def convertWeight(self, w, synapse_type):
if isinstance(w, list):
w = numpy.array(w)
if isinstance(w, RandomDistribution):
weight = RandomDistribution(w.name, w.parameters, w.rng)
if weight.name == "uniform":
(w_min, w_max) = weight.parameters
weight.parameters = (w_min, w_max)
elif weight.name == "normal":
(w_mean, w_std) = weight.parameters
weight.parameters = (w_mean, w_std)
else:
weight = w
if synapse_type == 'inhibitory':
if isinstance(weight, RandomDistribution):
if weight.name == "uniform":
myLogger.info(weight.name + ' ' + str(weight.parameters))
(w_min, w_max) = weight.parameters
if w_min >= 0 and w_max >= 0:
weight.parameters = (-w_max, -w_min)
elif weight.name == "normal":
(w_mean, w_std) = weight.parameters
if w_mean > 0:
weight.parameters = (-w_mean, w_std)
else:
myLogger.warn(
"No conversion of the inhibitory weights for this particular distribution"
)
# elif weight > 0:
#weight *= -1
# weight = weight
return weight
