ax5.scatter(source_xs, source_zs, c=source_amps)
if initial is not None:
ax5.scatter(cell.xmid[initial], cell.zmid[initial], marker='*', c='r')
fig.colorbar(im5, label='[mV / $\mu m^2$]')
ax6 = plt.subplot(236,
title='V_ext along the cell',
xlabel='z [$\mu$m]',
ylabel='mV')
for i in range(200, pulse_start + pulse_duration + 50, 10):
ax6.plot(np.arange(cell.totnsegs),
np.asarray(cell.v_ext).T[i][cell.zmid.argsort()][::-1])
plt.tight_layout()
pconv.mark_subplots(ax1, 'A', xpos=-.25, ypos=.99)
pconv.mark_subplots(ax2, 'B', xpos=-.25, ypos=.99)
pconv.mark_subplots(ax4, 'D', xpos=-.25, ypos=.99)
pconv.mark_subplots(ax5, 'E', xpos=-.25, ypos=.99)
# pconv.mark_subplots(ax3, 'C', xpos=-.25 , ypos=.99)
# pconv.mark_subplots(ax3, 'C')
pconv.mark_subplots(ax6, 'F', xpos=-.25, ypos=.99)
fig.subplots_adjust(left=.08,
right=.96,
bottom=.07,
top=.96,
wspace=None,
hspace=None)
plt.show()
def make_fig_1(totnsegs, imem, syninds, xmids, zmids, zips, cb_LFP_close,
cb_LFP_far, multi_dip_LFP_close, multi_dip_LFP_far,
db_LFP_close, db_LFP_far, LFP_max, time_max, multi_dips,
multi_dip_locs, single_dip, r_mid, X, Z, X_f, Z_f):
plt.interactive(1)
plt.close('all')
fig = plt.figure()
ax0 = plt.subplot2grid((3, 3), (0, 0))
ax1 = plt.subplot2grid((3, 3), (1, 0))
ax2 = plt.subplot2grid((3, 3), (2, 0))
ax3 = plt.subplot2grid((3, 3), (0, 1))
ax4 = plt.subplot2grid((3, 3), (1, 1))
ax5 = plt.subplot2grid((3, 3), (2, 1))
ax6 = plt.subplot2grid((3, 3), (0, 2))
ax7 = plt.subplot2grid((3, 3), (1, 2))
ax8 = plt.subplot2grid((3, 3), (2, 2))
# plot neuron morphology in top row
plot_neuron(ax0, syninds, xmids, zmids, zips, syn=True, lengthbar=True)
plot_neuron(ax3,
syninds,
xmids,
zmids,
zips,
syn=True,
lengthbar=True,
lb_clr='w')
plot_neuron(ax6,
syninds,
xmids,
zmids,
zips,
syn=True,
lengthbar=True,
lb_clr='w')
# plot transmembrane currents
for idx in range(totnsegs):
arrowlength = np.abs(imem[idx, time_max]) * 1e5
wdth = 1.
if [idx] == syninds:
arrowlength = -700.
wdth = 2.
ax0.arrow(
xmids[idx] - arrowlength,
zmids[idx],
arrowlength,
0.,
width=4.,
head_length=39.,
head_width=30.,
length_includes_head=True,
color='#0D325F',
# alpha=.5
)
else:
ax0.arrow(xmids[idx],
zmids[idx],
arrowlength,
0.,
width=wdth,
head_length=3.4,
head_width=7.,
length_includes_head=True,
color='#D90011',
alpha=.5)
# plt lfp close
plot_lfp(fig, ax1, cb_LFP_close, LFP_max, time_max, X, Z, lengthbar=True)
plot_lfp(fig,
ax4,
multi_dip_LFP_close,
LFP_max,
time_max,
X,
Z,
lengthbar=False)
plot_lfp(fig, ax7, db_LFP_close, LFP_max, time_max, X, Z, lengthbar=False)
# plot lfp far
plot_lfp_far(fig,
ax2,
cb_LFP_far,
LFP_max,
time_max,
X_f,
Z_f,
lengthbar=True)
plot_lfp_far(fig,
ax5,
multi_dip_LFP_far,
LFP_max,
time_max,
X_f,
Z_f,
lengthbar=False)
LFP, ep_intervals, ticks = plot_lfp_far(fig,
ax8,
db_LFP_far,
LFP_max,
time_max,
X_f,
Z_f,
lengthbar=False,
colorax=True)
# plot neurons in second row
for ax in [ax1, ax4]:
plot_neuron(ax, syninds, xmids, zmids, zips, syn=False, clr='w')
# plot multi-dipole arrows
for i in range(len(multi_dips)):
p = multi_dips[i, time_max] * 10
loc = multi_dip_locs[i]
w = np.linalg.norm(p) * 2
ax3.arrow(loc[0] - 0.5 * p[0],
loc[2] - 0.5 * p[2],
p[0],
p[2],
color='green',
alpha=0.8,
width=w,
head_width=4 * w,
length_includes_head=False)
# plot single dipole arrow
arrow = single_dip[time_max] * 25
arrow_colors = ['gray', 'w']
arrow_axes = [ax6, ax7]
for i in range(2):
arrow_axes[i].arrow(
r_mid[0] - 2 * arrow[0],
r_mid[2] - 2 * arrow[2],
arrow[0] * 4,
arrow[2] * 4, #fc = 'k',ec = 'k',
color=arrow_colors[i],
alpha=0.8,
width=12, #head_width = 60.,
length_includes_head=True) #,
# colorbar
cax = fig.add_axes([0.13, 0.03, 0.8, 0.01])
cbar = fig.colorbar(ep_intervals,
cax=cax,
orientation='horizontal',
format='%3.3f',
extend='max')
cbar.set_ticks(ticks)
labels = [
r'$-10^{\/\/2}$', r'$-10^{\/\/1}$', r'$-1.0$', r'$-10^{-1}$',
r'$-10^{-2}$', r'$\/\/10^{-2}$', r'$\/\/10^{-1}$', r'$\/\/1.0$',
r'$\/\/10^{\/\/1}$', r'$\/\/10^{\/\/2}$'
]
cax.set_xticklabels(labels, rotation=40)
cbar.set_label('$\phi$ (nV)', labelpad=-0.4)
cbar.outline.set_visible(False)
ax0.set_title('compartment-based', fontsize='x-small')
ax3.set_title('multi-dipole', fontsize='x-small')
ax6.set_title('single-dipole', fontsize='x-small')
fig.set_size_inches(8, 8)
plotting_convention.mark_subplots([ax0, ax3, ax6],
letters='ABC',
xpos=-0.02,
ypos=1.0)
plotting_convention.mark_subplots([ax1, ax4, ax7],
letters='DEF',
xpos=-0.02,
ypos=1.05)
plotting_convention.mark_subplots([ax2, ax5, ax8],
letters='GHI',
xpos=-0.02,
ypos=0.94)
for ax in [ax1, ax2, ax4, ax5, ax7, ax8, ax0, ax3, ax6]:
ax.set_aspect('equal', 'datalim')
plt.subplots_adjust(wspace=0.05,
hspace=0.05,
left=0.1,
bottom=0.05,
right=0.96,
top=0.93)
return fig
xlabel="x [$\mu$m]",
ylabel='y [$\mu$m]')
img12 = ax12.imshow(dd_field_xy2.T,
extent=[-x_extent, x_extent, -y_extent, y_extent],
**imshow_dict)
# ax2.scatter(source_xs, np.ones(len(source_xs)) * -300, c=source_amps, s=100, vmin=-1.4,
# vmax=1.4, edgecolor='k', lw=2, cmap=plt.cm.bwr, clip_on=False)
plt.colorbar(img12, label="mV / $\mu m^2$")
fig.subplots_adjust(wspace=0.06,
left=.02,
right=.95,
top=.92,
hspace=.5,
bottom=.08)
pconv.mark_subplots(ax1, 'A', xpos=-.05, ypos=.99)
pconv.mark_subplots(ax2, 'B', xpos=-.05, ypos=.99)
pconv.mark_subplots(ax3, 'C', xpos=-.05, ypos=.99)
pconv.mark_subplots(ax4, 'D', xpos=-.05, ypos=.99)
pconv.mark_subplots(ax5, 'E', xpos=-.05, ypos=.99)
pconv.mark_subplots(ax6, 'F', xpos=-.05, ypos=.99)
pconv.mark_subplots(ax7, 'G', xpos=-.05, ypos=.99)
pconv.mark_subplots(ax8, 'H', xpos=-.05, ypos=.99)
pconv.mark_subplots(ax9, 'I', xpos=-.05, ypos=.99)
pconv.mark_subplots(ax10, 'J', xpos=-.05, ypos=.99)
pconv.mark_subplots(ax11, 'K', xpos=-.05, ypos=1.)
pconv.mark_subplots(ax12, 'L', xpos=-.05, ypos=1.)
# plt.tight_layout()
plt.savefig("extracellular_potential_2nd_derivative_" + name_shape_ecog +
".png")
ax.set_title(r'$\sigma_{\mathrm{skull}} = \sigma_{\mathrm{brain}} / 80$',
fontsize=17)
l1, = plt.plot(theta, phi_80_98, 'g', lw=2)
l2, = plt.plot(theta, phi_80, 'k', lw=2)
l3, = plt.plot(theta[:50], phi_80_fit[:50], 'm+', lw=2)
l4, = plt.plot(theta, phi_80_c, 'b', lw=2)
l5, = plt.plot(theta, num_80, 'r.', lw=2)
ax.set_xticks(ax.get_xticks()[::2])
ax.tick_params(labelsize=15.)
ax.set_yticks(np.linspace(0, 110, 12))
ax.set_yticklabels(
['0', '', '20', '', '40', '', '60', '', '80', '', '100', ''])
lines = [l1, l2, l3, l4, l5]
line_names = [
'Srinivasan (1998)', 'Nunez & Srinivasan (2006)',
'Fit - Nunez & Srinivasan (2006) ', 'Present results - analytical',
'Present results - FEM'
]
fig.legend(lines,
line_names,
frameon=False,
ncol=1,
bbox_to_anchor=[1.01, 0.85],
fontsize=12)
simplify_axes(fig.axes)
mark_subplots(fig.axes, ypos=1.07, xpos=-0.)
plt.savefig(os.path.join('results', 'Potentials_scaled.png'), dpi=150)
# plt.show()
def simulate_laminar_LFP():
# DEPRECATED, and not updated for LFPy2.2 and newer
dt = 2**-5
cell_name = 'hay'
elec_z = np.linspace(-200, 1200, 15)
elec_x = np.ones(len(elec_z)) * 50
elec_y = np.zeros(len(elec_z))
h = np.abs(elec_z[1] - elec_z[0])
electrode_parameters = {
'sigma': 0.3, # extracellular conductivity
'x': elec_x, # x,y,z-coordinates of contact points
'y': elec_y,
'z': elec_z
}
elec_clr = lambda idx: plt.cm.viridis(idx / len(elec_z))
num_sims = 10
cells = []
summed_LFP = []
summed_cdm = []
for sim in range(num_sims):
print(sim + 1, "/", num_sims)
cell_wca, idx_clr, plot_idxs = run_cell_simulation_distributed_input(
dt=dt, cell_name=cell_name)
cells.append([cell_wca, idx_clr, plot_idxs])
for sim in range(num_sims):
cell_wca, idx_clr, plot_idxs = cells[sim]
fig = plt.figure(figsize=[7, 7])
fig.subplots_adjust(hspace=0.5,
left=0.0,
wspace=0.4,
right=0.96,
top=0.97,
bottom=0.1)
ax_m = fig.add_axes([-0.01, 0.05, 0.3, 0.97],
aspect=1,
frameon=False,
xlim=[-350, 350],
xticks=[],
yticks=[])
[
ax_m.plot([cell_wca.xstart[idx], cell_wca.xend[idx]],
[cell_wca.zstart[idx], cell_wca.zend[idx]],
c='k') for idx in range(cell_wca.totnsegs)
]
[
ax_m.plot(cell_wca.xmid[idx],
cell_wca.zmid[idx],
'o',
c=idx_clr[idx],
ms=13) for idx in plot_idxs
]
[
ax_m.plot(cell_wca.xmid[idx], cell_wca.zmid[idx], 'rd')
for idx in cell_wca.synidx
]
ax_top = 0.98
ax_h = 0.25
h_space = 0.1
ax_w = 0.17
ax_left = 0.4
cell = cell_wca
elec = LFPy.RecExtElectrode(cell, **electrode_parameters)
elec.calc_lfp()
ax_vm = fig.add_axes([ax_left, ax_top - ax_h, ax_w, ax_h],
ylim=[-80, 50],
xlim=[0, 80],
xlabel="Time (ms)")
ax_eap = fig.add_axes([ax_left + 0.3, 0.1, ax_w, 0.8],
xlim=[0, 80],
xlabel="Time (ms)")
ax_cdm = fig.add_axes(
[ax_left, 0.2, ax_w, ax_h],
xlabel="Time (ms)",
ylim=[-0.5, 1],
xlim=[0, 80],
)
ax_vm.set_ylabel("Membrane\npotential\n(mV)", labelpad=-3)
ax_eap.set_ylabel("Extracellular potential ($\mu$V)", labelpad=-3)
ax_cdm.set_ylabel("Curent dipole\nmoment\n($\mu$A$\cdot \mu$m)",
labelpad=-3)
[
ax_vm.plot(cell.tvec, cell.vmem[idx], c=idx_clr[idx])
for idx in plot_idxs
]
elec.LFP -= elec.LFP[:, 0, None]
cell.current_dipole_moment -= cell.current_dipole_moment[0, :]
summed_LFP.append(elec.LFP)
summed_cdm.append(cell.current_dipole_moment)
normalize = np.max(np.abs(elec.LFP))
for idx in range(len(elec_x)):
ax_eap.plot(cell.tvec,
elec.LFP[idx] / normalize * h + elec_z[idx],
c=elec_clr(idx))
ax_m.plot(elec_x[idx], elec_z[idx], c=elec_clr(idx), marker='D')
ax_cdm.plot(cell.tvec, 1e-3 * cell.current_dipole_moment[:, 2], c='k')
mark_subplots([ax_m], xpos=0.1, ypos=0.95)
simplify_axes(fig.axes)
plt.savefig(
join("figures",
'laminar_LFP_ca_spike_{}_{}.png'.format(cell_name, sim)))
# plt.savefig(join("figures", 'hay_ca_spike.pdf'))
plt.close("all")
summed_LFP = np.sum(summed_LFP, axis=0)
summed_cdm = np.sum(summed_cdm, axis=0)
normalize = np.max(np.abs(summed_LFP))
plt.subplot(121)
for idx in range(len(elec_x)):
plt.plot(cell.tvec,
summed_LFP[idx] / normalize * h + elec_z[idx],
c=elec_clr(idx))
plt.subplot(122)
plt.plot(cell.tvec, summed_cdm[:, 2])
plt.savefig(
join("figures",
'summed_LFP_CDM_{}_num:{}.png'.format(cell_name, num_sims)))
def simulate_spike_current_dipole_moment():
dt = 2**-5
cell_name = 'hay'
jitter_std = 10
num_trials = 1000
# Create a grid of measurement locations, in (mum)
grid_x, grid_z = np.mgrid[-750:751:25, -750:1301:25]
grid_y = np.zeros(grid_x.shape)
# Define electrode parameters
grid_elec_params = {
'sigma': 0.3, # extracellular conductivity
'x': grid_x.flatten(), # electrode requires 1d vector of positions
'y': grid_y.flatten(),
'z': grid_z.flatten()
}
elec_x = np.array([
30,
])
elec_y = np.array([
0,
])
elec_z = np.array([
0,
])
elec_params = {
'sigma': 0.3, # extracellular conductivity
'x': elec_x, # x,y,z-coordinates of contact points
'y': elec_y,
'z': elec_z
}
elec_clr = 'r'
cell_woca_data, idx_clr, plot_idxs = run_cell_simulation(
make_ca_spike=False,
dt=dt,
cell_name=cell_name,
grid_elec_params=grid_elec_params,
elec_params=elec_params)
cell_wca_data, idx_clr, plot_idxs = run_cell_simulation(
make_ca_spike=True,
dt=dt,
cell_name=cell_name,
grid_elec_params=grid_elec_params,
elec_params=elec_params)
fig = plt.figure(figsize=[8, 7])
fig.subplots_adjust(hspace=0.5,
left=0.0,
wspace=0.4,
right=0.96,
top=0.97,
bottom=0.1)
ax_m = fig.add_axes([-0.01, 0.05, 0.25, 0.97],
aspect=1,
frameon=False,
xticks=[],
yticks=[])
ax_m.plot(cell_wca_data["cell_x"].T, cell_wca_data["cell_z"].T, c='k')
[
ax_m.plot(cell_wca_data["cell_x"][idx].mean(),
cell_wca_data["cell_z"][idx].mean(),
'o',
c=idx_clr[idx],
ms=13) for idx in plot_idxs
]
ax_m.plot(elec_x, elec_z, elec_clr, marker='D')
ax_top = 0.98
ax_h = 0.15
h_space = 0.1
ax_w = 0.17
ax_left = 0.37
num = 11
levels = np.logspace(-2.5, 0, num=num)
scale_max = 100.
levels_norm = scale_max * np.concatenate((-levels[::-1], levels))
bwr_cmap = plt.cm.get_cmap('bwr_r')
colors_from_map = [
bwr_cmap(i * np.int(255 / (len(levels_norm) - 2)))
for i in range(len(levels_norm) - 1)
]
colors_from_map[num - 1] = (1.0, 1.0, 1.0, 1.0)
spike_plot_time_idxs = [1030, 1151]
summed_cdm_max = np.zeros(2)
for plot_row, cell_data in enumerate([cell_woca_data, cell_wca_data]):
ax_left += plot_row * 0.25
grid_LFP = cell_data["grid_LFP"]
cdm = cell_data["cdm"]
tvec = cell_data["tvec"]
vmem = cell_data["vmem"]
elec_LFP = cell_data["elec_LFP"]
time_idx = spike_plot_time_idxs[plot_row]
print(tvec[time_idx])
grid_LFP_ = grid_LFP[:, time_idx].reshape(grid_x.shape)
ax_ = fig.add_axes([0.75, 0.55 - plot_row * 0.46, 0.3, 0.45],
xticks=[],
yticks=[],
aspect=1,
frameon=False)
mark_subplots(ax_, [["D"], ["E"]][plot_row], ypos=0.95, xpos=0.35)
ax_.plot(cell_data["cell_x"].T, cell_data["cell_z"].T, c='k')
ep_intervals = ax_.contourf(grid_x,
grid_z,
grid_LFP_,
zorder=-2,
colors=colors_from_map,
levels=levels_norm,
extend='both')
ax_.contour(grid_x,
grid_z,
grid_LFP_,
colors='k',
linewidths=(1),
zorder=-2,
levels=levels_norm)
if plot_row == 1:
cax = fig.add_axes([0.82, 0.12, 0.16, 0.01])
cbar = fig.colorbar(ep_intervals,
cax=cax,
orientation='horizontal',
format='%.0E')
cbar.set_ticks(
np.array([-1, -0.1, -0.01, 0, 0.01, 0.1, 1]) * scale_max)
cax.set_xticklabels(np.array(
np.array([-1, -0.1, -0.01, 0, 0.01, 0.1, 1]) * scale_max,
dtype=int),
fontsize=11,
rotation=45)
cbar.set_label('$\phi$ (µV)', labelpad=-5)
sum_tvec, summed_cdm = sum_jittered_cdm(cdm[2, :], dt, jitter_std,
num_trials)
summed_cdm_max[plot_row] = np.max(np.abs(summed_cdm))
ax_vm = fig.add_axes([ax_left, ax_top - ax_h, ax_w, ax_h],
ylim=[-80, 50],
xlim=[0, 100])
ax_eap = fig.add_axes(
[ax_left, ax_top - 2 * ax_h - h_space, ax_w, ax_h],
ylim=[-120, 40],
xlim=[0, 100])
ax_cdm = fig.add_axes(
[ax_left, ax_top - 3 * ax_h - 2 * h_space, ax_w, ax_h],
ylim=[-0.5, 1],
xlim=[0, 100],
)
ax_cdm_sum = fig.add_axes(
[ax_left, ax_top - 4 * ax_h - 3 * h_space, ax_w, ax_h],
ylim=[-250, 100],
xlabel="Time (ms)",
xlim=[0, 140])
if plot_row == 0:
ax_vm.set_ylabel("Membrane\npotential\n(mV)", labelpad=-3)
ax_eap.set_ylabel("Extracellular\npotential\n(µV)", labelpad=-3)
ax_cdm.set_ylabel("Curent dipole\nmoment\n(µA$\cdot$µm)",
labelpad=-3)
ax_cdm_sum.set_ylabel("Jittered sum\n(µA$\cdot$µm)", labelpad=-3)
elif plot_row == 1:
ax_vm.text(65, -5, "Ca$^{2+}$ spike", fontsize=11, color='orange')
ax_vm.arrow(80, -10, -10, -18, color='orange', head_width=4)
mark_subplots(ax_vm, [["B1"], ["C1"]][plot_row], xpos=-0.3, ypos=0.93)
mark_subplots(ax_eap, [["B2"], ["C2"]][plot_row], xpos=-0.3, ypos=0.93)
mark_subplots(ax_cdm, [["B3"], ["C3"]][plot_row],
xpos=-0.35,
ypos=0.97)
mark_subplots(ax_cdm_sum, [["B4"], ["C4"]][plot_row],
xpos=-0.3,
ypos=0.93)
[ax_vm.plot(tvec, vmem[idx], c=idx_clr[idx]) for idx in plot_idxs]
ax_vm.axvline(tvec[time_idx], ls='--', c='gray')
[
ax_eap.plot(tvec, elec_LFP[idx], c=elec_clr)
for idx in range(len(elec_x))
]
ax_cdm.plot(tvec, 1e-3 * cdm[2, :], c='k')
ax_cdm_sum.plot(sum_tvec, 1e-3 * summed_cdm, c='k')
print(
"Summed CDM max (abs), ratio",
summed_cdm_max * 1e-3,
)
mark_subplots([ax_m], xpos=0.1, ypos=0.95)
simplify_axes(fig.axes)
plt.savefig(join("figures", 'Figure5.pdf'))
