def connect(self, projection):
# Determine number of processes and current rank
rank, num_processes = get_mpi_config()
# Assume that targets are equally distributed over processes
targets_per_process = int(len(projection.post) / num_processes)
# Calculate the number of synapses on each process
bino = RandomDistribution(
'binomial', [self.n, targets_per_process / len(projection.post)],
rng=self.rng)
num_conns_on_vp = numpy.zeros(num_processes)
sum_dist = 0
sum_partitions = 0
for k in xrange(num_processes):
p_local = targets_per_process / (len(projection.post) - sum_dist)
bino.parameters['p'] = p_local
bino.parameters['n'] = self.n - sum_partitions
num_conns_on_vp[k] = bino.next()
sum_dist += targets_per_process
sum_partitions += num_conns_on_vp[k]
# Draw random sources and targets
while num_conns_on_vp[rank] > 0:
s_index = self.rng.rng.randint(low=0,
high=len(projection.pre.all_cells))
t_index = self.rng.rng.randint(low=0,
high=len(
projection.post.local_cells))
t_index = numpy.where(projection.post.all_cells == int(
projection.post.local_cells[t_index]))[0][0]
# Evaluate the lazy arrays containing the synaptic parameters
parameter_space = self._parameters_from_synapse_type(projection)
connection_parameters = {}
for name, map in parameter_space.items():
if map.is_homogeneous:
connection_parameters[name] = map.evaluate(simplify=True)
else:
connection_parameters[name] = map[source_mask, col]
projection._convergent_connect(numpy.array([s_index]), t_index,
**connection_parameters)
num_conns_on_vp[rank] -= 1
def connect(self, projection):
# Determine number of processes and current rank
rank, num_processes = get_mpi_config()
# Assume that targets are equally distributed over processes
targets_per_process = int(len(projection.post)/num_processes)
# Calculate the number of synapses on each process
bino = RandomDistribution('binomial',[self.n,targets_per_process/len(projection.post)], rng=self.rng)
num_conns_on_vp = numpy.zeros(num_processes)
sum_dist = 0
sum_partitions = 0
for k in xrange(num_processes) :
p_local = targets_per_process / ( len(projection.post) - sum_dist)
bino.parameters['p'] = p_local
bino.parameters['n'] = self.n - sum_partitions
num_conns_on_vp[k] = bino.next()
sum_dist += targets_per_process
sum_partitions += num_conns_on_vp[k]
# Draw random sources and targets
while num_conns_on_vp[rank] > 0 :
s_index = self.rng.rng.randint(low=0, high=len(projection.pre.all_cells))
t_index = self.rng.rng.randint(low=0, high=len(projection.post.local_cells))
t_index = numpy.where(projection.post.all_cells == int(projection.post.local_cells[t_index]))[0][0]
# Evaluate the lazy arrays containing the synaptic parameters
parameter_space = self._parameters_from_synapse_type(projection)
connection_parameters = {}
for name, map in parameter_space.items():
if map.is_homogeneous:
connection_parameters[name] = map.evaluate(simplify=True)
else:
connection_parameters[name] = map[source_mask, col]
projection._convergent_connect(numpy.array([s_index]),t_index, **connection_parameters)
num_conns_on_vp[rank] -=1