.coveragerc

@@ -3,3 +3,4 @@ omit =
     test/integration/example_test.py
     test/integration/noise_realisation_test.py
     test/integration/other_test.py
+    bilby/_version.py

.gitignore

@@ -15,3 +15,4 @@ MANIFEST
 *.ipynb_checkpoints
 outdir/*
 .idea/*
+bilby/_version.py

AUTHORS.md

@@ -24,6 +24,7 @@ Eric Thrane
 Ethan Payne
 Francisco Javier Hernandez
 Gregory Ashton
+Hank Hua
 Hector Estelles
 Ignacio Magaña Hernandez
 Isobel Marguarethe Romero-Shaw
@@ -34,6 +35,7 @@ Jeremy G Baier
 John Veitch
 Joshua Brandt
 Josh Willis
+Karl Wette
 Katerina Chatziioannou
 Kaylee de Soto
 Khun Sang Phukon
@@ -59,6 +61,7 @@ Paul Easter
 Paul Lasky
 Philip Relton
 Rhys Green
+Richard Udall
 Rico Lo
 Roberto Cotesta
 Rory Smith
@@ -75,7 +78,9 @@ Stephen R Green
 Sumeet Kulkarni
 Sylvia Biscoveanu
 Tathagata Ghosh
+Tomasz Baka
+Will M. Farr
 Virginia d'Emilio
 Vivien Raymond
 Ka-Lok Lo
-Isaac Legred
+Isaac Legred
\ No newline at end of file

CHANGELOG.md

 # All notable changes will be documented in this file
 
+## [1.2.1] 2022-09-05
+Version 1.2.1 release of Bilby
+
+This release contains a few bug fixes following 1.2.0.
+
+### Changes
+- Improve how sampling seed is handled across samplers (!1134)
+- Make sure labels are included when evidences are in corner plot legend (!1135)
+- Remove calls to `getargspec` (!1136)
+- Make sure parameter reconstruction cache is not mangled when reading (!1126)
+- Enable the constant uncertainty calibration spline to have a specifiable boundary condition (!1137)
+- Fix a bug in checkpointing for `bilby_mcmc` (!1141)
+- Fix the `LALCBCWaveformGenerator` (!1140)
+- Switch to automatic versioning with `setuptools_scm` (!1125)
+- Improve the stability of the multivariate normal prior (!1142)
+- Extend mass conversions to include source-frame parameters (!1131)
+- Fix prior ranges for GW150914 example (!1129)
+
 ## [1.2.0] 2022-08-15
 Version 1.2.0 release of Bilby
 

MANIFEST.in

@@ -5,4 +5,5 @@ include gw_requirements.txt
 include mcmc_requirements.txt
 include optional_requirements.txt
 include sampler_requirements.txt
+include bilby/_version.py
 recursive-include test *.py *.prior

bilby/__init__.py

@@ -24,7 +24,10 @@ from .core import utils, likelihood, prior, result, sampler
 from .core.sampler import run_sampler
 from .core.likelihood import Likelihood
 
-__version__ = utils.get_version_information()
+try:
+    from ._version import version as __version__
+except ModuleNotFoundError:  # development mode
+    __version__ = 'unknown'
 
 
 if sys.version_info < (3,):

bilby/bilby_mcmc/sampler.py

@@ -202,6 +202,7 @@ class Bilby_MCMC(MCMCSampler):
             logger.warning("Burn-in inefficiency fraction greater than 10%")
 
     def _translate_kwargs(self, kwargs):
+        kwargs = super()._translate_kwargs(kwargs)
         if "printdt" not in kwargs:
             for equiv in ["print_dt", "print_update"]:
                 if equiv in kwargs:
@@ -1075,9 +1076,6 @@ class BilbyMCMCSampler(object):
         Eindex=0,
         use_ratio=False,
     ):
-        from ..core.sampler.base_sampler import _sampling_convenience_dump
-
-        self._sampling_helper = _sampling_convenience_dump
         self.beta = beta
         self.Tindex = Tindex
         self.Eindex = Eindex

bilby/core/prior/joint.py

@@ -382,6 +382,7 @@ class MultivariateGaussianDist(BaseJointPriorDist):
         self.covs = []
         self.corrcoefs = []
         self.sigmas = []
+        self.logprodsigmas = []   # log of product of sigmas, needed for "standard" multivariate normal
         self.weights = []
         self.eigvalues = []
         self.eigvectors = []
@@ -528,6 +529,9 @@ class MultivariateGaussianDist(BaseJointPriorDist):
             self.covs.append(np.eye(self.num_vars))
             self.sigmas.append(np.ones(self.num_vars))
 
+        # compute log of product of sigmas, needed for "standard" multivariate normal
+        self.logprodsigmas.append(np.log(np.prod(self.sigmas[-1])))
+
         # get eigen values and vectors
         try:
             evals, evecs = np.linalg.eig(self.corrcoefs[-1])
@@ -557,9 +561,16 @@ class MultivariateGaussianDist(BaseJointPriorDist):
         # add the mode
         self.nmodes += 1
 
-        # add multivariate Gaussian
+        # add "standard" multivariate normal distribution
+        # - when the typical scales of the parameters are very different,
+        #   multivariate_normal() may complain that the covariance matrix is singular
+        # - instead pass zero means and correlation matrix instead of covariance matrix
+        #   to get the equivalent of a standard normal distribution in higher dimensions
+        # - this modifies the multivariate normal PDF as follows:
+        #     multivariate_normal(mean=mus, cov=cov).logpdf(x)
+        #     = multivariate_normal(mean=0, cov=corrcoefs).logpdf((x - mus)/sigmas) - logprodsigmas
         self.mvn.append(
-            scipy.stats.multivariate_normal(mean=self.mus[-1], cov=self.covs[-1])
+            scipy.stats.multivariate_normal(mean=np.zeros(self.num_vars), cov=self.corrcoefs[-1])
         )
 
     def _rescale(self, samp, **kwargs):
@@ -630,7 +641,9 @@ class MultivariateGaussianDist(BaseJointPriorDist):
         for j in range(samp.shape[0]):
             # loop over the modes and sum the probabilities
             for i in range(self.nmodes):
-                lnprob[j] = np.logaddexp(lnprob[j], self.mvn[i].logpdf(samp[j]))
+                # self.mvn[i] is a "standard" multivariate normal distribution; see add_mode()
+                z = (samp[j] - self.mus[i]) / self.sigmas[i]
+                lnprob[j] = np.logaddexp(lnprob[j], self.mvn[i].logpdf(z) - self.logprodsigmas[i])
 
         # set out-of-bounds values to -inf
         lnprob[outbounds] = -np.inf

bilby/core/result.py

@@ -298,7 +298,7 @@ def reweight(result, label=None, new_likelihood=None, new_prior=None,
 
     if conversion_function is not None:
         data_frame = result.posterior
-        if "npool" in inspect.getargspec(conversion_function).args:
+        if "npool" in inspect.signature(conversion_function).parameters:
             data_frame = conversion_function(data_frame, new_likelihood, new_prior, npool=npool)
         else:
             data_frame = conversion_function(data_frame, new_likelihood, new_prior)
@@ -1389,7 +1389,7 @@ class Result(object):
             data_frame['log_prior'] = self.log_prior_evaluations
 
         if conversion_function is not None:
-            if "npool" in inspect.getargspec(conversion_function).args:
+            if "npool" in inspect.signature(conversion_function).parameters:
                 data_frame = conversion_function(data_frame, likelihood, priors, npool=npool)
             else:
                 data_frame = conversion_function(data_frame, likelihood, priors)
@@ -1939,12 +1939,17 @@ def plot_multiple(results, filename=None, labels=None, colours=None,
 
     if evidences:
         if np.isnan(results[0].log_bayes_factor):
-            template = r' $\mathrm{{ln}}(Z)={lnz:1.3g}$'
+            template = r'{label} $\mathrm{{ln}}(Z)={lnz:1.3g}$'
         else:
-            template = r' $\mathrm{{ln}}(B)={lnbf:1.3g}$'
-        labels = [template.format(lnz=result.log_evidence,
-                                  lnbf=result.log_bayes_factor)
-                  for ii, result in enumerate(results)]
+            template = r'{label} $\mathrm{{ln}}(B)={lnbf:1.3g}$'
+        labels = [
+            template.format(
+                label=label,
+                lnz=result.log_evidence,
+                lnbf=result.log_bayes_factor,
+            )
+            for label, result in zip(labels, results)
+        ]
 
     axes = fig.get_axes()
     ndim = int(np.sqrt(len(axes)))

bilby/core/sampler/base_sampler.py

@@ -196,7 +196,13 @@ class Sampler(object):
         "cores",
         "n_pool",
     ]
+    sampling_seed_equiv_kwargs = ["sampling_seed", "seed", "random_seed"]
     hard_exit = False
+    sampling_seed_key = None
+    """Name of keyword argument for setting the sampling for the specific sampler.
+    If a specific sampler does not have a sampling seed option, then it should be
+    left as None.
+    """
 
     def __init__(
         self,
@@ -289,8 +295,16 @@ class Sampler(object):
         self._verify_kwargs_against_default_kwargs()
 
     def _translate_kwargs(self, kwargs):
-        """Template for child classes"""
-        pass
+        """Translate keyword arguments.
+
+        Default only translates the sampling seed if the sampler has
+        :code:`sampling_seed_key` set.
+        """
+        if self.sampling_seed_key and self.sampling_seed_key not in kwargs:
+            for equiv in self.sampling_seed_equiv_kwargs:
+                if equiv in kwargs:
+                    kwargs[self.sampling_seed_key] = kwargs.pop(equiv)
+        return kwargs
 
     @property
     def external_sampler_name(self):

bilby/core/sampler/cpnest.py

@@ -54,8 +54,10 @@ class Cpnest(NestedSampler):
         n_periodic_checkpoint=8000,
     )
     hard_exit = True
+    sampling_seed_key = "seed"
 
     def _translate_kwargs(self, kwargs):
+        kwargs = super()._translate_kwargs(kwargs)
         if "nlive" not in kwargs:
             for equiv in self.npoints_equiv_kwargs:
                 if equiv in kwargs:

bilby/core/sampler/dnest4.py

@@ -114,6 +114,7 @@ class DNest4(_TemporaryFileSamplerMixin, NestedSampler):
     )
     short_name = "dn4"
     hard_exit = True
+    sampling_seed_key = "seed"
 
     def __init__(
         self,
@@ -254,6 +255,7 @@ class DNest4(_TemporaryFileSamplerMixin, NestedSampler):
         return self.result
 
     def _translate_kwargs(self, kwargs):
+        kwargs = super()._translate_kwargs(kwargs)
         if "num_steps" not in kwargs:
             for equiv in self.walks_equiv_kwargs:
                 if equiv in kwargs:

bilby/core/sampler/dynesty.py

@@ -239,6 +239,7 @@ class Dynesty(NestedSampler):
         }
 
     def _translate_kwargs(self, kwargs):
+        kwargs = super()._translate_kwargs(kwargs)
         if "nlive" not in kwargs:
             for equiv in self.npoints_equiv_kwargs:
                 if equiv in kwargs:

bilby/core/sampler/emcee.py

@@ -113,6 +113,7 @@ class Emcee(MCMCSampler):
         return emcee
 
     def _translate_kwargs(self, kwargs):
+        kwargs = super()._translate_kwargs(kwargs)
         if "nwalkers" not in kwargs:
             for equiv in self.nwalkers_equiv_kwargs:
                 if equiv in kwargs:

bilby/core/sampler/nessai.py

@@ -19,7 +19,7 @@ class Nessai(NestedSampler):
     """
 
     _default_kwargs = None
-    seed_equiv_kwargs = ["sampling_seed"]
+    sampling_seed_key = "seed"
 
     @property
     def default_kwargs(self):
@@ -165,6 +165,7 @@ class Nessai(NestedSampler):
         return self.result
 
     def _translate_kwargs(self, kwargs):
+        super()._translate_kwargs(kwargs)
         if "nlive" not in kwargs:
             for equiv in self.npoints_equiv_kwargs:
                 if equiv in kwargs:
@@ -175,10 +176,6 @@ class Nessai(NestedSampler):
                     kwargs["n_pool"] = kwargs.pop(equiv)
             if "n_pool" not in kwargs:
                 kwargs["n_pool"] = self._npool
-        if "seed" not in kwargs:
-            for equiv in self.seed_equiv_kwargs:
-                if equiv in kwargs:
-                    kwargs["seed"] = kwargs.pop(equiv)
 
     def _verify_kwargs_against_default_kwargs(self):
         """

bilby/core/sampler/nestle.py

@@ -42,6 +42,7 @@ class Nestle(NestedSampler):
     )
 
     def _translate_kwargs(self, kwargs):
+        kwargs = super()._translate_kwargs(kwargs)
         if "npoints" not in kwargs:
             for equiv in self.npoints_equiv_kwargs:
                 if equiv in kwargs:

bilby/core/sampler/polychord.py

@@ -50,6 +50,7 @@ class PyPolyChord(NestedSampler):
         nlives={},
     )
     hard_exit = True
+    sampling_seed_key = "seed"
 
     @signal_wrapper
     def run_sampler(self):
@@ -100,6 +101,7 @@ class PyPolyChord(NestedSampler):
             self.kwargs["num_repeats"] = self.ndim * 5
 
     def _translate_kwargs(self, kwargs):
+        kwargs = super()._translate_kwargs(kwargs)
         if "nlive" not in kwargs:
             for equiv in self.npoints_equiv_kwargs:
                 if equiv in kwargs:

bilby/core/sampler/ptemcee.py

@@ -286,6 +286,7 @@ class Ptemcee(MCMCSampler):
 
     def _translate_kwargs(self, kwargs):
         """Translate kwargs"""
+        kwargs = super()._translate_kwargs(kwargs)
         if "nwalkers" not in kwargs:
             for equiv in self.nwalkers_equiv_kwargs:
                 if equiv in kwargs:

bilby/core/sampler/ptmcmc.py

@@ -116,6 +116,7 @@ class PTMCMCSampler(MCMCSampler):
             )
 
     def _translate_kwargs(self, kwargs):
+        kwargs = super()._translate_kwargs(kwargs)
         if "Niter" not in kwargs:
             for equiv in self.nwalkers_equiv_kwargs:
                 if equiv in kwargs:

bilby/core/sampler/pymc3.py

@@ -91,6 +91,8 @@ class Pymc3(MCMCSampler):
 
     default_kwargs.update(default_nuts_kwargs)
 
+    sampling_seed_key = "random_seed"
+
     def __init__(
         self,
         likelihood,