error_at_max_1 = np.abs(y1[max_sig_idx] - y2[max_sig_idx]) / np.abs(y1[max_sig_idx])
error_at_max_2 = np.abs(y1[max_sig_idx] - y3[max_sig_idx]) / np.abs(y1[max_sig_idx])
error_at_max_3 = np.abs(y1[max_sig_idx] - y4[max_sig_idx]) / np.abs(y1[max_sig_idx])
max_error_1 = np.max(np.abs(y1[t0_plot_idx:t1_plot_idx] - y2[t0_plot_idx:t1_plot_idx]) /
np.max(np.abs(y1[t0_plot_idx:t1_plot_idx])))
max_error_2 = np.max(np.abs(y1[t0_plot_idx:t1_plot_idx] - y3[t0_plot_idx:t1_plot_idx]) /
np.max(np.abs(y1[t0_plot_idx:t1_plot_idx])))
max_error_3 = np.max(np.abs(y1[t0_plot_idx:t1_plot_idx] - y4[t0_plot_idx:t1_plot_idx]) /
np.max(np.abs(y1[t0_plot_idx:t1_plot_idx])))
print("Single dipole: error at sig max (t={:1.3f} ms): {:1.4f}. Max relative error: {:1.4f}".format(
tvec[max_sig_idx], error_at_max_1, max_error_1))
print("Single dipole opt pos: error at sig max (t={:1.3f} ms): {:1.4f}. Max relative error: {:1.4f}".format(
tvec[max_sig_idx], error_at_max_2, max_error_2))
print("Pop dipoles: error at sig max (t={:1.3f} ms): {:1.4f}. Max relative error: {:1.4f}".format(
tvec[max_sig_idx], error_at_max_3, max_error_3))
ax1.plot(tvec, y1, c="k", lw=2., label="Sum")
ax1.plot(tvec, y2, ":", c="gray", lw=2., label="Single combined dipole")
ax1.plot(tvec, y4, "--", c="r", lw=1., label="Population dipoles")
simplify_axes(ax1)
fig.legend(frameon=False, ncol=3, fontsize=8)
plt.savefig(join(sim_folder, "Figure_combined_EEG.png"))
plt.savefig(join(sim_folder, "Figure_combined_EEG.pdf"))
phi_sphere = phi_sphere.reshape(180, 180)[:, 0][0:90]
theta = params.theta.reshape(180, 180)[:, 0][0:90]
fig = plt.figure(figsize=[5, 4])
fig.subplots_adjust(left=0.2, bottom=0.14)
plt.subplot(111)
plt.xlim([0, 80])
plt.ylim([-15, 500.])
# plt.ylim([-0.5, 1.+1e-10]) # , xlim=[0, 50], ylim=[-0.2, 5])
# plt.plot(theta, phi_20, 'k', label='Nunez 20')
plt.plot(theta, phi_sphere, 'r', label='Homogeneous sphere')
plt.plot(theta, phi_nunsri06, 'k+', label='Nunez & Srinivasan (2006)')
plt.plot(theta, phi_sri98, 'g*', label='Srinivasan (1998)')
plt.plot(theta, phi_correct, 'b.', label='Present results - analytical')
plt.xlabel('Polar angle (degrees)', fontsize=14)
plt.ylabel(r'Potential ($\mathrm{\mu V}$)', fontsize=14.)
plt.tick_params(labelsize=15.)
plt.title(
'$\sigma_{\mathrm{skull}} = \sigma_{\mathrm{brain}} = \sigma_{\mathrm{csf}} = \sigma_{\mathrm{scalp}}$',
fontsize=19)
plt.legend(frameon=False, bbox_to_anchor=(1, 0.9), fontsize=11)
simplify_axes(fig.axes)
# plt.savefig(os.path.join(args.results, 'figure4_scaled.png'), dpi=150)
plt.savefig(os.path.join(args.results, 'figure4.eps'), dpi=150)
# plt.show()
def make_results_figure():
x = np.linspace(x0, x1, nx)
z = np.linspace(z0, z1, nz)
mea_x_plot_pos = np.array([np.argmin(np.abs(x - x_))
for x_ in mea_x_positions])
# print(mea_x_plot_pos, x[mea_x_plot_pos])
mea_analytic = np.zeros((len(mea_x_plot_pos), num_tsteps))
mea_fem = np.zeros((len(mea_x_plot_pos), num_tsteps))
phi_plane_xz = np.zeros((len(x), len(z), len(tvec)))
for t_idx in range(num_tsteps):
phi_plane_xz[:, :, t_idx] = 1000 * np.load(join(out_folder,
"phi_xz_t_vec_{}.npy".format(t_idx)))
# phi_plane_xy_ = np.load(join(out_folder, "phi_xy_t_vec_{}.npy".format(t_idx)))
mea_fem[:, t_idx] = 1000 * np.load(join(out_folder,
"phi_mea_t_vec_{}.npy".format(t_idx)))[mea_x_plot_pos]
mea_analytic[:, t_idx] = 1000 * np.load(join(out_folder,
"phi_mea_analytic_t_vec_{}.npy".format(t_idx)))[mea_x_plot_pos]
noise_level = 10 # uV
soma_height = 65
soma_diam = 8
soma_xpos = -200
z_idx = np.argmin(np.abs(z - soma_height))
x_idxs = (-150 + soma_diam > x) & (x > soma_xpos + soma_diam / 2)
eap_amp = np.zeros(len(x))
for x_idx in range(len(x)):
eap_amp[x_idx] = np.max(np.abs(phi_plane_xz[x_idx, z_idx]))
plt.close("all")
fig = plt.figure(figsize=[117 * 0.03937, 48 * 0.03937])
# fig = plt.figure(figsize=[117 * 0.03937 * 5, 48 * 0.03937 * 5])
fig.subplots_adjust(hspace=0.45, bottom=0.17, top=0.99,
left=0.16, wspace=1.0, right=0.96)
ax_h = 0.25
ax_w = 0.11
ax_left = 0.10
ax_setup = fig.add_axes([0.28, 0.13, 0.69, 0.85], #aspect=1,
xlim=[-240, 145], ylim=[-15, 117])
ax_vmem = fig.add_axes([ax_left, 0.13 + 2 * (ax_h + 0.04), ax_w, ax_h],
xlim=[0, tvec[-1]], ylim=[-110, 40])
ax_mea_free = fig.add_axes([ax_left, 0.13 + ax_h + 0.04, ax_w, ax_h],
xlim=[0, tvec[-1]], ylim=[-8, 4])
ax_mea_tunnel = fig.add_axes([ax_left, 0.13, ax_w, ax_h], xlim=[0, tvec[-1]],
ylim=[-600, 400])
ax_EAP_decay = fig.add_axes([0.57, 0.70, 0.08, 0.17],
xlim=[0, 50], facecolor='none')
ax_setup.set_xlabel('X (µm)', labelpad=0)
ax_setup.set_ylabel('Z (µm)', labelpad=-1)
ax_vmem.set_ylabel('Membrane\npotential (mV)', labelpad=0)
ax_vmem.set_xticklabels(["", ""])
# ax_vmem.set_xlabel('Time (ms)', labelpad=-0.1)
ax_mea_free.set_ylabel('OME (µV)', labelpad=9)
ax_mea_free.set_xticklabels(["", ""])
# ax_mea_free.set_xlabel('Time (ms)', labelpad=-0.1)
ax_mea_tunnel.set_ylabel('CME (µV)', labelpad=2)
ax_mea_tunnel.set_xlabel('Time (ms)', labelpad=-0.1)
ax_EAP_decay.set_ylabel('Peak\namplitude (µV)', labelpad=1)
ax_EAP_decay.set_xlabel('Distance from\nsoma (µm)', labelpad=-0.0)
# ax_mea_free.set_title("OµE", pad=-5)
# ax_mea_tunnel.set_title("CµE", pad=-5)
ax_setup.text(139, -7, "Substrate", ha='right', va='top')
dist_from_soma = x[x_idxs] - soma_xpos
ax_EAP_decay.plot(dist_from_soma, eap_amp[x_idxs], lw=1, c='b')
ax_setup.plot(x[x_idxs], np.ones(len(x[x_idxs])) * soma_height,
ls=':', c='blue', lw=1)
noise_level_dist_idx = np.argmin(np.abs(eap_amp[x_idxs] - noise_level))
noise_level_dist = dist_from_soma[noise_level_dist_idx]
ax_EAP_decay.axhline(noise_level, ls='--', c='gray', lw=0.5)
ax_EAP_decay.axvline(noise_level_dist, ls='--', c='gray', lw=0.5)
# ax_EAP_decay.text(noise_level_dist + 5, noise_level + 5,
# "{:1.1f}\n µm".format(noise_level_dist), fontsize=6)
# Draw set up with tunnel and axon
rect = mpatches.Rectangle([-structure_radius - 2, tunnel_radius],
2 * structure_radius + 4, structure_height,
ec="k", fc='0.8', linewidth=0.3)
ax_setup.add_patch(rect)
rect_bottom = mpatches.Rectangle([-1000, 0],
2000, -1000, ec="k", fc='0.7', linewidth=0.3)
ax_setup.add_patch(rect_bottom)
for source_idx in range(len(source_line_pos)):
xstart, xend = source_line_pos[source_idx, :, 0]
zstart, zend = source_line_pos[source_idx, :, 2]
ax_setup.plot([xstart, xend], [zstart, zend], c='#18e10c', lw=1,
solid_capstyle="round", solid_joinstyle="round")
ax_setup.plot(source_pos[0, 0], source_pos[0, 2], c='#18e10c', marker='o',
ms=14)
for counter, p_idx in enumerate(cell_plot_idxs):
ax_setup.plot(source_pos[p_idx, 0], source_pos[p_idx, 2],
c=plot_idx_clrs[counter], marker='o', ms=5)
if p_idx > 0:
rect_elec = mpatches.Rectangle([source_pos[p_idx, 0] - 5, 0],
10, -5, ec="k", fc='k',
linewidth=0.5)
ax_setup.add_patch(rect_elec)
ax_setup.text(source_pos[p_idx, 0], - 6,
["OME", "CME"][counter - 1], va='top', ha="center")
for counter, p_idx in enumerate(cell_plot_idxs):
ax_vmem.plot(tvec, vmem[p_idx, :], c=plot_idx_clrs[counter], lw=1)
num = 11
levels = np.logspace(-2., 0, num=num)
scale_max = 500
levels_norm = scale_max * np.concatenate((-levels[::-1], levels))
bwr_cmap = plt.cm.get_cmap('bwr') # rainbow, spectral, RdYlBu
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)
xz_masked = np.ma.masked_where(np.isnan(phi_plane_xz), phi_plane_xz)
ep_intervals = ax_setup.contourf(x, z, xz_masked[:, :, 0],
zorder=-2, colors=colors_from_map,
levels=levels_norm, extend='both')
ep_intervals_ = ax_setup.contour(x, z, xz_masked[:, :, 0].T, colors='k',
linewidths=(0.3), zorder=-2,
levels=levels_norm)
cax = fig.add_axes([0.85, 0.5, 0.01, 0.35])
cbar = plt.colorbar(ep_intervals, cax=cax, label="$\phi$ (µV)")
cbar.set_ticks(np.array([-1, -0.1, -0.02, 0.02, 0.1, 1]) * scale_max)
#ax_mea_free.plot(tvec, mea_analytic[0], lw=1, c='gray')
l, = ax_mea_free.plot(tvec, mea_fem[0], lw=1, c='k')
la, = ax_mea_tunnel.plot(tvec, mea_fem[1], lw=1, c='k', ls="-")
rel_error = np.max(np.abs((mea_analytic[0] - mea_fem[0])) / np.max(
np.abs(mea_fem[0])))
print("Relative error between FEM and MoI (free elec): {:1.4f}".format(
rel_error))
t1 = ax_vmem.axvline(tvec[0], c='gray', ls="--")
t2 = ax_mea_free.axvline(tvec[0], c='gray', ls="--")
t3 = ax_mea_tunnel.axvline(tvec[0], c='gray', ls="--")
simplify_axes([ax_setup, ax_mea_free, ax_mea_tunnel, ax_vmem, ax_EAP_decay])
#mark_subplots([ax_vmem, ax_mea_free, ax_mea_tunnel, ax_EAP_decay], "BCDE", xpos=-0.05, ypos=1.07)
## This is to make animation. Can be commented out to save time
for t_idx in range(num_tsteps):
for tp in ep_intervals.collections:
tp.remove()
ep_intervals = ax_setup.contourf(x, z, xz_masked[:, :, t_idx].T,
zorder=-2, colors=colors_from_map,
levels=levels_norm, extend='both')
for tp in ep_intervals_.collections:
tp.remove()
ep_intervals_ = ax_setup.contour(x, z, xz_masked[:, :, t_idx].T,
colors='k', linewidths=(1), zorder=-2,
levels=levels_norm)
t1.set_xdata(tvec[t_idx])
t2.set_xdata(tvec[t_idx])
t3.set_xdata(tvec[t_idx])
plt.savefig(join(fem_fig_folder, 'anim_results_{}_t_idx_{:04d}.png'.format(
sim_name, t_idx)), dpi=300)
cax.clear()
t1.set_xdata(100)
t2.set_xdata(100)
t3.set_xdata(100)
# num = 11
# levels = np.logspace(-2.0, 0, num=num)
# scale_max = 500
# levels_norm = scale_max * levels
# bwr_cmap = plt.cm.get_cmap('Reds') # rainbow, spectral, RdYlBu
# colors_from_map = [bwr_cmap(i * np.int(255 / (len(levels_norm) - 2)))
# for i in range(len(levels_norm) - 1)]
#
#
levels_norm = [0, 10.0, 1e9]#scale_max * levels
# bwr_cmap = plt.cm.get_cmap('Reds') # rainbow, spectral, RdYlBu
colors_from_map = ['0.95', '#ffbbbb', (0.5, 0.5, 0.5, 1)]
#colors_from_map[0] = (1.0, 1.0, 1.0, 1.0)
xz_crossmax = np.array(np.max(np.abs(xz_masked[:, :, :]), axis=-1).T)
for tp in ep_intervals.collections:
tp.remove()
ep_intervals = ax_setup.contourf(x, z, xz_crossmax,
zorder=-2, colors=colors_from_map,
levels=levels_norm, extend='both')
for tp in ep_intervals_.collections:
tp.remove()
# ep_intervals_ = ax_setup.contour(x, z, xz_crossmax, colors='k',
# linewidths=(0.3), zorder=-2,
# levels=levels_norm)
#img1 = ax_setup.imshow(np.max(np.abs(xz_masked), axis=-1).T,
# interpolation='nearest', origin='lower', cmap='Reds',
# extent=(x[0], x[-1], z[0], z[-1]), norm=LogNorm(0.002, vmax=1))
cbar = plt.colorbar(ep_intervals, cax=cax)
# cbar.set_ticks(np.array([0.01, 0.1, 1]) * scale_max)
# print(cbar.get_ticks())
cbar.set_ticks(np.array([5, 1e9/2]))
cbar.set_ticklabels(np.array(["<10 µV", ">10 µV"]))
#cax.set_xticklabels(np.array(np.array([-1, -0.1, -0.01, 0, 0.01, 0.1, 1])
# * scale_max, dtype=int), fontsize=7, rotation=0)
plt.savefig(join(root_folder, 'Fig_{}_4.png'.format(sim_name)), dpi=300)
plt.savefig(join(root_folder, 'Fig_{}_4.pdf'.format(sim_name)), dpi=300)
plot_data_folder = join(root_folder, 'figure_data')
os.makedirs(plot_data_folder, exist_ok=True)
np.save(join(plot_data_folder, "xz_max_amp.npy"), xz_crossmax)
np.save(join(plot_data_folder, "xz_x_values.npy"), x)
np.save(join(plot_data_folder, "xz_z_values.npy"), z)
np.save(join(plot_data_folder, "mea_phi_values.npy"), mea_fem)
np.save(join(plot_data_folder, "source_line_pos.npy"), source_line_pos)
np.save(join(plot_data_folder, "t_vec.npy"), tvec)
np.save(join(plot_data_folder, "memb_pot.npy"), vmem[cell_plot_idxs, :])
np.save(join(plot_data_folder, "eap_amp.npy"), eap_amp[x_idxs])
np.save(join(plot_data_folder, "eap_amp_dist.npy"), dist_from_soma)
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'))
def animate_ca_spike():
# Deprecated, and not updated for LFPy2.2 and newer
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,
])
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') # rainbow, spectral, RdYlBu
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)
cell, idx_clr, plot_idxs = run_cell_simulation(make_ca_spike=False,
dt=dt,
cell_name=cell_name)
grid_electrode = LFPy.RecExtElectrode(cell, **grid_elec_params)
grid_electrode.calc_lfp()
grid_LFP = 1e3 * grid_electrode.LFP
grid_LFP -= grid_LFP[:, 0, None]
for time_idx in range(len(cell.tvec))[2000:]:
plt.close("all")
fig = plt.figure(figsize=[5, 4])
fig.text(0.5,
0.95,
"T={:1.1f} ms".format(cell.tvec[time_idx]),
ha="center")
ax_ = fig.add_axes([0.4, 0.14, 0.6, 0.88],
xticks=[],
yticks=[],
aspect=1,
frameon=False)
cax = fig.add_axes([0.5, 0.15, 0.4, 0.01])
ax_vm = fig.add_axes([0.25, 0.65, 0.2, 0.3],
ylabel="membrane\npotential\n(mV)",
xlabel="time (ms)")
ax_cdm = fig.add_axes([0.25, 0.2, 0.2, 0.3],
ylabel="current dipole\nmoment\n(µA$\cdot$µm)",
xlabel="time (ms)")
grid_LFP_ = grid_LFP[:, time_idx].reshape(grid_x.shape)
[
ax_.plot([cell.xstart[idx], cell.xend[idx]],
[cell.zstart[idx], cell.zend[idx]],
c='gray') for idx in range(cell.totnsegs)
]
ax_.plot([350, 350], [-540, -640], c='k', lw=2)
ax_.text(360, -590, "100 $\mu$m", va='center')
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)
cbar = fig.colorbar(ep_intervals,
cax=cax,
orientation='horizontal',
format='%.0E',
extend='max')
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=7,
rotation=45)
cbar.set_label('$\phi$ (µV)', labelpad=-5)
[
ax_vm.plot(cell.tvec, cell.vmem[idx], c=idx_clr[idx])
for idx in plot_idxs
]
ax_cdm.plot(cell.tvec, cell.current_dipole_moment[:, 2], c='k')
ax_vm.axvline(cell.tvec[time_idx], c='gray', lw=1, ls='--')
ax_cdm.axvline(cell.tvec[time_idx], c='gray', lw=1, ls='--')
simplify_axes([ax_vm, ax_cdm])
anim_fig_folder = "anim_no_ca"
os.makedirs(anim_fig_folder, exist_ok=True)
plt.savefig(
join(anim_fig_folder, "cell_ca_cont_{:04d}.png".format(time_idx)))
if idx < 100:
ax1.plot(tvec, cdm[:, 0], lw=0.5, c="0.7")
ax2.plot(tvec, cdm[:, 1], lw=0.5, c="0.7")
ax3.plot(tvec, cdm[:, 2], lw=0.5, c="0.7")
ax1.plot(tvec, summed_cdm[:, 0] / len(files), lw=2, c="k")
ax2.plot(tvec, summed_cdm[:, 1] / len(files), lw=2, c="k")
ax3.plot(tvec, summed_cdm[:, 2] / len(files), lw=2, c="k")
return summed_cdm
for pop_name, subpops in sub_pop_groups_dict.items():
plt.close("all")
fig = plt.figure(figsize=[18, 9], )
fig.suptitle("{}".format(pop_name))
fig.subplots_adjust(hspace=0.5, left=0.3)
ax1 = fig.add_subplot(311, title="$P_x$", **ax_dict)
ax2 = fig.add_subplot(312, title="$P_y$", **ax_dict)
ax3 = fig.add_subplot(313, title="$P_z$", xlabel="Time (ms)", **ax_dict)
# ax1.axvline(900, color="gray", zorder=0, lw=0.5)
# ax2.axvline(900, color="gray", zorder=0, lw=0.5)
# ax3.axvline(900, color="gray", zorder=0, lw=0.5)
summed_cdm = plot_and_return_subgroup_cdms(subpops)
simplify_axes([ax1, ax2, ax3])
plt.savefig(join(sim_folder, "cdm_{}.png".format(pop_name)))
plt.savefig(join(sim_folder, "cdm_{}.pdf".format(pop_name)))
np.save(join(sim_folder, "cdm", "summed_cdm_{}.npy".format(pop_name)), summed_cdm)
