def default_analysis_figure(idx):
global fname, ftitle
tr = load_trajectory(fname)
tr.v_idx = idx
crun = tr.v_crun
pl.close()
fig = pl.figure()
# ----- 3x4 -----
s = 150
fig.set_size_inches(1920 / s * 5 / 4, 1080 / s * 7 / 3)
ax_lines, ax_cols = 7, 5
axs = {}
for x, y in itertools.product(range(ax_lines), range(ax_cols)):
axs['%d,%d' % (x + 1, y + 1)] = pl.subplot2grid((ax_lines, ax_cols),
(x, y))
midT = int(tr.T / tr.dt / 2)
if tr.T / second > 2:
raster_plot(axs['1,1'], tr, crun, tmin=0 * second, tmax=2 * second)
raster_plot(axs['1,2'],
tr,
crun,
tmin=tr.T / 2 - 1 * second,
tmax=tr.T / 2 + 1 * second)
raster_plot(axs['1,3'], tr, crun, tmin=tr.T - 2 * second, tmax=tr.T)
voltage_traces(axs['2,1'],
tr,
crun,
tmin=0 * second,
tmax=0.5 * second)
voltage_traces(axs['2,2'],
tr,
crun,
tmin=tr.T / 2 - 0.25 * second,
tmax=tr.T / 2 + 0.25 * second)
voltage_traces(axs['2,3'],
tr,
crun,
tmin=tr.T - 0.25 * second,
tmax=tr.T)
else:
raster_plot(axs['1,1'], tr, crun)
voltage_traces(axs['2,1'], tr, crun)
firing_rate_distribution_exc(axs['1,4'], tr, crun, steps=10)
firing_rate_distribution_inh(axs['2,4'], tr, crun, steps=15)
print_netw_params(axs['2,5'], tr, crun)
isi_distribution(axs['1,5'], tr, crun, bins=50)
conductance_mult_trace(axs['3,1'], tr, crun, tmin=0, tmax=2000)
conductance_mult_trace(axs['3,2'], tr, crun, tmin=midT, tmax=midT + 2000)
conductance_mult_trace(axs['3,3'], tr, crun, tmin=-2001, tmax=-1)
print_stdp_params(axs['3,5'], tr, crun)
synapse_weight_distribution_t(axs['4,1'], tr, crun, tstep=0)
synapse_weight_distribution_t(axs['4,2'], tr, crun, tstep=-1)
synapse_weight_distribution_log_t(axs['4,3'], tr, crun, bins=30, tstep=-1)
synapse_weight_distribution_log_t(axs['4,4'],
tr,
crun,
bins=30,
a_min=tr.a_insert,
tstep=-1)
synapse_weight_distribution_log_t_faulty(axs['4,5'], tr, crun, bins=30)
synapse_active_trace(axs['5,1'], tr, crun)
synapse_deathtime_distribution(axs['5,2'], tr, crun)
synapse_lifetime_distribution(axs['5,3'], tr, crun)
synapse_lifetime_distribution_loglog_linbin(axs['5,4'], tr, crun)
synapse_lifetime_distribution_loglog(axs['5,5'], tr, crun)
synapse_weight_traces(axs['6,1'], tr, crun)
synapse_weight_traces(axs['7,1'],
tr,
crun,
tmin=tr.T - 1 * second,
tmax=tr.T)
synapse_weight_change_on_spike(axs['6,2'], tr, crun)
synapse_weight_change_on_spike_log(axs['6,3'], tr, crun)
synapse_weight_change_on_spike_loglog(axs['6,4'], tr, crun)
synapse_lifetime_distribution_loglin(axs['6,5'], tr, crun)
# Apre_traces(axs['6,4'], tr, crun, tmin=-2001, tmax=-1)
# Apost_traces(axs['6,5'], tr, crun, tmin=-2001, tmax=-1)
membrane_threshold_distribution_t(axs['7,2'], tr, crun, tstep=0)
membrane_threshold_distribution_t(axs['7,3'], tr, crun, tstep=-1)
membrane_threshold_traces(axs['7,4'], tr, crun)
print_membrane_params(axs['7,5'], tr, crun)
pl.tight_layout()
directory = "figures/default_analysis".format(ftitle)
if not os.path.exists(directory):
os.makedirs(directory)
pl.savefig(directory + "/{:s}_{:s}.png".format(ftitle, crun),
dpi=100,
bbox_inches='tight')
def lognorm_analysis_figure(idx):
global fname, ftitle
tr = load_trajectory(fname)
tr.v_idx = idx
crun = tr.v_crun
pl.close()
fig = pl.figure()
ax_lines, ax_cols = 6, 8
s = 150
fig.set_size_inches(1920 / s * ax_cols / 4, 1080 / s * ax_lines / 3)
axs = {}
for x, y in itertools.product(range(ax_lines), range(ax_cols)):
axs['%d,%d' % (x + 1, y + 1)] = pl.subplot2grid((ax_lines, ax_cols),
(x, y))
midT = int(tr.T / tr.dt / 2)
fit = True
synapse_weight_distribution_log_t(axs['1,1'], tr, crun, tstep=1, bins=50)
synapse_weight_distribution_log_t(axs['1,2'], tr, crun, tstep=2, bins=50)
synapse_weight_distribution_log_t(axs['1,3'], tr, crun, tstep=3, bins=50)
synapse_weight_distribution_log_t(axs['1,4'], tr, crun, tstep=4, bins=50)
synapse_weight_distribution_log_t(axs['1,5'], tr, crun, tstep=5, bins=50)
synapse_weight_distribution_log_t(axs['1,6'], tr, crun, tstep=6, bins=50)
synapse_weight_distribution_log_t(axs['1,7'],
tr,
crun,
tstep=-1,
bins=50,
ax_text=True)
low_bound = 0.00000000000000001 * tr.ATotalMax
synapse_weight_distribution_log_t(axs['2,1'],
tr,
crun,
tstep=1,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['2,2'],
tr,
crun,
tstep=2,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['2,3'],
tr,
crun,
tstep=3,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['2,4'],
tr,
crun,
tstep=4,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['2,5'],
tr,
crun,
tstep=5,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['2,6'],
tr,
crun,
tstep=6,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['2,7'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=low_bound,
fit=fit,
ax_text=True)
low_bound = 0.00000000001 * tr.ATotalMax
synapse_weight_distribution_log_t(axs['3,1'],
tr,
crun,
tstep=1,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['3,2'],
tr,
crun,
tstep=2,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['3,3'],
tr,
crun,
tstep=3,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['3,4'],
tr,
crun,
tstep=4,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['3,5'],
tr,
crun,
tstep=5,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['3,6'],
tr,
crun,
tstep=6,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['3,7'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=low_bound,
fit=fit)
view_thrshld_ax_text(axs['3,8'], tr, low_bound=low_bound)
low_bound = 0.00000001 * tr.ATotalMax
synapse_weight_distribution_log_t(axs['4,1'],
tr,
crun,
tstep=1,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['4,2'],
tr,
crun,
tstep=2,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['4,3'],
tr,
crun,
tstep=3,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['4,4'],
tr,
crun,
tstep=4,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['4,5'],
tr,
crun,
tstep=5,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['4,6'],
tr,
crun,
tstep=6,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['4,7'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=low_bound,
fit=fit)
view_thrshld_ax_text(axs['4,8'], tr, low_bound=low_bound)
low_bound = 0.00001 * tr.ATotalMax
synapse_weight_distribution_log_t(axs['5,1'],
tr,
crun,
tstep=1,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['5,2'],
tr,
crun,
tstep=2,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['5,3'],
tr,
crun,
tstep=3,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['5,4'],
tr,
crun,
tstep=4,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['5,5'],
tr,
crun,
tstep=5,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['5,6'],
tr,
crun,
tstep=6,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['5,7'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=low_bound,
fit=fit)
view_thrshld_ax_text(axs['5,8'], tr, low_bound=low_bound)
low_bound = 0.001 * tr.ATotalMax
synapse_weight_distribution_log_t(axs['6,1'],
tr,
crun,
tstep=1,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['6,2'],
tr,
crun,
tstep=2,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['6,3'],
tr,
crun,
tstep=3,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['6,4'],
tr,
crun,
tstep=4,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['6,5'],
tr,
crun,
tstep=5,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['6,6'],
tr,
crun,
tstep=6,
bins=50,
low_bound=low_bound,
fit=fit)
synapse_weight_distribution_log_t(axs['6,7'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=low_bound,
fit=fit)
view_thrshld_ax_text(axs['6,8'], tr, low_bound=low_bound)
print_netw_params(axs['1,8'], tr, crun)
print_stdp_params(axs['2,8'], tr, crun)
ax_off(axs['3,8'])
ax_off(axs['4,8'])
ax_off(axs['5,8'])
ax_off(axs['6,8'])
pl.tight_layout()
subdir = "analysis/synaptic_weight_lognorm_t"
if not os.path.exists(subdir):
os.makedirs(subdir)
pl.savefig(subdir + "/syn_lnt_{:s}_{:s}.png".format(ftitle, crun),
dpi=100,
bbox_inches='tight')
def lognorm_analysis_figure(idx):
global fname, ftitle
tr = load_trajectory(fname)
tr.v_idx = idx
crun = tr.v_crun
pl.close()
fig = pl.figure()
ax_lines, ax_cols = 6, 4
s = 150
fig.set_size_inches(1920 / s * ax_cols / 4, 1080 / s * ax_lines / 3)
axs = {}
for x, y in itertools.product(range(ax_lines), range(ax_cols)):
axs['%d,%d' % (x + 1, y + 1)] = pl.subplot2grid((ax_lines, ax_cols),
(x, y))
midT = int(tr.T / tr.dt / 2)
synapse_weight_distribution_t(axs['1,1'], tr, crun, tstep=-1, bins=50)
synapse_weight_distribution_log_t(axs['1,2'], tr, crun, tstep=-1, bins=50)
synapse_weight_distribution_loglog_t(axs['1,3'],
tr,
crun,
tstep=-1,
bins=50)
synapse_weight_distribution_t(axs['2,1'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.00001 * tr.ATotalMax)
synapse_weight_distribution_log_t(axs['2,2'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.00001 * tr.ATotalMax)
synapse_weight_distribution_loglog_t(axs['2,3'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.00001 * tr.ATotalMax)
print_netw_params(axs['1,4'], tr, crun)
print_stdp_params(axs['2,4'], tr, crun)
ax_off(axs['3,4'])
ax_off(axs['4,4'])
ax_off(axs['5,4'])
ax_off(axs['6,4'])
synapse_weight_distribution_t(axs['3,1'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.0001 * tr.ATotalMax)
synapse_weight_distribution_log_t(axs['3,2'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.0001 * tr.ATotalMax)
synapse_weight_distribution_loglog_t(axs['3,3'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.0001 * tr.ATotalMax)
synapse_weight_distribution_t(axs['4,1'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.001 * tr.ATotalMax)
synapse_weight_distribution_log_t(axs['4,2'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.001 * tr.ATotalMax)
synapse_weight_distribution_loglog_t(axs['4,3'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.001 * tr.ATotalMax)
synapse_weight_distribution_t(axs['5,1'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.01 * tr.ATotalMax)
synapse_weight_distribution_log_t(axs['5,2'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.01 * tr.ATotalMax)
synapse_weight_distribution_loglog_t(axs['5,3'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.01 * tr.ATotalMax)
synapse_weight_distribution_t(axs['6,1'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.05 * tr.ATotalMax)
synapse_weight_distribution_log_t(axs['6,2'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.05 * tr.ATotalMax)
synapse_weight_distribution_loglog_t(axs['6,3'],
tr,
crun,
tstep=-1,
bins=50,
low_bound=0.05 * tr.ATotalMax)
# synapse_weight_distribution_t(axs['5,1'], tr, crun, tstep=0)
# synapse_weight_distribution_t(axs['5,2'], tr, crun, tstep=1)
# synapse_weight_distribution_t(axs['5,3'], tr, crun, tstep=2)
# synapse_weight_distribution_t(axs['5,4'], tr, crun, tstep=-1)
# synapse_weight_distribution_log_t(axs['6,1'], tr, crun, tstep=1)
# synapse_weight_distribution_log_t(axs['6,2'], tr, crun, tstep=2)
# synapse_weight_distribution_log_t(axs['6,3'], tr, crun, tstep=3)
# synapse_weight_distribution_log_t(axs['6,4'], tr, crun, tstep=-1)
# synapse_weight_distribution_log_t(axs['1,5'], tr, crun, tstep=1, bins=50)
# synapse_weight_distribution_log_t(axs['1,6'], tr, crun, tstep=2, bins=50)
# synapse_weight_distribution_log_t(axs['1,7'], tr, crun, tstep=3, bins=50)
# synapse_weight_distribution_log_t(axs['1,8'], tr, crun, tstep=-1, bins=50)
# synapse_weight_distribution_log_t(axs['2,5'], tr, crun, tstep=1, bins=100)
# synapse_weight_distribution_log_t(axs['2,6'], tr, crun, tstep=2, bins=100)
# synapse_weight_distribution_log_t(axs['2,7'], tr, crun, tstep=3, bins=100)
# synapse_weight_distribution_log_t(axs['2,8'], tr, crun, tstep=-1, bins=100)
# synapse_weight_distribution_log_t(axs['3,5'], tr, crun, tstep=1, bins=250)
# synapse_weight_distribution_log_t(axs['3,6'], tr, crun, tstep=2, bins=250)
# synapse_weight_distribution_log_t(axs['3,7'], tr, crun, tstep=3, bins=250)
# synapse_weight_distribution_log_t(axs['3,8'], tr, crun, tstep=-1, bins=250)
# synapse_weight_distribution_loglog_t(axs['4,5'], tr, crun, tstep=1, bins=50)
# synapse_weight_distribution_loglog_t(axs['4,6'], tr, crun, tstep=2, bins=50)
# synapse_weight_distribution_loglog_t(axs['4,7'], tr, crun, tstep=3, bins=50)
# synapse_weight_distribution_loglog_t(axs['4,8'], tr, crun, tstep=-1, bins=50)
# synapse_weight_distribution_loglog_t(axs['5,5'], tr, crun, tstep=1, bins=100)
# synapse_weight_distribution_loglog_t(axs['5,6'], tr, crun, tstep=2, bins=100)
# synapse_weight_distribution_loglog_t(axs['5,7'], tr, crun, tstep=3, bins=100)
# synapse_weight_distribution_loglog_t(axs['5,8'], tr, crun, tstep=-1, bins=100)
# synapse_weight_distribution_loglog_t(axs['6,5'], tr, crun, tstep=1, bins=250)
# synapse_weight_distribution_loglog_t(axs['6,6'], tr, crun, tstep=2, bins=250)
# synapse_weight_distribution_loglog_t(axs['6,7'], tr, crun, tstep=3, bins=250)
# synapse_weight_distribution_loglog_t(axs['6,8'], tr, crun, tstep=-1, bins=250)
# synapse_weight_distribution_t(axs['4,2'], tr, crun, tstep=-1)
# synapse_weight_distribution_log_t(axs['4,3'], tr, crun, bins=30,
# tstep=-1)
# synapse_weight_distribution_log_t(axs['4,4'], tr, crun, bins=30,
# a_min=tr.a_insert, tstep=-1)
# synapse_weight_distribution_log_t_faulty(axs['4,5'], tr, crun, bins=30)
# synapse_active_trace(axs['5,1'], tr, crun)
# synapse_deathtime_distribution(axs['5,2'], tr, crun)
# synapse_lifetime_distribution(axs['5,3'], tr, crun)
# synapse_lifetime_distribution_loglog_linbin(axs['5,4'], tr, crun)
# synapse_lifetime_distribution_loglog(axs['5,5'], tr, crun)
# synapse_weight_traces(axs['6,1'], tr, crun)
# synapse_weight_traces(axs['7,1'], tr, crun, tmin=tr.T-1*second, tmax=tr.T)
# synapse_weight_change_on_spike(axs['6,2'], tr, crun)
# synapse_weight_change_on_spike_log(axs['6,3'], tr, crun)
# synapse_weight_change_on_spike_loglog(axs['6,4'], tr, crun)
# synapse_lifetime_distribution_loglin(axs['6,5'], tr, crun)
# # Apre_traces(axs['6,4'], tr, crun, tmin=-2001, tmax=-1)
# # Apost_traces(axs['6,5'], tr, crun, tmin=-2001, tmax=-1)
# membrane_threshold_distribution_t(axs['7,2'], tr, crun, tstep=0)
# membrane_threshold_distribution_t(axs['7,3'], tr, crun, tstep=-1)
# membrane_threshold_traces(axs['7,4'], tr, crun)
# print_membrane_params(axs['7,5'], tr, crun)
pl.tight_layout()
subdir = "analysis/synaptic_weight_full".format(ftitle)
if not os.path.exists(subdir):
os.makedirs(subdir)
pl.savefig(subdir + "/synw_full_{:s}_{:s}.png".format(ftitle, crun),
dpi=100,
bbox_inches='tight')