def __csd_correlation(v, m):
'''compute correlation coefficient between CSD estimate and CSD
for a given source diameter'''
if m == 'delta':
icsd_input[m].update({'diam': v * pq.m})
_icsd = icsd.DeltaiCSD(**icsd_input[m])
corrcoef = pl.corrcoef(
CSD_filtered.flatten(),
pl.array(_icsd.filter_csd(_icsd.get_csd()) / pq.m).flatten())
elif m == 'step':
icsd_input[m].update({'diam': v * pq.m})
_icsd = icsd.StepiCSD(**icsd_input[m])
corrcoef = pl.corrcoef(
CSD_filtered.flatten(),
pl.array(_icsd.filter_csd(_icsd.get_csd()) / pq.m).flatten())
elif m == 'spline':
icsd_input[m].update({'diam': v * pq.m})
_icsd = icsd.SplineiCSD(**icsd_input[m])
corrcoef = pl.corrcoef(
CSD76ptF.flatten(),
pl.array(_icsd.filter_csd(_icsd.get_csd()) / pq.m).flatten())
else:
raise Exception, 'm = %s should be either [delta, step, spline]' % m
return corrcoef[0, -1]
def __csd_error(v, m):
'''using squared difference summed'''
if m == 'delta':
icsd_input[m].update({'diam': v * pq.m})
_icsd = icsd.DeltaiCSD(**icsd_input[m])
error = (
(pl.array(_icsd.filter_csd(_icsd.get_csd()) /
(100E-6 * pq.m)).reshape(CSD_filtered.size) * 1E-9 -
CSD_filtered.reshape(CSD_filtered.size))**2).sum()
elif m == 'step':
icsd_input[m].update({'diam': v * pq.m})
_icsd = icsd.StepiCSD(**icsd_input[m])
error = ((pl.array(_icsd.filter_csd(_icsd.get_csd())).reshape(
CSD_filtered.size) * 1E-9 -
CSD_filtered.reshape(CSD_filtered.size))**2).sum()
elif m == 'spline':
icsd_input[m].update({'diam': v * pq.m})
_icsd = icsd.SplineiCSD(**icsd_input[m])
error = ((pl.array(_icsd.filter_csd(_icsd.get_csd())).reshape(
CSD76ptF.size) * 1E-9 - CSD76ptF.reshape(CSD76ptF.size))**2).sum()
else:
raise Exception, 'm = %s should be either [delta, step, spline]' % m
return error
def test_SplineiCSD_01(self):
'''test using standard SI units, deep electrode coordinates'''
# set some parameters for ground truth csd and csd estimates., e.g.,
# we will use same source diameter as in ground truth
# contact point coordinates
z_j = np.arange(10, 31) * 1E-4 * pq.m
# source coordinates
z_i = z_j
# current source density magnitude
C_i = np.zeros(z_i.size) * pq.A / pq.m**3
C_i[7:12:2] += np.array([-.5, 1., -.5]) * pq.A / pq.m**3
# source radius (delta, step)
R_i = np.ones(z_i.size) * 1E-3 * pq.m
# source height (cylinder)
h_i = np.ones(z_i.size) * 1E-4 * pq.m
# conductivity, use same conductivity for top layer (z_j < 0)
sigma = 0.3 * pq.S / pq.m
sigma_top = sigma
# construct interpolators, spline method assume underlying source
# pattern generating LFPs that are cubic spline interpolates between
# contacts so we generate CSD data relying on the same assumption
f_C = interp1d(z_i, C_i, kind='cubic')
f_R = interp1d(z_i, R_i)
num_steps = 201
z_i_i = np.linspace(float(z_i[0]), float(
z_i[-1]), num_steps) * z_i.units
C_i_i = f_C(np.asarray(z_i_i)) * C_i.units
R_i_i = f_R(z_i_i) * R_i.units
h_i_i = np.ones(z_i_i.size) * np.diff(z_i_i).min()
# flag for debug plots
plot = False
# get LFP and CSD at contacts
phi_j, C_i = get_lfp_of_cylinders(z_j, z_i_i, C_i_i, R_i_i, h_i_i,
sigma, plot)
spline_input = {
'lfp': phi_j,
'coord_electrode': z_j,
'diam': R_i * 2,
'sigma': sigma,
'sigma_top': sigma,
'num_steps': num_steps,
'tol': 1E-12, # Tolerance in numerical integration
'f_type': 'gaussian',
'f_order': (3, 1),
}
spline_icsd = icsd.SplineiCSD(**spline_input)
csd = spline_icsd.get_csd()
self.assertEqual(C_i.units, csd.units)
nt.assert_array_almost_equal(C_i, csd, decimal=3)
_icsd = icsd.StepiCSD(**icsd_input[m])
icsd_output.update(
{'icsd_step': _icsd.filter_csd(_icsd.get_csd()) * 1E-9})
my_errors['step'], my_diams['step'], diam_best['step'] = \
minimize_icsd_error_brute(m)
icsd_input[m].update({'diam': diam_best['step'] * pq.m})
print 'best diameter step: %.5e' % diam_best['step']
_icsd = icsd.StepiCSD(**icsd_input[m])
icsd_output.update(
{'icsd_step': _icsd.filter_csd(_icsd.get_csd()) * 1E-9})
elif m == 'spline':
_icsd = icsd.SplineiCSD(**icsd_input[m])
icsd_output.update(
{'icsd_spline': _icsd.filter_csd(_icsd.get_csd()) * 1E-9})
my_errors['spline'], my_diams['spline'], diam_best['spline'] = \
minimize_icsd_error_brute(m)
icsd_input[m].update({'diam': diam_best['spline'] * pq.m})
print 'best diameter spline: %.5e' % diam_best['spline']
_icsd = icsd.SplineiCSD(**icsd_input[m])
icsd_output.update(
{'icsd_spline': _icsd.filter_csd(_icsd.get_csd()) * 1E-9})
elif m == 'std':
_icsd = icsd.StandardCSD(**icsd_input[m])
