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pyNN_spatial.py
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'''
@author: Daniel Hjertholm
Tests for spatially structured networks generated by PyNN.
'''
import numpy as np
import numpy.random as rnd
from numpy import exp
import pyNN.nest as sim
from testsuite.spatial_test import SpatialTester
class pyNN_SpatialTester(SpatialTester):
'''Tests for spatially structured networks generated by PyNN.'''
def __init__(self, L, N, kernel_string):
'''
Construct a test object.
Parameters
----------
L : Side length of area / volume.
N : Number of nodes.
kernel_string: Kernel, given as an evaluable string.
'''
sim.nest.set_verbosity('M_FATAL')
SpatialTester.__init__(self, L=L, N=N)
self._kernel_string = kernel_string
self._kernel = lambda d: eval(self._kernel_string)
def _reset(self, seed):
'''
Reset simulator and seed PRNGs.
Parameters
----------
seed: PRNG seed value.
'''
sim.end()
sim.setup()
if seed is None:
seed = rnd.randint(10 ** 10)
seed = 2 * seed # Reduces probability of overlapping seed values.
rnd.seed(seed)
self._rng = sim.NumpyRNG(seed=seed + 1)
def _build(self):
'''Create populations.'''
if self._dimensions == 2:
ldict = {'fill_order': 'sequential',
'dx': self._L, 'dy': self._L,
'x0': 0.0, 'y0': 0.0}
self._ls = sim.Population(1, sim.IF_cond_exp,
structure=sim.space.Grid2D(**ldict))
cube = sim.space.Cuboid(1.0, 1.0, 0.0)
else:
ldict = {'fill_order': 'sequential',
'dx': self._L, 'dy': self._L, 'dz': self._L,
'x0': 0.0, 'y0': 0.0, 'z0': 0.0}
self._ls = sim.Population(1, sim.IF_cond_exp,
structure=sim.space.Grid3D(**ldict))
cube = sim.space.Cuboid(1.0, 1.0, 1.0)
self._lt = sim.Population(self._N, sim.IF_cond_exp,
structure=sim.space.RandomStructure(cube, rng=self._rng))
def _connect(self):
'''Connect populations.'''
self._p = sim.Projection(self._ls, self._lt,
sim.DistanceDependentProbabilityConnector(self._kernel_string),
rng=self._rng)
def _positions(self):
'''Return list of position tuples for all nodes.'''
return [tuple(p) for p in np.transpose(self._lt.positions)]
def _target_positions(self):
'''Return list of position tuples of all connected target nodes.'''
return [tuple(c.target.position) for c in self._p]
def _distances(self):
'''Return list with distances to all nodes.'''
return [sim.space.distance(self._ls[0], t) for t in self._lt]
def _target_distances(self):
'''
Return list with distances from source node to all connected
target nodes.
'''
return [sim.space.distance(c.source, c.target) for c in self._p]
class Spatial2DTester(pyNN_SpatialTester):
'''Tests for 2D spatially structured networks generated by PyNN.'''
def __init__(self, L, N, kernel_string):
'''
Construct a test object.
Parameters
----------
L : Side length of area / volume.
N : Number of nodes.
kernel_string: Kernel, given as an evaluable string.
'''
self._dimensions = 2
pyNN_SpatialTester.__init__(self, L=L, N=N,
kernel_string=kernel_string)
class Spatial3DTester(pyNN_SpatialTester):
'''Tests for 3D spatially structured networks generated by PyNN.'''
def __init__(self, L, N, kernel_string):
'''
Construct a test object.
Parameters
----------
L : Side length of area / volume.
N : Number of nodes.
kernel_string: Kernel, given as an evaluable string.
'''
self._dimensions = 3
pyNN_SpatialTester.__init__(self, L=L, N=N,
kernel_string=kernel_string)
if __name__ == '__main__':
kernel_string = '%s + %s * exp(-((d - %s) ** 2 / (2.0 * %s ** 2)))' % \
(0.0, 1.0, 0.0, 1.0 / 4)
test = Spatial2DTester(L=1.0, N=10000, kernel_string=kernel_string)
ks, p = test.ks_test(control=False, seed=0)
print 'p-value of KS-test:', p
z, p = test.z_test(control=False, seed=0)
print 'p-value of Z-test:', p
test.show_network()
test.show_PDF()
test.show_CDF()