def set_weights(self, weight=None, elist=None, distribution=None,
parameters=None, noise_scale=None):
'''
Set the synaptic weights.
..todo ::
take elist into account in Connections.weights
Parameters
----------
weight : float or class:`numpy.array`, optional (default: None)
Value or list of the weights (for user defined weights).
elist : class:`numpy.array`, optional (default: None)
List of the edges (for user defined weights).
distribution : class:`string`, optional (default: None)
Type of distribution (choose among "constant", "uniform",
"gaussian", "lognormal", "lin_corr", "log_corr").
parameters : dict, optional (default: {})
Dictionary containing the properties of the weight distribution.
noise_scale : class:`int`, optional (default: None)
Scale of the multiplicative Gaussian noise that should be applied
on the weights.
'''
if isinstance(weight, float):
size = self.edge_nb() if elist is None else len(elist)
weight = np.repeat(weight, size)
elif not hasattr(weight, "__len__") and weight is not None:
raise AttributeError('''Invalid `weight` value: must be either
float, array-like or None.''')
if distribution is None:
distribution = self._w["distribution"]
if parameters is None:
parameters = self._w
Connections.weights(self, elist=elist, wlist=weight,
distribution=distribution, parameters=parameters,
noise_scale=noise_scale)
def set_weights(self, elist=None, wlist=None, distrib=None,
distrib_prop=None, correl=None, noise_scale=None):
'''
Set the synaptic weights.
@todo: take elist into account in Connections.weights
Parameters
----------
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: None)
Type of distribution (choose among "constant", "uniform",
"gaussian", "lognormal", "lin_corr", "log_corr").
distrib_prop : dict, optional (default: {})
Dictoinary containing the properties of the weight distribution.
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.
'''
if distrib is None:
distrib = self._w["distrib"]
if distrib_prop is None:
distrib_prop = (self._w["distrib_prop"] if "distrib_prop" in
self._w.keys() else {})
if correl is None:
correl = self._w["correl"] if "correl" in self._w.keys() else None
Connections.weights(self, elist=elist, wlist=wlist, distrib=distrib,
correl=correl, distrib_prop=distrib_prop, noise_scale=noise_scale)
