Renames log10temperature_min to log10beta_min

The variable name was misleading and hence renamed

examples/computing_the_Bayes_factor.py

@@ -19,7 +19,7 @@ theta_prior = {'F0': {'type': 'unif', 'lower': F0*(1-1e-6), 'upper': F0*(1+1e-6)
                }
 
 ntemps = 20
-log10temperature_min = -2
+log10beta_min = -2
 nwalkers = 100
 nsteps = [500, 500]
 
@@ -27,7 +27,7 @@ mcmc = MCMCSearch(label='computing_the_Bayes_factor', outdir='data',
                   sftfilepattern='data/*basic*sft', theta_prior=theta_prior,
                   tref=tref, tstart=tstart, tend=tend, nsteps=nsteps,
                   nwalkers=nwalkers, ntemps=ntemps,
-                  log10temperature_min=log10temperature_min)
+                  log10beta_min=log10beta_min)
 mcmc.run()
 mcmc.plot_corner(add_prior=True)
 mcmc.print_summary()

examples/follow_up.py

@@ -19,7 +19,7 @@ theta_prior = {'F0': {'type': 'unif', 'lower': F0*(1-1e-6), 'upper': F0*(1+1e-5)
                }
 
 ntemps = 1
-log10temperature_min = -1
+log10beta_min = -1
 nwalkers = 100
 run_setup = [(1000, 50), (1000, 25), (1000, 1, False),
              ((500, 500), 1, True)]
@@ -28,7 +28,7 @@ mcmc = pyfstat.MCMCFollowUpSearch(
     label='follow_up', outdir='data',
     sftfilepattern='data/*basic*sft', theta_prior=theta_prior, tref=tref,
     minStartTime=tstart, maxStartTime=tend, nwalkers=nwalkers,
-    ntemps=ntemps, log10temperature_min=log10temperature_min)
+    ntemps=ntemps, log10beta_min=log10beta_min)
 mcmc.run(run_setup, gen_tex_table=True)
 #mcmc.run(Nsegs0=50)
 mcmc.plot_corner(add_prior=True)

examples/fully_coherent_search_using_MCMC.py

@@ -47,7 +47,7 @@ theta_prior = {'F0': {'type': 'unif',
                }
 
 ntemps = 1
-log10temperature_min = -1
+log10beta_min = -1
 nwalkers = 100
 nsteps = [1000, 1000]
 
@@ -55,7 +55,7 @@ mcmc = pyfstat.MCMCSearch(
     label='fully_coherent_search_using_MCMC', outdir='data',
     sftfilepattern='data/*'+data_label+'*sft', theta_prior=theta_prior, tref=tref,
     minStartTime=tstart, maxStartTime=tend, nsteps=nsteps, nwalkers=nwalkers,
-    ntemps=ntemps, log10temperature_min=log10temperature_min)
+    ntemps=ntemps, log10beta_min=log10beta_min)
 mcmc.run(context='paper', subtractions=[30, -1e-10], c=2)
 mcmc.plot_corner(add_prior=True)
 mcmc.print_summary()

examples/fully_coherent_search_using_MCMC_on_glitching_data.py

@@ -21,7 +21,7 @@ theta_prior = {'F0': {'type': 'unif', 'lower': F0-1e-4, 'upper': F0+1e-4},
                }
 
 ntemps = 2
-log10temperature_min = -0.01
+log10beta_min = -0.01
 nwalkers = 100
 nsteps = [500, 500]
 
@@ -29,7 +29,7 @@ mcmc = MCMCSearch('fully_coherent_search_using_MCMC_on_glitching_data', 'data',
                   sftfilepattern='data/*_glitch*.sft',
                   theta_prior=theta_prior, tref=tref, minStartTime=tstart, maxStartTime=tend,
                   nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
-                  log10temperature_min=log10temperature_min)
+                  log10beta_min=log10beta_min)
 mcmc.run()
 mcmc.plot_corner(add_prior=True)
 mcmc.print_summary()

examples/glitch_robust_search.py

@@ -47,7 +47,7 @@ theta_prior = {'F0': {'type': 'unif',
 search = pyfstat.MCMCGlitchSearch(
     label=label, outdir=outdir, sftfilepattern=sftfilepattern,
     theta_prior=theta_prior, nglitch=1, tref=tref, nsteps=[500, 500],
-    ntemps=3, log10temperature_min=-0.5, minStartTime=tstart,
+    ntemps=3, log10beta_min=-0.5, minStartTime=tstart,
     maxStartTime=tstart+Tspan)
 search.run()
 search.plot_corner(label_offset=0.8, add_prior=True)

examples/semi_coherent_glitch_search_using_MCMC.py

@@ -25,7 +25,7 @@ theta_prior = {'F0': {'type': 'norm', 'loc': F0, 'scale': abs(1e-6*F0)},
                }
 
 ntemps = 4
-log10temperature_min = -1
+log10beta_min = -1
 nwalkers = 100
 nsteps = [5000, 1000, 1000]
 
@@ -34,7 +34,7 @@ mcmc = pyfstat.MCMCGlitchSearch(
     sftfilepattern='data/*_glitch*sft', theta_prior=theta_prior, tref=tref,
     tstart=tstart, tend=tend, nsteps=nsteps, nwalkers=nwalkers,
     scatter_val=1e-10, nglitch=1, ntemps=ntemps,
-    log10temperature_min=log10temperature_min)
+    log10beta_min=log10beta_min)
 
 mcmc.run()
 mcmc.plot_corner(add_prior=True)

examples/semi_coherent_search_using_MCMC.py

@@ -19,7 +19,7 @@ theta_prior = {'F0': {'type': 'unif', 'lower': F0*(1-1e-6), 'upper': F0*(1+1e-5)
                }
 
 ntemps = 1
-log10temperature_min = -1
+log10beta_min = -1
 nwalkers = 100
 nsteps = [500, 500, 500]
 
@@ -27,7 +27,7 @@ mcmc = pyfstat.MCMCSemiCoherentSearch(
     label='semi_coherent_search_using_MCMC', outdir='data', nsegs=20,
     sftfilepattern='data/*basic*sft', theta_prior=theta_prior, tref=tref,
     minStartTime=tstart, maxStartTime=tend, nsteps=nsteps, nwalkers=nwalkers,
-    ntemps=ntemps, log10temperature_min=log10temperature_min)
+    ntemps=ntemps, log10beta_min=log10beta_min)
 mcmc.run()
 mcmc.plot_corner(add_prior=True)
 mcmc.print_summary()

examples/transient_search_using_MCMC.py

@@ -34,7 +34,7 @@ theta_prior = {'F0': {'type': 'unif',
                }
 
 ntemps = 2
-log10temperature_min = -1
+log10beta_min = -1
 nwalkers = 100
 nsteps = [100, 100]
 
@@ -43,7 +43,7 @@ mcmc = pyfstat.MCMCTransientSearch(
     sftfilepattern='data/*simulated_transient_signal*sft',
     theta_prior=theta_prior, tref=tref, minStartTime=minStartTime,
     maxStartTime=maxStartTime, nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
-    log10temperature_min=log10temperature_min)
+    log10beta_min=log10beta_min)
 mcmc.run()
 mcmc.plot_corner(label_offset=0.7)
 mcmc.print_summary()

examples/twoF_cumulative.py

@@ -47,7 +47,7 @@ theta_prior = {'F0': {'type': 'unif',
                }
 
 ntemps = 1
-log10temperature_min = -1
+log10beta_min = -1
 nwalkers = 100
 nsteps = [50, 50]
 
@@ -55,7 +55,7 @@ mcmc = pyfstat.MCMCSearch(
     label='twoF_cumulative', outdir='data',
     sftfilepattern='data/*'+data_label+'*sft', theta_prior=theta_prior, tref=tref,
     minStartTime=tstart, maxStartTime=tend, nsteps=nsteps, nwalkers=nwalkers,
-    ntemps=ntemps, log10temperature_min=log10temperature_min)
+    ntemps=ntemps, log10beta_min=log10beta_min)
 mcmc.run(context='paper', subtractions=[30, -1e-10])
 mcmc.plot_corner(add_prior=True)
 mcmc.print_summary()

examples/weak_signal_follow_up.py

@@ -48,7 +48,7 @@ theta_prior = {'F0': {'type': 'unif', 'lower': F0-DeltaF0/2.,
                }
 
 ntemps = 3
-log10temperature_min = -0.5
+log10beta_min = -0.5
 nwalkers = 100
 scatter_val = 1e-10
 nsteps = [100, 100]
@@ -57,7 +57,7 @@ mcmc = pyfstat.MCMCFollowUpSearch(
     label='weak_signal_follow_up', outdir='data',
     sftfilepattern='data/*'+data_label+'*sft', theta_prior=theta_prior, tref=tref,
     minStartTime=tstart, maxStartTime=tend, nwalkers=nwalkers, nsteps=nsteps,
-    ntemps=ntemps, log10temperature_min=log10temperature_min,
+    ntemps=ntemps, log10beta_min=log10beta_min,
     scatter_val=scatter_val)
 
 fig, axes = plt.subplots(nrows=2, ncols=2)

pyfstat/mcmc_based_searches.py

@@ -48,9 +48,10 @@ class MCMCSearch(core.BaseSearchClass):
     nwalkers, ntemps: int,
         The number of walkers and temperates to use in the parallel
         tempered PTSampler.
-    log10temperature_min float < 0
-        The  log_10(tmin) value, the set of betas passed to PTSampler are
-        generated from `np.logspace(0, log10temperature_min, ntemps)`.
+    log10beta_min float < 0
+        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)
         A dictionary of distribution about which to distribute the
         initial walkers about
@@ -101,7 +102,7 @@ class MCMCSearch(core.BaseSearchClass):
     def __init__(self, label, outdir, theta_prior, tref, minStartTime,
                  maxStartTime, sftfilepattern=None, detectors=None,
                  nsteps=[100, 100], nwalkers=100, ntemps=1,
-                 log10temperature_min=-5, theta_initial=None,
+                 log10beta_min=-5, theta_initial=None,
                  rhohatmax=1000, binary=False, BSGL=False,
                  SSBprec=None, minCoverFreq=None, maxCoverFreq=None,
                  injectSources=None, assumeSqrtSX=None):
@@ -119,8 +120,8 @@ class MCMCSearch(core.BaseSearchClass):
         self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
         self._unpack_input_theta()
         self.ndim = len(self.theta_keys)
-        if self.log10temperature_min:
-            self.betas = np.logspace(0, self.log10temperature_min, self.ntemps)
+        if self.log10beta_min:
+            self.betas = np.logspace(0, self.log10beta_min, self.ntemps)
         else:
             self.betas = None
 
@@ -138,8 +139,8 @@ class MCMCSearch(core.BaseSearchClass):
         logging.info('nwalkers={}'.format(self.nwalkers))
         logging.info('nsteps = {}'.format(self.nsteps))
         logging.info('ntemps = {}'.format(self.ntemps))
-        logging.info('log10temperature_min = {}'.format(
-            self.log10temperature_min))
+        logging.info('log10beta_min = {}'.format(
+            self.log10beta_min))
 
     def _initiate_search_object(self):
         logging.info('Setting up search object')
@@ -1163,7 +1164,7 @@ class MCMCSearch(core.BaseSearchClass):
         d = dict(nsteps=self.nsteps, nwalkers=self.nwalkers,
                  ntemps=self.ntemps, theta_keys=self.theta_keys,
                  theta_prior=self.theta_prior,
-                 log10temperature_min=self.log10temperature_min,
+                 log10beta_min=self.log10beta_min,
                  BSGL=self.BSGL)
         return d
 
@@ -1571,7 +1572,7 @@ class MCMCGlitchSearch(MCMCSearch):
     def __init__(self, label, outdir, theta_prior, tref, minStartTime,
                  maxStartTime, sftfilepattern=None, detectors=None,
                  nsteps=[100, 100], nwalkers=100, ntemps=1,
-                 log10temperature_min=-5, theta_initial=None,
+                 log10beta_min=-5, theta_initial=None,
                  rhohatmax=1000, binary=False, BSGL=False,
                  SSBprec=None, minCoverFreq=None, maxCoverFreq=None,
                  injectSources=None, assumeSqrtSX=None,
@@ -1586,8 +1587,8 @@ class MCMCGlitchSearch(MCMCSearch):
         self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
         self._unpack_input_theta()
         self.ndim = len(self.theta_keys)
-        if self.log10temperature_min:
-            self.betas = np.logspace(0, self.log10temperature_min, self.ntemps)
+        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):
@@ -1702,7 +1703,7 @@ class MCMCGlitchSearch(MCMCSearch):
         d = dict(nsteps=self.nsteps, nwalkers=self.nwalkers,
                  ntemps=self.ntemps, theta_keys=self.theta_keys,
                  theta_prior=self.theta_prior,
-                 log10temperature_min=self.log10temperature_min,
+                 log10beta_min=self.log10beta_min,
                  theta0_idx=self.theta0_idx, BSGL=self.BSGL)
         return d
 
@@ -1780,7 +1781,7 @@ class MCMCSemiCoherentSearch(MCMCSearch):
     def __init__(self, label, outdir, theta_prior, tref, minStartTime,
                  maxStartTime, sftfilepattern=None, detectors=None,
                  nsteps=[100, 100], nwalkers=100, ntemps=1,
-                 log10temperature_min=-5, theta_initial=None,
+                 log10beta_min=-5, theta_initial=None,
                  rhohatmax=1000, binary=False, BSGL=False,
                  SSBprec=None, minCoverFreq=None, maxCoverFreq=None,
                  injectSources=None, assumeSqrtSX=None,
@@ -1795,8 +1796,8 @@ class MCMCSemiCoherentSearch(MCMCSearch):
         self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
         self._unpack_input_theta()
         self.ndim = len(self.theta_keys)
-        if self.log10temperature_min:
-            self.betas = np.logspace(0, self.log10temperature_min, self.ntemps)
+        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):
@@ -1816,7 +1817,7 @@ class MCMCSemiCoherentSearch(MCMCSearch):
         d = dict(nsteps=self.nsteps, nwalkers=self.nwalkers,
                  ntemps=self.ntemps, theta_keys=self.theta_keys,
                  theta_prior=self.theta_prior,
-                 log10temperature_min=self.log10temperature_min,
+                 log10beta_min=self.log10beta_min,
                  BSGL=self.BSGL, nsegs=self.nsegs)
         return d
 
@@ -1853,7 +1854,7 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
     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,
-                 log10temperature_min=self.log10temperature_min,
+                 log10beta_min=self.log10beta_min,
                  BSGL=self.BSGL, run_setup=self.run_setup)
         return d
 

tests.py

@@ -227,7 +227,7 @@ class TestMCMCSearch(Test):
             label=self.label, outdir=outdir, theta_prior=theta, tref=tref,
             sftfilepattern='{}/*{}*sft'.format(Writer.outdir, Writer.label),
             minStartTime=minStartTime, maxStartTime=maxStartTime,
-            nsteps=[100, 100], nwalkers=100, ntemps=2, log10temperature_min=-1)
+            nsteps=[100, 100], nwalkers=100, ntemps=2, log10beta_min=-1)
         search.setup_burnin_convergence_testing()
         search.run(create_plots=False)
         _, FS = search.get_max_twoF()