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 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 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))
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", {
"mu": -1.0,
"sigma": 0.5
def setUp(self):
self.rnglist = [random.NumpyRNG(seed=987), random.GSLRNG(seed=654)]