def show_potential_on_grid(cable, I):
import matplotlib.pyplot as plt
xx, yy = field.calc_grid([-10, 10], [-10, 10], 10)
v_ext = field.estimate_on_grid(cable, I, xx, yy)
cs = plt.contour(xx, yy, v_ext[0, :, :])
plt.clabel(cs)
plt.show()
def calc_field(all_params, seg_coords,results_sim):
# run the simulation for t_length
print 'simulate neuron'
grid_size = all_params['grid_size']
external_resistivity = all_params['external_resistivity']
record_x_range = all_params[
'record_x_range'] # the grid with defined cells might be of different size that the one where we calculate
record_y_range = all_params['record_y_range']
# estimates the field in every defined point of the grid
print 'estimate the field'
n_samp = grid_size # grid size will be n_samp x n_samp
xx, yy = field.calc_grid(record_x_range, record_y_range, n_samp) # define grid
v_ext = field.estimate_on_grid(seg_coords, results_sim['I'], xx, yy, eta=external_resistivity)
# find dipole moment for this cell
Q = field.calc_dipole_moment(seg_coords, results_sim['I_axial'])
Q_len = np.sqrt(np.sum(Q ** 2, 0))
results_sim = {'xx': xx, # grid xx
'yy': yy, # grid yy
'Q': Q, # dipole
'Q_len': Q_len, # dipole length
'v_ext': np.array(v_ext), # potential
'seg_coords': seg_coords}
return results_sim
def show_potential_on_grid(cable, I):
import matplotlib.pyplot as plt
xx, yy = field.calc_grid([-10,10], [-10,10], 10)
v_ext = field.estimate_on_grid(cable, I, xx, yy)
cs=plt.contour(xx, yy, v_ext[0,:,:])
plt.clabel(cs)
plt.show()
def calc_field(data, record_x_range, record_y_range, n_samp, savename):
ext_resist = 3.5 # Ohm.m
dt = data['dt']
I = data['I']
t=data['t']
seg_coords=data['seg_coords']
xx, yy = field.calc_grid(record_x_range, record_y_range, n_samp) # define grid
v_ext = field.estimate_on_grid(seg_coords, I, xx, yy, eta=ext_resist) # field
# save calculated lfp
np.savez(savename+'.npz', v_ext=v_ext,
xx=xx,yy=yy, n_samp=n_samp,
dt=dt, x_range = record_x_range, y_range = record_y_range,
t=t)
n_waveforms=4
#filter = None
pt_idx = 1094
filter = field.hp_fir(order, cutoff, dt)
# Simulation
cell.load_model('models/Mainen/demo_ext.hoc',
'models/Mainen/%s/.libs/libnrnmech.so' % ARCH)
cell.initialize(dt=dt)
t, I = cell.integrate(tstop)
# Calculation of field
xrange = np.array([-600, 600])-150
yrange = [-1500, 600]
coords = cell.get_seg_coords()
xx, yy = field.calc_grid(xrange, yrange, n_samp=Nsamp)
v_ext = field.estimate_on_grid(coords, I, xx, yy)
if filter:
for i in range(v_ext.shape[1]):
for j in range(v_ext.shape[2]):
v_ext[:, i, j] = filter(v_ext[:, i, j])
#sample spike waveforms
spikes = np.mgrid[xrange[0]:xrange[1]:n_waveforms*1j,
yrange[0]:yrange[1]:n_waveforms*1j]
xx_sp, yy_sp = spikes
xx_sp, yy_sp = xx_sp.flatten(), yy_sp.flatten()
xx_sp, yy_sp = xx_sp[:, np.newaxis], yy_sp[:, np.newaxis]
v_samples = field.estimate_on_grid(coords, I, xx_sp, yy_sp)
def plot_contour(x_range, y_range, n_contours=15):
xx, yy = field.calc_grid(x_range, y_range, n_samp=Nsamp)
vext = field.estimate_on_grid(coords, I, xx, yy)
vext_p2p = vext.max(0) - vext.min(0)
graph.logcontour(xx, yy, vext_p2p / 1000., n_contours=n_contours, linecolors='0.8', linewidths=1, unit=r'$\mathrm{\mu}$V', fontsize=8)
init_synapse(cell_no=0, syn_start=0.1)
init_synapse(cell_no=1, syn_start=0.5)
# initialise and run neuron simulations
cell.initialize(dt=dt)
t, I_cell = cell.integrate(t_length, i_axial=False, neuron_cells=cells)
# CALCULATION OF THE FIELD
seg_coords = cell.get_seg_coords()
n_samp = 40
x_range = [-210, 430]
y_range = [-255, 725]
# define grid
xx, yy = field.calc_grid(x_range, y_range, n_samp)
# join the currents together
I = I_cell.swapaxes(0, 1).reshape(I_cell.shape[1], -1)
v_ext = field.estimate_on_grid(seg_coords, I, xx, yy)
# PLOTS
synapses = [s for s in cell.get_point_processes()]
# plot neurons shape
graph.plot_neuron(seg_coords, colors='0.4')
# plot field potential
plt.imshow((v_ext[time_to_plot, :, :]),
interpolation="nearest",
n_waveforms = 4
#filter = None
pt_idx = 1094
filter = field.hp_fir(order, cutoff, dt)
# Simulation
cell.load_model('models/Mainen/demo_ext.hoc',
'models/Mainen/%s/.libs/libnrnmech.so' % ARCH)
cell.initialize(dt=dt)
t, I = cell.integrate(tstop)
# Calculation of field
xrange = np.array([-600, 600]) - 150
yrange = [-1500, 600]
coords = cell.get_seg_coords()
xx, yy = field.calc_grid(xrange, yrange, n_samp=Nsamp)
v_ext = field.estimate_on_grid(coords, I, xx, yy)
if filter:
for i in range(v_ext.shape[1]):
for j in range(v_ext.shape[2]):
v_ext[:, i, j] = filter(v_ext[:, i, j])
#sample spike waveforms
spikes = np.mgrid[xrange[0]:xrange[1]:n_waveforms * 1j,
yrange[0]:yrange[1]:n_waveforms * 1j]
xx_sp, yy_sp = spikes
xx_sp, yy_sp = xx_sp.flatten(), yy_sp.flatten()
xx_sp, yy_sp = xx_sp[:, np.newaxis], yy_sp[:, np.newaxis]
v_samples = field.estimate_on_grid(coords, I, xx_sp, yy_sp)
