# encoding: utf-8
"""
nrnpython implementation of the PyNN API.
:copyright: Copyright 2006-2016 by the PyNN team, see AUTHORS.
:license: CeCILL, see LICENSE for details.
"""
from copy import deepcopy
import numpy
import logging
try:
from itertools import izip
except ImportError:
izip = zip # Python 3 zip returns an iterator already
from itertools import repeat, chain
from collections import defaultdict
from pyNN import common, errors, core
from pyNN.random import RandomDistribution, NativeRNG
from pyNN.space import Space
from . import simulator
from .standardmodels.synapses import StaticSynapse, TsodyksMarkramSynapse
logger = logging.getLogger("PyNN")
_projections = [] # if a Projection is created but not assigned to a variable,
# the connections will not exist, so we store a reference here
[docs]class Projection(common.Projection):
__doc__ = common.Projection.__doc__
_simulator = simulator
_static_synapse_class = StaticSynapse
def __init__(self, presynaptic_population, postsynaptic_population,
connector, synapse_type=None, source=None, receptor_type=None,
space=Space(), label=None):
__doc__ = common.Projection.__init__.__doc__
common.Projection.__init__(self, presynaptic_population, postsynaptic_population,
connector, synapse_type, source, receptor_type,
space, label)
self._connections = dict((index, defaultdict(list)) for index in self.post._mask_local.nonzero()[0])
connector.connect(self)
self._presynaptic_components = dict((index, {}) for index in
self.pre._mask_local.nonzero()[0])
if self.synapse_type.presynaptic_type:
self._configure_presynaptic_components()
_projections.append(self)
logger.info("--- Projection[%s].__init__() ---" % self.label)
@property
def connections(self):
for x in self._connections.values():
for y in x.values():
for z in y:
yield z
[docs] def __getitem__(self, i):
__doc__ = common.Projection.__getitem__.__doc__
if isinstance(i, int):
if i < len(self):
return self.connections[i]
else:
raise IndexError("%d > %d" % (i, len(self) - 1))
elif isinstance(i, slice):
if i.stop < len(self):
return [self.connections[j] for j in range(*i.indices(i.stop))]
else:
raise IndexError("%d > %d" % (i.stop, len(self) - 1))
[docs] def __len__(self):
"""Return the number of connections on the local MPI node."""
return len(list(self.connections))
def _convergent_connect(self, presynaptic_indices, postsynaptic_index,
**connection_parameters):
"""
Connect a neuron to one or more other neurons with a static connection.
`presynaptic_cells` -- a 1D array of pre-synaptic cell IDs
`postsynaptic_cell` -- the ID of the post-synaptic cell.
`connection_parameters` -- each parameter should be either a
1D array of the same length as `sources`, or
a single value.
"""
#logger.debug("Convergent connect. Weights=%s" % connection_parameters['weight'])
postsynaptic_cell = self.post[postsynaptic_index]
if not isinstance(postsynaptic_cell, int) or not (0 <= postsynaptic_cell <= simulator.state.gid_counter):
errmsg = "Invalid post-synaptic cell: %s (gid_counter=%d)" % (postsynaptic_cell, simulator.state.gid_counter)
raise errors.ConnectionError(errmsg)
for name, value in connection_parameters.items():
if isinstance(value, (float, int)):
connection_parameters[name] = repeat(value)
assert postsynaptic_cell.local
for pre_idx, values in core.ezip(presynaptic_indices, *connection_parameters.values()):
parameters = dict(zip(connection_parameters.keys(), values))
#logger.debug("Connecting neuron #%s to neuron #%s with synapse type %s, receptor type %s, parameters %s", pre_idx, postsynaptic_index, self.synapse_type, self.receptor_type, parameters)
self._connections[postsynaptic_index][pre_idx].append(
self.synapse_type.connection_type(self, pre_idx, postsynaptic_index, **parameters))
def _configure_presynaptic_components(self):
"""
For gap junctions potentially other complex synapse types the presynaptic side of the
connection also needs to be initiated. This is a little tricky with sources distributed on
different nodes as the parameters need to be gathered to the node where the source is
hosted before it can be set
"""
# Get the list of all connections on all nodes
conn_list = numpy.array(self.get(self.synapse_type.get_parameter_names(), 'list',
gather='all', with_address=True))
# Loop through each of the connections where the presynaptic index (first column) is on
# the local node
mask_local = numpy.array(numpy.in1d(numpy.squeeze(conn_list[:, 0]),
numpy.nonzero(self.pre._mask_local)[0]), dtype=bool)
for conn in conn_list[mask_local, :]:
pre_idx = int(conn[0])
post_idx = int(conn[1])
params = dict(zip(self.synapse_type.get_parameter_names(), conn[2:]))
self._presynaptic_components[pre_idx][post_idx] = \
self.synapse_type.presynaptic_type(self, pre_idx, post_idx, **params)
def _set_attributes(self, parameter_space):
# If synapse has pre-synaptic components evaluate the parameters for them
if self.synapse_type.presynaptic_type:
presyn_param_space = deepcopy(parameter_space)
presyn_param_space.evaluate(mask=(slice(None), self.pre._mask_local))
for component, connection_parameters in zip(self._presynaptic_components.values(),
presyn_param_space.columns()):
for name, value in connection_parameters.items():
for index in component:
setattr(component[index], name, value[index])
# Evaluate the parameters for the post-synaptic components (typically the "Connection" object)
parameter_space.evaluate(mask=(slice(None), self.post._mask_local)) # only columns for connections that exist on this machine
for connection_group, connection_parameters in zip(self._connections.values(),
parameter_space.columns()):
for name, value in connection_parameters.items():
for index in connection_group:
for connection in connection_group[index]:
setattr(connection, name, value[index])
