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 set_up_surveys(self):
"""Set up surveys."""
surveys = []
for name in self.survey_names:
survey = Survey(name=name)
survey.set_beam(model='airy', n_sidelobes=1)
if name in ('chime-frb', 'wsrt-apertif', 'parkes-htru'):
survey.set_beam(model=name)
surveys.append(survey)
return surveys
def complex_rates(remake=REMAKE, alphas=ALPHAS, size=SIZE, surveys=SURVEYS):
"""Calculate expected rates for a complex 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'complex_alpha_{alpha}_{s}').source_rate
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:
if alpha <= -1.0 and ADAPTATIVE_SCALING:
size = 1e7
if alpha <= -1.5 and ADAPTATIVE_SCALING:
size = 1e8
pop = CosmicPopulation.complex(size)
pop.set_dist(model='vol_co',
z_max=2.5,
alpha=alpha,
H_0=67.74,
W_m=0.3089,
W_v=0.6911)
pop.set_lum(model='powerlaw', low=1e40, high=1e45, power=-1)
pop.name = f'complex_alpha_{alpha}'
pops.append(pop)
# Set up surveys
ss = []
for s in surveys:
survey = Survey(name=s)
survey.set_beam(model='airy', n_sidelobes=1)
ss.append(survey)
surv_pops = LargePopulation(pop, *ss).pops
for i, s in enumerate(surveys):
surv_rates = surv_pops[i].source_rate
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 first survey in list
for s in surveys:
if s != surveys[0]:
norm = []
for i, r in enumerate(rates[s]):
norm.append(r / rates[surveys[0]][i])
rates[s] = norm
rates[surveys[0]] = [r / r for r in rates[surveys[0]]]
return rates
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}').source_rate
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.set_dist(alpha=alpha)
pop.name = f'simple_alpha_{alpha}'
pops.append(pop)
# Set up surveys
ss = []
for s in surveys:
survey = Survey(name=s)
survey.set_beam(model='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].source_rate
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 != 'parkes-htru':
norm = []
for i, r in enumerate(rates[s]):
norm.append(r/rates['parkes-htru'][i])
rates[s] = norm
rates['parkes-htru'] = [r/r for r in rates['parkes-htru']]
return rates
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 analytical_rates(surveys=SURVEYS, alphas=ALPHAS):
"""Use a analytical model to scale detection rates to various alphas."""
rates = {}
for surv in surveys:
# Get survey parameters
surv1 = Survey(surv)
surv1.set_beam('perfect', n_sidelobes=0.5)
surv2 = Survey(surveys[0])
surv2.set_beam('perfect', n_sidelobes=0.5)
# Calculate rate per alpha
rate = []
for alpha in alphas:
rate.append(compare_surveys(surv1, surv2, alpha))
rates[surv] = rate
return rates
def get_data():
"""Get survey populations."""
# Don't always regenerate a population
if REMAKE is False:
# Check where a possible population would be located
path = ''
surv_pops = []
for telescope in TELESCOPES:
survey = telescope
if telescope == 'askap':
survey = 'askap-fly'
if telescope == 'parkes':
survey = 'parkes-htru'
name = f'{survey}'
path = paths.populations() + f'complex_{name}.p'
surv_pops.append(unpickle(path))
return surv_pops
cosmic_pop = CosmicPopulation.complex(SIZE, generate=False)
surveys = []
for telescope in TELESCOPES:
pattern = 'airy'
if telescope == 'parkes':
pattern = 'parkes-htru'
s = telescope
if telescope == 'askap':
s = 'askap-fly'
survey = Survey(s)
survey.set_beam(model=pattern, n_sidelobes=1)
surveys.append(survey)
return LargePopulation(cosmic_pop, *surveys).pops
from obs_rep_frac import calc_rep_frac
init_surv_time_frac = 0.05
MAKE = False
PLOT_CHIME = False
N_SRCS = 3.6e4
N_DAYS = 100
RATE = 9 # per day
# Chime started in Aug 2018. Assuming 2/day for one-offs.
# Total of 9 repeaters published on 9 Aug 2019. = ~year
N_CHIME = {'rep': 9, 'one-offs': 365 * 2, 'time': 365}
# Set up survey
chime = Survey('chime-frb', n_days=N_DAYS)
chime.set_beam(model='chime-frb')
if MAKE:
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)
from frbpoppy import CosmicPopulation, Survey
import frbpoppy.gen_dists as gd
# Set up a population
cosmic_pop = CosmicPopulation(1e4, generate=False)
# ... 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',
pop.set_dist(model='sfr', z_max=2.5, H_0=67.74, W_m=0.3089, W_v=0.6911)
pop.set_dm_igm(model='ioka', slope=1200, std=0)
pop.set_dm(mw=False, host=False, igm=True)
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
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation, hist
from tests.convenience import plot_aa_style, rel_path
from alpha_real import EXPECTED, poisson_interval
N_DAYS = 1 # Not used in eventual result
SCALE_TO = 'parkes-htru'
pop = CosmicPopulation.complex(n_srcs=1e5, n_days=N_DAYS)
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)
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(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()}')
linestyle = self.lz[pop.z_max]
colour = self.colours[int(i % (len(pops)/2))]
label = '_'.join(pop.name.split('_')[0:-1])
if i > (len(pops)/2 - 1): # Turn off some line labels
label = f'_{label}'
ax.step(*self.calc_cum_hist(pop), where='mid',
label=rf'{label}', linestyle=linestyle,
color=colour)
def calc_cum_hist(self, pop):
# Bin up parameter in a cumulative bin
edges, values = hist(pop.frbs.snr, bin_type='log')
if isinstance(edges, float):
return np.nan, np.nan
n_gt_s = np.cumsum(values[::-1])[::-1]
# Normalise to S/N of 10^5
log_diff = (np.log10(edges[0]) - np.log10(1e0))
edges = 10**(np.log10(edges) - log_diff)
return edges, n_gt_s
if __name__ == '__main__':
for s in SURVEYS:
survey = Survey(s)
survey.set_beam('gaussian')
if s == 'perfect':
survey.set_beam('perfect')
Flattening(SIZE, survey)
# Generate population with standard candles
pop = CosmicPopulation(n_srcs=5e5, n_days=1, name='standard')
pop.set_dist(model='sfr', z_max=2.5, H_0=67.74, W_m=0.3089, W_v=0.6911)
pop.set_dm_igm(model='ioka', slope=1200, std=0)
pop.set_dm(mw=False, host=False, igm=True)
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')
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:
def generate(parallel=False):
from joblib import Parallel, delayed
from frbpoppy import CosmicPopulation, Survey, pprint
from frbpoppy import SurveyPopulation
from tqdm import tqdm
# Set up rate dataframe
paras = [ALPHAS, LIS, SIS]
vals = np.array(np.meshgrid(*paras)).T.reshape(-1, len(paras))
cols = ['alpha', 'li', 'si']
df = pd.DataFrame(vals, columns=cols)
# Set up surveys
surveys = []
for name in SURVEY_NAMES:
survey = Survey(name=name)
survey.set_beam(model='airy', n_sidelobes=1)
surveys.append(survey)
df[name] = np.nan
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
if parallel:
n_cpu = min([3, os.cpu_count() - 1])
pprint(f'{os.cpu_count()} CPUs available')
r = range(len(ALPHAS))
temp_path = ('./temp.mmap')
# Make a temp memmap to have a sharedable memory object
temp = np.memmap(temp_path,
dtype=np.float64,
shape=(len(vals), len(SURVEY_NAMES)),
mode='w+')
Parallel(n_jobs=n_cpu)(delayed(iter_alpha)(i, parallel=temp)
for i in tqdm(r))
for name in SURVEY_NAMES:
col = SURVEY_NAMES.index(name)
df[name] = temp[:, col]
else:
for i in tqdm(range(len(ALPHAS)), desc='Alphas'):
iter_alpha(i)
df.to_csv(CSV_PATH)
"""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)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from matplotlib.lines import Line2D
from frbpoppy import Survey, int_pro_fixed
from tests.convenience import plot_aa_style, rel_path
T_OBS = 60 * 60 # seconds
OBJ_OFFSET = 1 # deg
INTENSITY_LIMIT = 1e-8
# Set up survey
survey = Survey('chime-frb', n_days=0.99)
survey.set_beam('chime-frb')
# Get beam properties
beam_array = survey.beam_array
pattern = survey.beam_pattern
pixel_scale = np.float64(survey.pixel_scale)
latitude = survey.latitude
mount_type = survey.mount_type
# Limit extent of intensity for plotting reasons
beam_array[beam_array < INTENSITY_LIMIT] = INTENSITY_LIMIT
# Set pointings
survey.gen_pointings()
pointings = survey.pointings
r.set_dist(z_max=2)
r.set_lum(model='powerlaw',
low=1e35,
high=1e45,
power=-1.7,
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('chime-frb', n_days=DAYS)
survey.set_beam(model='chime-frb')
survey.snr_limit = 1e-13
surv_pop = SurveyPopulation(r, survey)
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)'
"""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 gen_survey(self, name):
survey = Survey(name)
survey.set_beam('gaussian')
return survey
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 = []
for name in SURVEYS:
# Set up survey
survey = Survey(name)
if name in ('parkes-htru', 'wsrt-apertif'):
survey.set_beam(model=name)
# Set up CosmicPopulation
pop = CosmicPopulation.optimal(SIZE, generate=False)
# Only generate FRBs in the survey region
pop.set_direction(model='uniform',
min_ra=survey.ra_min,
max_ra=survey.ra_max,
min_dec=survey.dec_min,
max_dec=survey.dec_max)
# Parkes also has galactic limits:
if name == 'parkes-htru':
pop.gen_index()
pop.gen_dist()
