diff --git a/pyfstat/mcmc_based_searches.py b/pyfstat/mcmc_based_searches.py
index c03960500ded2331a7457d85166c80a3615bd174..41826f7c0d8f945b6dc8525b85ecfeedbfd7896e 100644
--- a/pyfstat/mcmc_based_searches.py
+++ b/pyfstat/mcmc_based_searches.py
@@ -110,7 +110,6 @@ class MCMCSearch(core.BaseSearchClass):
asini='', period='s', ecc='', tp='', argp='')
transform_dictionary = {}
- @helper_functions.initializer
def __init__(self, theta_prior, tref, label, outdir='data',
minStartTime=None, maxStartTime=None, sftfilepattern=None,
detectors=None, nsteps=[100, 100], nwalkers=100, ntemps=1,
@@ -120,6 +119,30 @@ class MCMCSearch(core.BaseSearchClass):
injectSources=None, assumeSqrtSX=None,
transientWindowType=None, tCWFstatMapVersion='lal'):
+ self.theta_prior = theta_prior
+ self.tref = tref
+ self.label = label
+ self.outdir = outdir
+ self.minStartTime = minStartTime
+ self.maxStartTime = maxStartTime
+ self.sftfilepattern = sftfilepattern
+ self.detectors = detectors
+ self.nsteps = nsteps
+ self.nwalkers = nwalkers
+ self.ntemps = ntemps
+ self.log10beta_min = log10beta_min
+ self.theta_initial = theta_initial
+ self.rhohatmax = rhohatmax
+ self.binary = binary
+ self.BSGL = BSGL
+ self.SSBprec = SSBprec
+ self.minCoverFreq = minCoverFreq
+ self.maxCoverFreq = maxCoverFreq
+ self.injectSources = injectSources
+ self.assumeSqrtSX = assumeSqrtSX
+ self.transientWindowType = transientWindowType
+ self.tCWFstatMapVersion = tCWFstatMapVersion
+
if os.path.isdir(outdir) is False:
os.mkdir(outdir)
self._add_log_file()
@@ -1889,17 +1912,63 @@ class MCMCGlitchSearch(MCMCSearch):
class MCMCSemiCoherentSearch(MCMCSearch):
""" MCMC search for a signal using the semi-coherent ComputeFstat
- See parent MCMCSearch for a list of all additional parameters, here we list
- only the additional init parameters of this class.
-
Parameters
----------
+ theta_prior: dict
+ Dictionary of priors and fixed values for the search parameters.
+ For each parameters (key of the dict), if it is to be held fixed
+ the value should be the constant float, if it is be searched, the
+ value should be a dictionary of the prior.
+ tref, minStartTime, maxStartTime: int
+ GPS seconds of the reference time, start time and end time. While tref
+ is requirede, minStartTime and maxStartTime default to None in which
+ case all available data is used.
+ label, outdir: str
+ A label and output directory (optional, defaults is `'data'`) to
+ name files
+ sftfilepattern: str, optional
+ Pattern to match SFTs using wildcards (*?) and ranges [0-9];
+ mutiple patterns can be given separated by colons.
+ detectors: str, optional
+ Two character reference to the detectors to use, specify None for no
+ contraint and comma separate for multiple references.
+ nsteps: list (2,), optional
+ Number of burn-in and production steps to take, [nburn, nprod]. See
+ `pyfstat.MCMCSearch.setup_initialisation()` for details on adding
+ initialisation steps.
+ nwalkers, ntemps: int, optional
+ The number of walkers and temperates to use in the parallel
+ tempered PTSampler.
+ log10beta_min float < 0, optional
+ The log_10(beta) value, if given the set of betas passed to PTSampler
+ are generated from `np.logspace(0, log10beta_min, ntemps)` (given
+ in descending order to ptemcee).
+ theta_initial: dict, array, optional
+ A dictionary of distribution about which to distribute the
+ initial walkers about
+ rhohatmax: float, optional
+ Upper bound for the SNR scale parameter (required to normalise the
+ Bayes factor) - this needs to be carefully set when using the
+ evidence.
+ binary: bool, optional
+ If true, search over binary parameters
+ BSGL: bool, optional
+ If true, use the BSGL statistic
+ SSBPrec: int, optional
+ SSBPrec (SSB precision) to use when calling ComputeFstat
+ minCoverFreq, maxCoverFreq: float, optional
+ Minimum and maximum instantaneous frequency which will be covered
+ over the SFT time span as passed to CreateFstatInput
+ injectSources: dict, optional
+ If given, inject these properties into the SFT files before running
+ the search
+ assumeSqrtSX: float, optional
+ Don't estimate noise-floors, but assume (stationary) per-IFO sqrt{SX}
nsegs: int
The number of segments
"""
- @helper_functions.initializer
def __init__(self, theta_prior, tref, label, outdir='data',
minStartTime=None, maxStartTime=None, sftfilepattern=None,
detectors=None, nsteps=[100, 100], nwalkers=100, ntemps=1,
@@ -1909,6 +1978,29 @@ class MCMCSemiCoherentSearch(MCMCSearch):
injectSources=None, assumeSqrtSX=None,
nsegs=None):
+ self.theta_prior = theta_prior
+ self.tref = tref
+ self.label = label
+ self.outdir = outdir
+ self.minStartTime = minStartTime
+ self.maxStartTime = maxStartTime
+ self.sftfilepattern = sftfilepattern
+ self.detectors = detectors
+ self.nsteps = nsteps
+ self.nwalkers = nwalkers
+ self.ntemps = ntemps
+ self.log10beta_min = log10beta_min
+ self.theta_initial = theta_initial
+ self.rhohatmax = rhohatmax
+ self.binary = binary
+ self.BSGL = BSGL
+ self.SSBprec = SSBprec
+ self.minCoverFreq = minCoverFreq
+ self.maxCoverFreq = maxCoverFreq
+ self.injectSources = injectSources
+ self.assumeSqrtSX = assumeSqrtSX
+ self.nsegs = nsegs
+
if os.path.isdir(outdir) is False:
os.mkdir(outdir)
self._add_log_file()
@@ -1975,10 +2067,128 @@ class MCMCSemiCoherentSearch(MCMCSearch):
class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
""" A follow up procudure increasing the coherence time in a zoom
- See parent MCMCSemiCoherentSearch for a list of all additional parameters
+ Parameters
+ ----------
+ theta_prior: dict
+ Dictionary of priors and fixed values for the search parameters.
+ For each parameters (key of the dict), if it is to be held fixed
+ the value should be the constant float, if it is be searched, the
+ value should be a dictionary of the prior.
+ tref, minStartTime, maxStartTime: int
+ GPS seconds of the reference time, start time and end time. While tref
+ is requirede, minStartTime and maxStartTime default to None in which
+ case all available data is used.
+ label, outdir: str
+ A label and output directory (optional, defaults is `'data'`) to
+ name files
+ sftfilepattern: str, optional
+ Pattern to match SFTs using wildcards (*?) and ranges [0-9];
+ mutiple patterns can be given separated by colons.
+ detectors: str, optional
+ Two character reference to the detectors to use, specify None for no
+ contraint and comma separate for multiple references.
+ nsteps: list (2,), optional
+ Number of burn-in and production steps to take, [nburn, nprod]. See
+ `pyfstat.MCMCSearch.setup_initialisation()` for details on adding
+ initialisation steps.
+ nwalkers, ntemps: int, optional
+ The number of walkers and temperates to use in the parallel
+ tempered PTSampler.
+ log10beta_min float < 0, optional
+ The log_10(beta) value, if given the set of betas passed to PTSampler
+ are generated from `np.logspace(0, log10beta_min, ntemps)` (given
+ in descending order to ptemcee).
+ theta_initial: dict, array, optional
+ A dictionary of distribution about which to distribute the
+ initial walkers about
+ rhohatmax: float, optional
+ Upper bound for the SNR scale parameter (required to normalise the
+ Bayes factor) - this needs to be carefully set when using the
+ evidence.
+ binary: bool, optional
+ If true, search over binary parameters
+ BSGL: bool, optional
+ If true, use the BSGL statistic
+ SSBPrec: int, optional
+ SSBPrec (SSB precision) to use when calling ComputeFstat
+ minCoverFreq, maxCoverFreq: float, optional
+ Minimum and maximum instantaneous frequency which will be covered
+ over the SFT time span as passed to CreateFstatInput
+ injectSources: dict, optional
+ If given, inject these properties into the SFT files before running
+ the search
+ assumeSqrtSX: float, optional
+ Don't estimate noise-floors, but assume (stationary) per-IFO sqrt{SX}
+
+ Attributes
+ ----------
+ symbol_dictionary: dict
+ Key, val pairs of the parameters (i.e. `F0`, `F1`), to Latex math
+ symbols for plots
+ unit_dictionary: dict
+ Key, val pairs of the parameters (i.e. `F0`, `F1`), and the
+ units (i.e. `Hz`)
+ transform_dictionary: dict
+ Key, val pairs of the parameters (i.e. `F0`, `F1`), where the key is
+ itself a dictionary which can item `multiplier`, `subtractor`, or
+ `unit` by which to transform by and update the units.
"""
+ def __init__(self, theta_prior, tref, label, outdir='data',
+ minStartTime=None, maxStartTime=None, sftfilepattern=None,
+ detectors=None, nsteps=[100, 100], nwalkers=100, ntemps=1,
+ log10beta_min=-5, theta_initial=None,
+ rhohatmax=1000, binary=False, BSGL=False,
+ SSBprec=None, minCoverFreq=None, maxCoverFreq=None,
+ injectSources=None, assumeSqrtSX=None):
+
+ self.theta_prior = theta_prior
+ self.tref = tref
+ self.label = label
+ self.outdir = outdir
+ self.minStartTime = minStartTime
+ self.maxStartTime = maxStartTime
+ self.sftfilepattern = sftfilepattern
+ self.detectors = detectors
+ self.nsteps = nsteps
+ self.nwalkers = nwalkers
+ self.ntemps = ntemps
+ self.log10beta_min = log10beta_min
+ self.theta_initial = theta_initial
+ self.rhohatmax = rhohatmax
+ self.binary = binary
+ self.BSGL = BSGL
+ self.SSBprec = SSBprec
+ self.minCoverFreq = minCoverFreq
+ self.maxCoverFreq = maxCoverFreq
+ self.injectSources = injectSources
+ self.assumeSqrtSX = assumeSqrtSX
+ self.nsegs = None
+
+ if os.path.isdir(outdir) is False:
+ os.mkdir(outdir)
+ self._add_log_file()
+ logging.info(('Set-up MCMC semi-coherent search for model {} on data'
+ '{}').format(
+ self.label, self.sftfilepattern))
+ self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
+ self._unpack_input_theta()
+ self.ndim = len(self.theta_keys)
+ if self.log10beta_min:
+ self.betas = np.logspace(0, self.log10beta_min, self.ntemps)
+ else:
+ self.betas = None
+ if args.clean and os.path.isfile(self.pickle_path):
+ os.rename(self.pickle_path, self.pickle_path+".old")
+
+ self._log_input()
+
+ if self.nsegs:
+ self._set_likelihoodcoef()
+ else:
+ logging.info('Value `nsegs` not yet provided')
+
def _get_data_dictionary_to_save(self):
d = dict(nwalkers=self.nwalkers, ntemps=self.ntemps,
theta_keys=self.theta_keys, theta_prior=self.theta_prior,