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)]