def connect(self, projection):
"""Connect-up a Projection."""
if projection.pre.size == projection.post.size:
N = projection.post.size
local = projection.post._mask_local
if isinstance(self.weights, basestring) or isinstance(
self.delays, basestring):
raise Exception(
'Expression for weights or delays is not supported for OneToOneConnector !'
)
weights_generator = WeightGenerator(self.weights, local,
projection, self.safe)
delays_generator = DelayGenerator(self.delays, local, self.safe)
weights = weights_generator.get(N)
delays = delays_generator.get(N)
self.progressbar(len(projection.post.local_cells))
count = 0
create = numpy.arange(0, N)[local]
sources = projection.pre.all_cells[create]
for tgt, src, w, d in zip(projection.post.local_cells, sources,
weights, delays):
# the float is in case the values are of type numpy.float64, which NEST chokes on
projection.connection_manager.connect(src, [tgt], [float(w)],
[float(d)])
self.progression(count)
count += 1
else:
raise errors.InvalidDimensionsError(
"OneToOneConnector does not support presynaptic and postsynaptic Populations of different sizes."
)
def connect(self, projection):
"""Connect-up a Projection."""
if projection.pre.dim == projection.post.dim:
N = projection.post.size
local = projection.post._mask_local.flatten()
if isinstance(self.weights, basestring) or isinstance(
self.delays, basestring):
raise Exception(
'Expression for weights or delays is not supported for OneToOneConnector !'
)
weights_generator = WeightGenerator(self.weights, local,
projection, self.safe)
delays_generator = DelayGenerator(self.delays, local, self.safe)
weights = weights_generator.get(N)
delays = delays_generator.get(N)
self.progressbar(len(projection.post.local_cells))
count = 0
for tgt, w, d in zip(projection.post.local_cells, weights, delays):
src = projection.pre.index(projection.post.id_to_index(tgt))
# the float is in case the values are of type numpy.float64, which NEST chokes on
projection._divergent_connect(src, [tgt], float(w), float(d))
self.progression(count)
count += 1
else:
raise errors.InvalidDimensionsError(
"OneToOneConnector does not support presynaptic and postsynaptic Populations of different sizes."
)
def update(self, **parameters):
"""
Update the contents of the parameter space according to the
`(key, value)` pairs in ``**parameters``. All values will be turned into
lazy arrays.
If the :class:`ParameterSpace` has a schema, the keys and the data types
of the values will be checked against the schema.
"""
if self.schema:
for name, value in parameters.items():
try:
expected_dtype = self.schema[name]
except KeyError:
if self.component:
model_name = self.component.__name__
else:
model_name = 'unknown'
raise errors.NonExistentParameterError(
name,
model_name,
valid_parameter_names=self.schema.keys())
if issubclass(expected_dtype, ArrayParameter) and isinstance(
value, collections.Sized):
if len(value) == 0:
value = ArrayParameter([])
elif not isinstance(
value[0], ArrayParameter
): # may be a more generic way to do it, but for now this special-casing seems like the most robust approach
if isinstance(
value[0],
collections.Sized): # e.g. list of tuples
value = type(value)(
[ArrayParameter(x) for x in value])
else:
value = ArrayParameter(value)
try:
self._parameters[name] = LazyArray(value,
shape=self._shape,
dtype=expected_dtype)
except (TypeError, errors.InvalidParameterValueError):
raise errors.InvalidParameterValueError(
"For parameter %s expected %s, got %s" %
(name, expected_dtype, type(value)))
except ValueError as err:
raise errors.InvalidDimensionsError(
err
) # maybe put the more specific error classes into lazyarray
else:
for name, value in parameters.items():
self._parameters[name] = LazyArray(value, shape=self._shape)
