def iter_alpha(i, surveys=surveys, parallel=None):
alpha = ALPHAS[i]
pop = CosmicPopulation.complex(SIZE)
pop.set_dist(model='vol_co', z_max=1.0, alpha=alpha)
pop.set_lum(model='powerlaw', low=1e40, high=1e45, power=-1)
pop.generate()
for li in LIS:
pop.set_lum(model='powerlaw', low=1e40, high=1e45, power=li)
pop.gen_lum()
for si in SIS:
pop.set_si(model='constant', value=si)
pop.gen_si()
pop.name = f'complex_alpha_{alpha}_lum_{li}_si_{si}'
for survey in surveys:
surv_pop = SurveyPopulation(pop, survey)
print(surv_pop.name)
surv_pop.save()
sr = surv_pop.source_rate
rate = sr.det / sr.days
mask = (df.alpha == alpha) & (df.li == li) & (df.si == si)
if parallel is not None:
i = df[mask].index
j = SURVEY_NAMES.index(survey.name)
parallel[i, j] = rate
else:
df.loc[mask, survey.name] = rate
def get_data():
"""Get the population data."""
# Construct population
pop = CosmicPopulation(n_srcs=SIZE, n_days=1, name='standard_candle')
pop.set_dist(model='sfr', z_max=2.5, H_0=67.74, W_m=0.3089, W_v=0.6911)
pop.set_dm_host(model='constant', value=100)
pop.set_dm_igm(model='ioka', slope=1000, std=None)
pop.set_dm_mw(model='ne2001')
pop.set_emission_range(low=10e6, high=10e9)
pop.set_lum(model='constant', value=1e36)
pop.set_w(model='constant', value=1.)
pop.set_si(model='constant', value=0)
pop.generate()
# Survey population
pops = {}
for b in BEAMPATTERNS:
pprint(f'Surveying with {b} beampattern')
n_s = 0
bp = b
if b.startswith('airy'):
bp, n_s = b.split('-')
n_s = int(n_s)
survey = Survey(name='perfect-small')
# Prevent beam from getting larger than the sky
survey.set_beam(model=bp, n_sidelobes=n_s, size=10)
surv_pop = SurveyPopulation(pop, survey)
print(surv_pop.source_rate)
pops[b] = surv_pop
return pops
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)
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 get_local(make=MAKE, size=SIZE):
# Construct populations
pop = get_cosmic_pop('alpha_simple',
size,
load=True,
overwrite=make,
alpha=-1.5)
# Survey populations
survey = Survey(name='perfect', gain_pattern='perfect', n_sidelobes=0.5)
surv_pop = SurveyPopulation(pop, survey)
return surv_pop.frbs.fluence
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()
def get_further(gamma, make=MAKE, size=SIZE):
"""Construct populations going further out."""
# Construct populations
pop = get_cosmic_pop('gamma',
size,
load=True,
overwrite=make,
gamma=gamma)
if gamma == 1:
pop.frbs.lum_bol = np.ones_like(pop.frbs.lum_bol)*10**43
# Survey populations
survey = Survey(name='perfect', gain_pattern='perfect', n_sidelobes=0.5)
surv_pop = SurveyPopulation(pop, survey)
return surv_pop.frbs.fluence
def get_data():
"""Get the population data."""
# Construct population
pop = CosmicPopulation(SIZE,
days=1,
name='standard_candle',
H_0=67.74,
W_m=0.3089,
W_v=0.6911,
dm_host_model='gaussian',
dm_host_mu=100,
dm_host_sigma=0,
dm_igm_index=1000,
dm_igm_sigma=None,
dm_mw_model='ne2001',
emission_range=[10e6, 10e9],
lum_range=[1e36, 1e36],
lum_index=0.,
n_model='sfr',
alpha=-1.5,
w_model='uniform',
w_range=[1., 1.],
w_mu=0.1,
w_sigma=0.,
si_mu=0.,
si_sigma=0.,
z_max=2.5)
# Survey population
pops = {}
for b in BEAMPATTERNS:
n_s = 0
bp = b
if b.startswith('airy'):
bp, n_s = b.split('-')
n_s = int(n_s)
survey = Survey(name='perfect-small')
survey.gain_pattern = bp
survey.n_sidelobes = n_s
surv_pop = SurveyPopulation(pop, survey)
pops[b] = surv_pop
return pops
def get_further(gamma):
"""Construct populations going further out."""
# Construct populations
pop = CosmicPopulation.simple(SIZE)
pop.si_mu = gamma
pop.z_max = 2.5
pop.lum_min = 10**42.5
pop.lum_max = pop.lum_min
pop.generate()
if gamma == 1:
pop.frbs.lum_bol = np.ones_like(pop.frbs.lum_bol)*10**43
# Survey populations
survey = Survey('perfect')
surv_pop = SurveyPopulation(pop, survey)
return surv_pop.frbs.s_peak
def get_further(gamma):
"""Construct populations going further out."""
# Construct populations
pop = CosmicPopulation.simple(SIZE)
pop.set_dist(z_max=2.5)
pop.set_si(model='constant', value=gamma)
pop.set_lum(model='constant', value=10**42.5)
pop.generate()
if gamma == 1:
pop.set_lum(model='constant', value=10**43)
pop.gen_lum()
# Survey populations
survey = Survey('perfect')
surv_pop = SurveyPopulation(pop, survey)
return surv_pop.frbs.s_peak
def iter_run(i):
r = CosmicPopulation(N_SRCS, n_days=N_DAYS, repeaters=True)
r.set_dist(model='vol_co', z_max=1.0)
r.set_dm_host(model='gauss', mean=100, std=200)
r.set_dm_igm(model='ioka', slope=1000, std=None)
r.set_dm(mw=True, igm=True, host=True)
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(RATE, 2, N_SRCS)
if LARGE_POP:
rate = lognormal(RATE, 2, int(MAX_SIZE)) # Not completely kosher
r.set_time(model='poisson', rate=rate)
# Set up survey
s = Survey('chime-frb', n_days=N_DAYS)
s.set_beam(model='chime-frb')
s.gen_pointings() # To ensure each sub pop has the same pointings
# 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)
if LARGE_POP:
surv_pop = LargePopulation(r, s, max_size=MAX_SIZE).pops[0]
else:
r.generate()
surv_pop = SurveyPopulation(r, s)
surv_pop.name = f'cosmic_chime_longer_{i}'
surv_pop.save()
print(surv_pop.source_rate)
print(surv_pop.burst_rate)
pprint(f'i: {i}')
pprint(f'# one-offs: {surv_pop.n_one_offs()}')
pprint(f'# repeaters: {surv_pop.n_repeaters()}')
def do_survey():
# Construct population
pop = get_cosmic_pop('standard_candle', SIZE, load=True, overwrite=MAKE)
# Survey population
pops = {}
for b in BEAMPATTERNS:
n_s = 0
bp = b
if b.startswith('airy'):
bp, n_s = b.split('-')
n_s = int(n_s)
survey = Survey(name='perfect-small', gain_pattern=bp, n_sidelobes=n_s)
surv_pop = SurveyPopulation(pop, survey)
pops[b] = surv_pop
return pops
def get_local():
"""Construct a local population."""
pop = CosmicPopulation.simple(SIZE, generate=True)
survey = Survey('perfect')
surv_pop = SurveyPopulation(pop, survey)
return surv_pop.frbs.s_peak
pop_obs = {}
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
pop.generate()
apertif = Survey('wsrt-apertif', n_days=N_DAYS)
apertif.set_beam(model='apertif_real')
if SCALE_TO == 'parkes-htru':
htru = Survey('parkes-htru', n_days=N_DAYS)
htru.set_beam(model='parkes')
if SCALE_TO == 'askap':
askap = Survey('askap-fly', n_days=N_DAYS)
askap.set_beam(model='gaussian', n_sidelobes=0.5)
days_per_frbs = []
for i in tqdm(range(2000), desc='Survey Run'):
apertif_pop = SurveyPopulation(pop, apertif, mute=True)
if SCALE_TO == 'parkes-htru':
htru_pop = SurveyPopulation(pop, htru, mute=True)
n_frbs_htru = EXPECTED['parkes-htru'][0]
n_days_htru = EXPECTED['parkes-htru'][1]
scaled_n_days = n_days_htru * (htru_pop.source_rate.det / n_frbs_htru)
if SCALE_TO == 'askap':
askap_pop = SurveyPopulation(pop, askap, mute=True)
n_frbs_askap = EXPECTED['askap-fly'][0]
n_days_askap = EXPECTED['askap-fly'][1]
scaled_n_days = n_days_askap * (askap_pop.source_rate.det /
n_frbs_askap)
days_per_frb = scaled_n_days / apertif_pop.source_rate.det
pop.set_emission_range(low=10e6, high=10e9)
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
def survey_pop(self, pop):
return SurveyPopulation(pop, self.survey)
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()}')
"""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)
pop.set_w(model='constant', value=1)
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
"""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]
pop[si].generate()
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]
"""How to access frb population parameters."""
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation
cosmic_pop = CosmicPopulation.simple(1e3, repeaters=True, generate=True)
dm = cosmic_pop.frbs.dm # Get dispersion measure values
survey_pop = SurveyPopulation(cosmic_pop, Survey('perfect'))
survey_dm = survey_pop.frbs.dm # Also works for SurveyPopulations
# Inbuilt functions help with detection rates
print(f'Number of sources: {survey_pop.n_sources()}')
print(f'Number of bursts: {survey_pop.n_bursts()}')
print(f'Number of repeating sources: {survey_pop.n_repeaters()}')
print(f'Number of one-off sources: {survey_pop.n_oneoffs()}')
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(RATE, 1, int(N_SRCS))
r.set_time(model='poisson', rate=rate)
# Only generate FRBs in CHIME's survey region
r.set_direction(model='uniform',
min_ra=chime.ra_min,
max_ra=chime.ra_max,
min_dec=chime.dec_min,
max_dec=chime.dec_max)
r.generate()
surv_pop = SurveyPopulation(r, chime)
surv_pop.save()
else:
surv_pop = unpickle('cosmic_chime')
# Limit population to above S/N >= 10
mask = (surv_pop.frbs.snr >= 10)
surv_pop.frbs.apply(mask)
# Set up plot style
plot_aa_style(cols=1)
f, ax1 = plt.subplots(1, 1)
# See how fraction changes over time
n_pointings = chime.n_pointings
days = np.linspace(0, N_DAYS, (N_DAYS * n_pointings) + 1)
"""Test perfect survey."""
import matplotlib.pyplot as plt
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation
from convenience import hist, plot_aa_style, rel_path
cosmic_pop = CosmicPopulation.simple(1e4, generate=False)
cosmic_pop.z_max = 0.01
cosmic_pop.lum_min = 1e38
cosmic_pop.generate()
survey = Survey('perfect')
survey_pop = SurveyPopulation(cosmic_pop, survey)
plot_aa_style()
plt.rcParams["figure.figsize"] = (5.75373, 5.75373)
f, axes = plt.subplots(2, 2)
s = survey_pop.frbs
axes[0, 0].step(*hist(s.snr, bin_type='log'), where='mid')
axes[0, 0].set_title('SNR')
axes[1, 0].step(*hist(s.T_sys, bin_type='log'), where='mid')
axes[1, 0].set_title(r'T$_{\text{sys}}$')
axes[1, 0].set_xlim(1, 1e2)
axes[1, 0].set_ylim(1e-3, 1)
axes[0, 1].step(*hist(s.s_peak, bin_type='log'), where='mid')
axes[0, 1].set_title(r'S$_{\text{peak}}$')
axes[0, 1].set_xlim(1e-3, 1)
# Assign the values
frbs = pop.frbs
frbs.ra, frbs.dec = coords[0], coords[1]
frbs.gl, frbs.gb = coords[2], coords[3]
# Continue with generation
pop.gen_gal_coords()
pop.gen_dm()
pop.gen_w()
pop.gen_lum()
pop.gen_si()
else:
pop.generate()
surv_pop = SurveyPopulation(pop, survey, scale_by_area=False)
surv_pop.source_rate.f_area = surv_f_area[name]
surv_pop.source_rate.scale_by_area()
# surv_pop.save()
surv_pops.append(surv_pop)
else:
surv_pops = []
for name in SURVEYS:
surv_pops.append(unpickle(f'optimal_{name}'))
# Start plot
plot_aa_style(cols=2)
plt.rcParams["figure.figsize"] = (3.556 * 3, 3.556)
fig, axes = plt.subplots(1, 3)
from frbpoppy import unpickle, pprint
from tests.convenience import plot_aa_style, rel_path
MAKE = True
SURVEYS = ('parkes-htru', 'fast-crafts', 'puma-full', 'chord', 'ska1-low',
'ska1-mid')
if MAKE:
surv_pops = []
pop = CosmicPopulation.complex(1e5, generate=False)
pop.generate()
for name in SURVEYS:
survey = Survey(name)
surv_pop = SurveyPopulation(pop, survey)
surv_pop.save()
surv_pops.append(surv_pop)
else:
surv_pops = []
for name in SURVEYS:
surv_pops.append(unpickle(f'complex_{name}'))
# Start plot
plot_aa_style()
fig, ax1 = plt.subplots(1, 1)
# Fluence plot
ax1.set_xlabel('S/N')
ax1.set_xscale('log')
ax1.set_ylabel(r'\#(${>}\text{S/N}$)')