From a42f0b0a46191f26c1114c7ec104b885ad5b9190 Mon Sep 17 00:00:00 2001
From: Gregory Ashton <gregory.ashton@ligo.org>
Date: Wed, 11 Oct 2017 09:31:08 +0200
Subject: [PATCH] Minor improvements to user interface
- Remove minStartTime, maxStartTime and outdir as default arguments
- Adds notes ot documentation on which arguments are optional
- Change log-level on some command line calls that dont' matter
- Reorganise the tests performed after loading the data
- If minStartTime and maxStartTime are None, set them using
SFT_timestamps
- Remove default labels from plot_twoF_cumulative
- If add_pfs is called, call generate_loudest automatically
- Save min/maxStartTime in pickle and load if required
---
pyfstat/core.py | 39 +++++++++++------
pyfstat/mcmc_based_searches.py | 80 +++++++++++++++++++++++-----------
2 files changed, 79 insertions(+), 40 deletions(-)
diff --git a/pyfstat/core.py b/pyfstat/core.py
index d54634d..a508765 100755
--- a/pyfstat/core.py
+++ b/pyfstat/core.py
@@ -423,12 +423,10 @@ class ComputeFstat(BaseSearchClass):
constraints.minStartTime = lal.LIGOTimeGPS(self.minStartTime)
if self.maxStartTime:
constraints.maxStartTime = lal.LIGOTimeGPS(self.maxStartTime)
-
logging.info('Loading data matching pattern {}'.format(
self.sftfilepattern))
SFTCatalog = lalpulsar.SFTdataFind(self.sftfilepattern, constraints)
- detector_names = list(set([d.header.name for d in SFTCatalog.data]))
- self.detector_names = detector_names
+
SFT_timestamps = [d.header.epoch for d in SFTCatalog.data]
self.SFT_timestamps = [float(s) for s in SFT_timestamps]
if len(SFT_timestamps) == 0:
@@ -440,21 +438,33 @@ class ComputeFstat(BaseSearchClass):
plot_hist(SFT_timestamps, height=5, bincount=50)
except ImportError:
pass
- if len(detector_names) == 0:
- raise ValueError('No data loaded.')
- logging.info('Loaded {} data files from detectors {}'.format(
- len(SFT_timestamps), detector_names))
+
cl_tconv1 = 'lalapps_tconvert {}'.format(int(SFT_timestamps[0]))
- output = helper_functions.run_commandline(cl_tconv1)
+ output = helper_functions.run_commandline(cl_tconv1,
+ log_level=logging.DEBUG)
tconvert1 = output.rstrip('\n')
cl_tconv2 = 'lalapps_tconvert {}'.format(int(SFT_timestamps[-1]))
- output = helper_functions.run_commandline(cl_tconv2)
+ output = helper_functions.run_commandline(cl_tconv2,
+ log_level=logging.DEBUG)
tconvert2 = output.rstrip('\n')
logging.info('Data spans from {} ({}) to {} ({})'.format(
int(SFT_timestamps[0]),
tconvert1,
int(SFT_timestamps[-1]),
tconvert2))
+
+ if self.minStartTime is None:
+ self.minStartTime = int(SFT_timestamps[0])
+ if self.maxStartTime is None:
+ self.maxStartTime = int(SFT_timestamps[-1])
+
+ detector_names = list(set([d.header.name for d in SFTCatalog.data]))
+ self.detector_names = detector_names
+ if len(detector_names) == 0:
+ raise ValueError('No data loaded.')
+ logging.info('Loaded {} data files from detectors {}'.format(
+ len(SFT_timestamps), detector_names))
+
return SFTCatalog
def init_computefstatistic_single_point(self):
@@ -735,7 +745,7 @@ class ComputeFstat(BaseSearchClass):
def plot_twoF_cumulative(self, label, outdir, add_pfs=False, N=15,
injectSources=None, ax=None, c='k', savefig=True,
- title=None, **kwargs):
+ title=None, plt_label=None, **kwargs):
""" Plot the twoF value cumulatively
Parameters
@@ -753,8 +763,8 @@ class ComputeFstat(BaseSearchClass):
Colour
savefig : bool
If true, save the figure in outdir
- title: str
- Figure title
+ title, plt_label: str
+ Figure title and label
Returns
-------
@@ -775,7 +785,7 @@ class ComputeFstat(BaseSearchClass):
pfs_input = None
taus, twoFs = self.calculate_twoF_cumulative(**kwargs)
- ax.plot(taus/86400., twoFs, label='All detectors', color=c)
+ ax.plot(taus/86400., twoFs, label=plt_label, color=c)
if len(self.detector_names) > 1:
detector_names = self.detector_names
detectors = self.detectors
@@ -819,7 +829,8 @@ class ComputeFstat(BaseSearchClass):
else:
ax.set_ylabel(r'$\widetilde{2\mathcal{F}}_{\rm cumulative}$')
ax.set_xlim(0, taus[-1]/86400)
- ax.legend(frameon=False, loc=2, fontsize=6)
+ if plt_label:
+ ax.legend(frameon=False, loc=2, fontsize=6)
if title:
ax.set_title(title)
if savefig:
diff --git a/pyfstat/mcmc_based_searches.py b/pyfstat/mcmc_based_searches.py
index 06445f8..277c5cb 100644
--- a/pyfstat/mcmc_based_searches.py
+++ b/pyfstat/mcmc_based_searches.py
@@ -26,52 +26,55 @@ class MCMCSearch(core.BaseSearchClass):
Parameters
----------
- label, outdir: str
- A label and directory to read/write data from/to
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
- sftfilepattern: str
+ 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
+ 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,)
+ 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,
+ nwalkers, ntemps: int, optional
The number of walkers and temperates to use in the parallel
tempered PTSampler.
- log10beta_min float < 0
+ 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 emcee).
- theta_initial: dict, array, (None)
+ theta_initial: dict, array, optional
A dictionary of distribution about which to distribute the
initial walkers about
- rhohatmax: float,
+ 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
+ binary: bool, optional
If true, search over binary parameters
- BSGL: bool
+ BSGL: bool, optional
If true, use the BSGL statistic
- SSBPrec: int
+ SSBPrec: int, optional
SSBPrec (SSB precision) to use when calling ComputeFstat
- minCoverFreq, maxCoverFreq: float
+ minCoverFreq, maxCoverFreq: float, optional
Minimum and maximum instantaneous frequency which will be covered
over the SFT time span as passed to CreateFstatInput
- injectSources: dict
+ injectSources: dict, optional
If given, inject these properties into the SFT files before running
the search
- assumeSqrtSX: float
+ assumeSqrtSX: float, optional
Don't estimate noise-floors, but assume (stationary) per-IFO sqrt{SX}
Attributes
@@ -99,9 +102,9 @@ class MCMCSearch(core.BaseSearchClass):
transform_dictionary = {}
@helper_functions.initializer
- def __init__(self, label, outdir, theta_prior, tref, minStartTime,
- maxStartTime, sftfilepattern=None, detectors=None,
- nsteps=[100, 100], nwalkers=100, ntemps=1,
+ 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,
@@ -151,6 +154,10 @@ class MCMCSearch(core.BaseSearchClass):
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
binary=self.binary, injectSources=self.injectSources,
assumeSqrtSX=self.assumeSqrtSX, SSBprec=self.SSBprec)
+ if self.minStartTime is None:
+ self.minStartTime = self.search.minStartTime
+ if self.maxStartTime is None:
+ self.maxStartTime = self.search.maxStartTime
def logp(self, theta_vals, theta_prior, theta_keys, search):
H = [self._generic_lnprior(**theta_prior[key])(p) for p, key in
@@ -830,6 +837,9 @@ class MCMCSearch(core.BaseSearchClass):
if key not in d:
d[key] = val
+ if 'add_pfs' in kwargs:
+ self.generate_loudest()
+
if hasattr(self, 'search') is False:
self._initiate_search_object()
if self.binary is False:
@@ -1165,7 +1175,8 @@ class MCMCSearch(core.BaseSearchClass):
ntemps=self.ntemps, theta_keys=self.theta_keys,
theta_prior=self.theta_prior,
log10beta_min=self.log10beta_min,
- BSGL=self.BSGL)
+ BSGL=self.BSGL, minStartTime=self.minStartTime,
+ maxStartTime=self.maxStartTime)
return d
def _save_data(self, sampler, samples, lnprobs, lnlikes, all_lnlikelihood):
@@ -1212,6 +1223,11 @@ class MCMCSearch(core.BaseSearchClass):
old_d.pop('lnlikes')
old_d.pop('all_lnlikelihood')
+ for key in 'minStartTime', 'maxStartTime':
+ if new_d[key] is None:
+ new_d[key] = old_d[key]
+ setattr(self, key, new_d[key])
+
mod_keys = []
for key in new_d.keys():
if key in old_d:
@@ -1569,9 +1585,9 @@ class MCMCGlitchSearch(MCMCSearch):
)
@helper_functions.initializer
- def __init__(self, label, outdir, theta_prior, tref, minStartTime,
- maxStartTime, sftfilepattern=None, detectors=None,
- nsteps=[100, 100], nwalkers=100, ntemps=1,
+ 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,
@@ -1610,6 +1626,10 @@ class MCMCGlitchSearch(MCMCSearch):
minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
detectors=self.detectors, BSGL=self.BSGL, nglitch=self.nglitch,
theta0_idx=self.theta0_idx, injectSources=self.injectSources)
+ if self.minStartTime is None:
+ self.minStartTime = self.search.minStartTime
+ if self.maxStartTime is None:
+ self.maxStartTime = self.search.maxStartTime
def logp(self, theta_vals, theta_prior, theta_keys, search):
if self.nglitch > 1:
@@ -1778,9 +1798,9 @@ class MCMCSemiCoherentSearch(MCMCSearch):
"""
@helper_functions.initializer
- def __init__(self, label, outdir, theta_prior, tref, minStartTime,
- maxStartTime, sftfilepattern=None, detectors=None,
- nsteps=[100, 100], nwalkers=100, ntemps=1,
+ 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,
@@ -1830,6 +1850,10 @@ class MCMCSemiCoherentSearch(MCMCSearch):
maxStartTime=self.maxStartTime, minCoverFreq=self.minCoverFreq,
maxCoverFreq=self.maxCoverFreq, detectors=self.detectors,
injectSources=self.injectSources, assumeSqrtSX=self.assumeSqrtSX)
+ if self.minStartTime is None:
+ self.minStartTime = self.search.minStartTime
+ if self.maxStartTime is None:
+ self.maxStartTime = self.search.maxStartTime
def logp(self, theta_vals, theta_prior, theta_keys, search):
H = [self._generic_lnprior(**theta_prior[key])(p) for p, key in
@@ -2144,6 +2168,10 @@ class MCMCTransientSearch(MCMCSearch):
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
BSGL=self.BSGL, binary=self.binary,
injectSources=self.injectSources)
+ if self.minStartTime is None:
+ self.minStartTime = self.search.minStartTime
+ if self.maxStartTime is None:
+ self.maxStartTime = self.search.maxStartTime
def logl(self, theta, search):
for j, theta_i in enumerate(self.theta_idxs):
--
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