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_interp(data_path, ds_step):
# calculate pixel (um) positions of model objects
top_l5 = 1466
soma_l5 = 177.5
bottom_l5 = -72
top_l2 = 1466
soma_l2 = 1000
bottom_l2 = 769
num_contacts_hnn = 20
hnn_values = [top_l2, soma_l2, bottom_l2, soma_l5, bottom_l5]
perlayer = (top_l5 - bottom_l5) / (num_contacts_hnn - 5.0)
spacing_um_hnn = perlayer
# print("contact spacing (microns)",spacing_um_hnn)
first_contact = top_l5 + 2 * spacing_um_hnn
last_contact = bottom_l5 - 2 * spacing_um_hnn
spacing_hnn = []
for i, x in enumerate(
np.linspace(first_contact, last_contact, num_contacts_hnn)):
# print("contact %d: %.2f" % (i+1, x))
spacing_hnn.append(x)
#load data
dir = data_path
sampr_hnn, LFP_hnn, dt_hnn, tt_hnn, CSD_hnn, maxlfp_hnn, ntrial_hnn = load_hnn.loadHNNdir(
dir, spacing_um_hnn)
#Down sample fixed interval
# ds_step = 10
tt_hnn = tt_hnn[::ds_step]
# Average iCSD from HNN
z_data_hnn = np.linspace(spacing_um_hnn * 1E-6, 2300E-6,
num_contacts_hnn) * pq.m # [m]
diam_hnn = 500E-6 * pq.m # [m]
h_hnn = spacing_um_hnn * 1E-6 * pq.m # [m]
sigma_hnn = 0.3 * pq.S / pq.m # [S/m] or [1/(ohm*m)]
sigma_top_hnn = 0.3 * pq.S / pq.m # [S/m] or [1/(ohm*m)]
# Create dictionary with iCSD calculations
icsd_hnn = {}
for key in LFP_hnn:
lfp_data_hnn = LFP_hnn[key] * 1E-6 * pq.V # [uV] -> [V]
lfp_data_hnn = lfp_data_hnn[:, ::ds_step]
# Input dictionaries for monkey data
delta_input_hnn = {
'lfp': lfp_data_hnn,
'coord_electrode': z_data_hnn,
'diam': diam_hnn, # source diameter
'sigma': sigma_hnn, # extracellular conductivity
'sigma_top': sigma_hnn, # conductivity on top of cortex
'f_type': 'gaussian', # gaussian filter
'f_order': (3, 1), # 3-point filter, sigma = 1.
}
delta_icsd_hnn = icsd.DeltaiCSD(**delta_input_hnn)
icsd_hnn[key] = delta_icsd_hnn.get_csd()
avgiCSD_hnn = load_hnn.getAvgERP(icsd_hnn, sampr_hnn, tt_hnn, maxlfp_hnn,
ntrial_hnn)
# avgiCSD_hnn = load_hnn.downsample(avgiCSD_hnn, sampr_hnn, 2000) #Down sample to LFP range
#Prepare HNN data
# set timerange from 30 to 80 ms
# ewindowms_hnn = 35
# tmin_hnn = 37
# tmax_hnn = tmin_hnn + ewindowms_hnn
# (idx_min_hnn, idx_max_hnn) = (np.where(tt_hnn==tmin_hnn)[0][0], np.where(tt_hnn==tmax_hnn)[0][0])
# X_hnn = tt_hnn[idx_min_hnn:idx_max_hnn]
X_hnn = tt_hnn
# mask = np.zeros(len(tt_hnn), dtype=bool)
# mask[idx_min_hnn:idx_max_hnn] = True
# avgCSD_trim_X_hnn = avgiCSD_hnn[:,mask]
avgCSD_trim_X_hnn = avgiCSD_hnn
Y_hnn = range(avgiCSD_hnn.shape[0])
# X_hnn = range(avgiCSD_hnn.shape[1])
# CSD_spline_hnn=scipy.interpolate.RectBivariateSpline(Y_hnn, X_hnn, avgCSD_trim_X_hnn)
CSD_spline_hnn = scipy.interpolate.RectBivariateSpline(
Y_hnn, X_hnn, avgCSD_trim_X_hnn)
# trim channels
(idx_min_hnn, idx_max_hnn) = (0, 18)
HNN_Y_plot = np.linspace(idx_min_hnn, idx_max_hnn, num=1000)
# print("HNN channels: %d-%d"%(HNN_Y_plot.min(), HNN_Y_plot.max()))
# mask = np.zeros(len(Y_hnn), dtype=bool)
# mask[idx_min_hnn:idx_max_hnn] = True
# avgCSD_trim_XY_hnn = avgCSD_trim_X_hnn[mask,:]
# HNN_ymax = (spacing_hnn[int(HNN_Y_plot.min())]-spacing_hnn[int(HNN_Y_plot.max())])
# HNN_ymin = 0
Z_hnn = CSD_spline_hnn(HNN_Y_plot, X_hnn)
# normalize to abs(Z.min)
Z_hnn = Z_hnn / abs(Z_hnn.min())
return Z_hnn