def test_check_weight_with_scalar():
assert_equal(4.3,
common.check_weight(4.3, 'excitatory', is_conductance=True))
assert_equal(4.3,
common.check_weight(4.3, 'excitatory', is_conductance=False))
assert_equal(4.3,
common.check_weight(4.3, 'inhibitory', is_conductance=True))
assert_equal(-4.3,
common.check_weight(-4.3, 'inhibitory', is_conductance=False))
assert_equal(common.DEFAULT_WEIGHT,
common.check_weight(None, 'excitatory', is_conductance=True))
assert_raises(errors.InvalidWeightError,
common.check_weight,
4.3,
'inhibitory',
is_conductance=False)
assert_raises(errors.InvalidWeightError,
common.check_weight,
-4.3,
'inhibitory',
is_conductance=True)
assert_raises(errors.InvalidWeightError,
common.check_weight,
-4.3,
'excitatory',
is_conductance=True)
assert_raises(errors.InvalidWeightError,
common.check_weight,
-4.3,
'excitatory',
is_conductance=False)
def test_check_weight_with_list():
w = range(10)
assert_equal(
w,
common.check_weight(w, 'excitatory', is_conductance=True).tolist())
assert_equal(
w,
common.check_weight(w, 'excitatory', is_conductance=False).tolist())
assert_equal(
w,
common.check_weight(w, 'inhibitory', is_conductance=True).tolist())
assert_raises(errors.InvalidWeightError,
common.check_weight,
w,
'inhibitory',
is_conductance=False)
w = range(-10, 0)
assert_equal(
w,
common.check_weight(w, 'inhibitory', is_conductance=False).tolist())
assert_raises(errors.InvalidWeightError,
common.check_weight,
w,
'inhibitory',
is_conductance=True)
assert_raises(errors.InvalidWeightError,
common.check_weight,
w,
'excitatory',
is_conductance=True)
assert_raises(errors.InvalidWeightError,
common.check_weight,
w,
'excitatory',
is_conductance=False)
w = range(-5, 5)
assert_raises(errors.InvalidWeightError,
common.check_weight,
w,
'excitatory',
is_conductance=True)
assert_raises(errors.InvalidWeightError,
common.check_weight,
w,
'excitatory',
is_conductance=False)
assert_raises(errors.InvalidWeightError,
common.check_weight,
w,
'inhibitory',
is_conductance=True)
assert_raises(errors.InvalidWeightError,
common.check_weight,
w,
'inhibitory',
is_conductance=False)
def test_check_weight_with_scalar():
assert_equal(4.3, common.check_weight(4.3, 'excitatory', is_conductance=True))
assert_equal(4.3, common.check_weight(4.3, 'excitatory', is_conductance=False))
assert_equal(4.3, common.check_weight(4.3, 'inhibitory', is_conductance=True))
assert_equal(-4.3, common.check_weight(-4.3, 'inhibitory', is_conductance=False))
assert_equal(common.DEFAULT_WEIGHT, common.check_weight(None, 'excitatory', is_conductance=True))
assert_raises(errors.InvalidWeightError, common.check_weight, 4.3, 'inhibitory', is_conductance=False)
assert_raises(errors.InvalidWeightError, common.check_weight, -4.3, 'inhibitory', is_conductance=True)
assert_raises(errors.InvalidWeightError, common.check_weight, -4.3, 'excitatory', is_conductance=True)
assert_raises(errors.InvalidWeightError, common.check_weight, -4.3, 'excitatory', is_conductance=False)
def connect(self, projection):
"""Connect-up a Projection."""
if isinstance(projection.rng, random.NativeRNG):
raise Exception("Warning: use of NativeRNG not implemented.")
for target in projection.post.local_cells.flat:
# pick n neurons at random
if hasattr(self, 'rand_distr'):
n = self.rand_distr.next()
else:
n = self.n
candidates = projection.pre.all_cells.flatten().tolist()
if not self.allow_self_connections and projection.pre == projection.post:
candidates.remove(target)
sources = []
while len(sources) < n: # if the number of requested cells is larger than the size of the
# presynaptic population, we allow multiple connections for a given cell
sources += [candidates[candidates.index(id)] for id in projection.rng.permutation(candidates)[0:n]]
# have to use index() because rng.permutation returns ints, not ID objects
sources = sources[:n]
weights = self.get_weights(n)
is_conductance = common.is_conductance(projection.post.index(0))
weights = common.check_weight(weights, projection.synapse_type, is_conductance)
delays = self.get_delays(n)
projection.connection_manager.convergent_connect(sources, [target], weights, delays)
def test_check_weight_with_list():
w = range(10)
assert_equal(w, common.check_weight(w, 'excitatory', is_conductance=True).tolist())
assert_equal(w, common.check_weight(w, 'excitatory', is_conductance=False).tolist())
assert_equal(w, common.check_weight(w, 'inhibitory', is_conductance=True).tolist())
assert_raises(errors.InvalidWeightError, common.check_weight, w, 'inhibitory', is_conductance=False)
w = range(-10,0)
assert_equal(w, common.check_weight(w, 'inhibitory', is_conductance=False).tolist())
assert_raises(errors.InvalidWeightError, common.check_weight, w, 'inhibitory', is_conductance=True)
assert_raises(errors.InvalidWeightError, common.check_weight, w, 'excitatory', is_conductance=True)
assert_raises(errors.InvalidWeightError, common.check_weight, w, 'excitatory', is_conductance=False)
w = range(-5,5)
assert_raises(errors.InvalidWeightError, common.check_weight, w, 'excitatory', is_conductance=True)
assert_raises(errors.InvalidWeightError, common.check_weight, w, 'excitatory', is_conductance=False)
assert_raises(errors.InvalidWeightError, common.check_weight, w, 'inhibitory', is_conductance=True)
assert_raises(errors.InvalidWeightError, common.check_weight, w, 'inhibitory', is_conductance=False)
def test_check_weight_with_NaN():
w = numpy.arange(10.0)
w[0] = numpy.nan
assert_arrays_equal(
w[1:],
common.check_weight(
w, 'excitatory',
is_conductance=True)[1:]) # NaN != NaN by definition
def _probabilistic_connect(self, projection, p):
"""
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 isinstance(projection.rng, random.NativeRNG):
raise Exception("Use of NativeRNG not implemented.")
else:
rng = projection.rng
local = projection.post._mask_local.flatten()
is_conductance = common.is_conductance(projection.post.index(0))
for src in projection.pre.all():
# ( the following two lines are a nice idea, but this needs some thought for
# the parallel case, to ensure reproducibility when varying the number
# of processors
# N = rng.binomial(npost,self.p_connect,1)[0]
# targets = sample(postsynaptic_neurons, N) # )
N = projection.post.size
# if running in parallel, rng.next(N) will not return N values, but only
# as many as are needed on this node, as determined by mask_local.
# Over the simulation as a whole (all nodes), N values will indeed be
# returned.
rarr = rng.next(N, 'uniform', (0, 1), mask_local=local)
if not common.is_listlike(rarr) and common.is_number(
rarr): # if N=1, rarr will be a single number
rarr = numpy.array([rarr])
if common.is_number(p):
create = rarr < p
else:
create = rarr < p[src][local]
if create.shape != projection.post.local_cells.shape:
logger.warning(
"Too many random numbers. Discarding the excess. Did you specify MPI rank and number of processes when you created the random number generator?"
)
create = create[:projection.post.local_cells.size]
targets = projection.post.local_cells[create].tolist()
weights = self.get_weights(N, local)[create]
weights = common.check_weight(weights, projection.synapse_type,
is_conductance)
delays = self.get_delays(N, local)[create]
if not self.allow_self_connections and projection.pre == projection.post and src in targets:
assert len(targets) == len(weights) == len(delays)
i = targets.index(src)
weights = numpy.delete(weights, i)
delays = numpy.delete(delays, i)
targets.remove(src)
if len(targets) > 0:
projection.connection_manager.connect(src, targets, weights,
delays)
def connect(self, projection):
"""Connect-up a Projection."""
if isinstance(projection.rng, random.NativeRNG):
raise Exception("Warning: use of NativeRNG not implemented.")
for source in projection.pre.all_cells.flat:
# pick n neurons at random
if hasattr(self, 'rand_distr'):
n = self.rand_distr.next()
else:
n = self.n
candidates = projection.post.all_cells.flatten().tolist()
if not self.allow_self_connections and projection.pre == projection.post:
candidates.remove(source)
targets = []
while len(
targets
) < n: # if the number of requested cells is larger than the size of the
# postsynaptic population, we allow multiple connections for a given cell
targets += [
candidates[candidates.index(id)]
for id in projection.rng.permutation(candidates)[0:n]
]
# have to use index() because rng.permutation returns ints, not ID objects
targets = numpy.array(targets[:n], dtype=common.IDMixin)
weights = self.get_weights(n)
is_conductance = common.is_conductance(projection.post.index(0))
weights = common.check_weight(weights, projection.synapse_type,
is_conductance)
delays = self.get_delays(n)
#local = numpy.array([tgt.local for tgt in targets])
#if local.size > 0:
# targets = targets[local]
# weights = weights[local]
# delays = delays[local]
targets = targets.tolist()
#print common.rank(), source, targets
if len(targets) > 0:
projection.connection_manager.connect(source, targets, weights,
delays)
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()
weights = self.get_weights(N, local)
is_conductance = common.is_conductance(projection.post.index(0))
weights = common.check_weight(weights, projection.synapse_type,
is_conductance)
delays = self.get_delays(N, local)
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.connection_manager.connect(src, [tgt], float(w),
float(d))
else:
raise common.InvalidDimensionsError(
"OneToOneConnector does not support presynaptic and postsynaptic Populations of different sizes."
)
def connect(self, projection):
"""Connect-up a Projection."""
# Timers
global rank
timer0 = 0.0
timer1 = 0.0
timer2 = 0.0
timer3 = 0.0
timer4 = 0.0
# Recuperate variables #
n = self.n
dist_factor = self.dist_factor
noise_factor = self.noise_factor
# Do some checking #
assert dist_factor >= 0
assert noise_factor >= 0
if isinstance(n, int):
assert n >= 0
else:
raise Exception("n must be an integer.")
# Get posts and pres #
listPostIDs = projection.post.local_cells
listPreIDs = projection.pre.all_cells
countPost = len(listPostIDs)
countPre = len(listPreIDs)
listPreIndexes = numpy.arange(countPre)
listPostIndexes = map(projection.post.id_to_index, listPostIDs)
# Prepare all distances #
allDistances = self.space.distances(projection.post.positions,projection.pre.positions)
# Get weights #
weights = numpy.empty(n)
weights[:] = self.weights
is_conductance = common.is_conductance(projection.post[listPostIndexes[0]])
weights = common.check_weight(weights, projection.synapse_type, is_conductance)
numpy.random.seed(12345)
for i in xrange(len(listPostIDs)):
currentPostIndex = listPostIndexes[i]
currentPostID = listPostIDs[i]
#currentPostIDAsList = [currentPostID]
# Pick n neurons at random in pre population
myTimer = time.time()
chosenPresIndexes = list(numpy.random.permutation(numpy.arange(countPre))[0:n])
chosenPresIDs = list(projection.pre[chosenPresIndexes].all_cells)
#if rank==0:
# print(chosenPresIDs)
#chosenPresIDs = chosenPresIDs.tolist()
timer0 += time.time() - myTimer
# Get distances
myTimer = time.time()
#distances = allDistances[currentPostIndex,chosenPresIndexes]
distances = allDistances[currentPostIndex,chosenPresIndexes]
timer1 += time.time() - myTimer
# Generate gamme noise
noise = numpy.random.gamma(1.0, noise_factor, n)
# Create delays with distance and noise
myTimer = time.time()
delays = dist_factor * distances * (1.0+noise)
timer2 += time.time() - myTimer
#delays[:] = 1.0
# Check for small and big delays
myTimer = time.time()
delaysClipped = numpy.clip(delays,sim.get_min_delay(),sim.get_max_delay())
howManyClipped = len((delays != delaysClipped).nonzero()[0])
if (howManyClipped > 1):
print("Warning: %d of %d delays were cliped because they were either bigger than the max delay or lower than the min delay." % (howManyClipped, n))
delaysClipped = delaysClipped.tolist()
timer3 += time.time() - myTimer
# Connect everything up
yTimer = time.time()
projection._convergent_connect(chosenPresIDs, currentPostID, weights, delaysClipped)
timer4 += time.time() - myTimer
# Print timings
if rank==0:
print("\033[2;46m" + ("Timer 0: %5.4f seconds" % timer0).ljust(60) + "\033[m")
print("\033[2;46m" + ("Timer 1: %5.4f seconds" % timer1).ljust(60) + "\033[m")
print("\033[2;46m" + ("Timer 2: %5.4f seconds" % timer2).ljust(60) + "\033[m")
print("\033[2;46m" + ("Timer 3: %5.4f seconds" % timer3).ljust(60) + "\033[m")
print("\033[2;46m" + ("Timer 4: %5.4f seconds" % timer4).ljust(60) + "\033[m")
def connect(self, projection):
"""Connect-up a Projection."""
# Timers
global rank
timer0 = 0.0
timer1 = 0.0
timer2 = 0.0
timer3 = 0.0
timer4 = 0.0
# Recuperate variables #
n = self.n
dist_factor = self.dist_factor
noise_factor = self.noise_factor
# Do some checking #
assert dist_factor >= 0
assert noise_factor >= 0
if isinstance(n, int):
assert n >= 0
else:
raise Exception("n must be an integer.")
# Get posts and pres #
listPostIDs = projection.post.local_cells
listPreIDs = projection.pre.all_cells
countPost = len(listPostIDs)
countPre = len(listPreIDs)
listPreIndexes = numpy.arange(countPre)
listPostIndexes = map(projection.post.id_to_index, listPostIDs)
# Prepare all distances #
allDistances = self.space.distances(projection.post.positions, projection.pre.positions)
# Get weights #
weights = numpy.empty(n)
weights[:] = self.weights
is_conductance = common.is_conductance(projection.post[listPostIndexes[0]])
weights = common.check_weight(weights, projection.synapse_type, is_conductance)
for i in xrange(len(listPostIDs)):
currentPostIndex = listPostIndexes[i]
currentPostID = listPostIDs[i]
#currentPostIDAsList = [currentPostID]
# Pick n neurons at random in pre population
myTimer = time.time()
chosenPresIndexes = list(numpy.random.permutation(numpy.arange(countPre))[0:n])
chosenPresIDs = list(projection.pre[chosenPresIndexes].all_cells)
#if rank==0:
# print(chosenPresIDs)
#chosenPresIDs = chosenPresIDs.tolist()
timer0 += time.time() - myTimer
# Get distances
myTimer = time.time()
#distances = allDistances[currentPostIndex,chosenPresIndexes]
distances = allDistances[currentPostIndex, chosenPresIndexes]
timer1 += time.time() - myTimer
# Generate gamme noise
noise = numpy.random.gamma(1.0, noise_factor, n)
# Create delays with distance and noise
myTimer = time.time()
delays = dist_factor * distances * (1.0 + noise)
timer2 += time.time() - myTimer
#delays[:] = 1.0
# Check for small and big delays
myTimer = time.time()
delaysClipped = numpy.clip(delays, common.get_min_delay(), common.get_max_delay())
howManyClipped = len((delays != delaysClipped).nonzero()[0])
if (howManyClipped > 1):
print("Warning: %d of %d delays were cliped because they were either bigger than the max delay or lower than the min delay." % (howManyClipped, n))
delaysClipped = delaysClipped.tolist()
timer3 += time.time() - myTimer
# Connect everything up
yTimer = time.time()
projection._convergent_connect(chosenPresIDs, currentPostID, weights, delaysClipped)
timer4 += time.time() - myTimer
# Print timings
if rank == 0:
print("\033[2;46m" + ("Timer 0: %5.4f seconds" % timer0).ljust(60) + "\033[m")
print("\033[2;46m" + ("Timer 1: %5.4f seconds" % timer1).ljust(60) + "\033[m")
print("\033[2;46m" + ("Timer 2: %5.4f seconds" % timer2).ljust(60) + "\033[m")
print("\033[2;46m" + ("Timer 3: %5.4f seconds" % timer3).ljust(60) + "\033[m")
print("\033[2;46m" + ("Timer 4: %5.4f seconds" % timer4).ljust(60) + "\033[m")
def test_check_weight_is_conductance_is_None():
# need to check that a log message was created
assert_equal(4.3, common.check_weight(4.3, 'excitatory', is_conductance=None))
def test_check_weight_with_NaN():
w = numpy.arange(10.0)
w[0] = numpy.nan
assert_arrays_equal(w[1:], common.check_weight(w, 'excitatory', is_conductance=True)[1:]) # NaN != NaN by definition
def test_check_weight_is_conductance_is_None():
# need to check that a log message was created
assert_equal(4.3,
common.check_weight(4.3, 'excitatory', is_conductance=None))
