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 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()
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]