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csa_spatial.py
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'''
@author: Daniel Hjertholm
Tests for spatially structured networks generated by CSA.
'''
import numpy as np
import numpy.random as rnd
import random
import csa
from testsuite.spatial_test import SpatialTester
class CSA_SpatialTester(SpatialTester):
'''Tests for spatially structured networks generated by CSA.'''
def __init__(self, L, N, kernel_name, kernel_params=None):
'''
Construct a test object.
Parameters
----------
L : Side length of area / volume.
N : Number of nodes.
kernel_name : Name of kernel to use.
kernel_params: Dict with params to update.
'''
SpatialTester.__init__(self, L=L, N=N)
assert kernel_name == 'gaussian', 'Currently, only a Gaussian kernel' \
'is supported by this implementation of the CSA.'
default_params = {'p_center': 1., 'sigma': self._L / 4.,
'mean': 0., 'c': 0.}
self._params = default_params
if kernel_params is not None:
assert kernel_params.keys() == ['sigma'], \
'Only valid kernel parameter is "sigma".'
self._params.update(kernel_params)
self._kernel = lambda D: (self._params['c'] + self._params['p_center'] *
np.e ** -((D - self._params['mean']) ** 2 /
(2. * self._params['sigma'] ** 2)))
def _reset(self, seed):
'''
Seed the PRNG.
Parameters
----------
seed: PRNG seed value.
'''
if seed is None:
seed = random.randint(0, 10 ** 10)
seed = 2 * seed # Reduces probability of overlapping seed values.
random.seed(seed) # CSA uses random.
rnd.seed(seed + 1) # _get_expected_distribution uses numpy.random.
def _build(self):
'''Create populations.'''
def pos(idx):
assert idx == 0, 'This population has only one node.'
return (0.5, 0.5)
# Source node is located at (0.5, 0.5) because csa.random2d() can
# only scatter nodes on the unit square.
self._g1 = pos
self._g2 = csa.random2d(self._N)
self._d = csa.euclidMetric2d(self._g1, self._g2)
def _connect(self):
'''Connect populations.'''
sigma = self._params['sigma']
cutoff = self._max_dist
self._cs = csa.cset(csa.cross([0], xrange(self._N - 1)) *
(csa.random * (csa.gaussian(sigma, cutoff) * self._d)), 1.0, 1.0)
def _distances(self):
'''Return distances to all nodes in target population.'''
return [self._d(0, i) for i in xrange(0, self._N)]
def _target_distances(self):
'''Return distances from source node to connected nodes.'''
target_nodes = [c[1] for c in self._cs]
return [self._d(0, t) for t in target_nodes]
def _positions(self):
'''Return positions of all nodes.'''
return [self._g2(i) for i in xrange(0, self._N)]
def _target_positions(self):
'''Return positions of all connected target nodes.'''
return [self._g2(c[1]) for c in self._cs]
class Spatial2DTester(CSA_SpatialTester):
'''Tests for 2D spatially structured networks generated by CSA.'''
def __init__(self, L, N, kernel_name, kernel_params=None):
'''
Construct a test object.
Parameters
----------
L : Side length of area / volume.
N : Number of nodes.
kernel_name : Name of kernel to use.
kernel_params: Dict with params to update.
'''
self._dimensions = 2
CSA_SpatialTester.__init__(self, L=L, N=N, kernel_name=kernel_name,
kernel_params=kernel_params)
class Spatial3DTester(CSA_SpatialTester):
'''Tests for 3D spatially structured networks generated by CSA.'''
def __init__(self, *args, **kwargs):
raise NotImplementedError('CSA does currently not support ' \
'construction of 3D spatial networks.')
if __name__ == '__main__':
test = Spatial2DTester(L=1.0, N=10000, kernel_name='gaussian')
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()