def weights(graph, elist=None, wlist=None, distribution="constant",
parameters={}, noise_scale=None):
'''
Compute the weights of the graph's edges.
@todo: take elist into account
Parameters
----------
graph : class:`~nngt.Graph` or subclass
Graph the nodes belong to.
elist : class:`numpy.array`, optional (default: None)
List of the edges (for user defined weights).
wlist : class:`numpy.array`, optional (default: None)
List of the weights (for user defined weights).
distribution : class:`string`, optional (default: "constant")
Type of distribution (choose among "constant", "uniform",
"lognormal", "gaussian", "user_def", "lin_corr", "log_corr").
parameters : class:`dict`, optional (default: {})
Dictionary containing the distribution parameters.
noise_scale : class:`int`, optional (default: None)
Scale of the multiplicative Gaussian noise that should be applied
on the weights.
Returns
-------
new_weights : class:`scipy.sparse.lil_matrix`
A sparse matrix containing *ONLY* the newly-computed weights.
'''
parameters["btype"] = parameters.get("btype", "edge")
parameters["use_weights"] = parameters.get("use_weights", False)
#~ elist = np.array(elist) if elist is not None else elist
elist = None
if wlist is not None:
num_edges = graph.edge_nb() if elist is None else elist.shape[0]
if len(wlist) != num_edges:
raise InvalidArgument(
'''`wlist` must have one entry per edge. For graph {},
there are {} edges while {} values where provided'''.format(
graph.name, num_edges, len(wlist)))
else:
wlist = eprop_distribution(graph, distribution, elist=elist,
**parameters)
# add to the graph container
bwlist = (np.max(wlist) - wlist if np.any(wlist)
else np.repeat(0, len(wlist)))
graph.set_edge_attribute(
WEIGHT, value_type="double", values=wlist, edges=elist)
graph.set_edge_attribute(
BWEIGHT, value_type="double", values=bwlist, edges=elist)
return wlist
def weights(cls, graph, elist=None, wlist=None, distrib="constant",
distrib_prop={}, correl=None, noise_scale=None):
'''
Compute the weights of the graph's edges.
@todo: take elist into account
Parameters
----------
graph : class:`~nngt.Graph` or subclass
Graph the nodes belong to.
elist : class:`numpy.array`, optional (default: None)
List of the edges (for user defined weights).
wlist : class:`numpy.array`, optional (default: None)
List of the weights (for user defined weights).
distrib : class:`string`, optional (default: "constant")
Type of distribution (choose among "constant", "uniform",
"lognormal", "gaussian", "user_def", "lin_corr", "log_corr").
distrib_prop : class:`dict`, optional (default: {})
Dictionary containing the distribution parameters.
correl : class:`string`, optional (default: None)
Property to which the weights should be correlated.
noise_scale : class:`int`, optional (default: None)
Scale of the multiplicative Gaussian noise that should be applied
on the weights.
Returns
-------
new_weights : class:`scipy.sparse.lil_matrix`
A sparse matrix containing *ONLY* the newly-computed weights.
'''
corr = correl
if issubclass(correl.__class__, str):
if correl == "betweenness":
corr = graph.get_betweenness(False)[1]
else:
corre = graph[correl]
if wlist is not None:
num_edges = graph.edge_nb() if elist is None else max(elist.shape)
if len(wlist) != num_edges:
raise InvalidArgument("`dlist` must have one entry per edge.")
else:
wlist = eprop_distribution(graph, distrib, elist=elist,
correl_attribute=corr, **distrib_prop)
# add to the graph container
bwlist = wlist.max() - wlist if wlist.any() else []
graph.set_edge_attribute(WEIGHT, value_type="double", values=wlist)
graph.set_edge_attribute(BWEIGHT, value_type="double", values=bwlist)
return wlist
def delays(graph, dlist=None, elist=None, distrib="constant",
distrib_prop={}, correl=None, noise_scale=None):
'''
Compute the delays of the neuronal connections.
Parameters
----------
graph : class:`~nngt.Graph` or subclass
Graph the nodes belong to.
dlist : class:`numpy.array`, optional (default: None)
List of user-defined delays).
elist : class:`numpy.array`, optional (default: None)
List of the edges which value should be updated.
distrib : class:`string`, optional (default: "constant")
Type of distribution (choose among "constant", "uniform",
"lognormal", "gaussian", "user_def", "lin_corr", "log_corr").
distrib_prop : class:`dict`, optional (default: {})
Dictionary containing the distribution parameters.
correl : class:`string`, optional (default: None)
Property to which the weights should be correlated.
noise_scale : class:`int`, optional (default: None)
Scale of the multiplicative Gaussian noise that should be applied
on the weights.
Returns
-------
new_delays : class:`scipy.sparse.lil_matrix`
A sparse matrix containing *ONLY* the newly-computed weights.
'''
corr = correl
if issubclass(correl.__class__, str):
if correl == "betweenness":
corr = graph.get_betweenness(False)[1]
else:
corre = graph[correl]
if dlist is not None:
num_edges = graph.edge_nb() if elist is None else len(elist)
if len(dlist) != num_edges:
raise InvalidArgument("`dlist` must have one entry per edge.")
else:
dlist = eprop_distribution(graph, distrib, elist=elist,
correl_attribute=corr, **distrib_prop)
# add to the graph container
graph.set_edge_attribute(DELAY, value_type="double", values=dlist)
return dlist
def delays(graph, dlist=None, elist=None, distribution="constant",
parameters={}, noise_scale=None):
'''
Compute the delays of the neuronal connections.
Parameters
----------
graph : class:`~nngt.Graph` or subclass
Graph the nodes belong to.
dlist : class:`numpy.array`, optional (default: None)
List of user-defined delays).
elist : class:`numpy.array`, optional (default: None)
List of the edges which value should be updated.
distribution : class:`string`, optional (default: "constant")
Type of distribution (choose among "constant", "uniform",
"lognormal", "gaussian", "user_def", "lin_corr", "log_corr").
parameters : class:`dict`, optional (default: {})
Dictionary containing the distribution parameters.
noise_scale : class:`int`, optional (default: None)
Scale of the multiplicative Gaussian noise that should be applied
on the weights.
Returns
-------
new_delays : class:`scipy.sparse.lil_matrix`
A sparse matrix containing *ONLY* the newly-computed weights.
'''
parameters["btype"] = parameters.get("btype", "edge")
parameters["use_weights"] = parameters.get("use_weights", False)
elist = np.array(elist) if elist is not None else elist
if dlist is not None:
num_edges = graph.edge_nb() if elist is None else elist.shape[0]
if len(dlist) != num_edges:
raise InvalidArgument("`dlist` must have one entry per edge.")
else:
dlist = eprop_distribution(graph, distribution, elist=elist,
**parameters)
# add to the graph container
graph.set_edge_attribute(
DELAY, value_type="double", values=dlist, edges=elist)
return dlist
