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.")
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."
)
class FastProbabilisticConnector(Connector):
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 _probabilistic_connect(self,
tgt,
p,
n_connections=None,
rewiring=None):
"""
Connect-up a Projection with connection probability p, where p may be either
a float 0<=p<=1, or a dict containing a float array for each pre-synaptic
cell, the array containing the connection probabilities for all the local
targets of that pre-synaptic cell.
"""
if numpy.isscalar(p) and p == 1:
precreate = numpy.arange(self.N)
else:
rarr = self.probas_generator.get(self.M)
if not core.is_listlike(rarr) and numpy.isscalar(
rarr): # if N=1, rarr will be a single number
rarr = numpy.array([rarr])
precreate = numpy.where(rarr < p)[0]
self.distance_matrix.set_source(tgt.position)
if not self.allow_self_connections and self.projection.pre == self.projection.post:
idx_tgt = numpy.where(self.candidates == tgt)
if len(idx_tgt) > 0:
i = numpy.where(precreate == idx_tgt[0])
if len(i) > 0:
precreate = numpy.delete(precreate, i[0])
if (rewiring is not None) and (rewiring > 0):
if not self.allow_self_connections and self.projection.pre == self.projection.post:
i = numpy.where(self.candidates == tgt)[0]
idx = numpy.delete(self.candidates, i)
rarr = self.probas_generator.get(self.M)[precreate]
rewired = numpy.where(rarr < rewiring)[0]
N = len(rewired)
if N > 0:
new_idx = (len(idx) - 1) * self.probas_generator.get(
self.M)[precreate]
precreate[rewired] = idx[new_idx.astype(int)]
if (n_connections is not None) and (len(precreate) > 0):
create = numpy.array([], int)
while len(
create
) < n_connections: # if the number of requested cells is larger than the size of the
# presynaptic population, we allow multiple connections for a given cell
create = numpy.concatenate(
(create, self.projection.rng.permutation(precreate)))
create = create[:n_connections]
else:
create = precreate
sources = self.candidates[create]
weights = self.weights_generator.get(self.M, self.distance_matrix,
create)
delays = self.delays_generator.get(self.M, self.distance_matrix,
create)
if len(sources) > 0:
self.projection.connection_manager.convergent_connect(
sources.tolist(), tgt, weights, delays)
class FastProbabilisticConnector(Connector):
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
if self.delays is None:
self.delays = projection._simulator.state.min_delay
self.N = projection.pre.size
mpi_rank = projection._simulator.state.mpi_rank
num_processes = projection._simulator.state.num_processes
idx = numpy.arange(self.N*mpi_rank, self.N*(mpi_rank+1), dtype=numpy.int)
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(self.delays, self.local_long, kernel=projection._simulator.state, safe=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 _probabilistic_connect(self, tgt, p, n_connections=None, rewiring=None):
"""
Connect-up a Projection with connection probability p, where p may be either
a float 0<=p<=1, or a dict containing a float array for each pre-synaptic
cell, the array containing the connection probabilities for all the local
targets of that pre-synaptic cell.
"""
if numpy.isscalar(p) and p == 1:
precreate = numpy.arange(self.N, dtype=numpy.int)
else:
rarr = self.probas_generator.get(self.M)
if not core.is_listlike(rarr) and numpy.isscalar(rarr): # if N=1, rarr will be a single number
rarr = numpy.array([rarr])
precreate = numpy.where(rarr < p)[0]
self.distance_matrix.set_source(tgt.position)
if not self.allow_self_connections and self.projection.pre == self.projection.post:
idx_tgt = numpy.where(self.candidates == tgt)
if len(idx_tgt) > 0:
i = numpy.where(precreate == idx_tgt[0])
if len(i) > 0:
precreate = numpy.delete(precreate, i[0])
if (rewiring is not None) and (rewiring > 0):
idx = numpy.arange(self.N, dtype=numpy.int)
if not self.allow_self_connections and self.projection.pre == self.projection.post:
i = numpy.where(self.candidates == tgt)[0]
idx = numpy.delete(idx, i)
rarr = self.probas_generator.get(self.M)[precreate]
rewired = numpy.where(rarr < rewiring)[0]
N = len(rewired)
if N > 0:
new_idx = (len(idx)-1) * self.probas_generator.get(self.M)[precreate]
precreate[rewired] = idx[new_idx.astype(int)]
if (n_connections is not None) and (len(precreate) > 0):
create = numpy.array([], dtype=numpy.int)
while len(create) < n_connections: # if the number of requested cells is larger than the size of the
# presynaptic population, we allow multiple connections for a given cell
create = numpy.concatenate((create, self.projection.rng.permutation(precreate)))
create = create[:n_connections]
else:
create = precreate
sources = self.candidates[create]
weights = self.weights_generator.get(self.M, self.distance_matrix, create)
delays = self.delays_generator.get(self.M, self.distance_matrix, create)
if len(sources) > 0:
self.projection._convergent_connect(sources.tolist(), tgt, weights, delays)