def testRSetNumpy(self):
"""Population.rset(): with numpy rng."""
rd1 = random.RandomDistribution(rng=random.NumpyRNG(seed=98765),
distribution='uniform',
parameters=[0.9, 1.1])
rd2 = random.RandomDistribution(rng=random.NumpyRNG(seed=98765),
distribution='uniform',
parameters=[0.9, 1.1])
self.net.rset('cm', rd1)
output_values = numpy.zeros((3, 3), numpy.float)
hoc_net = getattr(h, self.net.label)
for i in 0, 1, 2:
for j in 0, 1, 2:
id = 3 * i + j
if id in self.net.gidlist:
list_index = self.net.gidlist.index(id)
output_values[i,
j] = hoc_net.object(list_index).cell(0.5).cm
input_values = rd2.next(9)
output_values = output_values.reshape((9, ))
for i in range(9):
if i in self.net.gidlist:
self.assertAlmostEqual(input_values[i],
output_values[i],
places=5)
def test_permutation(self):
# only works for NumpyRNG at the moment. pygsl has a permutation module, but I can't find documentation for it.
random.get_mpi_config = lambda: (0, 2)
rng0 = random.NumpyRNG(seed=1000, parallel_safe=True)
random.get_mpi_config = lambda: (1, 2)
rng1 = random.NumpyRNG(seed=1000, parallel_safe=True)
A = range(10)
perm0 = rng0.permutation(A)
perm1 = rng1.permutation(A)
assert_arrays_almost_equal(perm0, perm1, 1e-99)
def testRSetNumpy(self):
"""Population.rset(): with numpy rng."""
rd1 = random.RandomDistribution(rng=random.NumpyRNG(seed=98765),
distribution='uniform',
parameters=[0.9,1.1])
rd2 = random.RandomDistribution(rng=random.NumpyRNG(seed=98765),
distribution='uniform',
parameters=[0.9,1.1])
self.net.rset('cm', rd1)
output_values = self.net.get('cm', as_array=True)
input_values = rd2.next(9).reshape(self.net.dim)
assert numpy.equal(input_values, output_values).all()
def test_permutation(self):
# only works for NumpyRNG at the moment. pygsl has a permutation module, but I can't find documentation for it.
random.mpi_rank = 0
random.num_processes = 2
rng0 = random.NumpyRNG(seed=1000, parallel_safe=True)
random.mpi_rank = 1
random.num_processes = 2
rng1 = random.NumpyRNG(seed=1000, parallel_safe=True)
A = range(10)
perm0 = rng0.permutation(A)
perm1 = rng1.permutation(A)
assert_arrays_almost_equal(perm0, perm1, 1e-99)
def testRSetNumpy(self):
"""Population.rset(): with numpy rng."""
rd1 = random.RandomDistribution(rng=random.NumpyRNG(seed=98765),
distribution='uniform',
parameters=[0.9, 1.1])
rd2 = random.RandomDistribution(rng=random.NumpyRNG(seed=98765),
distribution='uniform',
parameters=[0.9, 1.1])
self.net.rset('cm', rd1)
output_values = self.net.get('cm', as_array=True)
input_values = rd2.next(9)
for i in range(9):
self.assertAlmostEqual(input_values[i], output_values[i], places=5)
def test_1(self):
C = connectors.FixedTotalNumberConnector(n=12, rng=random.NumpyRNG())
syn = sim.StaticSynapse(weight="0.5*d")
prj = sim.Projection(self.p1, self.p2, C, syn)
connections = prj.get(["weight", "delay"], format='list', gather=False)
self.assertLess(len(connections), 12) # unlikely to be 12, since we have 2 MPI nodes
self.assertGreater(len(connections), 0) # unlikely to be 0
def setUp(self):
setup()
self.target = Population((3,3),IF_curr_alpha)
self.target = Population((3,3),IF_curr_alpha)
self.source = Population((3,3),SpikeSourcePoisson,{'rate': 100})
self.distrib_Numpy = random.RandomDistribution('uniform',(0,1),random.NumpyRNG(12345))
self.distrib_Native= random.RandomDistribution('Uniform',(0,1),NativeRNG(12345))
def setUp(self):
random.get_mpi_config = lambda: (0, 1)
self.rnglist = [random.NumpyRNG(seed=987)]
if random.have_gsl:
self.rnglist.append(random.GSLRNG(seed=654))
if have_nrn:
self.rnglist.append(NativeRNG(seed=321))
def testRSetNumpy(self):
"""Population.rset(): with numpy rng."""
rd1 = random.RandomDistribution(rng=random.NumpyRNG(seed=98765),
distribution='uniform',
parameters=[0.9,1.1])
rd2 = random.RandomDistribution(rng=random.NumpyRNG(seed=98765),
distribution='uniform',
parameters=[0.9,1.1])
self.popul1.rset('cm',rd1)
output_values = numpy.zeros((3,3),numpy.float)
for i in 0,1,2:
for j in 0,1,2:
output_values[i,j] = 1e9*simulator.net.object(self.popul1.getObjectID(self.popul1[i,j])).Cm
input_values = rd2.next(9)
output_values = output_values.reshape((9,))
for i in range(9):
self.assertAlmostEqual(input_values[i],output_values[i],places=5)
def testDistantDependentProbability(self):
"""For all connections created with "distanceDependentProbability" ..."""
# Test should be improved..."
distrib_Numpy = random.RandomDistribution('uniform',(0,1),random.NumpyRNG(12345))
distrib_Native= random.RandomDistribution('Uniform',(0,1),NativeRNG(12345))
prj1 = Projection(self.source33, self.target33, DistanceDependentProbabilityConnector([ 0.1, 2]), distrib_Numpy)
prj2 = Projection(self.source33, self.target33, DistanceDependentProbabilityConnector([ 0.1, 3]), distrib_Native)
assert (0 < len(prj1) < len(self.source33)*len(self.target33)) and (0 < len(prj2) < len(self.source33)*len(self.target33))
def setUp(self):
self.rnglist = [random.NumpyRNG(seed=987)]
for rng in self.rnglist:
rng.mpi_rank=0; rng.num_processes=1
if random.have_gsl:
self.rnglist.append(random.GSLRNG(seed=654))
if have_nrn:
self.rnglist.append(NativeRNG(seed=321))
def testPotentialRecording(self):
"""Population.record_v() and Population.print_v(): not a full test, just checking
# there are no Exceptions raised."""
rng = random.NumpyRNG(123)
v_reset = -65.0
v_thresh = -50.0
uniformDistr = random.RandomDistribution(rng=rng, distribution='uniform', parameters=[v_reset, v_thresh])
self.pop2.randomInit(uniformDistr)
self.pop2.record_v([self.pop2[0,0], self.pop2[1,1]])
simtime = 10.0
neuron.running = False
neuron.run(simtime)
self.pop2.print_v("temp_neuron.v", gather=True, compatible_output=True)
def testFixedProbability(self):
"""For all connections created with "fixedProbability"..."""
for srcP in [self.source5, self.source22]:
for tgtP in [self.target1, self.target6, self.target33]:
prj1 = sim.Projection(srcP,
tgtP,
sim.FixedProbabilityConnector(0.5),
rng=random.NumpyRNG(12345))
prj2 = sim.Projection(srcP,
tgtP,
sim.FixedProbabilityConnector(0.5),
rng=random.NativeRNG(12345))
for prj in prj1, prj2:
assert (0 < len(prj) < len(srcP) * len(tgtP)
), 'len(prj) = %d, len(srcP)*len(tgtP) = %d' % (
len(prj), len(srcP) * len(tgtP))
def setUp(self):
nest.setup(max_delay=0.5)
nest.Population.nPop = 0
self.target33 = nest.Population((3, 3), nest.IF_curr_alpha)
self.target6 = nest.Population((6, ), nest.IF_curr_alpha)
self.source5 = nest.Population((5, ), nest.SpikeSourcePoisson)
self.source22 = nest.Population((2, 2), nest.SpikeSourcePoisson)
self.prjlist = []
self.distrib_Numpy = random.RandomDistribution(
rng=random.NumpyRNG(12345),
distribution='uniform',
parameters=(0.1, 0.5))
for tgtP in [self.target6, self.target33]:
for srcP in [self.source5, self.source22]:
for method in ('allToAll', 'fixedProbability'):
self.prjlist.append(
nest.Projection(srcP, tgtP, method,
{'p_connect': 0.5}))
def setUp(self):
random.mpi_rank = 0
random.num_processes = 1
self.rnglist = [random.NumpyRNG(seed=987)]
if random.have_gsl:
self.rnglist.append(random.GSLRNG(seed=654))
def __init__(self,
presynaptic_neurons,
postsynaptic_neurons,
method,
source=None,
target=None,
synapse_dynamics=None,
label=None,
rng=None):
"""
presynaptic_neurons and postsynaptic_neurons - Population, PopulationView
or Assembly objects.
source - string specifying which attribute of the presynaptic cell
signals action potentials. This is only needed for
multicompartmental cells with branching axons or
dendrodendriticsynapses. All standard cells have a single
source, and this is the default.
target - string specifying which synapse on the postsynaptic cell to
connect to. For standard cells, this can be 'excitatory' or
'inhibitory'. For non-standard cells, it could be 'NMDA', etc.
If target is not given, the default values of 'excitatory' is
used.
method - a Connector object, encapsulating the algorithm to use for
connecting the neurons.
synapse_dynamics - a `standardmodels.SynapseDynamics` object specifying
which synaptic plasticity mechanisms to use.
rng - specify an RNG object to be used by the Connector.
"""
for prefix, pop in zip(("pre", "post"),
(presynaptic_neurons, postsynaptic_neurons)):
if not isinstance(pop, (BasePopulation, Assembly)):
raise errors.ConnectionError(
"%ssynaptic_neurons must be a Population, PopulationView or Assembly, not a %s"
% (prefix, type(pop)))
if isinstance(postsynaptic_neurons, Assembly):
if not postsynaptic_neurons._homogeneous_synapses:
raise Exception(
'Projection to an Assembly object can be made only with homogeneous synapses types'
)
self.pre = presynaptic_neurons # } these really
self.source = source # } should be
self.post = postsynaptic_neurons # } read-only
self.target = target # }
self.label = label
if isinstance(rng, random.AbstractRNG):
self.rng = rng
elif rng is None:
self.rng = random.NumpyRNG(seed=151985012)
else:
raise Exception(
"rng must be either None, or a subclass of pyNN.random.AbstractRNG"
)
self._method = method
self.synapse_dynamics = synapse_dynamics
#self.connection = None # access individual connections. To be defined by child, simulator-specific classes
self.weights = []
if label is None:
if self.pre.label and self.post.label:
self.label = "%s→%s" % (self.pre.label, self.post.label)
if self.synapse_dynamics:
assert isinstance(self.synapse_dynamics, models.BaseSynapseDynamics), \
"The synapse_dynamics argument, if specified, must be a models.BaseSynapseDynamics object, not a %s" % type(synapse_dynamics)
import matplotlib.gridspec as gridspec
import scipy.stats
import pyNN.random as random
try:
from neuron import h
except ImportError:
have_nrn = False
else:
have_nrn = True
from pyNN.neuron.random import NativeRNG
n = 100000
nbins = 100
rnglist = [random.NumpyRNG(seed=984527)]
if random.have_gsl:
rnglist.append(random.GSLRNG(seed=668454))
if have_nrn:
rnglist.append(NativeRNG(seed=321245))
cases = (
("uniform", {
"low": -65,
"high": -55
}, (-65, -55), scipy.stats.uniform(loc=-65, scale=10)),
("gamma", {
"k": 2.0,
"theta": 0.5
}, (0, 5), scipy.stats.gamma(2.0, loc=0.0, scale=0.5)),
("normal", {
def test_1(self):
C = connectors.FixedTotalNumberConnector(n=12, rng=random.NumpyRNG())
syn = sim.StaticSynapse(weight="0.5*d")
prj = sim.Projection(self.p1, self.p2, C, syn)
connections = prj.get(["weight", "delay"], format='list', gather=False)
self.assertEqual(len(connections), 12)
def setUp(self):
self.rnglist = [random.NumpyRNG(seed=987), random.GSLRNG(seed=654)]
def testRecordWithRNG(self):
"""Population.record(n, rng): not a full test, just checking there are no Exceptions raised."""
self.pop1.record(5, random.NumpyRNG())
def setUp(self):
self.target = neuron.Population((3,3), neuron.IF_curr_alpha)
self.source = neuron.Population((3,3), neuron.SpikeSourcePoisson,{'rate': 200})
self.distrib_Numpy = random.RandomDistribution(rng=random.NumpyRNG(12345), distribution='uniform', parameters=(0.2,1))
self.distrib_Native= random.RandomDistribution(rng=random.NativeRNG(12345), distribution='uniform', parameters=(0.2,1))
