Various improvements

- Add option to just update the search object in the follow-up
- Abstract loading in SFT data and check it is not already loaded before
  creating SFTCatalog
- Fix bug in plot_walkers in which the number of steps is not displaying
- Removal of unused code
- Fixes to docs

pyfstat.py

@@ -341,9 +341,9 @@ class ComputeFstat(object):
 
         self.init_computefstatistic_single_point()
 
-    def init_computefstatistic_single_point(self):
-        """ Initilisation step of run_computefstatistic for a single point """
-
+    def get_SFTCatalog(self):
+        if hasattr(self, 'SFTCatalog'):
+            return
         logging.info('Initialising SFTCatalog')
         constraints = lalpulsar.SFTConstraints()
         if self.detector:
@@ -376,6 +376,12 @@ class ComputeFstat(object):
             int(SFT_timestamps[-1]),
             subprocess.check_output('lalapps_tconvert {}'.format(
                 int(SFT_timestamps[-1])), shell=True).rstrip('\n')))
+        self.SFTCatalog = SFTCatalog
+
+    def init_computefstatistic_single_point(self):
+        """ Initilisation step of run_computefstatistic for a single point """
+
+        self.get_SFTCatalog()
 
         logging.info('Initialising ephems')
         ephems = lalpulsar.InitBarycenter(self.earth_ephem, self.sun_ephem)
@@ -412,24 +418,22 @@ class ComputeFstat(object):
             PP.Doppler.fkdot = np.array(self.injectSources['fkdot'])
             PP.Doppler.refTime = self.tref
             if 't0' not in self.injectSources:
-                #PP.Transient.t0 = int(self.minStartTime)
-                #PP.Transient.tau = int(self.maxStartTime - self.minStartTime)
                 PP.Transient.type = lalpulsar.TRANSIENT_NONE
             FstatOAs.injectSources = PPV
         else:
             FstatOAs.injectSources = lalpulsar.FstatOptionalArgsDefaults.injectSources
 
         if self.minCoverFreq is None or self.maxCoverFreq is None:
-            fAs = [d.header.f0 for d in SFTCatalog.data]
+            fAs = [d.header.f0 for d in self.SFTCatalog.data]
             fBs = [d.header.f0 + (d.numBins-1)*d.header.deltaF
-                   for d in SFTCatalog.data]
+                   for d in self.SFTCatalog.data]
             self.minCoverFreq = np.min(fAs) + 0.5
             self.maxCoverFreq = np.max(fBs) - 0.5
             logging.info('Min/max cover freqs not provided, using '
                          '{} and {}, est. from SFTs'.format(
                              self.minCoverFreq, self.maxCoverFreq))
 
-        self.FstatInput = lalpulsar.CreateFstatInput(SFTCatalog,
+        self.FstatInput = lalpulsar.CreateFstatInput(self.SFTCatalog,
                                                      self.minCoverFreq,
                                                      self.maxCoverFreq,
                                                      dFreq,
@@ -449,7 +453,7 @@ class ComputeFstat(object):
         self.FstatResults = lalpulsar.FstatResults()
 
         if self.BSGL:
-            if len(names) < 2:
+            if len(self.names) < 2:
                 raise ValueError("Can't use BSGL with single detector data")
             else:
                 logging.info('Initialising BSGL')
@@ -811,7 +815,7 @@ class MCMCSearch(BaseSearchClass):
     """ MCMC search using ComputeFstat"""
     @initializer
     def __init__(self, label, outdir, sftfilepath, theta_prior, tref,
-                 minStartTime, maxStartTime, nsteps=[100, 100, 100],
+                 minStartTime, maxStartTime, nsteps=[100, 100],
                  nwalkers=100, ntemps=1, log10temperature_min=-5,
                  theta_initial=None, scatter_val=1e-10,
                  binary=False, BSGL=False, minCoverFreq=None,
@@ -1323,14 +1327,14 @@ class MCMCSearch(BaseSearchClass):
             if fig is None and axes is None:
                 fig = plt.figure(figsize=(8, 4*ndim))
                 ax = fig.add_subplot(ndim+1, 1, 1)
-                axes = [ax] + [fig.add_subplot(ndim+1, 1, i, sharex=ax)
+                axes = [ax] + [fig.add_subplot(ndim+1, 1, i)
                                for i in range(2, ndim+1)]
 
             idxs = np.arange(chain.shape[1])
             if ndim > 1:
                 for i in range(ndim):
                     axes[i].ticklabel_format(useOffset=False, axis='y')
-                    if i < ndim:
+                    if i < ndim-1:
                         axes[i].set_xticklabels([])
                     cs = chain[:, :, i].T
                     if burnin_idx:
@@ -1749,16 +1753,17 @@ class MCMCGlitchSearch(MCMCSearch):
     """ MCMC search using the SemiCoherentGlitchSearch """
     @initializer
     def __init__(self, label, outdir, sftfilepath, theta_prior, tref,
-                 minStartTime, maxStartTime, nglitch=1, nsteps=[100, 100, 100],
+                 minStartTime, maxStartTime, nglitch=1, nsteps=[100, 100],
                  nwalkers=100, ntemps=1, log10temperature_min=-5,
                  theta_initial=None, scatter_val=1e-10, dtglitchmin=1*86400,
                  theta0_idx=0, detector=None, BSGL=False, minCoverFreq=None,
                  maxCoverFreq=None, earth_ephem=None, sun_ephem=None):
         """
         Parameters
+        ----------
         label, outdir: str
             A label and directory to read/write data from/to
-_        sftfilepath: str
+        sftfilepath: str
             File patern to match SFTs
         theta_prior: dict
             Dictionary of priors and fixed values for the search parameters.
@@ -2066,6 +2071,10 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
                  BSGL=self.BSGL, run_setup=self.run_setup)
         return d
 
+    def update_search_object(self):
+        logging.info('Update search object')
+        self.search.init_computefstatistic_single_point()
+
     def get_width_from_prior(self, prior, key):
         if prior[key]['type'] == 'unif':
             return prior[key]['upper'] - prior[key]['lower']
@@ -2230,7 +2239,7 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
 
             self.nsegs = nseg
             self.search.nsegs = nseg
-            self.inititate_search_object()
+            self.update_search_object()
             self.search.init_semicoherent_parameters()
             sampler = emcee.PTSampler(
                 self.ntemps, self.nwalkers, self.ndim, self.logl, self.logp,
@@ -2255,12 +2264,6 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
             fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols,
                                           fig=fig, axes=axes, burnin_idx=nburn,
                                           xoffset=nsteps_total, **kwargs)
-            #yvals = axes[0].get_ylim()
-            #axes[0].annotate(
-            #    #r'$T_{{\rm coh}}^{{\rm (days)}}{{=}}{:1.1f}$'.format(Tcoh),
-            #    r'{}'.format(j),
-            #    xy=(nsteps_total, yvals[0]*(1+1e-2*(yvals[1]-yvals[0])/yvals[1])),
-            #    fontsize=6)
             for ax in axes[:-1]:
                 ax.axvline(nsteps_total, color='k', ls='--')
             nsteps_total += nburn+nprod