pop.set_w(model='constant', value=1)
pop.set_dm(mw=False, igm=True, host=False)
# Add a distribution of Poisson burst rates
if SINGLE_INPUT_RATE:
rate_dist = RATE
else:
rate_dist = log10normal(RATE, 2, N_SRCS)
pop.set_time(model='poisson', rate=rate_dist)
pop.generate()
# Survey the high fluences
survey = Survey('perfect', n_days=N_DAYS)
survey.set_beam(model='perfect')
survey.snr_limit = 1e-6
survey.t_obs = 60 * 60 # seconds
# Check the burst rate
surv_pop = SurveyPopulation(pop, survey)
time = surv_pop.frbs.time
# Set up plot
plot_aa_style()
if isinstance(rate_dist, np.ndarray):
min_rate = np.log10(np.min(rate_dist[rate_dist != 0]))
max_rate = np.log10(max(rate_dist))
else:
min_rate = np.log10(RATE) - 1
max_rate = np.log10(RATE) + 1
rate_dist = np.array(rate_dist)
time = np.where(t < day, t, np.nan)
n_rep = ((~np.isnan(time)).sum(1) > 1).sum()
n_one_offs = ((~np.isnan(time)).sum(1) == 1).sum()
frac = n_rep / (n_rep + n_one_offs)
fracs.append(frac)
print(f'# one-offs: {n_one_offs}')
print(f'# repeaters: {n_rep}')
return days, fracs
r_pop = setup_pop(N_SRCS)
# Set up surveys
survey = Survey('perfect', n_days=MAX_DAYS)
survey.mount_type = 'transit'
survey.t_obs = 60*60*24
survey.set_beam(model='perfect')
survey.snr_limit = 10000
chime_survey = deepcopy(survey)
chime_survey.set_beam(model='chime-frb')
chime_survey.snr_limit = 1
# Set up plot style
plot_aa_style(cols=2)
f, (ax1, ax2) = plt.subplots(1, 2)
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
linestyles = ['solid', 'dotted']
dist_types = ['rep', 'dist', 'mix']