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 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 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')
############################################################################
# C part, CSD
############################################################################
ax5 = fig.add_subplot(gs[:, 2])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax5)
if show_ax_labels:
phlp.annotate_subplot(ax5,
ncols=1,
nrows=1,
letter='C',
linear_offset=0.065)
plot_signal_sum(ax5,
params,
fname=os.path.join(params.savefolder, 'CSDsum.h5'),
unit='$\mu$A mm$^{-3}$',
T=T,
ylim=[-1550, 50],
rasterized=False)
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,
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_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_kernel_lfp(savefolders, params, transient=200, T=[800., 1000.], X='L5E',
lags=[20, 20], channels=[0,3,7,11,13]):
'''
This function calculates the STA of LFP, extracts kernels and recontructs the LFP from kernels.
Arguments
::
transient : the time in milliseconds, after which the analysis should begin
so as to avoid any starting transients
X : id of presynaptic trigger population
'''
# Electrode geometry
zvec = np.r_[params.electrodeParams['z']]
alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
ana_params.set_PLOS_2column_fig_style(ratio=1)
# Start the figure
fig = plt.figure()
fig.subplots_adjust(left=0.06, right=0.95, bottom=0.05, top=0.95, hspace=0.23, wspace=0.55)
# create grid_spec
gs = gridspec.GridSpec(2*len(channels)+1, 7)
###########################################################################
# spikegen "network" activity
############################################################################
# path to simulation files
params.savefolder = os.path.join(os.path.split(params.savefolder)[0],
'simulation_output_spikegen')
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
# Get the spikegen LFP:
f = h5py.File(os.path.join(params.savefolder, 'LFPsum.h5'))
srate = f['srate'].value
tvec = np.arange(f['data'].shape[1]) * 1000. / srate
# slice
inds = (tvec < params.tstop) & (tvec >= transient)
data_sg_raw = f['data'].value.astype(float)
data_sg = data_sg_raw[:, inds]
f.close()
# kernel width
kwidth = 20
# create some dummy spike times
activationtimes = np.array([x*100 for x in range(3,11)] + [200])
networkSimSpikegen = CachedNetwork(**params.networkSimParams)
x, y = networkSimSpikegen.get_xy([transient, params.tstop])
###########################################################################
# Part A: spatiotemporal kernels, all presynaptic populations
############################################################################
titles = ['TC',
'L23E/I',
'LFP kernels \n L4E/I',
'L5E/I',
'L6E/I',
]
COUNTER = 0
for i, X__ in enumerate(([['TC']]) + zip(params.X[1::2], params.X[2::2])):
ax = fig.add_subplot(gs[:len(channels), i])
if i == 0:
phlp.annotate_subplot(ax, ncols=7, nrows=4, letter=alphabet[0], linear_offset=0.02)
for j, X_ in enumerate(X__):
# create spikegen histogram for population Y
cinds = np.arange(activationtimes[np.arange(-1, 8)][COUNTER]-kwidth,
activationtimes[np.arange(-1, 8)][COUNTER]+kwidth+2)
x0_sg = np.histogram(x[X_], bins=cinds)[0].astype(float)
if X_ == ('TC'):
color='k' if analysis_params.bw else analysis_params.colorE
# lw = plt.rcParams['lines.linewidth']
# zorder=1
else:
color=('k' if analysis_params.bw else analysis_params.colorE,
'gray' if analysis_params.bw else analysis_params.colorI)[j]
lw = 0.75 if color in ['gray', 'r', 'b'] else plt.rcParams['lines.linewidth']
zorder = 0 if 'I' in X_ else 1
# plot kernel as correlation of spikegen LFP signal with delta spike train
xcorr, vlimround = plotting_correlation(params,
x0_sg/x0_sg.sum()**2,
data_sg_raw[:, cinds[:-1]]*1E3,
ax, normalize=False,
lag=kwidth,
color=color,
scalebar=False,
lw=lw, zorder=zorder)
if i > 0:
ax.set_yticklabels([])
## Create scale bar
ax.plot([kwidth, kwidth],
[-1500 + j*3*100, -1400 + j*3*100], lw=2, color=color,
clip_on=False)
ax.text(kwidth*1.08, -1450 + j*3*100, '%.1f $\mu$V' % vlimround,
rotation='vertical', va='center')
ax.set_xlim((-5, kwidth))
ax.set_xticks([-20, 0, 20])
ax.set_xticklabels([-20, 0, 20])
COUNTER += 1
ax.set_title(titles[i])
################################################
# Iterate over savefolders
################################################
for i, (savefolder, lag) in enumerate(zip(savefolders, lags)):
# 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
#load spike as database inside function to avoid buggy behaviour
networkSim = CachedNetwork(**params.networkSimParams)
# Get the Compound LFP: LFPsum : data[nchannels, timepoints ]
f = h5py.File(os.path.join(params.savefolder, 'LFPsum.h5'))
data_raw = f['data'].value
srate = f['srate'].value
tvec = np.arange(data_raw.shape[1]) * 1000. / srate
# slice
inds = (tvec < params.tstop) & (tvec >= transient)
data = data_raw[:, inds]
# subtract mean
dataT = data.T - data.mean(axis=1)
data = dataT.T
f.close()
# Get the spikegen LFP:
f = h5py.File(os.path.join(os.path.split(params.savefolder)[0], 'simulation_output_spikegen', 'LFPsum.h5'))
data_sg_raw = f['data'].value
f.close()
########################################################################
# Part B: STA LFP
########################################################################
titles = ['stLFP(%s)\n(spont.)' % X, 'stLFP(%s)\n(AC. mod.)' % X]
ax = fig.add_subplot(gs[:len(channels), 5 + i])
if i == 0:
phlp.annotate_subplot(ax, ncols=15, nrows=4, letter=alphabet[i+1],
linear_offset=0.02)
#collect the spikes x is the times, y is the id of the cell.
x, y = networkSim.get_xy([0,params.tstop])
# Get the spikes for the population of interest given as 'Y'
bins = np.arange(0, params.tstop+2) + 0.5
x0_raw = np.histogram(x[X], bins=bins)[0]
x0 = x0_raw[inds].astype(float)
# correlation between firing rate and LFP deviation
# from mean normalized by the number of spikes
xcorr, vlimround = plotting_correlation(params,
x0/x0.sum(),
data*1E3,
ax, normalize=False,
#unit='%.3f mV',
lag=lag,
scalebar=False,
color='k',
title=titles[i],
)
# Create scale bar
ax.plot([lag, lag],
[-1500, -1400], lw=2, color='k',
clip_on=False)
ax.text(lag*1.08, -1450, '%.1f $\mu$V' % vlimround,
rotation='vertical', va='center')
[Xind] = np.where(np.array(networkSim.X) == X)[0]
# create spikegen histogram for population Y
x0_sg = np.zeros(x0.shape, dtype=float)
x0_sg[activationtimes[Xind]] += params.N_X[Xind]
ax.set_yticklabels([])
ax.set_xticks([-lag, 0, lag])
ax.set_xticklabels([-lag, 0, lag])
###########################################################################
# Part C, F: LFP and reconstructed LFP
############################################################################
# create grid_spec
gsb = gridspec.GridSpec(2*len(channels)+1, 8)
ax = fig.add_subplot(gsb[1+len(channels):, (i*4):(i*4+2)])
phlp.annotate_subplot(ax, ncols=8/2., nrows=4, letter=alphabet[i*3+2],
linear_offset=0.02)
# extract kernels, force negative lags to be zero
kernels = np.zeros((len(params.N_X), 16, kwidth*2))
for j in range(len(params.X)):
kernels[j, :, kwidth:] = data_sg_raw[:, (j+2)*100:kwidth+(j+2)*100]/params.N_X[j]
LFP_reconst_raw = np.zeros(data_raw.shape)
for j, pop in enumerate(params.X):
x0_raw = np.histogram(x[pop], bins=bins)[0].astype(float)
for ch in range(kernels.shape[1]):
LFP_reconst_raw[ch] += np.convolve(x0_raw, kernels[j, ch],
'same')
# slice
LFP_reconst = LFP_reconst_raw[:, inds]
# subtract mean
LFP_reconstT = LFP_reconst.T - LFP_reconst.mean(axis=1)
LFP_reconst = LFP_reconstT.T
vlimround = plot_signal_sum(ax, params,
fname=os.path.join(params.savefolder,
'LFPsum.h5'),
unit='mV', scalebar=True,
T=T, ylim=[-1550, 50],
color='k', label='$real$',
rasterized=False,
zorder=1)
plot_signal_sum(ax, params, fname=LFP_reconst_raw,
unit='mV', scaling_factor= 1., scalebar=False,
vlimround=vlimround,
T=T, ylim=[-1550, 50],
color='gray' if analysis_params.bw else analysis_params.colorP,
label='$reconstr$',
rasterized=False,
lw=1, zorder=0)
ax.set_title('LFP & population \n rate predictor')
if i > 0:
ax.set_yticklabels([])
###########################################################################
# Part D,G: Correlation coefficient
############################################################################
ax = fig.add_subplot(gsb[1+len(channels):, i*4+2:i*4+3])
phlp.remove_axis_junk(ax)
phlp.annotate_subplot(ax, ncols=8./1, nrows=4, letter=alphabet[i*3+3],
linear_offset=0.02)
cc = np.zeros(len(zvec))
for ch in np.arange(len(zvec)):
cc[ch] = np.corrcoef(data[ch], LFP_reconst[ch])[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 = LFP_reconst[ch]
x1 -= x1.mean()
if method == 'randphase':
x0 = data[ch]
x0 -= x0.mean()
X00 = np.fft.fft(x0)
for n in range(N):
if method == 'permute':
x0 = np.random.permutation(datas[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 // 2-1)
#half-sided real and imaginary component
real = abs(X0[1:X0.size // 2])*np.cos(theta)
imag = abs(X0[1:X0.size // 2])*np.sin(theta)
#account for the antisymmetric phase values
X0.imag[1:imag.size+1] = imag
X0.imag[imag.size+2:] = -imag[::-1]
X0.real[1:real.size+1] = real
X0.real[real.size+2:] = 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 h, x in enumerate(cc):
p += [(chance[h, ] >= 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()
ax.set_xlabel('$cc$ (-)', labelpad=0.1)
ax.set_title('corr. \n coef.')
print 'correlation coefficients:'
print cc
###########################################################################
# Part E,H: Power spectra
############################################################################
#compute PSDs ratio between ground truth and estimate
freqs, PSD_data = calc_signal_power(params, fname=data,
transient=transient, Df=None,
mlab=True,
NFFT=256, noverlap=128,
window=plt.mlab.window_hanning)
freqs, PSD_LFP_reconst = calc_signal_power(params, fname=LFP_reconst,
transient=transient, Df=None,
mlab=True,
NFFT=256, noverlap=128,
window=plt.mlab.window_hanning)
zv = np.r_[params.electrodeParams['z']]
zv = np.r_[zv, zv[-1] + np.diff(zv)[-1]]
inds = freqs >= 1 # frequencies greater than 1 Hz
for j, ch in enumerate(channels):
ax = fig.add_subplot(gsb[1+len(channels)+j, (i*4+3):(i*4+4)])
if j == 0:
phlp.annotate_subplot(ax, ncols=8./1, nrows=4.5*len(channels),
letter=alphabet[i*3+4], linear_offset=0.02)
ax.set_title('PSD')
phlp.remove_axis_junk(ax)
ax.loglog(freqs[inds], PSD_data[ch, inds], 'k', label='LFP',
clip_on=True, zorder=1)
ax.loglog(freqs[inds], PSD_LFP_reconst[ch, inds],
'gray' if analysis_params.bw else analysis_params.colorP,
label='predictor', clip_on=True, lw=1, zorder=0)
ax.set_xlim([4E0,4E2])
ax.set_ylim([1E-8, 1E-4])
ax.tick_params(axis='y', which='major', pad=0)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.text(0.8, 0.9, 'ch. %i' % (ch+1),
horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
if j == 0:
ax.set_ylabel('(mV$^2$/Hz)', labelpad=0.)
if j > 0:
ax.set_yticklabels([])
if j == len(channels)-1:
ax.set_xlabel(r'$f$ (Hz)', labelpad=0.)
else:
ax.set_xticklabels([])
return fig, PSD_LFP_reconst, PSD_data
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
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_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
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)
ax3.xaxis.set_ticks_position('bottom')
ax3.yaxis.set_ticks_position('left')
ax3.set_xlabel(r'$t$ (ms)', labelpad=0.1)
############################################################################
# C part, CSD
############################################################################
ax5 = fig.add_subplot(gs[:, 2])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax5)
if show_ax_labels:
phlp.annotate_subplot(ax5, ncols=1, nrows=1, letter='C', linear_offset=0.065)
plot_signal_sum(ax5, params,
fname=os.path.join(params.savefolder, 'CSDsum.h5'),
unit='$\mu$A mm$^{-3}$',
T=T,
ylim=[-1550, 50],
rasterized=False)
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)
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_kernel_lfp_EITN_II(savefolders, params, transient=200, T=[800., 1000.], X='L5E',
lags=[20, 20], channels=[0,3,7,11,13]):
'''
This function calculates the STA of LFP, extracts kernels and recontructs the LFP from kernels.
Arguments
::
transient : the time in milliseconds, after which the analysis should begin
so as to avoid any starting transients
X : id of presynaptic trigger population
'''
# Electrode geometry
zvec = np.r_[params.electrodeParams['z']]
alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
ana_params.set_PLOS_2column_fig_style(ratio=0.5)
# Start the figure
fig = plt.figure()
fig.subplots_adjust(left=0.06, right=0.95, bottom=0.08, top=0.9, hspace=0.23, wspace=0.55)
# create grid_spec
gs = gridspec.GridSpec(len(channels), 7)
###########################################################################
# spikegen "network" activity
############################################################################
# path to simulation files
savefolder = 'simulation_output_spikegen'
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
# Get the spikegen LFP:
f = h5py.File(os.path.join('simulation_output_spikegen', 'LFPsum.h5'))
srate = f['srate'].value
tvec = np.arange(f['data'].shape[1]) * 1000. / srate
# slice
inds = (tvec < params.tstop) & (tvec >= transient)
data_sg_raw = f['data'].value.astype(float)
data_sg = data_sg_raw[:, inds]
f.close()
# kernel width
kwidth = 20
# create some dummy spike times
activationtimes = np.array([x*100 for x in range(3,11)] + [200])
networkSimSpikegen = CachedNetwork(**params.networkSimParams)
x, y = networkSimSpikegen.get_xy([transient, params.tstop])
############################################################################
## Part A: spatiotemporal kernels, all presynaptic populations
#############################################################################
#
#titles = ['TC',
# 'L23E/I',
# 'LFP kernels \n L4E/I',
# 'L5E/I',
# 'L6E/I',
# ]
#
#COUNTER = 0
#for i, X__ in enumerate(([['TC']]) + zip(params.X[1::2], params.X[2::2])):
# ax = fig.add_subplot(gs[:len(channels), i])
# if i == 0:
# phlp.annotate_subplot(ax, ncols=7, nrows=4, letter=alphabet[0], linear_offset=0.02)
#
# for j, X_ in enumerate(X__):
# # create spikegen histogram for population Y
# cinds = np.arange(activationtimes[np.arange(-1, 8)][COUNTER]-kwidth,
# activationtimes[np.arange(-1, 8)][COUNTER]+kwidth+2)
# x0_sg = np.histogram(x[X_], bins=cinds)[0].astype(float)
#
# if X_ == ('TC'):
# color='r'
# else:
# color=('r', 'b')[j]
#
#
# # plot kernel as correlation of spikegen LFP signal with delta spike train
# xcorr, vlimround = plotting_correlation(params,
# x0_sg/x0_sg.sum()**2,
# data_sg_raw[:, cinds[:-1]]*1E3,
# ax, normalize=False,
# lag=kwidth,
# color=color,
# scalebar=False)
# if i > 0:
# ax.set_yticklabels([])
#
# ## Create scale bar
# ax.plot([kwidth, kwidth],
# [-1500 + j*3*100, -1400 + j*3*100], lw=2, color=color,
# clip_on=False)
# ax.text(kwidth*1.08, -1450 + j*3*100, '%.1f $\mu$V' % vlimround,
# rotation='vertical', va='center')
#
# ax.set_xlim((-5, kwidth))
# ax.set_xticks([-20, 0, 20])
# ax.set_xticklabels([-20, 0, 20])
#
# COUNTER += 1
#
# ax.set_title(titles[i])
################################################
# Iterate over savefolders
################################################
for i, (savefolder, lag) in enumerate(zip(savefolders, lags)):
# 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
#load spike as database inside function to avoid buggy behaviour
networkSim = CachedNetwork(**params.networkSimParams)
# Get the Compound LFP: LFPsum : data[nchannels, timepoints ]
f = h5py.File(os.path.join(params.savefolder, 'LFPsum.h5'))
data_raw = f['data'].value
srate = f['srate'].value
tvec = np.arange(data_raw.shape[1]) * 1000. / srate
# slice
inds = (tvec < params.tstop) & (tvec >= transient)
data = data_raw[:, inds]
# subtract mean
dataT = data.T - data.mean(axis=1)
data = dataT.T
f.close()
# Get the spikegen LFP:
f = h5py.File(os.path.join('simulation_output_spikegen', 'LFPsum.h5'))
data_sg_raw = f['data'].value
f.close()
#
#
#
#
#########################################################################
## Part B: STA LFP
#########################################################################
#
#titles = ['staLFP(%s)\n(spont.)' % X, 'staLFP(%s)\n(AC. mod.)' % X]
#ax = fig.add_subplot(gs[:len(channels), 5 + i])
#if i == 0:
# phlp.annotate_subplot(ax, ncols=15, nrows=4, letter=alphabet[i+1],
# linear_offset=0.02)
#
#collect the spikes x is the times, y is the id of the cell.
x, y = networkSim.get_xy([0,params.tstop])
#
## Get the spikes for the population of interest given as 'Y'
bins = np.arange(0, params.tstop+2) + 0.5
x0_raw = np.histogram(x[X], bins=bins)[0]
x0 = x0_raw[inds].astype(float)
#
## correlation between firing rate and LFP deviation
## from mean normalized by the number of spikes
#xcorr, vlimround = plotting_correlation(params,
# x0/x0.sum(),
# data*1E3,
# ax, normalize=False,
# #unit='%.3f mV',
# lag=lag,
# scalebar=False,
# color='k',
# title=titles[i],
# )
#
## Create scale bar
#ax.plot([lag, lag],
# [-1500, -1400], lw=2, color='k',
# clip_on=False)
#ax.text(lag*1.08, -1450, '%.1f $\mu$V' % vlimround,
# rotation='vertical', va='center')
#
#
#[Xind] = np.where(np.array(networkSim.X) == X)[0]
#
## create spikegen histogram for population Y
#x0_sg = np.zeros(x0.shape, dtype=float)
#x0_sg[activationtimes[Xind]] += params.N_X[Xind]
#
#
#ax.set_yticklabels([])
#ax.set_xticks([-lag, 0, lag])
#ax.set_xticklabels([-lag, 0, lag])
###########################################################################
# Part C, F: LFP and reconstructed LFP
############################################################################
# create grid_spec
gsb = gridspec.GridSpec(len(channels), 8)
ax = fig.add_subplot(gsb[:, (i*4):(i*4+2)])
phlp.annotate_subplot(ax, ncols=8/2., nrows=4, letter=alphabet[i*3+2],
linear_offset=0.02)
# extract kernels, force negative lags to be zero
kernels = np.zeros((len(params.N_X), 16, kwidth*2))
for j in range(len(params.X)):
kernels[j, :, kwidth:] = data_sg_raw[:, (j+2)*100:kwidth+(j+2)*100]/params.N_X[j]
LFP_reconst_raw = np.zeros(data_raw.shape)
for j, pop in enumerate(params.X):
x0_raw = np.histogram(x[pop], bins=bins)[0].astype(float)
for ch in range(kernels.shape[1]):
LFP_reconst_raw[ch] += np.convolve(x0_raw, kernels[j, ch],
'same')
# slice
LFP_reconst = LFP_reconst_raw[:, inds]
# subtract mean
LFP_reconstT = LFP_reconst.T - LFP_reconst.mean(axis=1)
LFP_reconst = LFP_reconstT.T
vlimround = plot_signal_sum(ax, params,
fname=os.path.join(params.savefolder,
'LFPsum.h5'),
unit='mV', scalebar=True,
T=T, ylim=[-1550, 50],
color='k', label='$real$',
rasterized=False)
plot_signal_sum(ax, params, fname=LFP_reconst_raw,
unit='mV', scaling_factor= 1., scalebar=False,
vlimround=vlimround,
T=T, ylim=[-1550, 50],
color='r', label='$reconstr$',
rasterized=False)
ax.set_title('LFP & population \n rate predictor')
if i > 0:
ax.set_yticklabels([])
###########################################################################
# Part D,G: Correlation coefficient
############################################################################
ax = fig.add_subplot(gsb[:, i*4+2:i*4+3])
phlp.remove_axis_junk(ax)
phlp.annotate_subplot(ax, ncols=8./1, nrows=4, letter=alphabet[i*3+3],
linear_offset=0.02)
cc = np.zeros(len(zvec))
for ch in np.arange(len(zvec)):
cc[ch] = np.corrcoef(data[ch], LFP_reconst[ch])[1, 0]
ax.barh(zvec, cc, height=90, align='center', color='1', linewidth=0.5)
ax.set_ylim([-1550, 50])
ax.set_yticklabels([])
ax.set_yticks(zvec)
ax.set_xlim([0.0, 1.])
ax.set_xticks([0.0, 0.5, 1])
ax.yaxis.tick_left()
ax.set_xlabel('$cc$ (-)', labelpad=0.1)
ax.set_title('corr. \n coef.')
print 'correlation coefficients:'
print cc
###########################################################################
# Part E,H: Power spectra
############################################################################
#compute PSDs ratio between ground truth and estimate
freqs, PSD_data = calc_signal_power(params, fname=data,
transient=transient, Df=None, mlab=True,
NFFT=256, noverlap=128,
window=plt.mlab.window_hanning)
freqs, PSD_LFP_reconst = calc_signal_power(params, fname=LFP_reconst,
transient=transient, Df=None, mlab=True,
NFFT=256, noverlap=128,
window=plt.mlab.window_hanning)
zv = np.r_[params.electrodeParams['z']]
zv = np.r_[zv, zv[-1] + np.diff(zv)[-1]]
inds = freqs >= 1 # frequencies greater than 1 Hz
for j, ch in enumerate(channels):
ax = fig.add_subplot(gsb[j, (i*4+3):(i*4+4)])
if j == 0:
phlp.annotate_subplot(ax, ncols=8./1, nrows=4.5*len(channels),
letter=alphabet[i*3+4], linear_offset=0.02)
ax.set_title('PSD')
phlp.remove_axis_junk(ax)
ax.loglog(freqs[inds], PSD_data[ch, inds], 'k', label='LFP', clip_on=True)
ax.loglog(freqs[inds], PSD_LFP_reconst[ch, inds], 'r', label='predictor', clip_on=True)
ax.set_xlim([4E0,4E2])
ax.set_ylim([1E-8, 1E-4])
ax.tick_params(axis='y', which='major', pad=0)
ax.set_yticks([1E-8,1E-6,1E-4])
ax.yaxis.set_minor_locator(plt.NullLocator())
ax.text(0.8, 0.9, 'ch. %i' % (ch+1),
horizontalalignment='left',
verticalalignment='center',
fontsize=6,
transform=ax.transAxes)
if j == 0:
ax.set_ylabel('(mV$^2$/Hz)', labelpad=0.)
if j > 0:
ax.set_yticklabels([])
if j == len(channels)-1:
ax.set_xlabel(r'$f$ (Hz)', labelpad=0.)
else:
ax.set_xticklabels([])
return fig, PSD_LFP_reconst, PSD_data
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