def plot_multi_scale_output_a(fig):
#get the mean somatic currents and voltages,
#write pickles if they do not exist:
if not os.path.isfile(os.path.join(params.savefolder, 'data_analysis',
'meanInpCurrents.pickle')):
meanInpCurrents = getMeanInpCurrents(params, params.n_rec_input_spikes,
os.path.join(params.spike_output_path,
'population_input_spikes'))
f = file(os.path.join(params.savefolder, 'data_analysis',
'meanInpCurrents.pickle'), 'wb')
pickle.dump(meanInpCurrents, f)
f.close()
else:
f = file(os.path.join(params.savefolder, 'data_analysis',
'meanInpCurrents.pickle'), 'rb')
meanInpCurrents = pickle.load(f)
f.close()
if not os.path.isfile(os.path.join(params.savefolder, 'data_analysis',
'meanVoltages.pickle')):
meanVoltages = getMeanVoltages(params, params.n_rec_voltage,
os.path.join(params.spike_output_path,
'voltages'))
f = file(os.path.join(params.savefolder, 'data_analysis',
'meanVoltages.pickle'), 'wb')
pickle.dump(meanVoltages, f)
f.close()
else:
f = file(os.path.join(params.savefolder, 'data_analysis',
'meanVoltages.pickle'), 'rb')
meanVoltages = pickle.load(f)
f.close()
#load spike as database
networkSim = CachedNetwork(**params.networkSimParams)
if analysis_params.bw:
networkSim.colors = phlp.get_colors(len(networkSim.X))
show_ax_labels = True
show_insets = False
transient=200
T=[800, 1000]
T_inset=[900, 920]
sep = 0.025/2 #0.017
left = 0.075
bottom = 0.55
top = 0.975
right = 0.95
axwidth = 0.16
numcols = 4
insetwidth = axwidth/2
insetheight = 0.5
lefts = np.linspace(left, right-axwidth, numcols)
#fig = plt.figure()
############################################################################
# A part, plot spike rasters
############################################################################
ax1 = fig.add_axes([lefts[0], bottom, axwidth, top-bottom])
#fig.text(0.005,0.95,'a',fontsize=8, fontweight='demibold')
if show_ax_labels:
phlp.annotate_subplot(ax1, ncols=4, nrows=1.02, letter='A', )
ax1.set_title('network activity')
plt.locator_params(nbins=4)
x, y = networkSim.get_xy(T, fraction=1)
networkSim.plot_raster(ax1, T, x, y, markersize=0.2, marker='_', alpha=1.,
legend=False, pop_names=True, rasterized=False)
phlp.remove_axis_junk(ax1)
ax1.set_xlabel(r'$t$ (ms)', labelpad=0.1)
ax1.set_ylabel('population', labelpad=0.1)
# Inset
if show_insets:
ax2 = fig.add_axes([lefts[0]+axwidth-insetwidth, top-insetheight, insetwidth, insetheight])
plt.locator_params(nbins=4)
x, y = networkSim.get_xy(T_inset, fraction=0.4)
networkSim.plot_raster(ax2, T_inset, x, y, markersize=0.25, alpha=1.,
legend=False)
phlp.remove_axis_junk(ax2)
ax2.set_xticks(T_inset)
ax2.set_yticks([])
ax2.set_yticklabels([])
ax2.set_ylabel('')
ax2.set_xlabel('')
############################################################################
# B part, plot firing rates
############################################################################
nrows = len(networkSim.X)-1
high = top
low = bottom
thickn = (high-low) / nrows - sep
bottoms = np.linspace(low, high-thickn, nrows)[::-1]
x, y = networkSim.get_xy(T, fraction=1)
#dummy ax to put label in correct location
ax_ = fig.add_axes([lefts[1], bottom, axwidth, top-bottom])
ax_.axis('off')
if show_ax_labels:
phlp.annotate_subplot(ax_, ncols=4, nrows=1, letter='B')
for i, X in enumerate(networkSim.X[:-1]):
ax3 = fig.add_axes([lefts[1], bottoms[i], axwidth, thickn])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax3)
networkSim.plot_f_rate(ax3, X, i, T, x, y, yscale='linear',
plottype='fill_between', show_label=False,
rasterized=False)
ax3.yaxis.set_major_locator(plt.MaxNLocator(3))
if i != nrows -1:
ax3.set_xticklabels([])
if i == 3:
ax3.set_ylabel(r'(s$^{-1}$)', labelpad=0.1)
if i == 0:
ax3.set_title(r'firing rates ')
ax3.text(0, 1, X,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax3.transAxes)
for loc, spine in ax3.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
ax3.xaxis.set_ticks_position('bottom')
ax3.yaxis.set_ticks_position('left')
ax3.set_xlabel(r'$t$ (ms)', labelpad=0.1)
############################################################################
# C part, plot somatic synapse input currents population resolved
############################################################################
#set up subplots
nrows = len(meanInpCurrents.keys())
high = top
low = bottom
thickn = (high-low) / nrows - sep
bottoms = np.linspace(low, high-thickn, nrows)[::-1]
ax_ = fig.add_axes([lefts[2], bottom, axwidth, top-bottom])
ax_.axis('off')
if show_ax_labels:
phlp.annotate_subplot(ax_, ncols=4, nrows=1, letter='C')
for i, Y in enumerate(params.Y):
value = meanInpCurrents[Y]
tvec = value['tvec']
inds = (tvec <= T[1]) & (tvec >= T[0])
ax3 = fig.add_axes([lefts[2], bottoms[i], axwidth, thickn])
plt.locator_params(nbins=4)
if i == 0:
ax3.plot(tvec[inds][::10],
helpers.decimate(value['E'][inds], 10),
'k' if analysis_params.bw else analysis_params.colorE, #lw=0.75, #'r',
rasterized=False,label='exc.')
ax3.plot(tvec[inds][::10],
helpers.decimate(value['I'][inds], 10),
'gray' if analysis_params.bw else analysis_params.colorI, #lw=0.75, #'b',
rasterized=False,label='inh.')
ax3.plot(tvec[inds][::10],
helpers.decimate(value['E'][inds] + value['I'][inds], 10),
'k', lw=1, rasterized=False, label='sum')
else:
ax3.plot(tvec[inds][::10], helpers.decimate(value['E'][inds], 10),
'k' if analysis_params.bw else analysis_params.colorE, #lw=0.75, #'r',
rasterized=False)
ax3.plot(tvec[inds][::10], helpers.decimate(value['I'][inds], 10),
'gray' if analysis_params.bw else analysis_params.colorI, #lw=0.75, #'b',
rasterized=False)
ax3.plot(tvec[inds][::10],
helpers.decimate(value['E'][inds] + value['I'][inds], 10),
'k', lw=1, rasterized=False)
phlp.remove_axis_junk(ax3)
ax3.axis(ax3.axis('tight'))
ax3.set_yticks([ax3.axis()[2], 0, ax3.axis()[3]])
ax3.set_yticklabels([np.round((value['I'][inds]).min(), decimals=1),
0,
np.round((value['E'][inds]).max(), decimals=1)])
ax3.text(0, 1, Y,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax3.transAxes)
if i == nrows-1:
ax3.set_xlabel('$t$ (ms)', labelpad=0.1)
else:
ax3.set_xticklabels([])
if i == 3:
ax3.set_ylabel(r'(nA)', labelpad=0.1)
if i == 0:
ax3.set_title('input currents')
ax3.legend(loc=1,prop={'size':4})
phlp.remove_axis_junk(ax3)
ax3.set_xlim(T)
############################################################################
# D part, plot membrane voltage population resolved
############################################################################
nrows = len(meanVoltages.keys())
high = top
low = bottom
thickn = (high-low) / nrows - sep
bottoms = np.linspace(low, high-thickn, nrows)[::-1]
colors = phlp.get_colors(len(params.Y))
ax_ = fig.add_axes([lefts[3], bottom, axwidth, top-bottom])
ax_.axis('off')
if show_ax_labels:
phlp.annotate_subplot(ax_, ncols=4, nrows=1, letter='D')
for i, Y in enumerate(params.Y):
value = meanVoltages[Y]
tvec = value['tvec']
inds = (tvec <= T[1]) & (tvec >= T[0])
ax4 = fig.add_axes([lefts[3], bottoms[i], axwidth, thickn])
ax4.plot(tvec[inds][::10], helpers.decimate(value['data'][inds], 10), color=colors[i],
zorder=0, rasterized=False)
phlp.remove_axis_junk(ax4)
plt.locator_params(nbins=4)
ax4.axis(ax4.axis('tight'))
ax4.yaxis.set_major_locator(plt.MaxNLocator(3))
ax4.text(0, 1, Y,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax4.transAxes)
if i == nrows-1:
ax4.set_xlabel('$t$ (ms)', labelpad=0.1)
else:
ax4.set_xticklabels([])
if i == 3:
ax4.set_ylabel(r'(mV)', labelpad=0.1)
if i == 0:
ax4.set_title('voltages')
def plot_multi_scale_output_a(fig):
#get the mean somatic currents and voltages,
#write pickles if they do not exist:
if not os.path.isfile(
os.path.join(params.savefolder, 'data_analysis',
'meanInpCurrents.pickle')):
meanInpCurrents = getMeanInpCurrents(
params, params.n_rec_input_spikes,
os.path.join(params.spike_output_path, 'population_input_spikes'))
f = open(
os.path.join(params.savefolder, 'data_analysis',
'meanInpCurrents.pickle'), 'wb')
pickle.dump(meanInpCurrents, f)
f.close()
else:
f = open(
os.path.join(params.savefolder, 'data_analysis',
'meanInpCurrents.pickle'), 'rb')
meanInpCurrents = pickle.load(f)
f.close()
if not os.path.isfile(
os.path.join(params.savefolder, 'data_analysis',
'meanVoltages.pickle')):
meanVoltages = getMeanVoltages(
params, params.n_rec_voltage,
os.path.join(params.spike_output_path, 'voltages'))
f = open(
os.path.join(params.savefolder, 'data_analysis',
'meanVoltages.pickle'), 'wb')
pickle.dump(meanVoltages, f)
f.close()
else:
f = open(
os.path.join(params.savefolder, 'data_analysis',
'meanVoltages.pickle'), 'rb')
meanVoltages = pickle.load(f)
f.close()
#load spike as database
networkSim = CachedNetwork(**params.networkSimParams)
if analysis_params.bw:
networkSim.colors = phlp.get_colors(len(networkSim.X))
show_ax_labels = True
show_insets = False
transient = 200
T = [800, 1000]
T_inset = [900, 920]
sep = 0.025 / 2 #0.017
left = 0.075
bottom = 0.55
top = 0.975
right = 0.95
axwidth = 0.16
numcols = 4
insetwidth = axwidth / 2
insetheight = 0.5
lefts = np.linspace(left, right - axwidth, numcols)
#fig = plt.figure()
############################################################################
# A part, plot spike rasters
############################################################################
ax1 = fig.add_axes([lefts[0], bottom, axwidth, top - bottom])
#fig.text(0.005,0.95,'a',fontsize=8, fontweight='demibold')
if show_ax_labels:
phlp.annotate_subplot(
ax1,
ncols=4,
nrows=1.02,
letter='A',
)
ax1.set_title('network activity')
plt.locator_params(nbins=4)
x, y = networkSim.get_xy(T, fraction=1)
networkSim.plot_raster(ax1,
T,
x,
y,
markersize=0.2,
marker='_',
alpha=1.,
legend=False,
pop_names=True,
rasterized=False)
phlp.remove_axis_junk(ax1)
ax1.set_xlabel(r'$t$ (ms)', labelpad=0.1)
ax1.set_ylabel('population', labelpad=0.1)
# Inset
if show_insets:
ax2 = fig.add_axes([
lefts[0] + axwidth - insetwidth, top - insetheight, insetwidth,
insetheight
])
plt.locator_params(nbins=4)
x, y = networkSim.get_xy(T_inset, fraction=0.4)
networkSim.plot_raster(ax2,
T_inset,
x,
y,
markersize=0.25,
alpha=1.,
legend=False)
phlp.remove_axis_junk(ax2)
ax2.set_xticks(T_inset)
ax2.set_yticks([])
ax2.set_yticklabels([])
ax2.set_ylabel('')
ax2.set_xlabel('')
############################################################################
# B part, plot firing rates
############################################################################
nrows = len(networkSim.X) - 1
high = top
low = bottom
thickn = (high - low) / nrows - sep
bottoms = np.linspace(low, high - thickn, nrows)[::-1]
x, y = networkSim.get_xy(T, fraction=1)
#dummy ax to put label in correct location
ax_ = fig.add_axes([lefts[1], bottom, axwidth, top - bottom])
ax_.axis('off')
if show_ax_labels:
phlp.annotate_subplot(ax_, ncols=4, nrows=1, letter='B')
for i, X in enumerate(networkSim.X[:-1]):
ax3 = fig.add_axes([lefts[1], bottoms[i], axwidth, thickn])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax3)
networkSim.plot_f_rate(ax3,
X,
i,
T,
x,
y,
yscale='linear',
plottype='fill_between',
show_label=False,
rasterized=False)
ax3.yaxis.set_major_locator(plt.MaxNLocator(3))
if i != nrows - 1:
ax3.set_xticklabels([])
if i == 3:
ax3.set_ylabel(r'(s$^{-1}$)', labelpad=0.1)
if i == 0:
ax3.set_title(r'firing rates ')
ax3.text(0,
1,
X,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax3.transAxes)
for loc, spine in ax3.spines.items():
if loc in ['right', 'top']:
spine.set_color('none')
ax3.xaxis.set_ticks_position('bottom')
ax3.yaxis.set_ticks_position('left')
ax3.set_xlabel(r'$t$ (ms)', labelpad=0.1)
############################################################################
# C part, plot somatic synapse input currents population resolved
############################################################################
#set up subplots
nrows = len(list(meanInpCurrents.keys()))
high = top
low = bottom
thickn = (high - low) / nrows - sep
bottoms = np.linspace(low, high - thickn, nrows)[::-1]
ax_ = fig.add_axes([lefts[2], bottom, axwidth, top - bottom])
ax_.axis('off')
if show_ax_labels:
phlp.annotate_subplot(ax_, ncols=4, nrows=1, letter='C')
for i, Y in enumerate(params.Y):
value = meanInpCurrents[Y]
tvec = value['tvec']
inds = (tvec <= T[1]) & (tvec >= T[0])
ax3 = fig.add_axes([lefts[2], bottoms[i], axwidth, thickn])
plt.locator_params(nbins=4)
if i == 0:
ax3.plot(
tvec[inds][::10],
helpers.decimate(value['E'][inds], 10),
'k' if analysis_params.bw else
analysis_params.colorE, #lw=0.75, #'r',
rasterized=False,
label='exc.')
ax3.plot(
tvec[inds][::10],
helpers.decimate(value['I'][inds], 10),
'gray' if analysis_params.bw else
analysis_params.colorI, #lw=0.75, #'b',
rasterized=False,
label='inh.')
ax3.plot(tvec[inds][::10],
helpers.decimate(value['E'][inds] + value['I'][inds], 10),
'k',
lw=1,
rasterized=False,
label='sum')
else:
ax3.plot(
tvec[inds][::10],
helpers.decimate(value['E'][inds], 10),
'k' if analysis_params.bw else
analysis_params.colorE, #lw=0.75, #'r',
rasterized=False)
ax3.plot(
tvec[inds][::10],
helpers.decimate(value['I'][inds], 10),
'gray' if analysis_params.bw else
analysis_params.colorI, #lw=0.75, #'b',
rasterized=False)
ax3.plot(tvec[inds][::10],
helpers.decimate(value['E'][inds] + value['I'][inds], 10),
'k',
lw=1,
rasterized=False)
phlp.remove_axis_junk(ax3)
ax3.axis(ax3.axis('tight'))
ax3.set_yticks([ax3.axis()[2], 0, ax3.axis()[3]])
ax3.set_yticklabels([
np.round((value['I'][inds]).min(), decimals=1), 0,
np.round((value['E'][inds]).max(), decimals=1)
])
ax3.text(0,
1,
Y,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax3.transAxes)
if i == nrows - 1:
ax3.set_xlabel('$t$ (ms)', labelpad=0.1)
else:
ax3.set_xticklabels([])
if i == 3:
ax3.set_ylabel(r'(nA)', labelpad=0.1)
if i == 0:
ax3.set_title('input currents')
ax3.legend(loc=1, prop={'size': 4})
phlp.remove_axis_junk(ax3)
ax3.set_xlim(T)
############################################################################
# D part, plot membrane voltage population resolved
############################################################################
nrows = len(list(meanVoltages.keys()))
high = top
low = bottom
thickn = (high - low) / nrows - sep
bottoms = np.linspace(low, high - thickn, nrows)[::-1]
colors = phlp.get_colors(len(params.Y))
ax_ = fig.add_axes([lefts[3], bottom, axwidth, top - bottom])
ax_.axis('off')
if show_ax_labels:
phlp.annotate_subplot(ax_, ncols=4, nrows=1, letter='D')
for i, Y in enumerate(params.Y):
value = meanVoltages[Y]
tvec = value['tvec']
inds = (tvec <= T[1]) & (tvec >= T[0])
ax4 = fig.add_axes([lefts[3], bottoms[i], axwidth, thickn])
ax4.plot(tvec[inds][::10],
helpers.decimate(value['data'][inds], 10),
color=colors[i],
zorder=0,
rasterized=False)
phlp.remove_axis_junk(ax4)
plt.locator_params(nbins=4)
ax4.axis(ax4.axis('tight'))
ax4.yaxis.set_major_locator(plt.MaxNLocator(3))
ax4.text(0,
1,
Y,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax4.transAxes)
if i == nrows - 1:
ax4.set_xlabel('$t$ (ms)', labelpad=0.1)
else:
ax4.set_xticklabels([])
if i == 3:
ax4.set_ylabel(r'(mV)', labelpad=0.1)
if i == 0:
ax4.set_title('voltages')
ax4.set_xlim(T)
letter='B',
linear_offset=0.065)
ax_.set_title('firing rates ')
ax_.axis('off')
x, y = networkSim.get_xy(T, fraction=1)
colors = ['k'] + phlp.get_colors(len(params.Y))
for i, X in enumerate(networkSim.X):
ax3 = fig.add_subplot(gs[i, 1])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax3)
networkSim.plot_f_rate(ax3,
X,
i,
T,
x,
y,
yscale='linear',
plottype='bar')
ax3.yaxis.set_major_locator(plt.MaxNLocator(3))
if i != len(networkSim.X) - 1:
ax3.set_xticklabels([])
if i == 4:
ax3.set_ylabel(r'(s$^{-1}$)', labelpad=0.1)
ax3.text(0.02,
1,
X,
horizontalalignment='left',
verticalalignment='center',
############################################################################
# create invisible axes to position labels correctly
ax_ = fig.add_subplot(gs[:, 1])
if show_ax_labels:
phlp.annotate_subplot(ax_, ncols=1, nrows=1, letter='B', linear_offset=0.065)
ax_.set_title('firing rates ')
ax_.axis('off')
x, y = networkSim.get_xy(T, fraction=1)
colors = ['k'] + phlp.get_colors(len(params.Y))
for i, X in enumerate(networkSim.X):
ax3 = fig.add_subplot(gs[i, 1])
plt.locator_params(nbins=4)
phlp.remove_axis_junk(ax3)
networkSim.plot_f_rate(ax3, X, i, T, x, y, yscale='linear',
plottype='bar')
ax3.yaxis.set_major_locator(plt.MaxNLocator(3))
if i != len(networkSim.X) -1:
ax3.set_xticklabels([])
if i == 4:
ax3.set_ylabel(r'(s$^{-1}$)', labelpad=0.1)
ax3.text(0.02, 1, X,
horizontalalignment='left',
verticalalignment='center',
transform=ax3.transAxes)
for loc, spine in ax3.spines.iteritems():
if loc in ['right', 'top']:
