def setup_pop(n_srcs):
pop = CosmicPopulation.simple(n_srcs, n_days=MAX_DAYS,
repeaters=True)
pop.set_dist(z_max=0.01)
pop.set_lum(model='powerlaw', per_source='different', low=1e35, high=1e40,
power=-1.7)
pop.set_w(model='constant', value=1)
pop.set_dm(mw=False, igm=True, host=False)
pop.set_dm_igm(model='ioka', slope=1000, std=0)
return pop
def simple_rates(remake=REMAKE, alphas=ALPHAS, size=SIZE, surveys=SURVEYS):
"""Calculate expected rates for a simple populations."""
rates = defaultdict(list)
# Don't always regenerate a population
if remake is False:
for alpha in alphas:
for s in surveys:
surv_rates = unpickle(f'simple_alpha_{alpha}_{s}').rates()
pprint(f'Alpha:{alpha:.2}, Survey: {s}, Det: {surv_rates.det}')
rate = (surv_rates.det / surv_rates.days)
rates[s].append(rate)
else:
pops = []
for alpha in alphas:
pop = CosmicPopulation.simple(size)
pop.alpha = alpha
pop.name = f'simple_alpha_{alpha}'
pops.append(pop)
# Set up surveys
ss = []
for s in surveys:
survey = Survey(name=s,
gain_pattern='perfect',
n_sidelobes=0.5)
ss.append(survey)
surv_pops = LargePopulation(pop, *ss).pops
for i, s in enumerate(surveys):
surv_rates = surv_pops[i].rates()
pprint(f'Alpha:{alpha:.2}, Survey: {s}, Det: {surv_rates.det}')
rate = (surv_rates.det / surv_rates.days)
rates[s].append(rate)
# Scale rates to HTRU
for s in surveys:
if s != 'htru':
norm = []
for i, r in enumerate(rates[s]):
norm.append(r / rates['htru'][i])
rates[s] = norm
rates['htru'] = [r / r for r in rates['htru']]
return rates
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
# ... or adapt the population per parameter, e.g.
cosmic_pop.set_dist(z_max=2.5)
# Or to adapt the luminosity
cosmic_pop.set_lum(model='powerlaw', low=1e44, high=1e45)
# Generate the population
cosmic_pop.generate()
# Setup a survey
survey = Survey('wsrt-apertif')
# and adapt the survey with
survey.set_beam('airy', n_sidelobes=2)
survey.snr_limit = 2
# For a full list of available arguments or parameters check the classes as
# defined in /frbpoppy/*.py
# Some more difficult examples
# Use a convolution of two distributions
cosmic_pop = CosmicPopulation.simple(n_srcs=1e4, repeaters=True)
# Draw the mean value per source from a normal distribution
mean_dist = gd.trunc_norm(mean=1, std=2, shape=int(1e4))
# And use those means as a _means_ to generate from a new Gaussian
# distribution per source
cosmic_pop.set_w(model='gauss',
per_source='different',
mean=mean_dist,
std=0.01 * mean_dist)
# And generate afterwards
cosmic_pop.generate()
"""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)
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import savgol_filter
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation
from convenience import plot_aa_style, rel_path
SIS = (-2, 0, 2) # Spectral indices
n_tot = 1e5 # Number of sources
pop = {}
for si in SIS:
if si == min(SIS):
pop[si] = CosmicPopulation.simple(n_tot)
pop[si].name = f'si-{si}'
pop[si].si_mu = si
pop[si].z_max = 2.5
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:
"""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]
"""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()}')
"""Compare the resulting logNlogS and logNLog(S/N)."""
import matplotlib.pyplot as plt
import numpy as np
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation, hist
from tests.convenience import plot_aa_style
# Generate an FRB population
cosmic_pop = CosmicPopulation.simple(1e4)
cosmic_pop.set_lum(model='powerlaw', low=1e40, high=1e45)
cosmic_pop.generate()
# Setup a survey
survey = Survey('perfect')
# Observe the FRB population
survey_pop = SurveyPopulation(cosmic_pop, survey)
plot_aa_style(cols=2)
f, axes = plt.subplots(1, 2)
s = survey_pop.frbs
bins, values = hist(s.snr, bin_type='log')
# Cumulative sum
values = np.cumsum(values[::-1])[::-1]
axes[0].step(bins, values, where='mid')
axes[0].set_title('SNR')
bins, values = hist(s.fluence, bin_type='log')
"""Plot the 'burstiness': the observed repetition rate versus redshift."""
import matplotlib.pyplot as plt
import numpy as np
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation, pprint, split_pop
from frbcat import ChimeRepeaters
from tests.convenience import plot_aa_style, rel_path
n_srcs = 1e5
n_days = 4
RATE = 3
CHIME_HIST = False
SCATTER = False
pop = CosmicPopulation.simple(n_srcs, n_days=n_days, repeaters=True)
pop.set_dist(z_max=2.0)
pop.set_w(model='constant', value=1)
pop.set_dm(mw=False, igm=True, host=False)
pop.set_dm_igm(model='ioka', slope=1000, std=0)
pop.set_time(model='poisson', rate=RATE)
pop.set_time(model='regular', rate=RATE)
pop.set_w('constant', value=1)
pop.generate()
survey = Survey('perfect', n_days=n_days)
survey.snr_limit = 4e6
plot_aa_style(cols=1)
f, ax1 = plt.subplots(1, 1)
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
"""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)
import frbpoppy.galacticops as go
from tests.convenience import plot_aa_style, rel_path
from tests.rates.alpha_real import EXPECTED
MAKE = True
SURVEYS = ('parkes-htru', 'wsrt-apertif', 'fast-crafts', 'puma-full', 'chord',
'ska1-low', 'ska1-mid')
SIZE = 5e4
if MAKE:
# Calculate the fraction of the sky that the survey covers
surv_f_area = {}
for name in SURVEYS:
pop = CosmicPopulation.simple(5e5)
pop.gen_direction()
survey = Survey(name)
mask = survey.in_region(pop.frbs.ra, pop.frbs.dec, pop.frbs.gl,
pop.frbs.gb)
in_surv_region = np.sum(mask)
tot_region = len(mask)
area_sky = 4 * np.pi * (180 / np.pi)**2 # In sq. degrees
f_area = (survey.beam_size / area_sky) * (tot_region / in_surv_region)
surv_f_area[name] = f_area
print(f'{name} covers {f_area*100}% of the sky')
surv_pops = []
"""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)
# Dispersion measure properties
pop.set_dm_host(model='gauss', mean=100, std=200)
pop.set_dm_igm(model='ioka', slope=1000, std=None)
pop.set_dm_mw(model='ne2001')
# Emission range of FRB sources
pop.set_emission_range(low=100e6, high=10e9)
# Luminsity of FRBs
# See the per_source argument? That allows you to give different properties
# to different bursts from the same source. You can do that for the luminosity,
# or any of the following parameters
pop.set_lum(model='powerlaw', low=1e38, high=1e38, power=0,
per_source='different')
# Pulse width
pop.set_w(model='uniform', low=10, high=10)
# Spectral index
pop.set_si(model='gauss', mean=0, std=0)
# If repeaters, how they repeat
pop.set_time(model='regular', rate=2)
# And then generate the population!
pop.generate()
# Or simply use some predefined models
pop_simple = CosmicPopulation.simple(1e4, generate=True)
pop_complex = CosmicPopulation.complex(1e4, generate=True)
"""Plot DM/SNR distributions of repeater populations."""
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import ks_2samp
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation, plot
from frbpoppy import split_pop, pprint, hist
from tests.convenience import plot_aa_style, rel_path
DAYS = 4
INTERACTIVE_PLOT = False
PLOTTING_LIMIT_N_SRCS = 0
SNR = False
r = CosmicPopulation.simple(n_srcs=int(1e5), n_days=DAYS, repeaters=True)
r.set_dist(z_max=0.01)
r.set_lum(model='powerlaw',
low=1e35,
high=1e45,
power=-1.5,
per_source='different')
r.set_time(model='poisson', rate=3)
r.set_dm_igm(model='ioka', slope=1000, std=0)
r.set_dm(mw=False, igm=True, host=False)
r.set_w('constant', value=1)
r.generate()
# Set up survey
survey = Survey('perfect', n_days=DAYS)
from tests.convenience import plot_aa_style, rel_path
MAKE = True
NUM_FRBS = True
DENSITY_FRBS = True
pop = {}
pop_types = ('cst', 'sfr', 'smd', 'shallow', 'steep')
titles = ('Constant', 'SFR', 'SMD', r'$\alpha_{\text{in}}=-0.5$',
r'$\alpha_{\text{in}}=-2.0$')
if MAKE:
n_frbs = int(1e5)
# Generate population following a constant number density / comoving volume
pop['cst'] = CosmicPopulation.simple(n_frbs)
pop['cst'].set_dist(model='vol_co', z_max=3.)
pop['cst'].name = 'cst'
pop['cst'].generate()
# Generate population following star forming rate
pop['sfr'] = CosmicPopulation.simple(n_frbs)
pop['sfr'].set_dist(model='sfr', z_max=3.)
pop['sfr'].name = 'sfr'
pop['sfr'].generate()
# Generate population following stellar mass density
pop['smd'] = CosmicPopulation.simple(n_frbs)
pop['smd'].set_dist(model='smd', z_max=3.)
pop['smd'].name = 'smd'
pop['smd'].generate()
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
def gen_def_pop(self):
self.default_pop = CosmicPopulation.simple(self.size)
self.default_pop.set_lum(model='constant', value=1e43)
self.default_pop.generate()
import numpy as np
import matplotlib.pyplot as plt
from frbpoppy import CosmicPopulation, log10normal, Survey, SurveyPopulation
from frbpoppy import hist
from tests.convenience import plot_aa_style, rel_path
N_DAYS = 50
N_SRCS = int(1e3)
RATE = 0.1 # per day
SINGLE_INPUT_RATE = True
init_surv_time_frac = 0.1
# Set up a population
pop = CosmicPopulation.simple(N_SRCS, n_days=N_DAYS, repeaters=True)
pop.set_dist(z_max=0.01)
pop.set_lum(model='powerlaw', low=1e30, high=1e40, power=-1)
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)