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 })