def __init__(self,
projection,
weights=0.0,
delays=None,
allow_self_connections=True,
space=Space(),
safe=True):
Connector.__init__(self, weights, delays, space, safe)
if isinstance(projection.rng, random.NativeRNG):
raise Exception("Use of NativeRNG not implemented.")
else:
self.rng = projection.rng
self.N = projection.pre.size
idx = numpy.arange(self.N * rank(), self.N * (rank() + 1))
self.M = num_processes() * self.N
self.local = numpy.ones(self.N, bool)
self.local_long = numpy.zeros(self.M, bool)
self.local_long[idx] = True
self.weights_generator = WeightGenerator(weights, self.local_long,
projection, safe)
self.delays_generator = DelayGenerator(delays, self.local_long, safe)
self.probas_generator = ProbaGenerator(
random.RandomDistribution('uniform', (0, 1), rng=self.rng),
self.local_long)
self.distance_matrix = DistanceMatrix(projection.pre.positions,
self.space, self.local)
self.projection = projection
self.candidates = projection.pre.all_cells
self.allow_self_connections = allow_self_connections
def connect(self, projection):
"""Connect-up a Projection."""
if self.delays is None:
self.delays = projection._simulator.state.min_delay
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,
kernel=projection._simulator.state,
safe=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]
homogeneous = numpy.isscalar(delays_generator.source)
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._divergent_connect(src, [tgt], float(w), float(d),
homogeneous)
self.progression(count, projection._simulator.state.mpi_rank)
count += 1
else:
raise errors.InvalidDimensionsError(
"OneToOneConnector does not support presynaptic and postsynaptic Populations of different sizes."
)
