def synapse_lifetime_distribution_loglog_linbin(ax,
tr,
crun='run_00000000',
bin_w=10.,
discard_t=0.):
'''
discard all values until discard_t
'''
if discard_t != 0.:
raise NotImplementedError
else:
print("not discarding any ts")
if not len(tr.crun.turnover) == 0:
_lt, _dt = extract_lifetimes(tr.crun.turnover, tr.N_e)
life_t, death_t = _lt * second, _dt * second
counts, edges = np.histogram(life_t / ms,
bins=np.arange(tr.dt / ms, tr.T / ms,
bin_w))
centers = (edges[:-1] + edges[1:]) / 2.
ax.plot(centers, counts, '.', markersize=0.5)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_title('Lifetime distribution')
ax.set_xlabel('time [ms]')
def synapse_lifetime_distribution_loglin(ax,
tr,
crun='run_00000000',
bins=50,
discard_t=0.):
'''
discard all values until discard_t
'''
if discard_t != 0.:
raise NotImplementedError
if not len(tr.crun.turnover) == 0:
_lt, _dt = extract_lifetimes(tr.crun.turnover, tr.N_e)
life_t, death_t = _lt * second, _dt * second
if len(life_t) == 0:
print("No recorded lifetimes. Not plotting.")
ax.set_title("No recorded lifetimes")
else:
b_min, b_max = tr.dt / ms, np.max(life_t / ms)
bins = np.linspace(np.log10(b_min), np.log10(b_max), bins)
ax.hist(life_t / ms, 10**bins, normed=False, histtype='step')
ax.set_title('Lifetime distribution')
ax.set_xlabel('time [ms]')
ax.set_xscale('log')
ax.set_xlim(b_min, b_max)
def synapse_deathtime_distribution(ax, tr, crun, discard_t=0.):
'''
discard all values until discard_t
'''
if discard_t != 0.:
raise NotImplementedError
if not len(tr.crun.turnover) == 0:
_lt, _dt = extract_lifetimes(tr.crun.turnover, tr.N_e)
life_t, death_t = _lt * second, _dt * second
ax.hist(death_t / ms)
ax.set_title('Deathtime distribution')
ax.set_xlabel('time [ms]')