def log_prior(self,x):
logP = super(MassFunctionModel,self).log_prior(x)
if np.isfinite(logP):
if (self.model == "LambdaCDM"):
self.O = cs.CosmologicalParameters(x['h'],x['om'],1.0-x['om'],truths['w0'],truths['w1'])
elif (self.model == "CLambdaCDM"):
self.O = cs.CosmologicalParameters(x['h'],x['om'],x['ol'],truths['w0'],truths['w1'])
elif (self.model == "LambdaCDMDE"):
self.O = cs.CosmologicalParameters(x['h'],x['om'],x['ol'],x['w0'],x['w1'])
elif (self.model == "DE"):
self.O = cs.CosmologicalParameters(truths['h'],truths['om'],truths['ol'],x['w0'],x['w1'])
# self.O = cs.CosmologicalParameters(truths['h'],truths['om'],truths['ol'],truths['w0'],truths['w1'])
self.mass_model = gal.GalaxyMassDistribution(self.O,
x['phistar'],
x['phistar_exponent'],
x['logMstar'],
x['logMstar_exponent'],
x['alpha'],
x['alpha_exponent'],
mmin,
mmax,
self.data[:,1].min(),
self.data[:,1].max(),
ramin,
ramax,
decmin,
decmax,
threshold,
slope_model_choice,
cutoff_model_choice,
density_model_choice)
return logP
def find_redshift_limits(self,
h_w=(0.6, 0.86),
om_w=(0.04, 0.5),
w0_w=(-1, -1),
w1_w=(0, 0)):
from cosmolisa.likelihood import find_redshift
def limits(O, Dmin, Dmax):
return find_redshift(O, Dmin), find_redshift(O, Dmax)
redshift_min = np.zeros(self.catalog.shape[0])
redshift_max = np.zeros(self.catalog.shape[0])
redshift_fiducial = np.zeros(self.catalog.shape[0])
for k in range(self.catalog.shape[0]):
sys.stderr.write(
"finding redshift limits for event {0} out of {1}\r".format(
k + 1, self.catalog.shape[0]))
z_min = np.zeros(100)
z_max = np.zeros(100)
for i in range(100):
h = np.random.uniform(h_w[0], h_w[1])
om = np.random.uniform(om_w[0], om_w[1])
ol = 1.0 - om
w0 = np.random.uniform(w0_w[0], w0_w[1])
w1 = np.random.uniform(w1_w[0], w1_w[1])
O = cs.CosmologicalParameters(h, om, ol, w0, w1)
z_min[i], z_max[i] = limits(
O, self.catalog[k, 1] *
(np.maximum(0.0, 1.0 - 3 * self.catalog[k, 5])),
self.catalog[k, 1] * (1.0 + 3 * self.catalog[k, 5]))
redshift_min[k] = z_min.min()
redshift_max[k] = z_max.max()
redshift_fiducial[k] = find_redshift(self.fiducial_O,
self.catalog[k, 1])
sys.stderr.write("\n")
self.catalog = np.column_stack(
(self.catalog, redshift_fiducial, redshift_min, redshift_max))
return self.catalog
colors = [
'royalblue', 'gold', 'olive', 'lime', 'orange', 'magenta', 'cyan',
'turquoise', 'teal', 'purple'
]
from gwmodel.utils.utils import McQ2Masses, chi_eff
# events = ['gw151226']
# path = "/Users/wdp/repositories/MLGW/paper/img/GWTC-1_results/"
path = "/Users/wdp/Desktop/GWTC1/LALPSD/"
init_plotting()
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
handles = []
O = cs.CosmologicalParameters(0.68, 0.31, 0.69, -1.0, 0.0)
for c, e in zip(colors, events):
try:
print(os.path.join(path, e + '/Nested_sampler/posterior.dat'))
# BBH_file = '/Users/wdp/Desktop/GWTC-1_sample_release'+e.upper()+'_GWTC-1.hdf5'
# BBH = h5py.File(BBH_file, 'r')
p = np.genfromtxt(os.path.join(path, e +
'/Nested_sampler/posterior.dat'),
names=True)
z = np.array(
[find_redshift(O, np.exp(d)) for d in p['logdistance']])
m1, m2 = McQ2Masses(p['mc'] / (1 + z), p['q'])
X, Y, PDF = twod_kde(m1, m2)
string = e.upper() + format_90CR(m1) + format_90CR(
m2) + format_90CR(p['mc'] /
(1 + z)) + format_90CR(p['q']) + format_90CR(
def __init__(self,
redshift_min=0.0,
redshift_max=1.0,
ra_min=0.0,
ra_max=2.0 * np.pi,
dec_min=-np.pi / 2.0,
dec_max=np.pi / 2.0,
*args,
**kwargs):
self.A0 = 0.000405736691211125 #in rads^2
self.V0 = 0.1358e5
self.SNR0 = 87.
self.ra_min = ra_min
self.ra_max = ra_max
self.dec_min = dec_min
self.dec_max = dec_max
self.z_min = redshift_min
self.z_max = redshift_max
for key, value in kwargs.items():
if not hasattr(self, key):
setattr(self, key, value)
try:
self.h = getattr(self, 'h')
except:
self.h = 0.73
try:
self.omega_m = getattr(self, 'omega_m')
except:
self.omega_m = 0.25
try:
self.omega_lambda = getattr(self, 'omega_lambda')
except:
self.omega_lambda = 0.75
try:
self.w0 = getattr(self, 'w0')
except:
self.w0 = -1.0
try:
self.w1 = getattr(self, 'w1')
except:
self.w1 = 0.0
try:
self.w2 = getattr(self, 'w2')
except:
self.w2 = 0.0
try:
self.r0 = getattr(self, 'r0')
except:
self.r0 = 1.0
try:
self.W = getattr(self, 'W')
except:
self.W = 0.0
try:
self.Q = getattr(self, 'Q')
except:
self.Q = 0.0
try:
self.R = getattr(self, 'R')
except:
self.R = 0.0
# galaxy population parameters
try:
self.phistar0 = getattr(self, 'phistar0')
except:
self.phistar0 = 1e-2 # in galaxies per Mpc^{-3}
try:
self.logMstar0 = getattr(self, 'logMstar0')
except:
self.logMstar0 = -20.7
try:
self.alpha0 = getattr(self, 'alpha0')
except:
self.alpha0 = -1.23
try:
self.Mmin = getattr(self, 'Mmin')
except:
self.Mmin = -25
try:
self.Mmax = getattr(self, 'Mmax')
except:
self.Mmax = -15
try:
self.m_threshold = getattr(self, 'm_threshold')
except:
self.m_threshold = 17.7
self.fiducial_O = cs.CosmologicalParameters(self.h, self.omega_m,
self.omega_lambda, self.w0,
self.w1)
# luminosity function evolution model
try:
self.slope_model_choice = getattr(self, 'slope_model_choice')
except:
self.slope_model_choice = 0
try:
self.cutoff_model_choice = getattr(self, 'cutoff_model_choice')
except:
self.cutoff_model_choice = 0
try:
self.density_model_choice = getattr(self, 'density_model_choice')
except:
self.density_model_choice = 0
if self.cutoff_model_choice == 1:
try:
self.logMstar_exponent = getattr(self, 'logMstar_exponent')
except:
print(
"must define an exponent for the cutoff luminosity evolution"
)
exit(-1)
else:
self.logMstar_exponent = 0
if self.slope_model_choice == 1:
try:
self.logMstar_exponent = getattr(self, 'alpha_exponent')
except:
print("must define an exponent for the slope evolution")
exit(-1)
else:
self.alpha_exponent = 0
if self.density_model_choice == 1:
try:
self.phistar_exponent = getattr(self, 'phistar_exponent')
except:
print("must define an exponent for the density evolution")
exit(-1)
else:
self.phistar_exponent = 0
# now we are ready to sample the EMRI according to the cosmology and rate that we specified
# find the maximum of the probability for efficiency
zt = np.linspace(0, self.z_max, 1000)
self.norm = lk.integrated_rate(self.r0, self.W, self.R, self.Q,
self.fiducial_O, self.z_min,
self.z_max) #how many EMRIs per time
self.rate = lambda z: cs.StarFormationDensity(
z, self.r0, self.W, self.R, self.Q
) * self.fiducial_O.UniformComovingVolumeDensity(z)
self.dist = lambda z: cs.StarFormationDensity(
z, self.r0, self.W, self.R, self.Q
) * self.fiducial_O.UniformComovingVolumeDensity(
z) / self.norm #dist to eventually sample
self.pmax = np.max([self.dist(zi) for zi in zt])
self.ra_pdf = scipy.stats.uniform(loc=self.ra_min,
scale=self.ra_max - self.ra_min)
# dec distributed as cos(dec) in [-np.pi/2, np.pi/2] implies sin(dec) uniformly distributed in [-1,1]
self.sindec_min = np.sin(self.dec_min)
self.sindec_max = np.sin(self.dec_max)
self.sindec_pdf = scipy.stats.uniform(loc=self.sindec_min,
scale=self.sindec_max -
self.sindec_min)
self.galaxy_pmax = None
self.galaxy_norm = None
# x = x[:100]
lM = np.linspace(9.0,13.0,100)
Z = np.linspace(data[:,1].min(),data[:,1].max(),100)
PMZ = np.zeros((x.shape[0],lM.shape[0],Z.shape[0]))
PM = np.zeros((x.shape[0],lM.shape[0]))
PZ = np.zeros((x.shape[0],Z.shape[0]))
alpha_law = np.zeros((x.shape[0], Z.shape[0]))
logMstar_law = np.zeros((x.shape[0], Z.shape[0]))
phistar_law = np.zeros((x.shape[0], Z.shape[0]))
OM, OZ = np.meshgrid(lM,Z)
for i,xi in enumerate(x):
sys.stderr.write("processing sample {0} of {1}\r".format(i+1,x.shape[0]))
mass_model = gal.GalaxyMassDistribution(cs.CosmologicalParameters(xi['h'],xi['om'],1.0-xi['om'],truths['w0'],truths['w1']),
xi['phistar'],
xi['phistar_exponent'],
xi['logMstar'],
xi['logMstar_exponent'],
xi['alpha'],
xi['alpha_exponent'],
mmin,
mmax,
data[:,1].min(),
data[:,1].max(),
ramin,
ramax,
decmin,
decmax,
threshold,
A = (ramax - ramin) * (np.cos(decmax + np.pi / 2) -
np.cos(decmin + np.pi / 2))
h = 0.73
om = 0.25
n0 = 1e-2
Mstar = -20.7
Mstar_exponent = -0.13
cutoff_model_choice = 0
alpha = -1.23
alpha_exponent = 0.054
slope_model_choice = 0
density_model_choice = 0
phistar_exponent = -0.1
threshold = 17.7
O = cs.CosmologicalParameters(h, om, 1.0 - om, -1.0, 0.0)
S = gal.GalaxyDistribution(O, n0, phistar_exponent, Mstar, Mstar_exponent,
alpha, alpha_exponent, mmin, mmax, zmin, zmax,
ramin, ramax, decmin, decmax, threshold, A,
slope_model_choice, cutoff_model_choice,
density_model_choice)
N = int(S.get_number_of_galaxies(zmin, zmax, 1))
# N = 1000
print("sampling {0} galaxies in a field of {1} square deg".format(
N, A * 3282.8063500117))
galaxies = S.sample_correlated(N,
zmin,
zmax,
ramin,
ramax,