def loop_surveys(self, cosmic_pop, z_max):
for survey in self.surveys:
surv_pop = SurveyPopulation(cosmic_pop, survey)
surv_pop.z_max = z_max
rate = surv_pop.source_rate.det
n = surv_pop.n_sources()
errs = [np.abs(e - n) for e in poisson_interval(n)]
d = {
'survey': survey.name,
'pop': cosmic_pop.name,
'rate': rate,
'err_low': errs[0],
'err_high': errs[1],
'z_max': z_max
}
self.data.append(d)
ax.set_aspect('auto')
# Get norm pop
y = 0
ys = []
names = []
rates = []
norm_sim_rate = surv_pops[0].source_rate.det
norm_real_rate = EXPECTED['parkes-htru'][0] / EXPECTED['parkes-htru'][1]
norm_rate = norm_sim_rate / norm_real_rate
for i, surv_pop in enumerate(surv_pops):
name = surv_pop.name.split('_')[-1]
pprint(name)
if surv_pop.n_sources() == 0:
print(surv_pop.source_rate)
print(f'{name} | no FRBs in population')
continue
names.append(name)
ys.append(y)
# Dimensions measure plot
ax = axes[0]
ax.set_xlabel(r'DM ($\textrm{pc}\ \textrm{cm}^{-3}$)')
ax.set_ylabel(r'\#')
ax.set_yscale('log')
bins, values = hist(surv_pop.frbs.dm,
bin_type='lin',