edge_ctr < N_edges:
# Update degree list & degree-related probability vector
indegs = A.sum(0).astype(float)
outdegs = A.sum(1).astype(float)
degs = indegs + outdegs
degs_prob = degs.copy()
# Pick random node to draw edge to
to_idx = np.random.randint(low=0, high=N)
# Skip this node if already fully connected
if outdegs[to_idx] == N:
continue
# Find unavailable cxns and set their probability to zero
unavail_mask = A[:, to_idx] > 0
degs_prob[unavail_mask] = 0
# Set self cxn probability to zero
degs_prob[to_idx] = 0
# Calculate cxn probabilities from degree & distance
P = (degs_prob ** gamma) * D_decay[:, to_idx]
# On the off changes that P == 0, skip
if P.sum() == 0:
continue
# Otherwise keep going on
P /= float(P.sum()) # Normalize probabilities to sum to 1
# Sample node from distribution
from_idx = np.random.choice(np.arange(N), p=P)
# Add edge to graph
if A[from_idx, to_idx] == 0:
G.add_edge(from_idx, to_idx, {'d': D[from_idx, to_idx]})
# Add edge to adjacency matrix
A[from_idx, to_idx] += 1
# Increment edge counter
edge_ctr += 1
# Set diagonals to zero
np.fill_diagonal(A, 0)
return G, A, D
def source_growth_total_degree(N=bc.num_brain_nodes, N_edges=bc.num_brain_edges_directed,
L=np.inf, gamma=1., brain_size=(7., 7., 7.)):
"""Create a graph based in which source growth depends on total, not out-degree.
Source selection probability is proportional to total degree^gamma, and
target selection is either random or dependent on distance (through length
constant L).
Parameters:
-----------
N: how many nodes
N_edges: how many edges
L: length constant
gamma: power to raise degree to
brain_size: size of space in which nodes are randomly placed
Returns:
--------
G, A, D: Networkx graph object, adjacency matrix, distance matrix"""
# Pick node positions & calculate distance matrix
centroids = np.random.uniform([0, 0, 0], brain_size, (N, 3))
# Calculate distance matrix and distance decay matrix
D = aux_tools.dist_mat(centroids)
D_decay = np.exp(-D / L)
# Initialize diagonal adjacency matrix
A = np.eye(N, dtype=float)
# Make graph object
G = nx.DiGraph()
G.add_nodes_from(np.arange(N))
G.centroids = centroids
# Randomly add edges
edge_ctr = 0
while edge_ctr < N_edges:
# Update degree list & degree-related probability vector
outdegs = A.sum(1).astype(float)
indegs = A.sum(0).astype(float)
total_degs = outdegs + indegs
total_degs_prob = total_degs.copy()
# Calculate source node probability
P = total_degs_prob ** gamma
# On the off chance that P == 0, skip
if P.sum() == 0:
continue
# Otherwise keep going on
P /= float(P.sum()) # Normalize probabilities to sum to 1
# Sample node from distribution
src_idx = np.random.choice(np.arange(N), p=P)
D_src = D_decay[src_idx, :]
# Find unavailable cxns and set their probability to zero
unavail_mask = A[src_idx, :] > 0
D_src[unavail_mask] = 0
# Set self-connection probability to 0
D_src[src_idx] = 0
D_src /= float(D_src.sum())
# Pick random node to draw edge to
targ_idx = np.random.choice(np.arange(N), p=D_src)
# Skip this node if already fully connected
if outdegs[src_idx] == N:
continue
# Add edge to graph
if A[src_idx, targ_idx] == 0:
G.add_edge(src_idx, targ_idx, {'d': D[src_idx, targ_idx]})
# Add edge to adjacency matrix
A[src_idx, targ_idx] += 1
# Increment edge counter
edge_ctr += 1
# Set diagonals to zero
np.fill_diagonal(A, 0)
return G, A.astype(int), D
def pure_geometric(N, N_edges, L, brain_size=[7., 7., 7.]):
"""
Create a pure geometric model in which nodes are embedded in 3-D space and connect preferentially to
physically nearby nodes.
:param N: number of nodes
:param N_edges: number of edges
:param L: length constant
:param brain_size: volume in which to distribute nodes in
:return: graph, adjacency matrix, distance matrix
"""
# randomly distribute nodes in space & compute distance matrix
centroids = np.random.uniform([0, 0, 0], brain_size, (N, 3))
D = aux_tools.dist_mat(centroids)
# make an adjacency matrix and graph
A = np.zeros((N, N), dtype=float)
G = nx.Graph()
G.add_nodes_from(range(N))
# initialize some helper variables
node_idxs = np.arange(N, dtype=int)
degs = np.zeros((N, ), dtype=int)
fully_connected = np.zeros((N, ), dtype=bool)
# add edges
edge_ctr = 0
while edge_ctr < N_edges:
# pick source randomly
src = np.random.choice(node_idxs[fully_connected == False])
# get distance-dependent probabilities to all available targets
d_probs = np.exp(-D[src, :]/L) # unnormalized
# compute which nodes are available to connect to
unavailable_targs = fully_connected.copy() # no fully connected nodes
unavailable_targs[src] = True # can't connect to self
unavailable_targs[A[src, :] == 1] = True # can't connect to already connected nodes
# set probability to zero if node unavailable
d_probs[unavailable_targs] = 0.
# normalize
d_probs /= d_probs.sum()
# randomly pick a target
targ = np.random.choice(node_idxs, p=d_probs)
# add edge to graph and adjacency matrix
G.add_edge(src, targ)
A[src, targ] = 1
A[targ, src] = 1
# update degrees
degs[src] += 1
degs[targ] += 1
# update available_from
if degs[src] == N:
fully_connected[src] = False
if degs[targ] == N:
fully_connected[targ] = False
edge_ctr += 1
return G, A, D
def biophysical_reverse_outdegree(N=bc.num_brain_nodes, N_edges=bc.num_brain_edges_directed,
L=np.inf, gamma=1., brain_size=(7., 7., 7.)):
"""Create a biophysically inspired graph. Source probability depends on
outdegree. Target probability depends on distance.
Args:
N: how many nodes
N_edges: how many edges
L: length constant
gamma: power to raise degree to
brain_size: size of space in which nodes are randomly placed
Returns:
Networkx graph object, adjacency matrix, distance matrix"""
# Pick node positions & calculate distance matrix
if brain_size == 'brain':
centroids_dict = aux_random_graphs.get_coords()
labels = centroids_dict.keys()
centroids = np.zeros((N, 3))
for ctr, label in enumerate(labels):
centroids[ctr, :] = centroids_dict[label] / 10.0
else:
centroids = np.random.uniform([0, 0, 0], brain_size, (N, 3))
# Calculate distance matrix and distance decay matrix
D = aux_tools.dist_mat(centroids)
D_decay = np.exp(-D / L)
# Initialize diagonal adjacency matrix
A = np.eye(N, dtype=float)
# Make graph object
G = nx.DiGraph()
G.add_nodes_from(np.arange(N))
G.centroids = centroids
# Randomly add edges
edge_ctr = 0
while edge_ctr < N_edges:
# Update degree list & degree-related probability vector
outdegs = A.sum(1).astype(float)
outdegs_prob = outdegs.copy()
# Calculate source node probability
P = outdegs_prob ** gamma
# On the off chance that P == 0, skip
if P.sum() == 0:
continue
# Otherwise keep going on
P /= float(P.sum()) # Normalize probabilities to sum to 1
# Sample node from distribution
src_idx = np.random.choice(np.arange(N), p=P)
D_src = D_decay[src_idx, :]
# Find unavailable cxns and set their probability to zero
unavail_mask = A[src_idx, :] > 0
D_src[unavail_mask] = 0
# Set self-connection probability to 0
D_src[src_idx] = 0
D_src /= float(D_src.sum())
# Pick random node to draw edge to
targ_idx = np.random.choice(np.arange(N), p=D_src)
# Skip this node if already fully connected
if outdegs[src_idx] == N:
continue
# Add edge to graph
if A[src_idx, targ_idx] == 0:
G.add_edge(src_idx, targ_idx, {'d': D[src_idx, targ_idx]})
# Add edge to adjacency matrix
A[src_idx, targ_idx] += 1
# Increment edge counter
edge_ctr += 1
# Set diagonals to zero
np.fill_diagonal(A, 0)
return G, A, D
def biophysical_reverse(N=bc.num_brain_nodes,
N_edges=bc.num_brain_edges_directed,
L=np.inf,
gamma=1.75,
brain_size=[7., 7., 7.]):
"""Create a biophysically inspired graph. Connection probabilities depend
on distance & degree.
Args:
N: how many nodes
N_edges: how many edges
L: length constant
gamma: power to raise degree to
brain_size: size of space in which nodes are randomly placed
Returns:
Networkx graph object, adjacency matrix, distance matrix"""
# Pick node positions & calculate distance matrix
centroids = np.random.uniform([0, 0, 0], brain_size, (N, 3))
# Calculate distance matrix and distance decay matrix
D = aux_tools.dist_mat(centroids)
D_decay = np.exp(-D / L)
# Initialize diagonal adjacency matrix
A = np.eye(N, dtype=float)
# Make graph object
G = nx.DiGraph()
G.add_nodes_from(np.arange(N))
# Randomly add edges
edge_ctr = 0
while edge_ctr < N_edges:
# Update degree list & degree-related probability vector
indegs = A.sum(0).astype(float)
outdegs = A.sum(1).astype(float)
degs = indegs + outdegs
degs_prob = degs.copy()
# Pick random node to draw edge to
to_idx = np.random.randint(low=0, high=N)
# Skip this node if already fully connected
if outdegs[to_idx] == N:
continue
# Find unavailable cxns and set their probability to zero
unavail_mask = A[:, to_idx] > 0
degs_prob[unavail_mask] = 0
# Set self cxn probability to zero
degs_prob[to_idx] = 0
# Calculate cxn probabilities from degree & distance
P = (degs_prob**gamma) * D_decay[:, to_idx]
# On the off changes that P == 0, skip
if P.sum() == 0:
continue
# Otherwise keep going on
P /= float(P.sum()) # Normalize probabilities to sum to 1
# Sample node from distribution
from_idx = np.random.choice(np.arange(N), p=P)
# Add edge to graph
if A[from_idx, to_idx] == 0:
G.add_edge(from_idx, to_idx, {'d': D[from_idx, to_idx]})
# Add edge to adjacency matrix
A[from_idx, to_idx] += 1
# Increment edge counter
edge_ctr += 1
# Set diagonals to zero
np.fill_diagonal(A, 0)
return G, A, D
def source_growth_total_degree(N=bc.num_brain_nodes,
N_edges=bc.num_brain_edges_directed,
L=np.inf,
gamma=1.,
brain_size=(7., 7., 7.)):
"""Create a graph based in which source growth depends on total, not out-degree.
Source selection probability is proportional to total degree^gamma, and
target selection is either random or dependent on distance (through length
constant L).
Parameters:
-----------
N: how many nodes
N_edges: how many edges
L: length constant
gamma: power to raise degree to
brain_size: size of space in which nodes are randomly placed
Returns:
--------
G, A, D: Networkx graph object, adjacency matrix, distance matrix"""
# Pick node positions & calculate distance matrix
centroids = np.random.uniform([0, 0, 0], brain_size, (N, 3))
# Calculate distance matrix and distance decay matrix
D = aux_tools.dist_mat(centroids)
D_decay = np.exp(-D / L)
# Initialize diagonal adjacency matrix
A = np.eye(N, dtype=float)
# Make graph object
G = nx.DiGraph()
G.add_nodes_from(np.arange(N))
G.centroids = centroids
# Randomly add edges
edge_ctr = 0
while edge_ctr < N_edges:
# Update degree list & degree-related probability vector
outdegs = A.sum(1).astype(float)
indegs = A.sum(0).astype(float)
total_degs = outdegs + indegs
total_degs_prob = total_degs.copy()
# Calculate source node probability
P = total_degs_prob**gamma
# On the off chance that P == 0, skip
if P.sum() == 0:
continue
# Otherwise keep going on
P /= float(P.sum()) # Normalize probabilities to sum to 1
# Sample node from distribution
src_idx = np.random.choice(np.arange(N), p=P)
D_src = D_decay[src_idx, :]
# Find unavailable cxns and set their probability to zero
unavail_mask = A[src_idx, :] > 0
D_src[unavail_mask] = 0
# Set self-connection probability to 0
D_src[src_idx] = 0
D_src /= float(D_src.sum())
# Pick random node to draw edge to
targ_idx = np.random.choice(np.arange(N), p=D_src)
# Skip this node if already fully connected
if outdegs[src_idx] == N:
continue
# Add edge to graph
if A[src_idx, targ_idx] == 0:
G.add_edge(src_idx, targ_idx, {'d': D[src_idx, targ_idx]})
# Add edge to adjacency matrix
A[src_idx, targ_idx] += 1
# Increment edge counter
edge_ctr += 1
# Set diagonals to zero
np.fill_diagonal(A, 0)
return G, A.astype(int), D
