bilby/core/sampler/emcee.py

-from __future__ import absolute_import, print_function
-
 from collections import namedtuple
 import os
 import signal
@@ -12,8 +10,7 @@ from pandas import DataFrame
 from distutils.version import LooseVersion
 import dill as pickle
 
-from ..utils import (
-    logger, get_progress_bar, check_directory_exists_and_if_not_mkdir)
+from ..utils import logger, check_directory_exists_and_if_not_mkdir
 from .base_sampler import MCMCSampler, SamplerError
 
 
@@ -25,8 +22,8 @@ class Emcee(MCMCSampler):
     documentation for that class for further help. Under Other Parameters, we
     list commonly used kwargs and the bilby defaults.
 
-    Other Parameters
-    ----------------
+    Parameters
+    ==========
     nwalkers: int, (100)
         The number of walkers
     nsteps: int, (100)
@@ -44,7 +41,6 @@ class Emcee(MCMCSampler):
     a: float (2)
         The proposal scale factor
 
-
     """
 
     default_kwargs = dict(
@@ -224,7 +220,7 @@ class Emcee(MCMCSampler):
         """ Defines various things related to checkpointing and storing data
 
         Returns
-        -------
+        =======
         checkpoint_info: named_tuple
             An object with attributes `sampler_file`, `chain_file`, and
             `chain_template`. The first two give paths to where the sampler and
@@ -353,7 +349,7 @@ class Emcee(MCMCSampler):
         self.pos0 = self.sampler.chain[:, -1, :]
 
     def run_sampler(self):
-        tqdm = get_progress_bar()
+        from tqdm.auto import tqdm
         sampler_function_kwargs = self.sampler_function_kwargs
         iterations = sampler_function_kwargs.pop('iterations')
         iterations -= self._previous_iterations

bilby/core/sampler/fake_sampler.py

-from __future__ import absolute_import
 
 import numpy as np
 from .base_sampler import Sampler
@@ -13,7 +12,7 @@ class FakeSampler(Sampler):
     See base class for parameters. Added parameters are described below.
 
     Parameters
-    ----------
+    ==========
     sample_file: str
         A string pointing to the posterior data file to be loaded.
     """

bilby/core/sampler/kombine.py

-from __future__ import absolute_import, print_function
-from ..utils import logger, get_progress_bar
+from ..utils import logger
 import numpy as np
 import os
 from .emcee import Emcee
@@ -13,8 +12,8 @@ class Kombine(Emcee):
     documentation for that class for further help. Under Other Parameters, we
     list commonly used kwargs and the bilby defaults.
 
-    Other Parameters
-    ----------------
+    Parameters
+    ==========
     nwalkers: int, (500)
         The number of walkers
     iterations: int, (100)
@@ -32,7 +31,6 @@ class Kombine(Emcee):
     burn_in_act: float (3.)
         The number of autocorrelation times to discard as burn-in
 
-
     """
 
     default_kwargs = dict(nwalkers=500, args=[], pool=None, transd=False,
@@ -141,7 +139,7 @@ class Kombine(Emcee):
                 logger.info("Kombine auto-burnin complete. Removing {} samples from chains".format(self.nburn))
                 self._set_pos0_for_resume()
 
-        tqdm = get_progress_bar()
+        from tqdm.auto import tqdm
         sampler_function_kwargs = self.sampler_function_kwargs
         iterations = sampler_function_kwargs.pop('iterations')
         iterations -= self._previous_iterations

bilby/core/sampler/nessai.py

+import numpy as np
+from pandas import DataFrame
+
+from .base_sampler import NestedSampler
+from ..utils import logger, check_directory_exists_and_if_not_mkdir, load_json
+
+
+class Nessai(NestedSampler):
+    """bilby wrapper of nessai (https://github.com/mj-will/nessai)
+
+    All positional and keyword arguments passed to `run_sampler` are propogated
+    to `nessai.flowsampler.FlowSampler`
+
+    See the documentation for an explanation of the different kwargs.
+
+    Documentation: https://nessai.readthedocs.io/
+    """
+    default_kwargs = dict(
+        output=None,
+        nlive=1000,
+        stopping=0.1,
+        resume=True,
+        max_iteration=None,
+        checkpointing=True,
+        seed=1234,
+        acceptance_threshold=0.01,
+        analytic_priors=False,
+        maximum_uninformed=1000,
+        uninformed_proposal=None,
+        uninformed_proposal_kwargs=None,
+        flow_class=None,
+        flow_config=None,
+        training_frequency=None,
+        reset_weights=False,
+        reset_permutations=False,
+        reset_acceptance=False,
+        train_on_empty=True,
+        cooldown=100,
+        memory=False,
+        poolsize=None,
+        drawsize=None,
+        max_poolsize_scale=10,
+        update_poolsize=False,
+        latent_prior='truncated_gaussian',
+        draw_latent_kwargs=None,
+        compute_radius_with_all=False,
+        min_radius=False,
+        max_radius=50,
+        check_acceptance=False,
+        fuzz=1.0,
+        expansion_fraction=1.0,
+        rescale_parameters=True,
+        rescale_bounds=[-1, 1],
+        update_bounds=False,
+        boundary_inversion=False,
+        inversion_type='split', detect_edges=False,
+        detect_edges_kwargs=None,
+        reparameterisations=None,
+        n_pool=None,
+        max_threads=1,
+        pytorch_threads=None,
+        plot=None,
+        proposal_plots=False
+    )
+    seed_equiv_kwargs = ['sampling_seed']
+
+    def log_prior(self, theta):
+        """
+
+        Parameters
+        ----------
+        theta: list
+            List of sampled values on a unit interval
+
+        Returns
+        -------
+        float: Joint ln prior probability of theta
+
+        """
+        return self.priors.ln_prob(theta, axis=0)
+
+    def run_sampler(self):
+        from nessai.flowsampler import FlowSampler
+        from nessai.model import Model as BaseModel
+        from nessai.livepoint import dict_to_live_points
+        from nessai.posterior import compute_weights
+        from nessai.utils import setup_logger
+
+        class Model(BaseModel):
+            """A wrapper class to pass our log_likelihood and priors into nessai
+
+            Parameters
+            ----------
+            names : list of str
+                List of parameters to sample
+            priors : :obj:`bilby.core.prior.PriorDict`
+                Priors to use for sampling. Needed for the bounds and the
+                `sample` method.
+            """
+            def __init__(self, names, priors):
+                self.names = names
+                self.priors = priors
+                self._update_bounds()
+
+            @staticmethod
+            def log_likelihood(x, **kwargs):
+                """Compute the log likelihood"""
+                theta = [x[n].item() for n in self.search_parameter_keys]
+                return self.log_likelihood(theta)
+
+            @staticmethod
+            def log_prior(x, **kwargs):
+                """Compute the log prior"""
+                theta = {n: x[n] for n in self._search_parameter_keys}
+                return self.log_prior(theta)
+
+            def _update_bounds(self):
+                self.bounds = {key: [self.priors[key].minimum, self.priors[key].maximum]
+                               for key in self.names}
+
+            def new_point(self, N=1):
+                """Draw a point from the prior"""
+                prior_samples = self.priors.sample(size=N)
+                samples = {n: prior_samples[n] for n in self.names}
+                return dict_to_live_points(samples)
+
+            def new_point_log_prob(self, x):
+                """Proposal probability for new the point"""
+                return self.log_prior(x)
+
+        # Setup the logger for nessai using the same settings as the bilby logger
+        setup_logger(self.outdir, label=self.label,
+                     log_level=logger.getEffectiveLevel())
+        model = Model(self.search_parameter_keys, self.priors)
+        out = None
+        while out is None:
+            try:
+                out = FlowSampler(model, **self.kwargs)
+            except TypeError as e:
+                raise TypeError("Unable to initialise nessai sampler with error: {}".format(e))
+        try:
+            out.run(save=True, plot=self.plot)
+        except SystemExit as e:
+            import sys
+            logger.info("Caught exit code {}, exiting with signal {}".format(e.args[0], self.exit_code))
+            sys.exit(self.exit_code)
+
+        # Manually set likelihood evaluations because parallelisation breaks the counter
+        self.result.num_likelihood_evaluations = out.ns.likelihood_evaluations[-1]
+
+        self.result.posterior = DataFrame(out.posterior_samples)
+        self.result.nested_samples = DataFrame(out.nested_samples)
+        self.result.nested_samples.rename(
+            columns=dict(logL='log_likelihood', logP='log_prior'), inplace=True)
+        self.result.posterior.rename(
+            columns=dict(logL='log_likelihood', logP='log_prior'), inplace=True)
+        _, log_weights = compute_weights(np.array(self.result.nested_samples.log_likelihood),
+                                         np.array(out.ns.state.nlive))
+        self.result.nested_samples['weights'] = np.exp(log_weights)
+        self.result.log_evidence = out.ns.log_evidence
+        self.result.log_evidence_err = np.sqrt(out.ns.information / out.ns.nlive)
+
+        return self.result
+
+    def _translate_kwargs(self, kwargs):
+        if 'nlive' not in kwargs:
+            for equiv in self.npoints_equiv_kwargs:
+                if equiv in kwargs:
+                    kwargs['nlive'] = kwargs.pop(equiv)
+        if 'n_pool' not in kwargs:
+            for equiv in self.npool_equiv_kwargs:
+                if equiv in kwargs:
+                    kwargs['n_pool'] = kwargs.pop(equiv)
+        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):
+        """
+        Set the directory where the output will be written
+        and check resume and checkpoint status.
+        """
+        if 'config_file' in self.kwargs:
+            d = load_json(self.kwargs['config_file'], None)
+            self.kwargs.update(d)
+            self.kwargs.pop('config_file')
+
+        if not self.kwargs['plot']:
+            self.kwargs['plot'] = self.plot
+
+        if self.kwargs['n_pool'] == 1 and self.kwargs['max_threads'] == 1:
+            logger.warning('Setting pool to None (n_pool=1 & max_threads=1)')
+            self.kwargs['n_pool'] = None
+
+        if not self.kwargs['output']:
+            self.kwargs['output'] = self.outdir + '/{}_nessai/'.format(self.label)
+        if self.kwargs['output'].endswith('/') is False:
+            self.kwargs['output'] = '{}/'.format(self.kwargs['output'])
+
+        check_directory_exists_and_if_not_mkdir(self.kwargs['output'])
+        NestedSampler._verify_kwargs_against_default_kwargs(self)

bilby/core/sampler/nestle.py

-from __future__ import absolute_import
 
 import numpy as np
 from pandas import DataFrame
@@ -14,8 +13,8 @@ class Nestle(NestedSampler):
     that function for further help. Under Other Parameters, we list commonly
     used kwargs and the bilby defaults
 
-    Other Parameters
-    ----------------
+    Parameters
+    ==========
     npoints: int
         The number of live points, note this can also equivalently be given as
         one of [nlive, nlives, n_live_points]
@@ -52,7 +51,7 @@ class Nestle(NestedSampler):
         """ Runs Nestle sampler with given kwargs and returns the result
 
         Returns
-        -------
+        =======
         bilby.core.result.Result: Packaged information about the result
 
         """
@@ -74,6 +73,7 @@ class Nestle(NestedSampler):
             sorted_samples=self.result.samples)
         self.result.log_evidence = out.logz
         self.result.log_evidence_err = out.logzerr
+        self.result.information_gain = out.h
         self.calc_likelihood_count()
         return self.result
 
@@ -83,7 +83,7 @@ class Nestle(NestedSampler):
         kwargs without actually running to the end
 
         Returns
-        -------
+        =======
         bilby.core.result.Result: Dummy container for sampling results.
 
         """

bilby/core/sampler/polychord.py

-from __future__ import absolute_import
 
 import numpy as np
 
@@ -84,7 +83,7 @@ class PyPolyChord(NestedSampler):
         The second column are the log likelihoods, the remaining columns are the physical parameters
 
         Returns
-        -------
+        =======
         array_like, array_like: The log_likelihoods and the associated parameters
 
         """

bilby/core/sampler/proposal.py

@@ -43,12 +43,12 @@ class JumpProposal(object):
         """ A generic class for jump proposals
 
         Parameters
-        ----------
+        ==========
         priors: bilby.core.prior.PriorDict
             Dictionary of priors used in this sampling run
 
         Attributes
-        ----------
+        ==========
         log_j: float
             Log Jacobian of the proposal. Characterises whether or not detailed balance
             is preserved. If not, log_j needs to be adjusted accordingly.
@@ -60,12 +60,12 @@ class JumpProposal(object):
         """ A generic wrapper for the jump proposal function
 
         Parameters
-        ----------
+        ==========
         args: Arguments that are going to be passed into the proposal function
         kwargs: Keyword arguments that are going to be passed into the proposal function
 
         Returns
-        -------
+        =======
         dict: A dictionary with the new samples. Boundary conditions are applied.
 
         """
@@ -106,7 +106,7 @@ class JumpProposalCycle(object):
         """ A generic wrapper class for proposal cycles
 
         Parameters
-        ----------
+        ==========
         proposal_functions: list
             A list of callable proposal functions/objects
         weights: list
@@ -153,7 +153,7 @@ class JumpProposalCycle(object):
         """
 
         Returns
-        -------
+        =======
         Normalised proposal weights
 
         """
@@ -175,7 +175,7 @@ class NormJump(JumpProposal):
         A normal distributed step centered around the old sample
 
         Parameters
-        ----------
+        ==========
         step_size: float
             The scalable step size
         priors:
@@ -196,8 +196,9 @@ class EnsembleWalk(JumpProposal):
                  **random_number_generator_args):
         """
         An ensemble walk
+
         Parameters
-        ----------
+        ==========
         random_number_generator: func, optional
             A random number generator. Default is random.random
         n_points: int, optional
@@ -233,7 +234,7 @@ class EnsembleStretch(JumpProposal):
         Stretch move. Calculates the log Jacobian which can be used in cpnest to bias future moves.
 
         Parameters
-        ----------
+        ==========
         scale: float, optional
             Stretching scale. Default is 2.0.
         """
@@ -257,7 +258,7 @@ class DifferentialEvolution(JumpProposal):
         old sample based on them using some Gaussian randomisation in the step.
 
         Parameters
-        ----------
+        ==========
         sigma: float, optional
             Random spread in the evolution step. Default is 1e-4
         mu: float, optional
@@ -280,7 +281,7 @@ class EnsembleEigenVector(JumpProposal):
         Ensemble step based on the ensemble eigenvectors.
 
         Parameters
-        ----------
+        ==========
         priors:
             See superclass
         """

bilby/core/sampler/ptmcmc.py

-from __future__ import absolute_import, print_function
 
 import glob
 import shutil
@@ -17,8 +16,8 @@ class PTMCMCSampler(MCMCSampler):
     documentation for that class for further help. Under Other Parameters, we
     list commonly used kwargs and the bilby defaults.
 
-    Other Parameters
-    ----------------
+    Parameters
+    ==========
     Niter: int (2*10**4 + 1)
         The number of mcmc steps
     burn: int (5 * 10**3)
@@ -33,10 +32,13 @@ class PTMCMCSampler(MCMCSampler):
         (https://github.com/rgreen1995/PTMCMCSampler/blob/master/examples/simple.ipynb)
         and
         (http://jellis18.github.io/PTMCMCSampler/PTMCMCSampler.html#ptmcmcsampler-ptmcmcsampler-module)
-        for examples and more info.  logl_grad: func (None) Gradient of
-        likelihood  if known (default = None) logp_grad: func (None) Gradient
-        of prior if known (default = None) verbose: bool (True) Update current
-        run-status to the screen
+        for examples and more info.
+    logl_grad: func (None)
+        Gradient of likelihood  if known (default = None)
+    logp_grad: func (None)
+        Gradient of prior if known (default = None)
+    verbose: bool (True)
+        Update current run-status to the screen
 
     """
 

bilby/core/sampler/pymc3.py

-from __future__ import absolute_import, print_function
 
 from collections import OrderedDict
 from distutils.version import StrictVersion
@@ -21,8 +20,8 @@ class Pymc3(MCMCSampler):
     class for further help. Under Other Parameters, we list commonly used
     kwargs and the bilby, or where appropriate, PyMC3 defaults.
 
-    Other Parameters
-    ----------------
+    Parameters
+    ==========
     draws: int, (1000)
         The number of sample draws from the posterior per chain.
     chains: int, (2)

bilby/core/sampler/pymultinest.py

@@ -24,8 +24,8 @@ class Pymultinest(NestedSampler):
     for that class for further help. Under Other Parameters, we list commonly
     used kwargs and the bilby defaults.
 
-    Other Parameters
-    ----------------
+    Parameters
+    ==========
     npoints: int
         The number of live points, note this can also equivalently be given as
         one of [nlive, nlives, n_live_points]
@@ -129,9 +129,9 @@ class Pymultinest(NestedSampler):
 
     def _apply_multinest_boundaries(self):
         if self.kwargs["wrapped_params"] is None:
-            self.kwargs["wrapped_params"] = []
-            for param, value in self.priors.items():
-                if value.boundary == "periodic":
+            self.kwargs["wrapped_params"] = list()
+            for param in self.search_parameter_keys:
+                if self.priors[param].boundary == "periodic":
                     self.kwargs["wrapped_params"].append(1)
                 else:
                     self.kwargs["wrapped_params"].append(0)

bilby/core/sampler/ultranest.py

-from __future__ import absolute_import
 
 import datetime
 import distutils.dir_util
@@ -28,8 +27,8 @@ class Ultranest(NestedSampler):
     `ultranest.NestedSampler` will be used, otherwise the
     `ultranest.ReactiveNestedSampler` will be used.
 
-    Other Parameters
-    ----------------
+    Parameters
+    ==========
     num_live_points: int
         The number of live points, note this can also equivalently be given as
         one of [nlive, nlives, n_live_points, num_live_points]. If not given
@@ -61,7 +60,7 @@ class Ultranest(NestedSampler):
         log_interval=None,
         dlogz=None,
         max_iters=None,
-        update_interval_iter_fraction=0.2,
+        update_interval_volume_fraction=0.2,
         viz_callback=None,
         dKL=0.5,
         frac_remain=0.01,
@@ -232,7 +231,7 @@ class Ultranest(NestedSampler):
             ]
         else:
             keys = [
-                "update_interval_iter_fraction",
+                "update_interval_volume_fraction",
                 "update_interval_ncall",
                 "log_interval",
                 "show_status",
@@ -366,6 +365,8 @@ class Ultranest(NestedSampler):
         self.result.nested_samples = nested_samples
         self.result.log_evidence = out["logz"]
         self.result.log_evidence_err = out["logzerr"]
+        if self.kwargs["num_live_points"] is not None:
+            self.result.information_gain = np.power(out["logzerr"], 2) * self.kwargs["num_live_points"]
 
         self.result.outputfiles_basename = self.outputfiles_basename
         self.result.sampling_time = datetime.timedelta(seconds=self.total_sampling_time)

bilby/core/series.py

@@ -7,7 +7,7 @@ class CoupledTimeAndFrequencySeries(object):
         """ A waveform generator
 
     Parameters
-    ----------
+    ==========
     sampling_frequency: float, optional
         The sampling frequency
     duration: float, optional
@@ -32,7 +32,7 @@ class CoupledTimeAndFrequencySeries(object):
         """ Frequency array for the waveforms. Automatically updates if sampling_frequency or duration are updated.
 
         Returns
-        -------
+        =======
         array_like: The frequency array
         """
         if not self._frequency_array_updated:
@@ -60,7 +60,7 @@ class CoupledTimeAndFrequencySeries(object):
         """ Time array for the waveforms. Automatically updates if sampling_frequency or duration are updated.
 
         Returns
-        -------
+        =======
         array_like: The time array
         """
 
@@ -91,7 +91,7 @@ class CoupledTimeAndFrequencySeries(object):
         """ Allows one to set the time duration and automatically updates the frequency and time array.
 
         Returns
-        -------
+        =======
         float: The time duration.
 
         """
@@ -108,7 +108,7 @@ class CoupledTimeAndFrequencySeries(object):
         """ Allows one to set the sampling frequency and automatically updates the frequency and time array.
 
         Returns
-        -------
+        =======
         float: The sampling frequency.
 
         """

bilby/gw/cosmology.py

@@ -27,7 +27,7 @@ def set_cosmology(cosmology=None):
     as the last used cosmology.
 
     Parameters
-    ----------
+    ==========
     cosmology: astropy.cosmology.FLRW, str, dict
         Description of cosmology, one of:
             None - Use DEFAULT_COSMOLOGY
@@ -37,7 +37,7 @@ def set_cosmology(cosmology=None):
             class.
 
     Returns
-    -------
+    =======
     cosmo: astropy.cosmology.FLRW
         Cosmology instance
     """

bilby/gw/detector/detectors/A1.interferometer

+# LIGO India Aundha at Aplus sensitvity.
+# LIGO-T2000158
+# https://dcc.ligo.org/LIGO-T2000012/public
+name = 'A1'
+power_spectral_density = PowerSpectralDensity(asd_file='Aplus_asd.txt')
+minimum_frequency = 20
+maximum_frequency = 2048
+length = 4
+latitude = 19 + 36. / 60 + 47.9017 / 3600
+longitude = 77 + 1. / 60 + 51.0997 / 3600
+elevation = 440.0
+xarm_azimuth = 117.6157
+yarm_azimuth = 207.6165