parser.add_option('-m',
'--model',
default='LAMBDACDM',
type='string',
metavar='model',
help='model (default: LAMBDACDM)')
parser.add_option('-s',
'--source',
default=None,
type='string',
metavar='source',
help='source class')
(opts, args) = parser.parse_args()
init_plotting()
# read in the events
events = readdata.read_event(opts.source, opts.data)
# read in the posterior samples
try:
f**k
posteriors = np.genfromtxt(opts.posteriors, names=True)
except:
from cpnest import nest2pos
x = np.genfromtxt('test_logP_dv/chain_1000_1234.txt', names=True)
posteriors = nest2pos.draw_posterior_many([x], [1000], verbose=False)
names = ''
for n in posteriors.dtype.names:
names += n + '\t'
np.savetxt('test_logP_dv/posterior.dat', posteriors, header=names)
dl = [e.dl / 1e3 for e in events]
ztrue = [e.potential_galaxy_hosts[0].redshift for e in events]
parser.add_option('--postprocess',
default=0,
type='int',
metavar='postprocess',
help='run only the postprocessing')
(opts, args) = parser.parse_args()
em_selection = opts.em_selection
if opts.event_class == "MBH":
# if running on SMBH override the selection functions
em_selection = 0
if opts.event_class == "EMRI" and opts.joint != 0:
np.random.seed(opts.seed)
events = readdata.read_event(opts.event_class, opts.data, None)
N = opts.joint #np.int(np.random.poisson(len(events)*4./10.))
print("Will run a random catalog selection of {0} events:".format(N))
print("==================================================")
selected_events = []
count = 0
if 1:
while len(selected_events) < N - count and not (len(events) == 0):
while True:
if len(events) > 0:
idx = np.random.randint(len(events))
selected_event = events.pop(idx)
else:
break
if selected_event.z_true < opts.zhorizon:
metavar='model',
help='model (default: LAMBDACDM)')
parser.add_option('-s',
'--source',
default=None,
type='string',
metavar='source',
help='source class')
(opts, args) = parser.parse_args()
init_plotting()
os.system('mkdir -p {0}'.format(opts.outdir))
# Read in the events
events = readdata.read_event(opts.source,
opts.data,
None,
snr_threshold=100.0)
# Read in the posterior samples
filename = os.path.join(opts.posteriors, 'CPNest', 'cpnest.h5')
print("\nReading {} averaged posterior stored in {}".format(
opts.source, filename))
h5_file = h5py.File(filename, 'r')
posteriors = h5_file['combined'].get('posterior_samples')
# Injected cosmology
omega_true = CosmologicalParameters(0.73, 0.25, 0.75, -1, 0)
dlmeasured = []
zmeasured = []
dzmeasured = []
ddlmeasured = []
parser.add_option('-o','--out',action='store',type='string',default=None,help='Output folder', dest='output')
parser.add_option('-d',action='store',type='string',default=None,help='data folder', dest='data')
parser.add_option('-c',action='store',type='string',default=None,help='source class (MBH, EMRI, sBH)', dest='source_class')
parser.add_option('-m',action='store',type='string',default='LambdaCDM',help='model (LambdaCDM, LambdaCDMDE)', dest='model')
parser.add_option('-r',action='store',type='int',default=0,help='realisation number', dest='realisation')
parser.add_option('-p',action='store',type='int',default=0,help='unpickle precomputed posteriors', dest='pickle')
parser.add_option('-N',action='store',type='int',default=None,help='Number of bins for the grid sampling', dest='N')
parser.add_option('-e',action='store',type='int',default=None,help='Number of events to combine', dest='Nevents')
parser.add_option('--nlive',action='store',type='int',default=5000,help='Number of live points', dest='nlive')
(options,args)=parser.parse_args()
out_folder = os.path.join(options.output,str(options.realisation))
os.system("mkdir -p %s"%out_folder)
np.random.seed(options.realisation)
events = readdata.read_event(options.source_class, options.data, None)
N = len(events)
# if options.source_class == "EMRI":
# N = np.int(np.random.poisson(len(events)*4./10.))#len(events)
# events = np.random.choice(events,size = N,replace=False)
omega_true = CosmologicalParameters(truths['h'],
truths['om'],
truths['ol'],
truths['w0'],
truths['w1'])
Nbins = options.N
joint_posterior = np.zeros((Nbins,Nbins),dtype=np.float64)#-2.0*np.log(Nbins)
import numpy as np
import readdata
events = readdata.read_event(
"EMRI", "./data/EMRI_SAMPLE_MODEL101_TTOT10yr_SIG2_GAUSS", None)
densities = np.array([e.n_hosts / e.VC for e in events])
densities.sort()
print([d for d in densities])
print(np.average(densities, weights=[e.VC for e in events]))
import matplotlib.pyplot as plt
zs = []
for e in events:
zs.append(np.average([g.redshift for g in e.potential_galaxy_hosts]))
x = [e.VC for e in events]
y = densities
p = np.polyfit(np.log(x), np.log(y), 1)
print(p)
plt.scatter(np.log(x), np.log(y))
xp = np.linspace(np.log(x).min(), np.log(x).max(), 1000)
plt.plot(xp, np.poly1d(p)(xp))
plt.show()