def test_KCSD_1D_zero_csd(self):
"""if measured potential is 0, the calculated CSD should be 0"""
params = {'n_sources': 20, 'dist_density': 20,
'source_type': 'gauss_lim'}
k_zero = KCSD(elec_pos=np.array([[1.0], [2.0], [3.0], [4.0], [5.0]]),
sampled_pots=np.array([[0.0], [0.0], [0.0], [0.0], [0.0]]),
params=params)
k_zero.estimate_csd()
self.assertAlmostEqual(norm(k_zero.solver.estimated_csd), 0.0, places=10)
def csd_est_using_kCSD(probe_loc, lfp_bar, lambd=0.0, sigma = sigma):
elec_pos = np.expand_dims(probe_loc, axis=1)
pots = np.expand_dims(lfp_bar, axis=1)
params = {'gdX': np.diff(probe_loc)[0], 'gdY': np.diff(probe_loc)[0], sigma:sigma, lambd:lambd }
k = KCSD(elec_pos, pots, params)
k.solver.lambd = lambd
k.estimate_pots()
k.estimate_csd()
return k.solver.space_X.ravel(), k.solver.estimated_csd.ravel()*sigma
def test_KCSD_3D_zero_pot(self):
"""if the input pots are zero, estimated pots and csd should be zero"""
elec_pos = np.array([(0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0),
(0, 1, 1), (1, 1, 0), (1, 0, 1), (1, 1, 1)])
pots = np.array([[0], [0], [0], [0], [0], [0], [0], [0]])
params = {'gdX': 0.2, 'gdY': 0.2, 'gdZ': 0.2, 'n_src': 10}
k = KCSD(elec_pos, pots, params)
k.estimate_pots()
k.estimate_csd()
for pot in k.solver.estimated_pots.flatten():
self.assertAlmostEqual(pot, 0.0, places=5)
for csd in k.solver.estimated_csd.flatten():
self.assertAlmostEqual(csd, 0.0, places=5)
def test_KCSD_1D_csd_reconstruction(self):
"""reconstructed csd should be similar to model csd"""
params = {'sigma': self.sigma, 'source_type': 'gauss_lim',
'x_min': self.xmin, 'x_max': self.xmax, 'h': self.h}
k = KCSD(self.elec_pos, self.meas_pot, params)
k.estimate_csd()
"""print np.max(k.estimated_csd)
plot(k.estimated_csd)
plot(self.true_csd)
show()
print k.X_src"""
for estimated_csd, true_csd in zip(k.solver.estimated_csd, self.true_csd):
self.assertAlmostEqual(estimated_csd, true_csd, places=1)
def test_KCSD_1D_zero_csd(self):
"""if measured potential is 0, the calculated CSD should be 0"""
params = {
'n_sources': 20,
'dist_density': 20,
'source_type': 'gauss_lim'
}
k_zero = KCSD(elec_pos=np.array([[1.0], [2.0], [3.0], [4.0], [5.0]]),
sampled_pots=np.array([[0.0], [0.0], [0.0], [0.0],
[0.0]]),
params=params)
k_zero.estimate_csd()
self.assertAlmostEqual(norm(k_zero.solver.estimated_csd),
0.0,
places=10)
def test_KCSD_3D_zero_pot(self):
"""if the input pots are zero, estimated pots and csd should be zero"""
elec_pos = np.array([(0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0),
(0, 1, 1), (1, 1, 0), (1, 0, 1), (1, 1, 1)])
pots = np.array([[0], [0], [0], [0], [0], [0], [0], [0]])
params = {
'gdX': 0.2,
'gdY': 0.2,
'gdZ': 0.2,
'n_src': 10
}
k = KCSD(elec_pos, pots, params)
k.estimate_pots()
k.estimate_csd()
for pot in k.solver.estimated_pots.flatten():
self.assertAlmostEqual(pot, 0.0, places=5)
for csd in k.solver.estimated_csd.flatten():
self.assertAlmostEqual(csd, 0.0, places=5)
def test_KCSD_1D_csd_reconstruction(self):
"""reconstructed csd should be similar to model csd"""
params = {
'sigma': self.sigma,
'source_type': 'gauss_lim',
'x_min': self.xmin,
'x_max': self.xmax,
'h': self.h
}
k = KCSD(self.elec_pos, self.meas_pot, params)
k.estimate_csd()
"""print np.max(k.estimated_csd)
plot(k.estimated_csd)
plot(self.true_csd)
show()
print k.X_src"""
for estimated_csd, true_csd in zip(k.solver.estimated_csd,
self.true_csd):
self.assertAlmostEqual(estimated_csd, true_csd, places=1)
