def plot_multi_scale_output_b(fig, X='L5E'):
'''docstring me'''
show_ax_labels = True
show_insets = False
show_images = False
T=[800, 1000]
T_inset=[900, 920]
left = 0.075
bottom = 0.05
top = 0.475
right = 0.95
axwidth = 0.16
numcols = 4
insetwidth = axwidth/2
insetheight = 0.5
lefts = np.linspace(left, right-axwidth, numcols)
lefts += axwidth/2
#lower row of panels
#fig = plt.figure()
#fig.subplots_adjust(left=0.12, right=0.9, bottom=0.36, top=0.9, wspace=0.2, hspace=0.3)
############################################################################
# E part, soma locations
############################################################################
ax4 = fig.add_axes([lefts[0], bottom, axwidth, top-bottom], frameon=False)
plt.locator_params(nbins=4)
ax4.xaxis.set_ticks([])
ax4.yaxis.set_ticks([])
if show_ax_labels:
phlp.annotate_subplot(ax4, ncols=4, nrows=1, letter='E')
plot_population(ax4, params, isometricangle=np.pi/24, rasterized=False)
############################################################################
# F part, CSD
############################################################################
ax5 = fig.add_axes([lefts[1], bottom, axwidth, top-bottom])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax5)
if show_ax_labels:
phlp.annotate_subplot(ax5, ncols=4, nrows=1, letter='F')
plot_signal_sum(ax5, params, fname=os.path.join(params.savefolder, 'CSDsum.h5'),
unit='$\mu$A mm$^{-3}$',
T=T,
ylim=[ax4.axis()[2], ax4.axis()[3]],
rasterized=False)
ax5.set_title('CSD', va='center')
# Inset
if show_insets:
ax6 = fig.add_axes([lefts[1]+axwidth-insetwidth, top-insetheight, insetwidth, insetheight])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax6)
plot_signal_sum_colorplot(ax6, params, os.path.join(params.savefolder, 'CSDsum.h5'),
unit=r'$\mu$Amm$^{-3}$', T=T_inset,
ylim=[ax4.axis()[2], ax4.axis()[3]],
fancy=False,colorbar=False,cmap='bwr_r')
ax6.set_xticks(T_inset)
ax6.set_yticklabels([])
#show traces superimposed on color image
if show_images:
plot_signal_sum_colorplot(ax5, params, os.path.join(params.savefolder, 'CSDsum.h5'),
unit=r'$\mu$Amm$^{-3}$', T=T,
ylim=[ax4.axis()[2], ax4.axis()[3]],
fancy=False,colorbar=False,cmap='jet_r')
############################################################################
# G part, LFP
############################################################################
ax7 = fig.add_axes([lefts[2], bottom, axwidth, top-bottom])
plt.locator_params(nbins=4)
if show_ax_labels:
phlp.annotate_subplot(ax7, ncols=4, nrows=1, letter='G')
phlp.remove_axis_junk(ax7)
plot_signal_sum(ax7, params, fname=os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV', T=T, ylim=[ax4.axis()[2], ax4.axis()[3]],
rasterized=False)
ax7.set_title('LFP',va='center')
# Inset
if show_insets:
ax8 = fig.add_axes([lefts[2]+axwidth-insetwidth, top-insetheight, insetwidth, insetheight])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax8)
plot_signal_sum_colorplot(ax8, params, os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV', T=T_inset,
ylim=[ax4.axis()[2], ax4.axis()[3]],
fancy=False,colorbar=False,cmap='bwr_r')
ax8.set_xticks(T_inset)
ax8.set_yticklabels([])
#show traces superimposed on color image
if show_images:
plot_signal_sum_colorplot(ax7, params, os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV', T=T,
ylim=[ax4.axis()[2], ax4.axis()[3]],
fancy=False,colorbar=False,cmap='bwr_r')
def fig_lfp_decomposition(fig,
axes,
params,
transient=200,
X=['L23E', 'L6E'],
show_xlabels=True):
# ana_params.set_PLOS_2column_fig_style(ratio=0.5)
# fig, axes = plt.subplots(1,5)
# fig.subplots_adjust(left=0.06, right=0.96, wspace=0.4, hspace=0.2)
if analysis_params.bw:
# linestyles = ['-', '-', '--', '--', '-.', '-.', ':', ':']
linestyles = ['-', '-', '-', '-', '-', '-', '-', '-']
markerstyles = ['s', 's', 'v', 'v', 'o', 'o', '^', '^']
else:
if plt.matplotlib.__version__ == '1.5.x':
linestyles = ['-', ':'] * (len(params.Y) / 2)
print(
'CSD variance semi log plots may fail with matplotlib.__version__ {}'
.format(plt.matplotlib.__version__))
else:
linestyles = ['-',
(0, (1, 1))] * (len(params.Y) / 2) #cercor version
# markerstyles = ['s', 's', 'v', 'v', 'o', 'o', '^', '^']
markerstyles = [None] * len(params.Y)
linewidths = [1.25 for i in range(len(linestyles))]
plt.delaxes(axes[0])
#population plot
axes[0] = fig.add_subplot(261)
axes[0].xaxis.set_ticks([])
axes[0].yaxis.set_ticks([])
axes[0].set_frame_on(False)
plot_population(axes[0],
params,
aspect='tight',
isometricangle=np.pi / 32,
plot_somas=False,
plot_morphos=True,
num_unitsE=1,
num_unitsI=1,
clip_dendrites=False,
main_pops=True,
rasterized=False)
phlp.annotate_subplot(axes[0], ncols=5, nrows=1, letter='A')
axes[0].set_aspect('auto')
axes[0].set_ylim(-1550, 50)
axis = axes[0].axis()
phlp.remove_axis_junk(axes[1])
plot_signal_sum(axes[1],
params,
fname=os.path.join(params.populations_path,
X[0] + '_population_LFP.h5'),
unit='mV',
T=[800, 1000],
ylim=[axis[2], axis[3]],
rasterized=False)
# CSD background colorplot
im = plot_signal_sum_colorplot(
axes[1],
params,
os.path.join(params.populations_path, X[0] + '_population_CSD.h5'),
unit=r'$\mu$Amm$^{-3}$',
T=[800, 1000],
colorbar=False,
ylim=[axis[2], axis[3]],
fancy=False,
cmap=plt.get_cmap('gray', 21) if analysis_params.bw else plt.get_cmap(
'bwr_r', 21),
rasterized=False)
cb = phlp.colorbar(fig,
axes[1],
im,
width=0.05,
height=0.5,
hoffset=-0.05,
voffset=0.5)
cb.set_label('($\mu$Amm$^{-3}$)', labelpad=0.)
axes[1].set_ylim(-1550, 50)
axes[1].set_title('LFP and CSD ({})'.format(X[0]), va='baseline')
phlp.annotate_subplot(axes[1], ncols=3, nrows=1, letter='B')
#quickfix on first axes
axes[0].set_ylim(-1550, 50)
if show_xlabels:
axes[1].set_xlabel(r'$t$ (ms)', labelpad=0.)
else:
axes[1].set_xlabel('')
phlp.remove_axis_junk(axes[2])
plot_signal_sum(axes[2],
params,
fname=os.path.join(params.populations_path,
X[1] + '_population_LFP.h5'),
ylabels=False,
unit='mV',
T=[800, 1000],
ylim=[axis[2], axis[3]],
rasterized=False)
# CSD background colorplot
im = plot_signal_sum_colorplot(
axes[2],
params,
os.path.join(params.populations_path, X[1] + '_population_CSD.h5'),
unit=r'$\mu$Amm$^{-3}$',
T=[800, 1000],
ylabels=False,
colorbar=False,
ylim=[axis[2], axis[3]],
fancy=False,
cmap=plt.get_cmap('gray', 21) if analysis_params.bw else plt.get_cmap(
'bwr_r', 21),
rasterized=False)
cb = phlp.colorbar(fig,
axes[2],
im,
width=0.05,
height=0.5,
hoffset=-0.05,
voffset=0.5)
cb.set_label('($\mu$Amm$^{-3}$)', labelpad=0.)
axes[2].set_ylim(-1550, 50)
axes[2].set_title('LFP and CSD ({})'.format(X[1]), va='baseline')
phlp.annotate_subplot(axes[2], ncols=1, nrows=1, letter='C')
if show_xlabels:
axes[2].set_xlabel(r'$t$ (ms)', labelpad=0.)
else:
axes[2].set_xlabel('')
plotPowers(axes[3],
params,
params.Y,
'CSD',
linestyles=linestyles,
transient=transient,
markerstyles=markerstyles,
linewidths=linewidths)
axes[3].axis(axes[3].axis('tight'))
axes[3].set_ylim(-1550, 50)
axes[3].set_yticks(-np.arange(16) * 100)
if show_xlabels:
axes[3].set_xlabel(r'$\sigma^2$ ($(\mu$Amm$^{-3})^2$)', va='center')
axes[3].set_title('CSD variance', va='baseline')
axes[3].set_xlim(left=1E-7)
phlp.remove_axis_junk(axes[3])
phlp.annotate_subplot(axes[3], ncols=1, nrows=1, letter='D')
plotPowers(axes[4],
params,
params.Y,
'LFP',
linestyles=linestyles,
transient=transient,
markerstyles=markerstyles,
linewidths=linewidths)
axes[4].axis(axes[4].axis('tight'))
axes[4].set_ylim(-1550, 50)
axes[4].set_yticks(-np.arange(16) * 100)
if show_xlabels:
axes[4].set_xlabel(r'$\sigma^2$ (mV$^2$)', va='center')
axes[4].set_title('LFP variance', va='baseline')
axes[4].legend(bbox_to_anchor=(1.37, 1.0), frameon=False)
axes[4].set_xlim(left=1E-7)
phlp.remove_axis_junk(axes[4])
phlp.annotate_subplot(axes[4], ncols=1, nrows=1, letter='E')
return fig
def plot_multi_scale_output_b(fig, X='L5E'):
'''docstring me'''
show_ax_labels = True
show_insets = False
show_images = False
T = [800, 1000]
T_inset = [900, 920]
left = 0.075
bottom = 0.05
top = 0.475
right = 0.95
axwidth = 0.16
numcols = 4
insetwidth = axwidth / 2
insetheight = 0.5
lefts = np.linspace(left, right - axwidth, numcols)
lefts += axwidth / 2
#lower row of panels
#fig = plt.figure()
#fig.subplots_adjust(left=0.12, right=0.9, bottom=0.36, top=0.9, wspace=0.2, hspace=0.3)
############################################################################
# E part, soma locations
############################################################################
ax4 = fig.add_axes([lefts[0], bottom, axwidth, top - bottom],
frameon=False)
plt.locator_params(nbins=4)
ax4.xaxis.set_ticks([])
ax4.yaxis.set_ticks([])
if show_ax_labels:
phlp.annotate_subplot(ax4, ncols=4, nrows=1, letter='E')
plot_population(ax4, params, isometricangle=np.pi / 24, rasterized=False)
############################################################################
# F part, CSD
############################################################################
ax5 = fig.add_axes([lefts[1], bottom, axwidth, top - bottom])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax5)
if show_ax_labels:
phlp.annotate_subplot(ax5, ncols=4, nrows=1, letter='F')
plot_signal_sum(ax5,
params,
fname=os.path.join(params.savefolder, 'CSDsum.h5'),
unit='$\mu$A mm$^{-3}$',
T=T,
ylim=[ax4.axis()[2], ax4.axis()[3]],
rasterized=False)
ax5.set_title('CSD', va='center')
# Inset
if show_insets:
ax6 = fig.add_axes([
lefts[1] + axwidth - insetwidth, top - insetheight, insetwidth,
insetheight
])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax6)
plot_signal_sum_colorplot(ax6,
params,
os.path.join(params.savefolder, 'CSDsum.h5'),
unit=r'$\mu$Amm$^{-3}$',
T=T_inset,
ylim=[ax4.axis()[2],
ax4.axis()[3]],
fancy=False,
colorbar=False,
cmap='bwr_r')
ax6.set_xticks(T_inset)
ax6.set_yticklabels([])
#show traces superimposed on color image
if show_images:
plot_signal_sum_colorplot(ax5,
params,
os.path.join(params.savefolder, 'CSDsum.h5'),
unit=r'$\mu$Amm$^{-3}$',
T=T,
ylim=[ax4.axis()[2],
ax4.axis()[3]],
fancy=False,
colorbar=False,
cmap='jet_r')
############################################################################
# G part, LFP
############################################################################
ax7 = fig.add_axes([lefts[2], bottom, axwidth, top - bottom])
plt.locator_params(nbins=4)
if show_ax_labels:
phlp.annotate_subplot(ax7, ncols=4, nrows=1, letter='G')
phlp.remove_axis_junk(ax7)
plot_signal_sum(ax7,
params,
fname=os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV',
T=T,
ylim=[ax4.axis()[2], ax4.axis()[3]],
rasterized=False)
ax7.set_title('LFP', va='center')
# Inset
if show_insets:
ax8 = fig.add_axes([
lefts[2] + axwidth - insetwidth, top - insetheight, insetwidth,
insetheight
])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax8)
plot_signal_sum_colorplot(ax8,
params,
os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV',
T=T_inset,
ylim=[ax4.axis()[2],
ax4.axis()[3]],
fancy=False,
colorbar=False,
cmap='bwr_r')
ax8.set_xticks(T_inset)
ax8.set_yticklabels([])
#show traces superimposed on color image
if show_images:
plot_signal_sum_colorplot(ax7,
params,
os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV',
T=T,
ylim=[ax4.axis()[2],
ax4.axis()[3]],
fancy=False,
colorbar=False,
cmap='bwr_r')
def fig_intro(params,
ana_params,
T=[800, 1000],
fraction=0.05,
rasterized=False):
'''set up plot for introduction'''
ana_params.set_PLOS_2column_fig_style(ratio=0.5)
#load spike as database
networkSim = CachedNetwork(**params.networkSimParams)
if analysis_params.bw:
networkSim.colors = phlp.get_colors(len(networkSim.X))
#set up figure and subplots
fig = plt.figure()
gs = gridspec.GridSpec(3, 4)
fig.subplots_adjust(left=0.05, right=0.95, wspace=0.5, hspace=0.)
#network diagram
ax0_1 = fig.add_subplot(gs[:, 0], frameon=False)
ax0_1.set_title('point-neuron network', va='bottom')
network_sketch(ax0_1, yscaling=1.3)
ax0_1.xaxis.set_ticks([])
ax0_1.yaxis.set_ticks([])
phlp.annotate_subplot(ax0_1,
ncols=4,
nrows=1,
letter='A',
linear_offset=0.065)
#network raster
ax1 = fig.add_subplot(gs[:, 1], frameon=True)
phlp.remove_axis_junk(ax1)
phlp.annotate_subplot(ax1,
ncols=4,
nrows=1,
letter='B',
linear_offset=0.065)
x, y = networkSim.get_xy(T, fraction=fraction)
# networkSim.plot_raster(ax1, T, x, y, markersize=0.1, alpha=1.,legend=False, pop_names=True)
networkSim.plot_raster(ax1,
T,
x,
y,
markersize=0.2,
marker='_',
alpha=1.,
legend=False,
pop_names=True,
rasterized=rasterized)
ax1.set_ylabel('')
ax1.xaxis.set_major_locator(plt.MaxNLocator(4))
ax1.set_title('spiking activity', va='bottom')
a = ax1.axis()
ax1.vlines(x['TC'][0], a[2], a[3], 'k', lw=0.25)
#population
ax2 = fig.add_subplot(gs[:, 2], frameon=False)
ax2.xaxis.set_ticks([])
ax2.yaxis.set_ticks([])
plot_population(ax2,
params,
isometricangle=np.pi / 24,
plot_somas=False,
plot_morphos=True,
num_unitsE=1,
num_unitsI=1,
clip_dendrites=True,
main_pops=True,
title='',
rasterized=rasterized)
ax2.set_title('multicompartment\nneurons',
va='bottom',
fontweight='normal')
phlp.annotate_subplot(ax2,
ncols=4,
nrows=1,
letter='C',
linear_offset=0.065)
#LFP traces in all channels
ax3 = fig.add_subplot(gs[:, 3], frameon=True)
phlp.remove_axis_junk(ax3)
plot_signal_sum(ax3,
params,
fname=os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV',
vlimround=0.8,
T=T,
ylim=[ax2.axis()[2], ax2.axis()[3]],
rasterized=False)
ax3.set_title('LFP', va='bottom')
ax3.xaxis.set_major_locator(plt.MaxNLocator(4))
phlp.annotate_subplot(ax3,
ncols=4,
nrows=1,
letter='D',
linear_offset=0.065)
a = ax3.axis()
ax3.vlines(x['TC'][0], a[2], a[3], 'k', lw=0.25)
#draw some arrows:
ax = plt.gca()
ax.annotate(
"",
xy=(0.27, 0.5),
xytext=(.24, 0.5),
xycoords="figure fraction",
arrowprops=dict(facecolor='black', arrowstyle='simple'),
)
ax.annotate(
"",
xy=(0.52, 0.5),
xytext=(.49, 0.5),
xycoords="figure fraction",
arrowprops=dict(facecolor='black', arrowstyle='simple'),
)
ax.annotate(
"",
xy=(0.78, 0.5),
xytext=(.75, 0.5),
xycoords="figure fraction",
arrowprops=dict(facecolor='black', arrowstyle='simple'),
)
return fig
def fig_intro(params, fraction=0.05, rasterized=False):
'''set up plot for introduction'''
plt.close("all")
#load spike as database
networkSim = CachedNetwork(**params.networkSimParams)
# num_pops = 8
fig = plt.figure(figsize=[4.5, 3.5])
fig.subplots_adjust(left=0.03, right=0.98, wspace=0.5, hspace=0.)
ax_spikes = fig.add_axes([0.09, 0.4, 0.2, 0.55])
ax_morph = fig.add_axes([0.37, 0.3, 0.3, 0.75],
frameon=False,
aspect=1,
xticks=[],
yticks=[])
ax_lfp = fig.add_axes([0.73, 0.4, 0.23, 0.55], frameon=True)
ax_4s = fig.add_axes([0.42, 0.05, 0.25, 0.2],
frameon=False,
aspect=1,
title='head model',
xticks=[],
yticks=[])
ax_top_EEG = fig.add_axes([0.65, 0.02, 0.33, 0.32],
frameon=False,
xticks=[],
yticks=[],
ylim=[-0.5, .25])
dt = 1
t_idx = 875
T = [t_idx, t_idx + 75]
fig.text(0.55, 0.97, "multicompartment neurons", fontsize=6, ha="center")
ax_spikes.set_title("spiking activity", fontsize=6)
ax_lfp.set_title("LFP", fontsize=6)
#network raster
ax_spikes.xaxis.set_major_locator(plt.MaxNLocator(4))
phlp.remove_axis_junk(ax_spikes)
phlp.annotate_subplot(ax_spikes,
ncols=4,
nrows=1,
letter='A',
linear_offset=0.045)
x, y = networkSim.get_xy(T, fraction=fraction)
networkSim.plot_raster(ax_spikes,
T,
x,
y,
markersize=0.2,
marker='_',
alpha=1.,
legend=False,
pop_names=True,
rasterized=rasterized)
#population
plot_population(ax_morph,
params,
isometricangle=np.pi / 24,
plot_somas=False,
plot_morphos=True,
num_unitsE=1,
num_unitsI=1,
clip_dendrites=True,
main_pops=True,
title='',
rasterized=rasterized)
# ax_morph.set_title('multicompartment neurons', va='top')
phlp.annotate_subplot(ax_morph,
ncols=5,
nrows=1,
letter='B',
linear_offset=0.005)
phlp.remove_axis_junk(ax_lfp)
#ax_lfp.set_title('LFP', va='bottom')
ax_lfp.xaxis.set_major_locator(plt.MaxNLocator(4))
phlp.annotate_subplot(ax_lfp,
ncols=4,
nrows=2,
letter='C',
linear_offset=0.025)
#print(ax_morph.axis())
plot_signal_sum(ax_lfp,
params,
fname=join(params.savefolder, 'LFPsum.h5'),
unit='mV',
vlimround=0.8,
T=T,
ylim=[-1600, 100],
rasterized=False)
plot_cdms(fig, params, dt, T)
plot_foursphere_to_ax(ax_4s)
phlp.annotate_subplot(ax_4s,
ncols=3,
nrows=7,
letter='E',
linear_offset=0.05)
# Plot EEG at top of head
# ax_top_EEG.xaxis.set_major_locator(plt.MaxNLocator(4))
phlp.annotate_subplot(ax_top_EEG,
ncols=1,
nrows=1,
letter='F',
linear_offset=-0.08)
# ax_top_EEG.set_ylabel("$\mu$V", labelpad=-3)
summed_top_EEG = np.load(join(params.savefolder, "summed_EEG.npy"))
simple_EEG_single_pop = np.load(
join(params.savefolder, "simple_EEG_single_pop.npy"))
simple_EEG_pops_with_pos = np.load(
join(params.savefolder, "simple_EEG_pops_with_pos.npy"))
tvec = np.arange(len(summed_top_EEG)) * dt
# sub_pops = ["L5I", "L4I", "L6I", "L23I", "L5E", "L4E", "L6E", "L23E"]
pops = np.unique(next(zip(*params.mapping_Yy)))
colors = phlp.get_colors(np.unique(pops).size)
for p_idx, pop in enumerate(pops):
pop_eeg = np.load(join(params.savefolder, "EEG_{}.npy".format(pop)))
pop_eeg -= np.average(pop_eeg)
# pop_sum.append(pop_eeg)
ax_top_EEG.plot(pop_eeg, c=colors[p_idx], lw=1)
ax_top_EEG.plot([878, 878], [-0.1, -0.3], c='k', lw=1)
ax_top_EEG.plot([878, 888], [-0.3, -0.3], c='k', lw=1)
ax_top_EEG.text(879, -0.2, "0.2 $\mu$V", va="center")
ax_top_EEG.text(885, -0.32, "10 ms", va="top", ha="center")
y0 = summed_top_EEG - np.average(summed_top_EEG)
y1 = simple_EEG_single_pop - np.average(simple_EEG_single_pop)
y2 = simple_EEG_pops_with_pos - np.average(simple_EEG_pops_with_pos)
l3, = ax_top_EEG.plot(tvec, y0 - y2, lw=1.5, c='orange', ls='-')
l1, = ax_top_EEG.plot(tvec, y0, lw=1.5, c='k')
l2, = ax_top_EEG.plot(tvec, y2, lw=1.5, c='r', ls='--')
t0_plot_idx = np.argmin(np.abs(tvec - 875))
t1_plot_idx = np.argmin(np.abs(tvec - 950))
max_sig_idx = np.argmax(np.abs(y0[t0_plot_idx:])) + t0_plot_idx
EEG_error_at_max_1 = np.abs(y0[max_sig_idx] - y1[max_sig_idx]) / np.abs(
y0[max_sig_idx])
EEG_error_at_max_2 = np.abs(y0[max_sig_idx] - y2[max_sig_idx]) / np.abs(
y0[max_sig_idx])
max_EEG_error_1 = np.max(
np.abs(y0[t0_plot_idx:t1_plot_idx] - y1[t0_plot_idx:t1_plot_idx]) /
np.max(np.abs(y0[t0_plot_idx:t1_plot_idx])))
max_EEG_error_2 = np.max(
np.abs(y0[t0_plot_idx:t1_plot_idx] - y2[t0_plot_idx:t1_plot_idx]) /
np.max(np.abs(y0[t0_plot_idx:t1_plot_idx])))
print(
"Error with single pop at sig max (t={:1.3f} ms): {:1.4f}. Max relative error: {:1.4f}"
.format(tvec[max_sig_idx], EEG_error_at_max_1, max_EEG_error_1))
print(
"Error with multipop at sig max (t={:1.3f} ms): {:1.4f}. Max relative error: {:1.4f}"
.format(tvec[max_sig_idx], EEG_error_at_max_2, max_EEG_error_2))
ax_top_EEG.legend([l1, l2, l3], ["full sum", "pop. dipole", "difference"],
frameon=False,
loc=(0.5, 0.1))
# phlp.remove_axis_junk(ax_top_EEG)
ax_top_EEG.axvline(900, c='gray', ls='--')
ax_lfp.axvline(900, c='gray', ls='--')
ax_top_EEG.set_xlim(T)
fig.savefig(join("..", "figures", 'Figure6.png'), dpi=300)
fig.savefig(join("..", "figures", 'Figure6.pdf'), dpi=300)
def fig_lfp_decomposition(fig, axes, params, transient=200, X=['L23E', 'L6E'], show_xlabels=True):
# ana_params.set_PLOS_2column_fig_style(ratio=0.5)
# fig, axes = plt.subplots(1,5)
# fig.subplots_adjust(left=0.06, right=0.96, wspace=0.4, hspace=0.2)
if analysis_params.bw:
# linestyles = ['-', '-', '--', '--', '-.', '-.', ':', ':']
linestyles = ['-', '-', '-', '-', '-', '-', '-', '-']
markerstyles = ['s', 's', 'v', 'v', 'o', 'o', '^', '^']
else:
if plt.matplotlib.__version__ == '1.5.x':
linestyles = ['-', ':']*(len(params.Y) / 2)
print('CSD variance semi log plots may fail with matplotlib.__version__ {}'.format(plt.matplotlib.__version__))
else:
linestyles = ['-', (0, (1,1))]*(len(params.Y) / 2) #cercor version
# markerstyles = ['s', 's', 'v', 'v', 'o', 'o', '^', '^']
markerstyles = [None]*len(params.Y)
linewidths = [1.25 for i in range(len(linestyles))]
plt.delaxes(axes[0])
#population plot
axes[0] = fig.add_subplot(261)
axes[0].xaxis.set_ticks([])
axes[0].yaxis.set_ticks([])
axes[0].set_frame_on(False)
plot_population(axes[0], params, aspect='tight', isometricangle=np.pi/32,
plot_somas = False, plot_morphos = True,
num_unitsE = 1, num_unitsI=1,
clip_dendrites=False, main_pops=True,
rasterized=False)
phlp.annotate_subplot(axes[0], ncols=5, nrows=1, letter='A')
axes[0].set_aspect('auto')
axes[0].set_ylim(-1550, 50)
axis = axes[0].axis()
phlp.remove_axis_junk(axes[1])
plot_signal_sum(axes[1], params,
fname=os.path.join(params.populations_path, X[0] + '_population_LFP.h5'),
unit='mV', T=[800,1000], ylim=[axis[2], axis[3]],
rasterized=False)
# CSD background colorplot
im = plot_signal_sum_colorplot(axes[1], params, os.path.join(params.populations_path, X[0] + '_population_CSD.h5'),
unit=r'$\mu$Amm$^{-3}$', T=[800,1000],
colorbar=False,
ylim=[axis[2], axis[3]], fancy=False,
cmap=plt.get_cmap('gray', 21) if analysis_params.bw else plt.get_cmap('bwr_r', 21),
rasterized=False)
cb = phlp.colorbar(fig, axes[1], im,
width=0.05, height=0.5,
hoffset=-0.05, voffset=0.5)
cb.set_label('($\mu$Amm$^{-3}$)', labelpad=0.)
axes[1].set_ylim(-1550, 50)
axes[1].set_title('LFP and CSD ({})'.format(X[0]), va='baseline')
phlp.annotate_subplot(axes[1], ncols=3, nrows=1, letter='B')
#quickfix on first axes
axes[0].set_ylim(-1550, 50)
if show_xlabels:
axes[1].set_xlabel(r'$t$ (ms)',labelpad=0.)
else:
axes[1].set_xlabel('')
phlp.remove_axis_junk(axes[2])
plot_signal_sum(axes[2], params,
fname=os.path.join(params.populations_path, X[1] + '_population_LFP.h5'), ylabels=False,
unit='mV', T=[800,1000], ylim=[axis[2], axis[3]],
rasterized=False)
# CSD background colorplot
im = plot_signal_sum_colorplot(axes[2], params, os.path.join(params.populations_path, X[1] + '_population_CSD.h5'),
unit=r'$\mu$Amm$^{-3}$', T=[800,1000], ylabels=False,
colorbar=False,
ylim=[axis[2], axis[3]], fancy=False,
cmap=plt.get_cmap('gray', 21) if analysis_params.bw else plt.get_cmap('bwr_r', 21),
rasterized=False)
cb = phlp.colorbar(fig, axes[2], im,
width=0.05, height=0.5,
hoffset=-0.05, voffset=0.5)
cb.set_label('($\mu$Amm$^{-3}$)', labelpad=0.)
axes[2].set_ylim(-1550, 50)
axes[2].set_title('LFP and CSD ({})'.format(X[1]), va='baseline')
phlp.annotate_subplot(axes[2], ncols=1, nrows=1, letter='C')
if show_xlabels:
axes[2].set_xlabel(r'$t$ (ms)',labelpad=0.)
else:
axes[2].set_xlabel('')
plotPowers(axes[3], params, params.Y, 'CSD', linestyles=linestyles, transient=transient, markerstyles=markerstyles, linewidths=linewidths)
axes[3].axis(axes[3].axis('tight'))
axes[3].set_ylim(-1550, 50)
axes[3].set_yticks(-np.arange(16)*100)
if show_xlabels:
axes[3].set_xlabel(r'$\sigma^2$ ($(\mu$Amm$^{-3})^2$)', va='center')
axes[3].set_title('CSD variance', va='baseline')
axes[3].set_xlim(left=1E-7)
phlp.remove_axis_junk(axes[3])
phlp.annotate_subplot(axes[3], ncols=1, nrows=1, letter='D')
plotPowers(axes[4], params, params.Y, 'LFP', linestyles=linestyles, transient=transient, markerstyles=markerstyles, linewidths=linewidths)
axes[4].axis(axes[4].axis('tight'))
axes[4].set_ylim(-1550, 50)
axes[4].set_yticks(-np.arange(16)*100)
if show_xlabels:
axes[4].set_xlabel(r'$\sigma^2$ (mV$^2$)', va='center')
axes[4].set_title('LFP variance', va='baseline')
axes[4].legend(bbox_to_anchor=(1.37, 1.0), frameon=False)
axes[4].set_xlim(left=1E-7)
phlp.remove_axis_junk(axes[4])
phlp.annotate_subplot(axes[4], ncols=1, nrows=1, letter='E')
return fig
def fig_intro(params, ana_params, T=[800, 1000], fraction=0.05, rasterized=False):
'''set up plot for introduction'''
ana_params.set_PLOS_2column_fig_style(ratio=0.5)
#load spike as database
networkSim = CachedNetwork(**params.networkSimParams)
if analysis_params.bw:
networkSim.colors = phlp.get_colors(len(networkSim.X))
#set up figure and subplots
fig = plt.figure()
gs = gridspec.GridSpec(3, 4)
fig.subplots_adjust(left=0.05, right=0.95, wspace=0.5, hspace=0.)
#network diagram
ax0_1 = fig.add_subplot(gs[:, 0], frameon=False)
ax0_1.set_title('point-neuron network', va='bottom')
network_sketch(ax0_1, yscaling=1.3)
ax0_1.xaxis.set_ticks([])
ax0_1.yaxis.set_ticks([])
phlp.annotate_subplot(ax0_1, ncols=4, nrows=1, letter='A', linear_offset=0.065)
#network raster
ax1 = fig.add_subplot(gs[:, 1], frameon=True)
phlp.remove_axis_junk(ax1)
phlp.annotate_subplot(ax1, ncols=4, nrows=1, letter='B', linear_offset=0.065)
x, y = networkSim.get_xy(T, fraction=fraction)
# networkSim.plot_raster(ax1, T, x, y, markersize=0.1, alpha=1.,legend=False, pop_names=True)
networkSim.plot_raster(ax1, T, x, y, markersize=0.2, marker='_', alpha=1.,legend=False, pop_names=True, rasterized=rasterized)
ax1.set_ylabel('')
ax1.xaxis.set_major_locator(plt.MaxNLocator(4))
ax1.set_title('spiking activity', va='bottom')
a = ax1.axis()
ax1.vlines(x['TC'][0], a[2], a[3], 'k', lw=0.25)
#population
ax2 = fig.add_subplot(gs[:, 2], frameon=False)
ax2.xaxis.set_ticks([])
ax2.yaxis.set_ticks([])
plot_population(ax2, params, isometricangle=np.pi/24, plot_somas=False,
plot_morphos=True, num_unitsE=1, num_unitsI=1,
clip_dendrites=True, main_pops=True, title='',
rasterized=rasterized)
ax2.set_title('multicompartment\nneurons', va='bottom', fontweight='normal')
phlp.annotate_subplot(ax2, ncols=4, nrows=1, letter='C', linear_offset=0.065)
#LFP traces in all channels
ax3 = fig.add_subplot(gs[:, 3], frameon=True)
phlp.remove_axis_junk(ax3)
plot_signal_sum(ax3, params, fname=os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV', vlimround=0.8,
T=T, ylim=[ax2.axis()[2], ax2.axis()[3]],
rasterized=False)
ax3.set_title('LFP', va='bottom')
ax3.xaxis.set_major_locator(plt.MaxNLocator(4))
phlp.annotate_subplot(ax3, ncols=4, nrows=1, letter='D', linear_offset=0.065)
a = ax3.axis()
ax3.vlines(x['TC'][0], a[2], a[3], 'k', lw=0.25)
#draw some arrows:
ax = plt.gca()
ax.annotate("", xy=(0.27, 0.5), xytext=(.24, 0.5),
xycoords="figure fraction",
arrowprops=dict(facecolor='black', arrowstyle='simple'),
)
ax.annotate("", xy=(0.52, 0.5), xytext=(.49, 0.5),
xycoords="figure fraction",
arrowprops=dict(facecolor='black', arrowstyle='simple'),
)
ax.annotate("", xy=(0.78, 0.5), xytext=(.75, 0.5),
xycoords="figure fraction",
arrowprops=dict(facecolor='black', arrowstyle='simple'),
)
return fig