"""Example of simulating a perfect survey."""
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation, plot
# Generate an FRB population
cosmic_pop = CosmicPopulation.simple(1e4, generate=True)
# Setup a survey
survey = Survey('perfect')
# Observe the FRB population
survey_pop = SurveyPopulation(cosmic_pop, survey)
# Check the detection rates
print(survey_pop.rates())
# Note that due to redshift you won't see all bursts, as some will have
# redshifted out of the observing time. But no matter how faint, you'll see
# all bursts within the observing time
# Plot populations
plot(cosmic_pop, survey_pop, frbcat=False, mute=False)
"""Generate a repeater population and split into repeaters and one-offs."""
import numpy as np
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation
from frbpoppy import split_pop, pprint, plot
DAYS = 1
r = CosmicPopulation.simple(int(1e4), n_days=DAYS, repeaters=True)
r.set_time(model='regular', rate=2)
r.set_lum(model='powerlaw', low=1e40, high=1e45, per_source='different')
r.generate()
survey = Survey('chime-frb', n_days=DAYS)
survey.set_beam(model='perfect')
surv_pop = SurveyPopulation(r, survey)
# Split population into seamingly one-off and repeater populations
mask = ((~np.isnan(surv_pop.frbs.time)).sum(1) > 1)
pop_ngt1, pop_nle1 = split_pop(surv_pop, mask)
pop_ngt1.name += ' (> 1 burst)'
pop_nle1.name += ' (1 burst)'
pops = [pop_nle1, pop_ngt1]
pprint(f'{surv_pop.n_sources()} sources detected')
pprint(f'{surv_pop.n_bursts()} bursts detected')
plot(surv_pop)
"""Short example of how frbpoppy works."""
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation, plot
PLOT = False
# Generate an FRB population
cosmic_pop = CosmicPopulation(10000, days=1, name='example')
# Setup a survey
survey = Survey('htru')
# Observe the FRB population
survey_pop = SurveyPopulation(cosmic_pop, survey)
# Check the detection rates
print(survey_pop.rates())
# Plot populations
if PLOT:
plot(cosmic_pop, survey_pop, frbcat='parkes')
"""Short example of how frbpoppy works."""
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation, plot
PLOT = True
# Generate an FRB population
cosmic_pop = CosmicPopulation(10000, days=7, name='example')
# Setup a survey
survey = Survey('apertif')
# Observe the FRB population
survey_pop = SurveyPopulation(cosmic_pop, survey)
# Check the detection rates
print(survey_pop.rates())
# Plot populations
if PLOT:
plot(cosmic_pop, survey_pop, mute=False)
pprint(f'{r.n_bursts()}:{surv_pop.n_bursts()}')
pprint(f'{surv_pop.n_sources()} sources detected')
if r.n_bursts() < PLOTTING_LIMIT_N_SRCS:
pprint('Not sufficient FRB sources for plotting')
exit()
# Split population into seamingly one-off and repeater populations
mask = ((~np.isnan(surv_pop.frbs.time)).sum(1) > 1)
pop_rep, pop_one = split_pop(surv_pop, mask)
pop_rep.name += ' (> 1 burst)'
pop_one.name += ' (1 burst)'
if INTERACTIVE_PLOT:
plot(r, pop_rep, pop_one, tns=False, mute=False)
# Plot dm distribution
if SNR:
plot_aa_style(cols=2)
f, (ax1, ax2) = plt.subplots(1, 2)
else:
plot_aa_style(cols=1)
f, ax1 = plt.subplots(1, 1)
prop_cycle = plt.rcParams['axes.prop_cycle']
colors = prop_cycle.by_key()['color']
pops = (r, pop_rep, pop_one)
for i, pop in enumerate(pops):
pprint(f'{r.n_bursts()}:{surv_pop.n_bursts()}')
pprint(f'{surv_pop.n_sources()} sources detected')
if r.n_bursts() < PLOTTING_LIMIT_N_SRCS:
pprint('Not sufficient FRB sources for plotting')
exit()
# Split population into seamingly one-off and repeater populations
mask = ((~np.isnan(surv_pop.frbs.time)).sum(1) > 1)
pop_rep, pop_one = split_pop(surv_pop, mask)
pop_rep.name += ' (> 1 burst)'
pop_one.name += ' (1 burst)'
if INTERACTIVE_PLOT:
plot(r, pop_rep, pop_one, frbcat=False, mute=False)
# Plot dm distribution
if SNR:
plot_aa_style(cols=2)
f, (ax1, ax2) = plt.subplots(1, 2)
else:
plot_aa_style(cols=1)
f, ax1 = plt.subplots(1, 1)
prop_cycle = plt.rcParams['axes.prop_cycle']
colors = prop_cycle.by_key()['color']
pops = (r, pop_rep, pop_one)
for i, pop in enumerate(pops):
"""Short example of how frbpoppy works.
The first time you run frbpoppy, a series of cosmological databases will be
constructed to set up subsequent runs. This first run can take ~2h on a 4 core
machine. Subsequent runs will take mere seconds.
"""
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation, plot
# Set up an FRB population of one-offs
# Add repeaters=True to turn into an FRB population of repeaters
cosmic_pop = CosmicPopulation.complex(1e5, n_days=0.01)
# Generate your FRB population
cosmic_pop.generate()
# Setup a survey
survey = Survey('parkes-htru')
survey.set_beam(model='parkes-htru')
# Observe the FRB population
survey_pop = SurveyPopulation(cosmic_pop, survey)
# Check the detection rates
print(survey_pop.source_rate)
# Plot populations in a browser
plot(cosmic_pop, survey_pop, tns='parkes')
def main():
r = RepeaterPopulation(N,
days=1,
dm_host_model='gaussian',
dm_host_mu=100,
dm_host_sigma=0,
dm_igm_index=1000,
dm_igm_sigma=0,
dm_mw_model='zero',
emission_range=[10e6, 10e9],
lum_range=[1e38, 1e38],
lum_index=0,
n_model='vol_co',
alpha=-1.5,
w_model='uniform',
w_range=[1., 1.],
w_mu=0.1,
w_sigma=0.5,
si_mu=-1.4,
si_sigma=0.,
z_max=2.5,
lum_rep_model='independent',
lum_rep_sigma=1e3,
si_rep_model='same',
si_rep_sigma=0.1,
times_rep_model='even',
w_rep_model='independent',
w_rep_sigma=0.05,
generate=True)
s = Survey('perfect')
s.gain_pattern = 'perfect'
# Setup pointings
n_p = 1 # # of pointings
decs = np.linspace(s.dec_min, s.dec_max, n_p + 2)[1:n_p + 1]
ras = np.linspace(s.ra_min, s.ra_max, n_p + 2)[1:n_p + 1]
s.pointings = list(zip(ras, decs))
pop = limit_ra_dec(r, s.pointings)
pop.name = 'Cosmic Population'
s.snr_limit = 1
surv_pop_low_snr = SurveyPopulation(r, s)
surv_pop_low_snr.name = 'Low SNR'
s.snr_limit = 10
surv_pop_high_snr = SurveyPopulation(r, s)
surv_pop_high_snr.name = 'High SNR'
pops = [pop]
# Split population into seamingly one-off and repeater populations
for pop in (surv_pop_low_snr, surv_pop_high_snr):
mask = ((~np.isnan(pop.frbs.time)).sum(1) > 1)
pop_ngt1, pop_nle1 = split_pop(pop, mask)
pop_ngt1.name += ' (> 1 burst)'
pop_nle1.name += ' (1 burst)'
pops.append(pop_nle1)
pops.append(pop_ngt1)
plot(*pops, frbcat=False, mute=False, show=True)