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_exc_inh_contrib(fig, axes, params, savefolders, T=[800, 1000], transient=200,
panel_labels = 'FGHIJ', show_xlabels=True):
'''
plot time series LFPs and CSDs with signal variances as function of depth
for the cases with all synapses intact, or knocking out excitatory
input or inhibitory input to the postsynaptic target region
args:
::
fig :
axes :
savefolders : list of simulation output folders
T : list of ints, first and last time sample
transient : int, duration of transient period
returns:
::
matplotlib.figure.Figure object
'''
# params = multicompartment_params()
# ana_params = analysis_params.params()
#file name types
file_names = ['CSDsum.h5', 'LFPsum.h5']
#panel titles
panel_titles = [
'LFP&CSD\nexc. syn.',
'LFP&CSD\ninh. syn.',
'LFP&CSD\ncompound',
'CSD variance',
'LFP variance',]
#labels
labels = [
'exc. syn.',
'inh. syn.',
'SUM']
#some colors for traces
if analysis_params.bw:
colors = ['k', 'gray', 'k']
# lws = [0.75, 0.75, 1.5]
lws = [1.25, 1.25, 1.25]
else:
colors = [analysis_params.colorE, analysis_params.colorI, 'k']
# colors = 'rbk'
# lws = [0.75, 0.75, 1.5]
lws = [1.25, 1.25, 1.25]
#scalebar labels
units = ['$\mu$A mm$^{-3}$', 'mV']
#depth of each contact site
depth = params.electrodeParams['z']
# #set up figure
# #figure aspect
# 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)
#clean up
for ax in axes.flatten():
phlp.remove_axis_junk(ax)
for i, file_name in enumerate(file_names):
#get the global data scaling bar range for use in latter plots
#TODO: find nicer solution without creating figure
dum_fig, dum_ax = plt.subplots(1)
vlim_LFP = 0
vlim_CSD = 0
for savefolder in savefolders:
vlimround0 = plot_signal_sum(dum_ax, params,
os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
rasterized=False)
if vlimround0 > vlim_LFP:
vlim_LFP = vlimround0
im = plot_signal_sum_colorplot(dum_ax, params, os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
cmap=plt.get_cmap('gray', 21) if analysis_params.bw else plt.get_cmap('bwr_r', 21),
rasterized=False)
if abs(im.get_array()).max() > vlim_CSD:
vlim_CSD = abs(im.get_array()).max()
plt.close(dum_fig)
for j, savefolder in enumerate(savefolders):
ax = axes[j]
if i == 1:
plot_signal_sum(ax, params, os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
unit=units[i], T=T,
color='k',
# color='k' if analysis_params.bw else colors[j],
vlimround=vlim_LFP, rasterized=False)
elif i == 0:
im = plot_signal_sum_colorplot(ax, params, os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
unit=r'($\mu$Amm$^{-3}$)', T=T, ylabels=True,
colorbar=False,
fancy=False, cmap=plt.get_cmap('gray', 21) if analysis_params.bw else plt.get_cmap('bwr_r', 21),
absmax=vlim_CSD,
rasterized=False)
ax.axis((T[0], T[1], -1550, 50))
ax.set_title(panel_titles[j], va='baseline')
if i == 0:
phlp.annotate_subplot(ax, ncols=1, nrows=1, letter=panel_labels[j])
if j != 0:
ax.set_yticklabels([])
if i == 0:#and j == 2:
cb = phlp.colorbar(fig, ax, im,
width=0.05, height=0.5,
hoffset=-0.05, voffset=0.5)
cb.set_label('($\mu$Amm$^{-3}$)', labelpad=0.)
ax.xaxis.set_major_locator(plt.MaxNLocator(3))
if show_xlabels:
ax.set_xlabel(r'$t$ (ms)', labelpad=0.)
else:
ax.set_xlabel('')
#power in CSD
ax = axes[3]
datas = []
for j, savefolder in enumerate(savefolders):
f = h5py.File(os.path.join(os.path.split(params.savefolder)[0], savefolder, 'CSDsum.h5'))
var = f['data'][()][:, transient:].var(axis=1)
ax.semilogx(var, depth,
color=colors[j], label=labels[j], lw=lws[j], clip_on=False)
datas.append(f['data'][()][:, transient:])
f.close()
#control variances
vardiff = datas[0].var(axis=1) + datas[1].var(axis=1) + np.array([2*np.cov(x,y)[0,1] for (x,y) in zip(datas[0], datas[1])]) - datas[2].var(axis=1)
#ax.semilogx(abs(vardiff), depth, color='gray', lw=1, label='control')
ax.axis(ax.axis('tight'))
ax.set_ylim(-1550, 50)
ax.set_yticks(-np.arange(16)*100)
if show_xlabels:
ax.set_xlabel(r'$\sigma^2$ ($(\mu$Amm$^{-3})^2$)', labelpad=0.)
ax.set_title(panel_titles[3], va='baseline')
phlp.annotate_subplot(ax, ncols=1, nrows=1, letter=panel_labels[3])
ax.set_yticklabels([])
#power in LFP
ax = axes[4]
datas = []
for j, savefolder in enumerate(savefolders):
f = h5py.File(os.path.join(os.path.split(params.savefolder)[0], savefolder, 'LFPsum.h5'))
var = f['data'][()][:, transient:].var(axis=1)
ax.semilogx(var, depth,
color=colors[j], label=labels[j], lw=lws[j], clip_on=False)
datas.append(f['data'][()][:, transient:])
f.close()
#control variances
vardiff = datas[0].var(axis=1) + datas[1].var(axis=1) + np.array([2*np.cov(x,y)[0,1] for (x,y) in zip(datas[0], datas[1])]) - datas[2].var(axis=1)
ax.axis(ax.axis('tight'))
ax.set_ylim(-1550, 50)
ax.set_yticks(-np.arange(16)*100)
if show_xlabels:
ax.set_xlabel(r'$\sigma^2$ (mV$^2$)', labelpad=0.)
ax.set_title(panel_titles[4], va='baseline')
phlp.annotate_subplot(ax, ncols=1, nrows=1, letter=panel_labels[4])
ax.legend(bbox_to_anchor=(1.3, 1.0), frameon=False)
ax.set_yticklabels([])
def fig_exc_inh_contrib(fig, axes, params, savefolders, T=[800, 1000], transient=200,
panel_labels = 'FGHIJ', show_xlabels=True):
'''
plot time series LFPs and CSDs with signal variances as function of depth
for the cases with all synapses intact, or knocking out excitatory
input or inhibitory input to the postsynaptic target region
args:
::
fig :
axes :
savefolders : list of simulation output folders
T : list of ints, first and last time sample
transient : int, duration of transient period
returns:
::
matplotlib.figure.Figure object
'''
# params = multicompartment_params()
# ana_params = analysis_params.params()
#file name types
file_names = ['CSDsum.h5', 'LFPsum.h5']
#panel titles
panel_titles = [
'LFP&CSD\nexc. syn.',
'LFP&CSD\ninh. syn.',
'LFP&CSD\ncompound',
'CSD variance',
'LFP variance',]
#labels
labels = [
'exc. syn.',
'inh. syn.',
'SUM']
#some colors for traces
if analysis_params.bw:
colors = ['k', 'gray', 'k']
# lws = [0.75, 0.75, 1.5]
lws = [1.25, 1.25, 1.25]
else:
colors = [analysis_params.colorE, analysis_params.colorI, 'k']
# colors = 'rbk'
# lws = [0.75, 0.75, 1.5]
lws = [1.25, 1.25, 1.25]
#scalebar labels
units = ['$\mu$A mm$^{-3}$', 'mV']
#depth of each contact site
depth = params.electrodeParams['z']
# #set up figure
# #figure aspect
# 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)
#clean up
for ax in axes.flatten():
phlp.remove_axis_junk(ax)
for i, file_name in enumerate(file_names):
#get the global data scaling bar range for use in latter plots
#TODO: find nicer solution without creating figure
dum_fig, dum_ax = plt.subplots(1)
vlim_LFP = 0
vlim_CSD = 0
for savefolder in savefolders:
vlimround0 = plot_signal_sum(dum_ax, params,
os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
rasterized=False)
if vlimround0 > vlim_LFP:
vlim_LFP = vlimround0
im = plot_signal_sum_colorplot(dum_ax, params, os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
cmap=plt.get_cmap('gray', 21) if analysis_params.bw else plt.get_cmap('bwr_r', 21),
rasterized=False)
if abs(im.get_array()).max() > vlim_CSD:
vlim_CSD = abs(im.get_array()).max()
plt.close(dum_fig)
for j, savefolder in enumerate(savefolders):
ax = axes[j]
if i == 1:
plot_signal_sum(ax, params, os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
unit=units[i], T=T,
color='k',
# color='k' if analysis_params.bw else colors[j],
vlimround=vlim_LFP, rasterized=False)
elif i == 0:
im = plot_signal_sum_colorplot(ax, params, os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
unit=r'($\mu$Amm$^{-3}$)', T=T, ylabels=True,
colorbar=False,
fancy=False, cmap=plt.get_cmap('gray', 21) if analysis_params.bw else plt.get_cmap('bwr_r', 21),
absmax=vlim_CSD,
rasterized=False)
ax.axis((T[0], T[1], -1550, 50))
ax.set_title(panel_titles[j], va='baseline')
if i == 0:
phlp.annotate_subplot(ax, ncols=1, nrows=1, letter=panel_labels[j])
if j != 0:
ax.set_yticklabels([])
if i == 0:#and j == 2:
cb = phlp.colorbar(fig, ax, im,
width=0.05, height=0.5,
hoffset=-0.05, voffset=0.5)
cb.set_label('($\mu$Amm$^{-3}$)', labelpad=0.)
ax.xaxis.set_major_locator(plt.MaxNLocator(3))
if show_xlabels:
ax.set_xlabel(r'$t$ (ms)', labelpad=0.)
else:
ax.set_xlabel('')
#power in CSD
ax = axes[3]
datas = []
for j, savefolder in enumerate(savefolders):
f = h5py.File(os.path.join(os.path.split(params.savefolder)[0], savefolder, 'CSDsum.h5'))
var = f['data'].value[:, transient:].var(axis=1)
ax.semilogx(var, depth,
color=colors[j], label=labels[j], lw=lws[j], clip_on=False)
datas.append(f['data'].value[:, transient:])
f.close()
#control variances
vardiff = datas[0].var(axis=1) + datas[1].var(axis=1) + np.array([2*np.cov(x,y)[0,1] for (x,y) in zip(datas[0], datas[1])]) - datas[2].var(axis=1)
#ax.semilogx(abs(vardiff), depth, color='gray', lw=1, label='control')
ax.axis(ax.axis('tight'))
ax.set_ylim(-1550, 50)
ax.set_yticks(-np.arange(16)*100)
if show_xlabels:
ax.set_xlabel(r'$\sigma^2$ ($(\mu$Amm$^{-3})^2$)', labelpad=0.)
ax.set_title(panel_titles[3], va='baseline')
phlp.annotate_subplot(ax, ncols=1, nrows=1, letter=panel_labels[3])
ax.set_yticklabels([])
#power in LFP
ax = axes[4]
datas = []
for j, savefolder in enumerate(savefolders):
f = h5py.File(os.path.join(os.path.split(params.savefolder)[0], savefolder, 'LFPsum.h5'))
var = f['data'].value[:, transient:].var(axis=1)
ax.semilogx(var, depth,
color=colors[j], label=labels[j], lw=lws[j], clip_on=False)
datas.append(f['data'].value[:, transient:])
f.close()
#control variances
vardiff = datas[0].var(axis=1) + datas[1].var(axis=1) + np.array([2*np.cov(x,y)[0,1] for (x,y) in zip(datas[0], datas[1])]) - datas[2].var(axis=1)
ax.axis(ax.axis('tight'))
ax.set_ylim(-1550, 50)
ax.set_yticks(-np.arange(16)*100)
if show_xlabels:
ax.set_xlabel(r'$\sigma^2$ (mV$^2$)', labelpad=0.)
ax.set_title(panel_titles[4], va='baseline')
phlp.annotate_subplot(ax, ncols=1, nrows=1, letter=panel_labels[4])
ax.legend(bbox_to_anchor=(1.3, 1.0), frameon=False)
ax.set_yticklabels([])
def fig_network_input_structure(fig,
params,
bottom=0.1,
top=0.9,
transient=200,
T=[800, 1000],
Df=0.,
mlab=True,
NFFT=256,
srate=1000,
window=plt.mlab.window_hanning,
noverlap=256 * 3 / 4,
letters='abcde',
flim=(4, 400),
show_titles=True,
show_xlabels=True,
show_CSD=False):
'''
This figure is the top part for plotting a comparison between the PD-model
and the modified-PD model
'''
#load spike as database
networkSim = CachedNetwork(**params.networkSimParams)
if analysis_params.bw:
networkSim.colors = phlp.get_colors(len(networkSim.X))
# ana_params.set_PLOS_2column_fig_style(ratio=ratio)
# fig = plt.figure()
# fig.subplots_adjust(left=0.06, right=0.94, bottom=0.09, top=0.92, wspace=0.5, hspace=0.2)
#use gridspec to get nicely aligned subplots througout panel
gs1 = gridspec.GridSpec(5, 5, bottom=bottom, top=top)
############################################################################
# A part, full dot display
############################################################################
ax0 = fig.add_subplot(gs1[:, 0])
phlp.remove_axis_junk(ax0)
phlp.annotate_subplot(ax0,
ncols=5,
nrows=1,
letter=letters[0],
linear_offset=0.065)
x, y = networkSim.get_xy(T, fraction=1)
networkSim.plot_raster(ax0,
T,
x,
y,
markersize=0.2,
marker='_',
alpha=1.,
legend=False,
pop_names=True,
rasterized=False)
ax0.set_ylabel('population', labelpad=0.)
ax0.set_xticks([800, 900, 1000])
if show_titles:
ax0.set_title('spiking activity', va='center')
if show_xlabels:
ax0.set_xlabel(r'$t$ (ms)', labelpad=0.)
else:
ax0.set_xlabel('')
############################################################################
# B part, firing rate spectra
############################################################################
# Get the firing rate from Potjan Diesmann et al network activity
#collect the spikes x is the times, y is the id of the cell.
T_all = [transient, networkSim.simtime]
bins = np.arange(transient, networkSim.simtime + 1)
x, y = networkSim.get_xy(T_all, fraction=1)
# create invisible axes to position labels correctly
ax_ = fig.add_subplot(gs1[:, 1])
phlp.annotate_subplot(ax_,
ncols=5,
nrows=1,
letter=letters[1],
linear_offset=0.065)
if show_titles:
ax_.set_title('firing rate PSD', va='center')
ax_.axis('off')
colors = phlp.get_colors(len(params.Y)) + ['k']
COUNTER = 0
label_set = False
t**s = ['L23E/I', 'L4E/I', 'L5E/I', 'L6E/I', 'TC']
if x['TC'].size > 0:
TC = True
else:
TC = False
BAxes = []
for i, X in enumerate(networkSim.X):
if i % 2 == 0:
ax1 = fig.add_subplot(gs1[COUNTER, 1])
phlp.remove_axis_junk(ax1)
if x[X].size > 0:
ax1.text(0.05,
0.85,
t**s[COUNTER],
horizontalalignment='left',
verticalalignment='bottom',
transform=ax1.transAxes)
BAxes.append(ax1)
#firing rate histogram
hist = np.histogram(x[X], bins=bins)[0].astype(float)
hist -= hist.mean()
if mlab:
Pxx, freqs = plt.mlab.psd(hist,
NFFT=NFFT,
Fs=srate,
noverlap=noverlap,
window=window)
else:
[freqs, Pxx] = hlp.powerspec([hist],
tbin=1.,
Df=Df,
pointProcess=False)
mask = np.where(freqs >= 0.)
freqs = freqs[mask]
Pxx = Pxx.flatten()
Pxx = Pxx[mask]
Pxx = Pxx / (T_all[1] - T_all[0])**2
if x[X].size > 0:
ax1.loglog(freqs[1:],
Pxx[1:],
label=X,
color=colors[i],
clip_on=True)
ax1.axis(ax1.axis('tight'))
ax1.set_ylim([5E-4, 5E2])
ax1.set_yticks([1E-3, 1E-1, 1E1])
if label_set == False:
ax1.set_ylabel(r'(s$^{-2}$/Hz)', labelpad=0.)
label_set = True
if i > 1:
ax1.set_yticklabels([])
if i >= 6 and not TC and show_xlabels or X == 'TC' and TC and show_xlabels:
ax1.set_xlabel('$f$ (Hz)', labelpad=0.)
if TC and i < 8 or not TC and i < 6:
ax1.set_xticklabels([])
else:
ax1.axis('off')
ax1.set_xlim(flim)
if i % 2 == 0:
COUNTER += 1
ax1.yaxis.set_minor_locator(plt.NullLocator())
############################################################################
# c part, LFP traces and CSD color plots
############################################################################
ax2 = fig.add_subplot(gs1[:, 2])
phlp.annotate_subplot(ax2,
ncols=5,
nrows=1,
letter=letters[2],
linear_offset=0.065)
phlp.remove_axis_junk(ax2)
plot_signal_sum(ax2,
params,
fname=os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV',
T=T,
ylim=[-1600, 40],
rasterized=False)
# CSD background colorplot
if show_CSD:
im = plot_signal_sum_colorplot(ax2,
params,
os.path.join(params.savefolder,
'CSDsum.h5'),
unit=r'($\mu$Amm$^{-3}$)',
T=[800, 1000],
colorbar=False,
ylim=[-1600, 40],
fancy=False,
cmap=plt.cm.get_cmap('bwr_r', 21),
rasterized=False)
cb = phlp.colorbar(fig,
ax2,
im,
width=0.05,
height=0.4,
hoffset=-0.05,
voffset=0.3)
cb.set_label('($\mu$Amm$^{-3}$)', labelpad=0.1)
ax2.set_xticks([800, 900, 1000])
ax2.axis(ax2.axis('tight'))
if show_titles:
if show_CSD:
ax2.set_title('LFP & CSD', va='center')
else:
ax2.set_title('LFP', va='center')
if show_xlabels:
ax2.set_xlabel(r'$t$ (ms)', labelpad=0.)
else:
ax2.set_xlabel('')
############################################################################
# d part, LFP power trace for each layer
############################################################################
freqs, PSD = calc_signal_power(params,
fname=os.path.join(params.savefolder,
'LFPsum.h5'),
transient=transient,
Df=Df,
mlab=mlab,
NFFT=NFFT,
noverlap=noverlap,
window=window)
channels = [0, 3, 7, 11, 13]
# create invisible axes to position labels correctly
ax_ = fig.add_subplot(gs1[:, 3])
phlp.annotate_subplot(ax_,
ncols=5,
nrows=1,
letter=letters[3],
linear_offset=0.065)
if show_titles:
ax_.set_title('LFP PSD', va='center')
ax_.axis('off')
for i, ch in enumerate(channels):
ax = fig.add_subplot(gs1[i, 3])
phlp.remove_axis_junk(ax)
if i == 0:
ax.set_ylabel('(mV$^2$/Hz)', labelpad=0)
ax.loglog(freqs[1:], PSD[ch][1:], color='k')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
if i < 4:
ax.set_xticklabels([])
ax.text(0.75,
0.85,
'ch. %i' % (channels[i] + 1),
horizontalalignment='left',
verticalalignment='bottom',
fontsize=6,
transform=ax.transAxes)
ax.tick_params(axis='y', which='minor', bottom='off')
ax.axis(ax.axis('tight'))
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.set_xlim(flim)
ax.set_ylim(1E-7, 2E-4)
if i != 0:
ax.set_yticklabels([])
if show_xlabels:
ax.set_xlabel('$f$ (Hz)', labelpad=0.)
############################################################################
# e part signal power
############################################################################
ax4 = fig.add_subplot(gs1[:, 4])
phlp.annotate_subplot(ax4,
ncols=5,
nrows=1,
letter=letters[4],
linear_offset=0.065)
fname = os.path.join(params.savefolder, 'LFPsum.h5')
im = plot_signal_power_colorplot(ax4,
params,
fname=fname,
transient=transient,
Df=Df,
mlab=mlab,
NFFT=NFFT,
window=window,
cmap=plt.cm.get_cmap('gray_r', 12),
vmin=1E-7,
vmax=1E-4)
phlp.remove_axis_junk(ax4)
ax4.set_xlim(flim)
cb = phlp.colorbar(fig,
ax4,
im,
width=0.05,
height=0.5,
hoffset=-0.05,
voffset=0.5)
cb.set_label('(mV$^2$/Hz)', labelpad=0.1)
if show_titles:
ax4.set_title('LFP PSD', va='center')
if show_xlabels:
ax4.set_xlabel(r'$f$ (Hz)', labelpad=0.)
else:
ax4.set_xlabel('')
return fig
ax5,
params,
os.path.join(params.savefolder, 'CSDsum.h5'),
unit=r'$\mu$Amm$^{-3}$',
T=T,
ylim=[-1550, 50],
fancy=False,
colorbar=False,
cmap=plt.get_cmap('gray', 21)
if analysis_params.bw else plt.get_cmap('bwr_r', 21),
rasterized=False)
cb = phlp.colorbar(fig,
ax5,
im,
width=0.05,
height=0.5,
hoffset=-0.05,
voffset=0.5)
cb.set_label('($\mu$Amm$^{-3}$)', labelpad=0.1)
############################################################################
# D part, LFP
############################################################################
ax7 = fig.add_subplot(gs[:, 3])
plt.locator_params(nbins=4)
if show_ax_labels:
phlp.annotate_subplot(ax7,
ncols=1,
nrows=1,
letter='D',
def fig_lfp_scaling(fig, params, bottom=0.55, top=0.95, channels=[0,3,7,11,13], T=[800.,1000.], Df=None,
mlab=True, NFFT=256, noverlap=128,
window=plt.mlab.window_hanning, letters='ABCD',
lag=20, show_titles=True, show_xlabels=True):
fname_fullscale=os.path.join(params.savefolder, 'LFPsum.h5')
fname_downscaled=os.path.join(params.savefolder, 'populations','subsamples', 'LFPsum_10_0.h5')
# ana_params.set_PLOS_2column_fig_style(ratio=0.5)
gs = gridspec.GridSpec(len(channels), 8, bottom=bottom, top=top)
# fig = plt.figure()
# fig.subplots_adjust(left=0.075, right=0.95, bottom=0.075, wspace=0.8, hspace=0.1)
scaling_factor = np.sqrt(10)
##################################
### LFP traces ###
##################################
ax = fig.add_subplot(gs[:, :3])
phlp.annotate_subplot(ax, ncols=8/3., nrows=1, letter=letters[0], linear_offset=0.065)
plot_signal_sum(ax, params, fname=os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV', scaling_factor= 1., scalebar=True,
vlimround=None,
T=T, ylim=[-1600, 50] ,color='k',label='$\Phi$',
rasterized=False,
zorder=1,)
plot_signal_sum(ax, params, fname=os.path.join(params.savefolder, 'populations',
'subsamples', 'LFPsum_10_0.h5'),
unit='mV', scaling_factor= scaling_factor,scalebar=False,
vlimround=None,
T=T, ylim=[-1600, 50],
color='gray' if analysis_params.bw else analysis_params.colorP,
label='$\hat{\Phi}^{\prime}$',
rasterized=False,
lw=1, zorder=0)
if show_titles:
ax.set_title('LFP & low-density predictor')
if show_xlabels:
ax.set_xlabel('$t$ (ms)', labelpad=0.)
else:
ax.set_xlabel('')
#################################
### Correlations ###
#################################
ax = fig.add_subplot(gs[:, 3])
phlp.annotate_subplot(ax, ncols=8, nrows=1, letter=letters[1], linear_offset=0.065)
phlp.remove_axis_junk(ax)
datas = []
files = [os.path.join(params.savefolder, 'LFPsum.h5'),
os.path.join(params.savefolder, 'populations',
'subsamples', 'LFPsum_10_0.h5')]
for fil in files:
f = h5py.File(fil)
datas.append(f['data'].value[:, 200:])
f.close()
zvec = np.r_[params.electrodeParams['z']]
cc = np.zeros(len(zvec))
for ch in np.arange(len(zvec)):
x0 = datas[0][ch]
x0 -= x0.mean()
x1 = datas[1][ch]
x1 -= x1.mean()
cc[ch] = np.corrcoef(x0, x1)[1, 0]
ax.barh(zvec, cc, height=80, align='center', color='0.5', linewidth=0.5)
# superimpose the chance level, obtained by mixing one input vector N times
# while keeping the other fixed. We show boxes drawn left to right where
# these denote mean +/- two standard deviations.
N = 1000
method = 'randphase' #or 'permute'
chance = np.zeros((cc.size, N))
for ch in np.arange(len(zvec)):
x1 = datas[1][ch]
x1 -= x1.mean()
if method == 'randphase':
x0 = datas[0][ch]
x0 -= x0.mean()
X00 = np.fft.fft(x0)
for n in range(N):
if method == 'permute':
x0 = np.random.permutation(datas[0][ch])
elif method == 'randphase':
X0 = np.copy(X00)
#random phase information such that spectra is preserved
theta = np.random.uniform(0, 2*np.pi, size=X0.size // 2)
#half-sided real and imaginary component
real = abs(X0[1:X0.size // 2 + 1])*np.cos(theta)
imag = abs(X0[1:X0.size // 2 + 1])*np.sin(theta)
#account for the antisymmetric phase values
X0.imag[1:imag.size+1] = imag
X0.imag[imag.size+1:] = -imag[::-1]
X0.real[1:real.size+1] = real
X0.real[real.size+1:] = real[::-1]
x0 = np.fft.ifft(X0).real
chance[ch, n] = np.corrcoef(x0, x1)[1, 0]
# p-values, compute the fraction of chance correlations > cc at each channel
p = []
for i, x in enumerate(cc):
p += [(chance[i, ] >= x).sum() / float(N)]
print('p-values:', p)
#compute the 99% percentile of the chance data
right = np.percentile(chance, 99, axis=-1)
ax.plot(right, zvec, ':', color='k', lw=1.)
ax.set_ylim([-1550, 50])
ax.set_yticklabels([])
ax.set_yticks(zvec)
ax.set_xlim([0., 1.])
ax.set_xticks([0.0, 0.5, 1])
ax.yaxis.tick_left()
if show_titles:
ax.set_title('corr.\ncoef.')
if show_xlabels:
ax.set_xlabel('$cc$ (-)', labelpad=0.)
##################################
### Single channel PSDs ###
##################################
freqs, PSD_fullscale = calc_signal_power(params, fname=fname_fullscale,
transient=200, Df=Df, mlab=mlab,
NFFT=NFFT, noverlap=noverlap,
window=window)
freqs, PSD_downscaled = calc_signal_power(params, fname=fname_downscaled,
transient=200, Df=Df, mlab=mlab,
NFFT=NFFT, noverlap=noverlap,
window=window)
inds = freqs >= 1 # frequencies greater than 4 Hz
for i, ch in enumerate(channels):
ax = fig.add_subplot(gs[i, 4:6])
if i == 0:
phlp.annotate_subplot(ax, ncols=8/2., nrows=len(channels), letter=letters[2], linear_offset=0.065)
phlp.remove_axis_junk(ax)
ax.loglog(freqs[inds],PSD_fullscale[ch][inds],
color='k', label='$\gamma=1.0$',
zorder=1,)
ax.loglog(freqs[inds],PSD_downscaled[ch][inds]*scaling_factor**2,
lw=1,
color='gray' if analysis_params.bw else analysis_params.colorP,
label='$\gamma=0.1, \zeta=\sqrt{10}$',
zorder=0,)
ax.loglog(freqs[inds],PSD_downscaled[ch][inds]*scaling_factor**4,
lw=1,
color='0.75', label='$\gamma=0.1, \zeta=10$',
zorder=0)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.text(0.8,0.9,'ch. %i' %(ch+1),horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
if i < len(channels)-1:
#ax.set_xticks([])
ax.set_xticklabels([])
ax.tick_params(axis='y',which='minor',bottom='off')
ax.set_xlim([4E0,4E2])
ax.set_ylim([3E-8,1E-4])
if i == 0:
ax.tick_params(axis='y', which='major', pad=0)
ax.set_ylabel('(mV$^2$/Hz)', labelpad=0.)
if show_titles:
ax.set_title('power spectra')
#ax.set_yticks([1E-9,1E-7,1E-5])
if i > 0:
ax.set_yticklabels([])
if show_xlabels:
ax.set_xlabel(r'$f$ (Hz)', labelpad=0.)
##################################
### PSD ratios ###
##################################
ax = fig.add_subplot(gs[:, 6:8])
phlp.annotate_subplot(ax, ncols=8./2, nrows=1, letter=letters[3], linear_offset=0.065)
PSD_ratio = PSD_fullscale/(PSD_downscaled*scaling_factor**2)
zvec = np.r_[params.electrodeParams['z']]
zvec = np.r_[zvec, zvec[-1] + np.diff(zvec)[-1]]
inds = freqs >= 1 # frequencies greater than 4 Hz
im = ax.pcolormesh(freqs[inds], zvec+40, PSD_ratio[:, inds],
rasterized=False,
cmap=plt.get_cmap('gray_r', 18) if analysis_params.bw else plt.cm.get_cmap('Reds', 18),
vmin=1E0,vmax=1.E1)
ax.set_xlim([4E0,4E2])
ax.set_xscale('log')
ax.set_yticks(zvec)
yticklabels = ['ch. %i' %i for i in np.arange(len(zvec))+1]
ax.set_yticklabels(yticklabels)
plt.axis('tight')
cb = phlp.colorbar(fig, ax, im,
width=0.05, height=0.5,
hoffset=-0.05, voffset=0.0)
cb.set_label('(-)', labelpad=0.)
phlp.remove_axis_junk(ax)
if show_titles:
ax.set_title('power ratio')
if show_xlabels:
ax.set_xlabel(r'$f$ (Hz)', labelpad=0.)
return fig
def fig_network_input_structure(fig, params, bottom=0.1, top=0.9, transient=200, T=[800, 1000], Df= 0., mlab= True, NFFT=256, srate=1000,
window=plt.mlab.window_hanning, noverlap=256*3/4, letters='abcde', flim=(4, 400),
show_titles=True, show_xlabels=True, show_CSD=False):
'''
This figure is the top part for plotting a comparison between the PD-model
and the modified-PD model
'''
#load spike as database
networkSim = CachedNetwork(**params.networkSimParams)
if analysis_params.bw:
networkSim.colors = phlp.get_colors(len(networkSim.X))
# ana_params.set_PLOS_2column_fig_style(ratio=ratio)
# fig = plt.figure()
# fig.subplots_adjust(left=0.06, right=0.94, bottom=0.09, top=0.92, wspace=0.5, hspace=0.2)
#use gridspec to get nicely aligned subplots througout panel
gs1 = gridspec.GridSpec(5, 5, bottom=bottom, top=top)
############################################################################
# A part, full dot display
############################################################################
ax0 = fig.add_subplot(gs1[:, 0])
phlp.remove_axis_junk(ax0)
phlp.annotate_subplot(ax0, ncols=5, nrows=1, letter=letters[0],
linear_offset=0.065)
x, y = networkSim.get_xy(T, fraction=1)
networkSim.plot_raster(ax0, T, x, y,
markersize=0.2, marker='_',
alpha=1.,
legend=False, pop_names=True,
rasterized=False)
ax0.set_ylabel('population', labelpad=0.)
ax0.set_xticks([800,900,1000])
if show_titles:
ax0.set_title('spiking activity',va='center')
if show_xlabels:
ax0.set_xlabel(r'$t$ (ms)', labelpad=0.)
else:
ax0.set_xlabel('')
############################################################################
# B part, firing rate spectra
############################################################################
# Get the firing rate from Potjan Diesmann et al network activity
#collect the spikes x is the times, y is the id of the cell.
T_all=[transient, networkSim.simtime]
bins = np.arange(transient, networkSim.simtime+1)
x, y = networkSim.get_xy(T_all, fraction=1)
# create invisible axes to position labels correctly
ax_ = fig.add_subplot(gs1[:, 1])
phlp.annotate_subplot(ax_, ncols=5, nrows=1, letter=letters[1],
linear_offset=0.065)
if show_titles:
ax_.set_title('firing rate PSD', va='center')
ax_.axis('off')
colors = phlp.get_colors(len(params.Y))+['k']
COUNTER = 0
label_set = False
t**s = ['L23E/I', 'L4E/I', 'L5E/I', 'L6E/I', 'TC']
if x['TC'].size > 0:
TC = True
else:
TC = False
BAxes = []
for i, X in enumerate(networkSim.X):
if i % 2 == 0:
ax1 = fig.add_subplot(gs1[COUNTER, 1])
phlp.remove_axis_junk(ax1)
if x[X].size > 0:
ax1.text(0.05, 0.85, t**s[COUNTER],
horizontalalignment='left',
verticalalignment='bottom',
transform=ax1.transAxes)
BAxes.append(ax1)
#firing rate histogram
hist = np.histogram(x[X], bins=bins)[0].astype(float)
hist -= hist.mean()
if mlab:
Pxx, freqs=plt.mlab.psd(hist, NFFT=NFFT,
Fs=srate, noverlap=noverlap, window=window)
else:
[freqs, Pxx] = hlp.powerspec([hist], tbin= 1.,
Df=Df, pointProcess=False)
mask = np.where(freqs >= 0.)
freqs = freqs[mask]
Pxx = Pxx.flatten()
Pxx = Pxx[mask]
Pxx = Pxx/(T_all[1]-T_all[0])**2
if x[X].size > 0:
ax1.loglog(freqs[1:], Pxx[1:],
label=X, color=colors[i],
clip_on=True)
ax1.axis(ax1.axis('tight'))
ax1.set_ylim([5E-4,5E2])
ax1.set_yticks([1E-3,1E-1,1E1])
if label_set == False:
ax1.set_ylabel(r'(s$^{-2}$/Hz)', labelpad=0.)
label_set = True
if i > 1:
ax1.set_yticklabels([])
if i >= 6 and not TC and show_xlabels or X == 'TC' and TC and show_xlabels:
ax1.set_xlabel('$f$ (Hz)', labelpad=0.)
if TC and i < 8 or not TC and i < 6:
ax1.set_xticklabels([])
else:
ax1.axis('off')
ax1.set_xlim(flim)
if i % 2 == 0:
COUNTER += 1
ax1.yaxis.set_minor_locator(plt.NullLocator())
############################################################################
# c part, LFP traces and CSD color plots
############################################################################
ax2 = fig.add_subplot(gs1[:, 2])
phlp.annotate_subplot(ax2, ncols=5, nrows=1, letter=letters[2],
linear_offset=0.065)
phlp.remove_axis_junk(ax2)
plot_signal_sum(ax2, params,
fname=os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV', T=T, ylim=[-1600, 40],
rasterized=False)
# CSD background colorplot
if show_CSD:
im = plot_signal_sum_colorplot(ax2, params, os.path.join(params.savefolder, 'CSDsum.h5'),
unit=r'($\mu$Amm$^{-3}$)', T=[800, 1000],
colorbar=False,
ylim=[-1600, 40], fancy=False, cmap=plt.cm.get_cmap('bwr_r', 21),
rasterized=False)
cb = phlp.colorbar(fig, ax2, im,
width=0.05, height=0.4,
hoffset=-0.05, voffset=0.3)
cb.set_label('($\mu$Amm$^{-3}$)', labelpad=0.1)
ax2.set_xticks([800,900,1000])
ax2.axis(ax2.axis('tight'))
if show_titles:
if show_CSD:
ax2.set_title('LFP & CSD', va='center')
else:
ax2.set_title('LFP', va='center')
if show_xlabels:
ax2.set_xlabel(r'$t$ (ms)', labelpad=0.)
else:
ax2.set_xlabel('')
############################################################################
# d part, LFP power trace for each layer
############################################################################
freqs, PSD = calc_signal_power(params, fname=os.path.join(params.savefolder,
'LFPsum.h5'),
transient=transient, Df=Df, mlab=mlab,
NFFT=NFFT, noverlap=noverlap,
window=window)
channels = [0, 3, 7, 11, 13]
# create invisible axes to position labels correctly
ax_ = fig.add_subplot(gs1[:, 3])
phlp.annotate_subplot(ax_, ncols=5, nrows=1, letter=letters[3],
linear_offset=0.065)
if show_titles:
ax_.set_title('LFP PSD',va='center')
ax_.axis('off')
for i, ch in enumerate(channels):
ax = fig.add_subplot(gs1[i, 3])
phlp.remove_axis_junk(ax)
if i == 0:
ax.set_ylabel('(mV$^2$/Hz)', labelpad=0)
ax.loglog(freqs[1:],PSD[ch][1:], color='k')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
if i < 4:
ax.set_xticklabels([])
ax.text(0.75, 0.85,'ch. %i' %(channels[i]+1),
horizontalalignment='left',
verticalalignment='bottom',
fontsize=6,
transform=ax.transAxes)
ax.tick_params(axis='y', which='minor', bottom='off')
ax.axis(ax.axis('tight'))
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.set_xlim(flim)
ax.set_ylim(1E-7,2E-4)
if i != 0 :
ax.set_yticklabels([])
if show_xlabels:
ax.set_xlabel('$f$ (Hz)', labelpad=0.)
############################################################################
# e part signal power
############################################################################
ax4 = fig.add_subplot(gs1[:, 4])
phlp.annotate_subplot(ax4, ncols=5, nrows=1, letter=letters[4],
linear_offset=0.065)
fname=os.path.join(params.savefolder, 'LFPsum.h5')
im = plot_signal_power_colorplot(ax4, params, fname=fname, transient=transient, Df=Df,
mlab=mlab, NFFT=NFFT, window=window,
cmap=plt.cm.get_cmap('gray_r', 12),
vmin=1E-7, vmax=1E-4)
phlp.remove_axis_junk(ax4)
ax4.set_xlim(flim)
cb = phlp.colorbar(fig, ax4, im,
width=0.05, height=0.5,
hoffset=-0.05, voffset=0.5)
cb.set_label('(mV$^2$/Hz)', labelpad=0.1)
if show_titles:
ax4.set_title('LFP PSD', va='center')
if show_xlabels:
ax4.set_xlabel(r'$f$ (Hz)', labelpad=0.)
else:
ax4.set_xlabel('')
return fig
def fig_lfp_corr(params, savefolders, transient=200,
channels=[0,3,7,11,13], Df=None,
mlab=True, NFFT=256, noverlap=128,
window=plt.mlab.window_hanning,
letterslist=['AB', 'CD'], data_type = 'LFP'):
'''This figure compares power spectra for correlated and uncorrelated signals
'''
ana_params.set_PLOS_2column_fig_style(ratio=0.5)
fig = plt.figure()
fig.subplots_adjust(left=0.07, right=0.95, bottom=0.1, wspace=0.3, hspace=0.1)
gs = gridspec.GridSpec(5, 4)
for i, (savefolder, letters) in enumerate(zip(savefolders, letterslist)):
# path to simulation files
params.savefolder = os.path.join(os.path.split(params.savefolder)[0],
savefolder)
params.figures_path = os.path.join(params.savefolder, 'figures')
params.spike_output_path = os.path.join(params.savefolder,
'processed_nest_output')
params.networkSimParams['spike_output_path'] = params.spike_output_path
## Including correlations
f = h5py.File(os.path.join(params.savefolder, ana_params.analysis_folder, data_type + ana_params.fname_psd),'r')
freqs = f['freqs'][()]
LFP_PSD_corr = f['psd'][()]
f.close()
## Excluding correlations
f = h5py.File(os.path.join(params.savefolder, ana_params.analysis_folder, data_type + ana_params.fname_psd_uncorr),'r')
freqs = f['freqs'][()]
LFP_PSD_uncorr = f['psd'][()]
f.close()
##################################
### Single channel LFP PSDs ###
##################################
ax = fig.add_subplot(gs[0, (i % 2)*2])
phlp.remove_axis_junk(ax)
ax.loglog(freqs,LFP_PSD_corr[channels[0]], color='k', label='$P$')
ax.loglog(freqs,LFP_PSD_uncorr[channels[0]],
color='gray' if analysis_params.bw else analysis_params.colorP,
lw=1,
label='$\tilde{P}$')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.text(0.80,0.82,'ch. %i' %(channels[0]+1),horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.set_ylabel('(mV$^2$/Hz)', labelpad=0.)
ax.set_xticks([])
ax.set_xticklabels([])
ax.tick_params(axis='y',which='minor',bottom='off')
ax.set_xlim(4E0,4E2)
ax.set_ylim(1E-8,1.5E-4)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.set_title('power spectra')
phlp.annotate_subplot(ax, ncols=4, nrows=5, letter=letters[0],
linear_offset=0.065)
ax = fig.add_subplot(gs[1, (i % 2)*2])
phlp.remove_axis_junk(ax)
ax.loglog(freqs,LFP_PSD_corr[channels[1]], color='k', label='corr')
ax.loglog(freqs,LFP_PSD_uncorr[channels[1]],
color='gray' if analysis_params.bw else analysis_params.colorP,
lw=1,
label='uncorr')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.text(0.80,0.82,'ch. %i' %(channels[1]+1),horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.set_xticks([])
ax.set_xticklabels([])
ax.tick_params(axis='y',which='minor',bottom='off')
ax.set_xlim(4E0,4E2)
ax.set_ylim(1E-8,1.5E-4)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.set_yticklabels([])
ax = fig.add_subplot(gs[2, (i % 2)*2])
phlp.remove_axis_junk(ax)
ax.loglog(freqs,LFP_PSD_corr[channels[2]], color='k', label='corr')
ax.loglog(freqs,LFP_PSD_uncorr[channels[2]],
color='gray' if analysis_params.bw else analysis_params.colorP,
lw=1,
label='uncorr')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.text(0.80,0.82,'ch. %i' %(channels[2]+1),horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.set_xticks([])
ax.set_xticklabels([])
ax.tick_params(axis='y',which='minor',bottom='off')
ax.set_xlim(4E0,4E2)
ax.set_ylim(1E-8,1.5E-4)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.set_yticklabels([])
ax = fig.add_subplot(gs[3, (i % 2)*2])
phlp.remove_axis_junk(ax)
ax.loglog(freqs,LFP_PSD_corr[channels[3]], color='k', label='corr')
ax.loglog(freqs,LFP_PSD_uncorr[channels[3]],
color='gray' if analysis_params.bw else analysis_params.colorP,
lw=1,
label='uncorr')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.text(0.80,0.82,'ch. %i' %(channels[3]+1),horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.set_xticks([])
ax.set_xticklabels([])
ax.tick_params(axis='y',which='minor',bottom='off')
ax.set_xlim(4E0,4E2)
ax.set_ylim(1E-8,1.5E-4)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.set_yticklabels([])
ax = fig.add_subplot(gs[4, (i % 2)*2])
phlp.remove_axis_junk(ax)
ax.loglog(freqs,LFP_PSD_corr[channels[4]], color='k', label='corr')
ax.loglog(freqs,LFP_PSD_uncorr[channels[4]],
color='gray' if analysis_params.bw else analysis_params.colorP,
lw=1,
label='uncorr')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.set_xlabel(r'$f$ (Hz)', labelpad=0.2)
ax.text(0.80,0.82,'ch. %i' %(channels[4]+1),horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.tick_params(axis='y',which='minor',bottom='off')
ax.set_xlim(4E0,4E2)
ax.set_ylim(1E-8,1.5E-4)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.set_yticklabels([])
##################################
### LFP PSD ratios ###
##################################
ax = fig.add_subplot(gs[:, (i % 2)*2 + 1])
phlp.annotate_subplot(ax, ncols=4, nrows=1, letter=letters[1],
linear_offset=0.065)
phlp.remove_axis_junk(ax)
ax.set_title('power ratio')
PSD_ratio = LFP_PSD_corr/LFP_PSD_uncorr
zvec = np.r_[params.electrodeParams['z']]
zvec = np.r_[zvec, zvec[-1] + np.diff(zvec)[-1]]
inds = freqs >= 1 # frequencies greater than 4 Hz
im = ax.pcolormesh(freqs[inds], zvec+40, PSD_ratio[:, inds],
rasterized=False,
cmap=plt.get_cmap('gray_r', 12) if analysis_params.bw else plt.get_cmap('Reds', 12),
vmin=10**-0.25,vmax=10**2.75,norm=LogNorm())
ax.set_xscale('log')
ax.set_yticks(zvec)
yticklabels = ['ch. %i' %i for i in np.arange(len(zvec))+1]
ax.set_yticklabels(yticklabels)
ax.set_xlabel(r'$f$ (Hz)',labelpad=0.2)
plt.axis('tight')
ax.set_xlim([4E0, 4E2])
cb = phlp.colorbar(fig, ax, im,
width=0.05, height=0.5,
hoffset=-0.05, voffset=0.0)
cb.set_label('(-)', labelpad=0.1)
return fig
def fig_lfp_corr(params, savefolders, transient=200,
channels=[0,3,7,11,13], Df=None,
mlab=True, NFFT=256, noverlap=128,
window=plt.mlab.window_hanning,
letterslist=['AB', 'CD'], data_type = 'LFP'):
'''This figure compares power spectra for correlated and uncorrelated signals
'''
ana_params.set_PLOS_2column_fig_style(ratio=0.5)
fig = plt.figure()
fig.subplots_adjust(left=0.07, right=0.95, bottom=0.1, wspace=0.3, hspace=0.1)
gs = gridspec.GridSpec(5, 4)
for i, (savefolder, letters) in enumerate(zip(savefolders, letterslist)):
# path to simulation files
params.savefolder = os.path.join(os.path.split(params.savefolder)[0],
savefolder)
params.figures_path = os.path.join(params.savefolder, 'figures')
params.spike_output_path = os.path.join(params.savefolder,
'processed_nest_output')
params.networkSimParams['spike_output_path'] = params.spike_output_path
## Including correlations
f = h5py.File(os.path.join(params.savefolder, ana_params.analysis_folder, data_type + ana_params.fname_psd),'r')
freqs = f['freqs'].value
LFP_PSD_corr = f['psd'].value
f.close()
## Excluding correlations
f = h5py.File(os.path.join(params.savefolder, ana_params.analysis_folder, data_type + ana_params.fname_psd_uncorr),'r')
freqs = f['freqs'].value
LFP_PSD_uncorr = f['psd'].value
f.close()
##################################
### Single channel LFP PSDs ###
##################################
ax = fig.add_subplot(gs[0, (i % 2)*2])
phlp.remove_axis_junk(ax)
ax.loglog(freqs,LFP_PSD_corr[channels[0]], color='k', label='$P$')
ax.loglog(freqs,LFP_PSD_uncorr[channels[0]],
color='gray' if analysis_params.bw else analysis_params.colorP,
lw=1,
label='$\tilde{P}$')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.text(0.80,0.82,'ch. %i' %(channels[0]+1),horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.set_ylabel('(mV$^2$/Hz)', labelpad=0.)
ax.set_xticks([])
ax.set_xticklabels([])
ax.tick_params(axis='y',which='minor',bottom='off')
ax.set_xlim(4E0,4E2)
ax.set_ylim(1E-8,1.5E-4)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.set_title('power spectra')
phlp.annotate_subplot(ax, ncols=4, nrows=5, letter=letters[0],
linear_offset=0.065)
ax = fig.add_subplot(gs[1, (i % 2)*2])
phlp.remove_axis_junk(ax)
ax.loglog(freqs,LFP_PSD_corr[channels[1]], color='k', label='corr')
ax.loglog(freqs,LFP_PSD_uncorr[channels[1]],
color='gray' if analysis_params.bw else analysis_params.colorP,
lw=1,
label='uncorr')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.text(0.80,0.82,'ch. %i' %(channels[1]+1),horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.set_xticks([])
ax.set_xticklabels([])
ax.tick_params(axis='y',which='minor',bottom='off')
ax.set_xlim(4E0,4E2)
ax.set_ylim(1E-8,1.5E-4)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.set_yticklabels([])
ax = fig.add_subplot(gs[2, (i % 2)*2])
phlp.remove_axis_junk(ax)
ax.loglog(freqs,LFP_PSD_corr[channels[2]], color='k', label='corr')
ax.loglog(freqs,LFP_PSD_uncorr[channels[2]],
color='gray' if analysis_params.bw else analysis_params.colorP,
lw=1,
label='uncorr')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.text(0.80,0.82,'ch. %i' %(channels[2]+1),horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.set_xticks([])
ax.set_xticklabels([])
ax.tick_params(axis='y',which='minor',bottom='off')
ax.set_xlim(4E0,4E2)
ax.set_ylim(1E-8,1.5E-4)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.set_yticklabels([])
ax = fig.add_subplot(gs[3, (i % 2)*2])
phlp.remove_axis_junk(ax)
ax.loglog(freqs,LFP_PSD_corr[channels[3]], color='k', label='corr')
ax.loglog(freqs,LFP_PSD_uncorr[channels[3]],
color='gray' if analysis_params.bw else analysis_params.colorP,
lw=1,
label='uncorr')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.text(0.80,0.82,'ch. %i' %(channels[3]+1),horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.set_xticks([])
ax.set_xticklabels([])
ax.tick_params(axis='y',which='minor',bottom='off')
ax.set_xlim(4E0,4E2)
ax.set_ylim(1E-8,1.5E-4)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.set_yticklabels([])
ax = fig.add_subplot(gs[4, (i % 2)*2])
phlp.remove_axis_junk(ax)
ax.loglog(freqs,LFP_PSD_corr[channels[4]], color='k', label='corr')
ax.loglog(freqs,LFP_PSD_uncorr[channels[4]],
color='gray' if analysis_params.bw else analysis_params.colorP,
lw=1,
label='uncorr')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.set_xlabel(r'$f$ (Hz)', labelpad=0.2)
ax.text(0.80,0.82,'ch. %i' %(channels[4]+1),horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.tick_params(axis='y',which='minor',bottom='off')
ax.set_xlim(4E0,4E2)
ax.set_ylim(1E-8,1.5E-4)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.set_yticklabels([])
##################################
### LFP PSD ratios ###
##################################
ax = fig.add_subplot(gs[:, (i % 2)*2 + 1])
phlp.annotate_subplot(ax, ncols=4, nrows=1, letter=letters[1],
linear_offset=0.065)
phlp.remove_axis_junk(ax)
ax.set_title('power ratio')
PSD_ratio = LFP_PSD_corr/LFP_PSD_uncorr
zvec = np.r_[params.electrodeParams['z']]
zvec = np.r_[zvec, zvec[-1] + np.diff(zvec)[-1]]
inds = freqs >= 1 # frequencies greater than 4 Hz
im = ax.pcolormesh(freqs[inds], zvec+40, PSD_ratio[:, inds],
rasterized=False,
cmap=plt.get_cmap('gray_r', 12) if analysis_params.bw else plt.get_cmap('Reds', 12),
vmin=10**-0.25,vmax=10**2.75,norm=LogNorm())
ax.set_xscale('log')
ax.set_yticks(zvec)
yticklabels = ['ch. %i' %i for i in np.arange(len(zvec))+1]
ax.set_yticklabels(yticklabels)
ax.set_xlabel(r'$f$ (Hz)',labelpad=0.2)
plt.axis('tight')
ax.set_xlim([4E0, 4E2])
cb = phlp.colorbar(fig, ax, im,
width=0.05, height=0.5,
hoffset=-0.05, voffset=0.0)
cb.set_label('(-)', labelpad=0.1)
return fig
def fig_lfp_scaling(fig,
params,
bottom=0.55,
top=0.95,
channels=[0, 3, 7, 11, 13],
T=[800., 1000.],
Df=None,
mlab=True,
NFFT=256,
noverlap=128,
window=plt.mlab.window_hanning,
letters='ABCD',
lag=20,
show_titles=True,
show_xlabels=True):
fname_fullscale = os.path.join(params.savefolder, 'LFPsum.h5')
fname_downscaled = os.path.join(params.savefolder, 'populations',
'subsamples', 'LFPsum_10_0.h5')
# ana_params.set_PLOS_2column_fig_style(ratio=0.5)
gs = gridspec.GridSpec(len(channels), 8, bottom=bottom, top=top)
# fig = plt.figure()
# fig.subplots_adjust(left=0.075, right=0.95, bottom=0.075, wspace=0.8, hspace=0.1)
scaling_factor = np.sqrt(10)
##################################
### LFP traces ###
##################################
ax = fig.add_subplot(gs[:, :3])
phlp.annotate_subplot(ax,
ncols=8 / 3.,
nrows=1,
letter=letters[0],
linear_offset=0.065)
plot_signal_sum(
ax,
params,
fname=os.path.join(params.savefolder, 'LFPsum.h5'),
unit='mV',
scaling_factor=1.,
scalebar=True,
vlimround=None,
T=T,
ylim=[-1600, 50],
color='k',
label='$\Phi$',
rasterized=False,
zorder=1,
)
plot_signal_sum(
ax,
params,
fname=os.path.join(params.savefolder, 'populations', 'subsamples',
'LFPsum_10_0.h5'),
unit='mV',
scaling_factor=scaling_factor,
scalebar=False,
vlimround=None,
T=T,
ylim=[-1600, 50],
color='gray' if analysis_params.bw else analysis_params.colorP,
label='$\hat{\Phi}^{\prime}$',
rasterized=False,
lw=1,
zorder=0)
if show_titles:
ax.set_title('LFP & low-density predictor')
if show_xlabels:
ax.set_xlabel('$t$ (ms)', labelpad=0.)
else:
ax.set_xlabel('')
#################################
### Correlations ###
#################################
ax = fig.add_subplot(gs[:, 3])
phlp.annotate_subplot(ax,
ncols=8,
nrows=1,
letter=letters[1],
linear_offset=0.065)
phlp.remove_axis_junk(ax)
datas = []
files = [
os.path.join(params.savefolder, 'LFPsum.h5'),
os.path.join(params.savefolder, 'populations', 'subsamples',
'LFPsum_10_0.h5')
]
for fil in files:
f = h5py.File(fil)
datas.append(f['data'][()][:, 200:])
f.close()
zvec = np.r_[params.electrodeParams['z']]
cc = np.zeros(len(zvec))
for ch in np.arange(len(zvec)):
x0 = datas[0][ch]
x0 -= x0.mean()
x1 = datas[1][ch]
x1 -= x1.mean()
cc[ch] = np.corrcoef(x0, x1)[1, 0]
ax.barh(zvec, cc, height=80, align='center', color='0.5', linewidth=0.5)
# superimpose the chance level, obtained by mixing one input vector N times
# while keeping the other fixed. We show boxes drawn left to right where
# these denote mean +/- two standard deviations.
N = 1000
method = 'randphase' #or 'permute'
chance = np.zeros((cc.size, N))
for ch in np.arange(len(zvec)):
x1 = datas[1][ch]
x1 -= x1.mean()
if method == 'randphase':
x0 = datas[0][ch]
x0 -= x0.mean()
X00 = np.fft.fft(x0)
for n in range(N):
if method == 'permute':
x0 = np.random.permutation(datas[0][ch])
elif method == 'randphase':
X0 = np.copy(X00)
#random phase information such that spectra is preserved
theta = np.random.uniform(0, 2 * np.pi, size=X0.size // 2)
#half-sided real and imaginary component
real = abs(X0[1:X0.size // 2 + 1]) * np.cos(theta)
imag = abs(X0[1:X0.size // 2 + 1]) * np.sin(theta)
#account for the antisymmetric phase values
X0.imag[1:imag.size + 1] = imag
X0.imag[imag.size + 1:] = -imag[::-1]
X0.real[1:real.size + 1] = real
X0.real[real.size + 1:] = real[::-1]
x0 = np.fft.ifft(X0).real
chance[ch, n] = np.corrcoef(x0, x1)[1, 0]
# p-values, compute the fraction of chance correlations > cc at each channel
p = []
for i, x in enumerate(cc):
p += [(chance[i, ] >= x).sum() / float(N)]
print('p-values:', p)
#compute the 99% percentile of the chance data
right = np.percentile(chance, 99, axis=-1)
ax.plot(right, zvec, ':', color='k', lw=1.)
ax.set_ylim([-1550, 50])
ax.set_yticklabels([])
ax.set_yticks(zvec)
ax.set_xlim([0., 1.])
ax.set_xticks([0.0, 0.5, 1])
ax.yaxis.tick_left()
if show_titles:
ax.set_title('corr.\ncoef.')
if show_xlabels:
ax.set_xlabel('$cc$ (-)', labelpad=0.)
##################################
### Single channel PSDs ###
##################################
freqs, PSD_fullscale = calc_signal_power(params,
fname=fname_fullscale,
transient=200,
Df=Df,
mlab=mlab,
NFFT=NFFT,
noverlap=noverlap,
window=window)
freqs, PSD_downscaled = calc_signal_power(params,
fname=fname_downscaled,
transient=200,
Df=Df,
mlab=mlab,
NFFT=NFFT,
noverlap=noverlap,
window=window)
inds = freqs >= 1 # frequencies greater than 4 Hz
for i, ch in enumerate(channels):
ax = fig.add_subplot(gs[i, 4:6])
if i == 0:
phlp.annotate_subplot(ax,
ncols=8 / 2.,
nrows=len(channels),
letter=letters[2],
linear_offset=0.065)
phlp.remove_axis_junk(ax)
ax.loglog(
freqs[inds],
PSD_fullscale[ch][inds],
color='k',
label='$\gamma=1.0$',
zorder=1,
)
ax.loglog(
freqs[inds],
PSD_downscaled[ch][inds] * scaling_factor**2,
lw=1,
color='gray' if analysis_params.bw else analysis_params.colorP,
label='$\gamma=0.1, \zeta=\sqrt{10}$',
zorder=0,
)
ax.loglog(freqs[inds],
PSD_downscaled[ch][inds] * scaling_factor**4,
lw=1,
color='0.75',
label='$\gamma=0.1, \zeta=10$',
zorder=0)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.text(0.8,
0.9,
'ch. %i' % (ch + 1),
horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
ax.yaxis.set_minor_locator(plt.NullLocator())
if i < len(channels) - 1:
#ax.set_xticks([])
ax.set_xticklabels([])
ax.tick_params(axis='y', which='minor', bottom='off')
ax.set_xlim([4E0, 4E2])
ax.set_ylim([3E-8, 1E-4])
if i == 0:
ax.tick_params(axis='y', which='major', pad=0)
ax.set_ylabel('(mV$^2$/Hz)', labelpad=0.)
if show_titles:
ax.set_title('power spectra')
#ax.set_yticks([1E-9,1E-7,1E-5])
if i > 0:
ax.set_yticklabels([])
if show_xlabels:
ax.set_xlabel(r'$f$ (Hz)', labelpad=0.)
##################################
### PSD ratios ###
##################################
ax = fig.add_subplot(gs[:, 6:8])
phlp.annotate_subplot(ax,
ncols=8. / 2,
nrows=1,
letter=letters[3],
linear_offset=0.065)
PSD_ratio = PSD_fullscale / (PSD_downscaled * scaling_factor**2)
zvec = np.r_[params.electrodeParams['z']]
zvec = np.r_[zvec, zvec[-1] + np.diff(zvec)[-1]]
inds = freqs >= 1 # frequencies greater than 4 Hz
im = ax.pcolormesh(freqs[inds],
zvec + 40,
PSD_ratio[:, inds],
rasterized=False,
cmap=plt.get_cmap('gray_r', 18)
if analysis_params.bw else plt.cm.get_cmap('Reds', 18),
vmin=1E0,
vmax=1.E1)
ax.set_xlim([4E0, 4E2])
ax.set_xscale('log')
ax.set_yticks(zvec)
yticklabels = ['ch. %i' % i for i in np.arange(len(zvec)) + 1]
ax.set_yticklabels(yticklabels)
plt.axis('tight')
cb = phlp.colorbar(fig,
ax,
im,
width=0.05,
height=0.5,
hoffset=-0.05,
voffset=0.0)
cb.set_label('(-)', labelpad=0.)
phlp.remove_axis_junk(ax)
if show_titles:
ax.set_title('power ratio')
if show_xlabels:
ax.set_xlabel(r'$f$ (Hz)', labelpad=0.)
return fig
ax5.set_title('CSD')
a = ax5.axis()
ax5.vlines(x['TC'][0], a[2], a[3], 'k', lw=0.25)
#show traces superimposed on color image
if show_images:
im = plot_signal_sum_colorplot(ax5, params, os.path.join(params.savefolder, 'CSDsum.h5'),
unit=r'$\mu$Amm$^{-3}$', T=T,
ylim=[-1550, 50],
fancy=False, colorbar=False,
cmap=plt.get_cmap('gray', 21) if analysis_params.bw else plt.get_cmap('bwr_r', 21),
rasterized=False)
cb = phlp.colorbar(fig, ax5, im,
width=0.05, height=0.5,
hoffset=-0.05, voffset=0.5)
cb.set_label('($\mu$Amm$^{-3}$)', labelpad=0.1)
############################################################################
# D part, LFP
############################################################################
ax7 = fig.add_subplot(gs[:, 3])
plt.locator_params(nbins=4)
if show_ax_labels:
phlp.annotate_subplot(ax7, ncols=1, nrows=1, letter='D', linear_offset=0.065)
phlp.remove_axis_junk(ax7)
plot_signal_sum(ax7, params,
fname=os.path.join(params.savefolder, 'LFPsum.h5'),
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
