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 test_DeltaiCSD_04(self):
'''test non-continous z_j array'''
# 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(21) * 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**2
C_i[7:12:2] += np.array([-.5, 1., -.5]) * pq.A / pq.m**2
# source radius (delta, step)
R_i = np.ones(z_j.size) * 1E-3 * pq.m
# conductivity, use same conductivity for top layer (z_j < 0)
sigma = 0.3 * pq.S / pq.m
sigma_top = sigma
# flag for debug plots
plot = False
# get LFP and CSD at contacts
phi_j, C_i = get_lfp_of_disks(z_j, z_i, C_i, R_i, sigma,
plot)
inds = np.delete(np.arange(21), 5)
delta_input = {
'lfp': phi_j[inds],
'coord_electrode': z_j[inds],
'diam': R_i[inds] * 2, # source diameter
'sigma': sigma, # extracellular conductivity
'sigma_top': sigma_top, # conductivity on top of cortex
'f_type': 'gaussian', # gaussian filter
'f_order': (3, 1), # 3-point filter, sigma = 1.
}
delta_icsd = icsd.DeltaiCSD(**delta_input)
csd = delta_icsd.get_csd()
self.assertEqual(C_i.units, csd.units)
nt.assert_array_almost_equal(C_i[inds], csd)
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
'axis': 'tight',
'legend': False,
'new_fig': True
}
icsd_input = input_init(lfp_data=electrodeLFP[:16, :] * pq.mV,
z_data=pl.linspace(100, 1600, 16) * 1E-6 * pq.m,
diam=icsd_diam * pq.m)
icsd_output = {}
diam_best = {}
my_errors = {}
my_diams = {}
for m in icsd_input:
if m == 'delta':
_icsd = icsd.DeltaiCSD(**icsd_input[m])
icsd_output.update({
'icsd_delta':
_icsd.filter_csd(_icsd.get_csd()) / (100E-6 * pq.m) * 1E-9
})
my_errors['delta'], my_diams['delta'], diam_best['delta'] = \
minimize_icsd_error_brute(m)
icsd_input[m].update({'diam': diam_best['delta'] * pq.m})
print 'best diameter delta: %.5e' % diam_best['delta']
_icsd = icsd.DeltaiCSD(**icsd_input[m])
icsd_output.update({
'icsd_delta':
_icsd.filter_csd(_icsd.get_csd()) / (100E-6 * pq.m) * 1E-9
})
