# Save the samples in a dump folder if the data shouldn't be saved
# This overwrites any data saved before of the same model
folder_path = 'dump'
# Define PolyChord settings
settings = PolyChordSettings(
nDims,
nDerived,
)
settings.do_clustering = args_params.noclust
settings.nlive = nDims * args_params.nlive
settings.base_dir = os.path.join(dirname, folder_path)
settings.file_root = 'nathan_model{}'.format(model)
settings.num_repeats = nDims * args_params.nrep
settings.precision_criterion = args_params.prec
settings.read_resume = False
# Change settings if resume is true
# if args_params.resume:
# settings.read_resume = args_params.resume
# settings.base_dir = dirname+prev_run
# Save Parameter names list
try:
pickle.dump(parnames, open(settings.base_dir + '/parnames.p', 'wb'))
except:
os.makedirs(settings.base_dir)
pickle.dump(parnames, open(settings.base_dir + '/parnames.p', 'wb'))
# Initialize start time to measure run time
start = time.time()
return theta
# Define PolyChord settings
settings = PolyChordSettings(
nDims,
nDerived,
)
settings.do_clustering = args_params.noclust
settings.nlive = nDims * args_params.nlive
settings.base_dir = base_dir
settings.file_root = 'hd40307_k{}'.format(nplanets) # modelpath[12:-3]
settings.num_repeats = nDims * args_params.nrep
settings.precision_criterion = args_params.prec
settings.read_resume = False
# Change settings if resume is true
if args_params.resume:
settings.read_resume = args_params.resume
settings.base_dir = dirname + prev_run
# Run PolyChord
output = PPC.run_polychord(logLikelihood, nDims, nDerived, settings, prior)
# Parameter names
# latexnames = [r'\sigma_J', r'C']
# for j in range(nplanets):
# latexnames.extend(
# [fr'K_{j}', fr'P_{j}', fr'e_{j}', fr'\omega_{j}', fr'M_{j}'])
# paramnames = [(x, latexnames[i]) for i, x in enumerate(parnames)]
def PolyChordrun(LogLikelihood,n_dims,n_params,**kwargs):
"""
Function author: yuanfang
PolyChord settings
For full details of nested sampling and PolyChord, please refer to:
arxiv:1506.00171[astro-ph.IM], 0809.34371[astro-ph]
Parameters
----------
nDims: int
Dimensionality of the model, i.e. the number of physical parameters.
nDerived: int
The number of derived parameters (can be 0).
Keyword arguments
-----------------
nlive: int
(Default: nDims*25)
The number of live points.
Increasing nlive increases the accuracy of posteriors and evidences,
and proportionally increases runtime ~ O(nlive).
num_repeats : int
(Default: nDims*5)
The number of slice slice-sampling steps to generate a new point.
Increasing num_repeats increases the reliability of the algorithm.
Typically
* for reliable evidences need num_repeats ~ O(5*nDims).
* for reliable posteriors need num_repeats ~ O(nDims)
nprior : int
(Default: nlive)
The number of prior samples to draw before starting compression.
do_clustering : boolean
(Default: True)
Whether or not to use clustering at run time.
feedback : {0,1,2,3}
(Default: 1)
How much command line feedback to give
# Degree of feedback to provide
#-1 | nothing
# 0 | just header and tail
# 1 | run time evidences and clustering information
# 2 | fancy output
# 3 | verbose
precision_criterion : float
(Default: 0.001)
Termination criterion. Nested sampling terminates when the evidence
contained in the live points is precision_criterion fraction of the
total evidence.
max_ndead : int
(Default: -1)
Alternative termination criterion. Stop after max_ndead iterations.
Set negative to ignore (default).
boost_posterior : float
(Default: 0.0)
Increase the number of posterior samples produced. This can be set
arbitrarily high, but you won't be able to boost by more than
num_repeats
Warning: in high dimensions PolyChord produces _a lot_ of posterior
samples. You probably don't need to change this
posteriors : boolean
(Default: True)
Produce (weighted) posterior samples. Stored in <root>.txt.
equals : boolean
(Default: True)
Produce (equally weighted) posterior samples. Stored in
<root>_equal_weights.txt
cluster_posteriors : boolean
(Default: True)
Produce posterior files for each cluster?
Does nothing if do_clustering=False.
write_resume : boolean
(Default: True)
Create a resume file.
read_resume : boolean
(Default: True)
Read from resume file.
write_stats : boolean
(Default: True)
Write an evidence statistics file.
write_live : boolean
(Default: True)
Write a live points file.
write_dead : boolean
(Default: True)
Write a dead points file.
write_dead : boolean
(Default: True)
Write a prior points file.
update_files : int
(Default: nlive)
How often to update the files in <base_dir>.
base_dir : string
(Default: 'chains')
Where to store output files.
file_root : string
(Default: 'test')
Root name of the files produced.
grade_frac : List[float]
(Default: 1)
The amount of time to spend in each speed.
grade_dims : List[int]
(Default: 1)
The number of parameters within each speed.
"""
try:
import math
from numpy import pi, log, sqrt
import PyPolyChord
from PyPolyChord.settings import PolyChordSettings
from PyPolyChord.priors import UniformPrior
except ImportError:
print("INFO(Scanner):PyPolyChord module import error.exit...")
sys.exit()
# pass settings to polychord
nDerived=n_params-n_dims
settings = PolyChordSettings(n_dims, nDerived)
# settings.nlive = kwargs.pop('nlive', n_dims*25)
settings.nlive = kwargs.pop('n_live_points', n_dims*25)
# settings.num_repeats = kwargs.pop('num_repeats', n_dims*5)
settings.num_repeats = kwargs.pop('num_repeats', n_dims*2) # proposed by polychord eggbox
settings.nprior = kwargs.pop('nprior', -1)
settings.do_clustering = kwargs.pop('do_clustering', True)
settings.feedback = kwargs.pop('feedback', 1)
settings.precision_criterion = kwargs.pop('precision_criterion', 0.001)
settings.max_ndead = kwargs.pop('max_ndead', -1)
settings.boost_posterior = kwargs.pop('boost_posterior', 0.0)
settings.posteriors = kwargs.pop('posteriors', True)
settings.equals = kwargs.pop('equals', True)
settings.cluster_posteriors = kwargs.pop('cluster_posteriors', True)
settings.write_resume = kwargs.pop('write_resume', True)
settings.write_paramnames = kwargs.pop('write_paramnames', False)
settings.read_resume = kwargs.pop('read_resume', True)
settings.write_stats = kwargs.pop('write_stats', True)
settings.write_live = kwargs.pop('write_live', True)
settings.write_dead = kwargs.pop('write_dead', True)
settings.write_prior = kwargs.pop('write_prior', True)
settings.update_files = kwargs.pop('update_files', settings.nlive)
settings.base_dir = kwargs.pop('base_dir', 'chains')
settings.file_root = kwargs.pop('file_root', 'test')
settings.grade_dims = kwargs.pop('grade_dims', [n_dims])
settings.max_ndead=kwargs.pop('max_ndead ',-1)
ES=kwargs.pop('ES')
# settings.grade_frac = kwargs.pop('grade_frac', [1.0]*len(self.grade_dims))
if kwargs:
raise TypeError('Unexpected **kwargs in Contours constructor: %r' % kwargs)
# to speed up, set do_clustring = false
# settings.do_clustering=False
def my_prior(hypercube):
'''
theta: the hypercube for input parameters
len(theta)=n_dims
Get the phiscal parameters from hypercube
'''
theta=[0.0]*n_dims
for i,name in enumerate(ES.InPar):
if ES.InputPar[name][1].lower() == 'flat':
min = float(ES.InputPar[name][2])
max = float(ES.InputPar[name][3])
theta[i] = hypercube[i] * (max - min) + min
elif ES.InputPar[name][1].lower() == 'log':
min = math.log10(float(ES.InputPar[name][2]))
max = math.log10(float(ES.InputPar[name][3]))
theta[i] = 10.0**(hypercube[i]*(max - min) + min )
else:
sf.ErrorStop( 'Not ready. Only "flat" and "log" prior can be used.' )
# print 'my_prior: theta=',theta
return theta
def my_Loglikelihood(theta):
phi=[0.0]*nDerived
derived=[0.0]*nDerived
theta.extend(derived)
cube=theta
logL=LogLikelihood(cube,n_dims,n_params)
# print 'my_Loglikelihood after log',cube
phi=cube[n_dims:]
# print 'my loglikelihood: phi=',phi
# print 'my log likelihood: LogL=',logL
return logL,phi
output = PyPolyChord.run_polychord(my_Loglikelihood, n_dims, nDerived, settings, my_prior)