def iter_alpha(i):
alpha = alphas[i]
pop = CosmicPopulation.complex(self.pop_size)
pop.set_dist(model='vol_co', z_max=1.0, alpha=alpha)
pop.set_lum(model='constant', value=1)
if not np.isnan(w_mean):
pop.set_w(model='lognormal', mean=w_mean, std=w_std)
if not np.isnan(dm_igm_slope):
pop.set_dm_igm(model='ioka', slope=dm_igm_slope)
pop.set_dm_host(model='constant', value=dm_host)
pop.generate()
for si in sis:
pop.set_si(model='constant', value=si)
pop.gen_si()
for li in lis:
pop.set_lum(model='powerlaw',
low=1e40,
high=1e45,
power=li)
if not np.isnan(lum_min):
pop.set_lum(model='powerlaw',
low=lum_min,
high=lum_max,
index=li)
pop.gen_lum()
for survey in self.surveys:
surv_pop = SurveyPopulation(pop, survey)
# Get unique identifier
mask = (self.so.df.par_set == 1)
mask &= (self.so.df.run == run)
mask &= (self.so.df.alpha == alpha)
mask &= (self.so.df.si == si)
mask &= (self.so.df.li == li)
mask &= (self.so.df.survey == survey.name)
uuid = self.so.df[mask].uuid.iloc[0]
surv_pop.name = f'mc/run_{run}/{uuid}'
surv_pop.save()
def adapt_pop(e):
w_mean, w_std = e
t_pop = deepcopy(pop)
t_pop.set_w(model='lognormal', mean=w_mean, std=w_std)
t_pop.gen_w()
for survey in self.surveys:
surv_pop = SurveyPopulation(t_pop, survey)
# Get unique identifier
mask = (self.so.df.par_set == 3)
mask &= (self.so.df.run == run)
mask &= (self.so.df.run == run)
mask &= (self.so.df.w_mean == w_mean)
mask &= (self.so.df.w_std == w_std)
mask &= (self.so.df.survey == survey.name)
uuid = self.so.df[mask].uuid.iloc[0]
surv_pop.name = f'mc/run_{run}/{uuid}'
surv_pop.save()
def get_survey_pop(pop, survey, overwrite=False):
"""Quickly get survey populations.
Args:
pop (CosmicPopulation): Population to survey
survey (Survey): Survey to use
overwrite (bool): Check whether a population has already
been run. If overwrite is true, it will always make a new
instance.
Returns:
pop: Desired population.
"""
observe = True
# Check where a possible population would be located
path = ''
if isinstance(pop, str):
name = f'{pop}_{survey.name}'
path = paths.populations() + name + '.p'
# Check if the file exists
if not overwrite:
if os.path.isfile(path):
observe = False
return unpickle(path)
# If all else fails observe again
if observe:
if isinstance(pop, str):
m = f'No survey population at {path}, yet no surveying requested'
raise ValueError(m)
surv_pop = SurveyPopulation(pop, survey)
surv_pop.name = f'{pop.name}_{survey.name}'
surv_pop.save()
return surv_pop
def adapt_pop(e):
dm_igm_slope, dm_host = e
t_pop = deepcopy(pop)
t_pop.set_dm_igm(model='ioka', slope=dm_igm_slope)
t_pop.gen_dm_igm()
t_pop.set_dm_host(model='constant', value=dm_host)
t_pop.gen_dm_host()
t_pop.frbs.dm = t_pop.frbs.dm_mw + t_pop.frbs.dm_igm
t_pop.frbs.dm += t_pop.frbs.dm_host
for survey in self.surveys:
surv_pop = SurveyPopulation(t_pop, survey)
# Get unique identifier
mask = (self.so.df.par_set == 4)
mask &= (self.so.df.run == run)
mask &= (self.so.df.dm_igm_slope == dm_igm_slope)
mask &= (self.so.df.dm_host == dm_host)
mask &= (self.so.df.survey == survey.name)
uuid = self.so.df[mask].uuid.iloc[0]
surv_pop.name = f'mc/run_{run}/{uuid}'
surv_pop.save()
def adapt_pop(e):
li, lum_min, lum_max = e
if lum_max < lum_min:
return
t_pop = deepcopy(pop)
t_pop.set_lum(model='powerlaw',
low=lum_min,
high=lum_max,
power=li)
t_pop.gen_lum()
for survey in self.surveys:
surv_pop = SurveyPopulation(t_pop, survey)
# Get unique identifier
mask = (self.so.df.par_set == 2)
mask &= (self.so.df.run == run)
mask &= (self.so.df.li == li)
mask &= (self.so.df.lum_min == lum_min)
mask &= (self.so.df.lum_max == lum_max)
mask &= (self.so.df.survey == survey.name)
uuid = self.so.df[mask].uuid.iloc[0]
surv_pop.name = f'mc/run_{run}/{uuid}'
surv_pop.save()
if OBSERVE:
for n in (35, 36, 37):
if not CREATE:
pop[n] = unpickle(f'sc-{n}')
# Create Survey
perfect = Survey('perfect-small',
gain_pattern='gaussian',
n_sidelobes=8)
# Observe populations
pop_obs[n] = SurveyPopulation(pop[n], perfect)
pop_obs[n].name = f'sc-{n}-obs'
pop_obs[n].rates()
pop_obs[n].save()
else:
for n in (35, 36, 37):
pop_obs[n] = unpickle(f'sc-{n}-obs')
f, (ax1) = plt.subplots(1, 1)
for n in pop_obs:
pop = pop_obs[n]
limit = 1e-9
s_peak = pop.frbs.s_peak
pop.set_lum(model='constant', value=1e36)
pop.set_w(model='constant', value=1)
pop.set_si(model='constant', value=0)
pop.generate()
pop_obs = {}
survey = Survey('perfect-small')
for pattern in BEAMPATTERNS:
survey.set_beam(model=pattern, n_sidelobes=0, size=90)
# Observe populations
pop_obs[pattern] = SurveyPopulation(pop, survey)
pop_obs[pattern].name = f'obs-{pattern}'
pop_obs[pattern].source_rate
print(pop_obs[pattern].source_rate)
pop_obs[pattern].save()
plot_aa_style()
f, (ax1) = plt.subplots(1, 1)
for p in BEAMPATTERNS:
pop = pop_obs[p]
limit = 1e-9
s_peak = pop.frbs.s_peak
dm_igm = pop.frbs.dm_igm
r.set_emission_range(low=100e6, high=10e9)
r.set_lum(model='powerlaw',
per_source='different',
low=1e40,
high=1e45,
power=0)
r.set_si(model='gauss', mean=-1.4, std=1)
r.set_w(model='lognormal', per_source='different', mean=0.1, std=1)
rate = lognormal(ra, 1, int(n))
r.set_time(model='poisson', rate=rate)
# Set up survey
s = Survey('chime-frb', n_days=N_DAYS)
s.set_beam(model='chime-frb')
# Only generate FRBs in CHIME's survey region
r.set_direction(model='uniform',
min_ra=s.ra_min,
max_ra=s.ra_max,
min_dec=s.dec_min,
max_dec=s.dec_max)
r.generate()
surv_pop = SurveyPopulation(r, s)
surv_pop.name = 'cosmic_chime'
print(surv_pop.source_rate)
print(surv_pop.burst_rate)
pprint(f'# one-offs: {surv_pop.n_one_offs()}')
pprint(f'# repeaters: {surv_pop.n_repeaters()}')
pop.set_si(model='constant', value=0)
pop.generate()
pop_obs = {}
survey = Survey('perfect-small')
survey.beam_size_at_fwhm = 10.
survey.set_beam(model='airy')
for sidelobe in SIDELOBES:
survey.set_beam(model='airy', n_sidelobes=sidelobe)
# Observe populations
pop_obs[sidelobe] = SurveyPopulation(pop, survey)
pop_obs[sidelobe].name = f'obs-{sidelobe}'
pop_obs[sidelobe].source_rate
pop_obs[sidelobe].save()
plot_aa_style()
f, (ax1) = plt.subplots(1, 1)
for p in SIDELOBES:
pop = pop_obs[p]
limit = 1e-9
s_peak = pop.frbs.s_peak
dm_igm = pop.frbs.dm_igm
mini = min(s_peak)
pop[si].save()
else:
pop[si] = copy.deepcopy(pop[min(SIS)])
pop[si].frbs.si = np.random.normal(si, 0, int(n_tot))
pop[si].name = f'si-{si}'
pop[si].save()
pop_obs = {}
for si in SIS:
perfect = Survey('perfect')
# Observe populations
pop_obs[si] = SurveyPopulation(pop[si], perfect)
pop_obs[si].name = f'si-{si}-obs'
pop_obs[si].rates()
pop_obs[si].save()
plot_aa_style()
# Plot log N and alpha versus log S
f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
min_s = 1e99
max_s = -1e99
for si in SIS:
pop = pop_obs[si]
"""Check the log N log F slope of a local population."""
import numpy as np
import matplotlib.pyplot as plt
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation
from frbpoppy.population import unpickle
from tests.convenience import plot_aa_style, rel_path
MAKE = True
if MAKE:
population = CosmicPopulation.simple(1e5, generate=True)
survey = Survey('perfect')
surv_pop = SurveyPopulation(population, survey)
surv_pop.name = 'lognlogflocal'
surv_pop.save()
else:
surv_pop = unpickle('lognlogflocal')
# Get parameter
parms = surv_pop.frbs.fluence
min_p = min(parms)
max_p = max(parms)
# Bin up
min_f = np.log10(min(parms))
max_f = np.log10(max(parms))
log_bins = np.logspace(min_f, max_f, 50)
hist, edges = np.histogram(parms, bins=log_bins)
n_gt_s = np.cumsum(hist[::-1])[::-1]
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)
dm_igm_sigma=0,
dm_mw_model='zero',
emission_range=[10e6, 10e9],
lum_index=0,
pulse_model='uniform',
pulse_range=[1., 1.],
pulse_mu=1.,
pulse_sigma=0.)
population.name = 'test'
# Setup a survey
survey = Survey('perfect', gain_pattern='perfect')
# Observe the FRB population
surv_pop = SurveyPopulation(population, survey)
surv_pop.name = 'lognlogslocal'
surv_pop.save()
else:
surv_pop = unpickle('lognlogslocal')
parms = surv_pop.frbs.fluence
min_p = min(parms)
max_p = max(parms)
# Bin up
min_f = np.log10(min(parms))
max_f = np.log10(max(parms))
log_bins = np.logspace(min_f, max_f, 50)
hist, edges = np.histogram(parms, bins=log_bins)
n_gt_s = np.cumsum(hist[::-1])[::-1]
pop_obs = {}
if OBSERVE:
if not CREATE:
pop = unpickle(f'simple')
for n in SIDELOBES:
# Create Survey
survey = Survey('perfect-small', gain_pattern='airy', n_sidelobes=n)
# Observe populations
pop_obs[n] = SurveyPopulation(pop, survey)
pop_obs[n].name = f'obs-airy-{n}'
pop_obs[n].rates()
pop_obs[n].save()
else:
for n in SIDELOBES:
pop_obs[n] = unpickle(f'obs-airy-{n}')
f, (ax1) = plt.subplots(1, 1)
for sidelobe in SIDELOBES:
pop = pop_obs[sidelobe]
limit = 1e-9
s_peak = pop.frbs.s_peak