Merge branch 'master' of https://git.ligo.org/lscsoft/bilby

 Conflicts:
	bilby/core/prior/analytical.py

.gitlab-ci.yml

@@ -43,7 +43,7 @@ basic-3.7:
 # test example on python 3.7
 python-3.7:
   stage: test
-  image: bilbydev/v2-dockerfile-test-suite-python37
+  image: quay.io/bilbydev/v2-dockerfile-test-suite-python37
   script:
     - python -m pip install .
 
@@ -69,7 +69,7 @@ python-3.7:
 # test example on python 3.8
 python-3.8:
   stage: test
-  image: bilbydev/v2-dockerfile-test-suite-python38
+  image: quay.io/bilbydev/v2-dockerfile-test-suite-python38
   script:
     - python -m pip install .
 
@@ -78,7 +78,7 @@ python-3.8:
 # test example on python 3.6
 python-3.6:
   stage: test
-  image: bilbydev/v2-dockerfile-test-suite-python36
+  image: quay.io/bilbydev/v2-dockerfile-test-suite-python36
   script:
     - python -m pip install .
 
@@ -87,7 +87,7 @@ python-3.6:
 # test samplers on python 3.7
 python-3.7-samplers:
   stage: test
-  image: bilbydev/v2-dockerfile-test-suite-python37
+  image: quay.io/bilbydev/v2-dockerfile-test-suite-python37
   script:
     - python -m pip install .
 
@@ -97,7 +97,7 @@ python-3.7-samplers:
 # test samplers on python 3.6
 python-3.6-samplers:
   stage: test
-  image: bilbydev/v2-dockerfile-test-suite-python36
+  image: quay.io/bilbydev/v2-dockerfile-test-suite-python36
   script:
     - python -m pip install .
 
@@ -106,7 +106,7 @@ python-3.6-samplers:
 # Test containers are up to date
 containers:
   stage: test
-  image: bilbydev/v2-dockerfile-test-suite-python37
+  image: quay.io/bilbydev/v2-dockerfile-test-suite-python37
   script:
     - cd containers
     - python write_dockerfiles.py
@@ -117,7 +117,7 @@ containers:
 # Tests run at a fixed schedule rather than on push
 scheduled-python-3.7:
   stage: test
-  image: bilbydev/v2-dockerfile-test-suite-python37
+  image: quay.io/bilbydev/v2-dockerfile-test-suite-python37
   only:
     - schedules
   script:
@@ -129,7 +129,7 @@ scheduled-python-3.7:
 
 plotting:
   stage: test
-  image: bilbydev/bilby-test-suite-python37
+  image: quay.io/bilbydev/v2-dockerfile-test-suite-python37
   only:
     - schedules
   script:
@@ -140,7 +140,7 @@ plotting:
 
 authors:
   stage: test
-  image: bilbydev/bilby-test-suite-python37
+  image: quay.io/bilbydev/v2-dockerfile-test-suite-python37
   script:
     - python test/check_author_list.py
 
@@ -162,7 +162,7 @@ pages:
 
 deploy_release:
   stage: deploy
-  image: bilbydev/v2-dockerfile-test-suite-python37
+  image: quay.io/bilbydev/v2-dockerfile-test-suite-python37
   variables:
     TWINE_USERNAME: $PYPI_USERNAME
     TWINE_PASSWORD: $PYPI_PASSWORD
@@ -177,7 +177,7 @@ deploy_release:
 
 precommits-py3.7:
   stage: test
-  image: bilbydev/v2-dockerfile-test-suite-python37
+  image: quay.io/bilbydev/v2-dockerfile-test-suite-python37
   script:
     - source activate python37
     - mkdir -p .pip37

CHANGELOG.md

 # All notable changes will be documented in this file
 
+## [1.0.3] 2020-11-23
+Version 1.0.4 release of bilby
+
+### Added
+- Added a chirp-mass and mass-ratio prior which are uniform in component masses (!891)
+
+### Changes
+- Fixed issue in the CI
+
 ## [1.0.3] 2020-10-23
 
 Version 1.0.3 release of bilby

bilby/__init__.py

@@ -16,7 +16,6 @@ https://lscsoft.docs.ligo.org/bilby/installation.html.
 """
 
 
-from __future__ import absolute_import
 import sys
 
 from . import core, gw, hyper

bilby/core/__init__.py

-from __future__ import absolute_import
 from . import grid, likelihood, prior, result, sampler, series, utils

bilby/core/grid.py

-from __future__ import division
-
 import numpy as np
 import os
 import json

bilby/core/likelihood.py

-from __future__ import division, print_function
 import copy
 
 import numpy as np

bilby/core/prior/analytical.py

@@ -389,7 +389,7 @@ class Cosine(Prior):
         boundary: str
             See superclass
         """
-        super(Cosine, self).__init__(minimum=minimum, maximum=maximum,  name=name,
+        super(Cosine, self).__init__(minimum=minimum, maximum=maximum, name=name,
                                      latex_label=latex_label, unit=unit, boundary=boundary)
 
     def rescale(self, val):

bilby/core/prior/dict.py

@@ -3,7 +3,6 @@ from io import open as ioopen
 import json
 import os
 
-from future.utils import iteritems
 from matplotlib.cbook import flatten
 import numpy as np
 
@@ -185,7 +184,7 @@ class PriorDict(dict):
 
     def from_dictionary(self, dictionary):
         eval_dict = dict(inf=np.inf)
-        for key, val in iteritems(dictionary):
+        for key, val in dictionary.items():
             if isinstance(val, Prior):
                 continue
             elif isinstance(val, (int, float)):

bilby/core/result.py

-from __future__ import division
-
 import inspect
 import os
 from collections import OrderedDict, namedtuple
@@ -238,8 +236,9 @@ class Result(object):
                  sampler_kwargs=None, injection_parameters=None,
                  meta_data=None, posterior=None, samples=None,
                  nested_samples=None, log_evidence=np.nan,
-                 log_evidence_err=np.nan, log_noise_evidence=np.nan,
-                 log_bayes_factor=np.nan, log_likelihood_evaluations=None,
+                 log_evidence_err=np.nan, information_gain=np.nan,
+                 log_noise_evidence=np.nan, log_bayes_factor=np.nan,
+                 log_likelihood_evaluations=None,
                  log_prior_evaluations=None, sampling_time=None, nburn=None,
                  num_likelihood_evaluations=None, walkers=None,
                  max_autocorrelation_time=None, use_ratio=None,
@@ -269,6 +268,8 @@ class Result(object):
             An array of the output posterior samples and the unweighted samples
         log_evidence, log_evidence_err, log_noise_evidence, log_bayes_factor: float
             Natural log evidences
+        information_gain: float
+            The Kullback-Leibler divergence
         log_likelihood_evaluations: array_like
             The evaluations of the likelihood for each sample point
         num_likelihood_evaluations: int
@@ -321,6 +322,7 @@ class Result(object):
         self.use_ratio = use_ratio
         self.log_evidence = log_evidence
         self.log_evidence_err = log_evidence_err
+        self.information_gain = information_gain
         self.log_noise_evidence = log_noise_evidence
         self.log_bayes_factor = log_bayes_factor
         self.log_likelihood_evaluations = log_likelihood_evaluations
@@ -573,7 +575,7 @@ class Result(object):
             'log_noise_evidence', 'log_bayes_factor', 'priors', 'posterior',
             'injection_parameters', 'meta_data', 'search_parameter_keys',
             'fixed_parameter_keys', 'constraint_parameter_keys',
-            'sampling_time', 'sampler_kwargs', 'use_ratio',
+            'sampling_time', 'sampler_kwargs', 'use_ratio', 'information_gain',
             'log_likelihood_evaluations', 'log_prior_evaluations',
             'num_likelihood_evaluations', 'samples', 'nested_samples',
             'walkers', 'nburn', 'parameter_labels', 'parameter_labels_with_unit',

bilby/core/sampler/base_sampler.py

-from __future__ import absolute_import
 import datetime
 import distutils.dir_util
 import numpy as np

bilby/core/sampler/cpnest.py

-from __future__ import absolute_import
 
 import array
 import copy
@@ -89,8 +88,8 @@ class Cpnest(NestedSampler):
                 prior_samples = self.priors.sample()
                 self._update_bounds()
                 point = LivePoint(
-                    self.names, array.array(
-                        'f', [prior_samples[name] for name in self.names]))
+                    self.names, array.array('d', [prior_samples[name] for name in self.names])
+                )
                 return point
 
         self._resolve_proposal_functions()
@@ -132,6 +131,7 @@ class Cpnest(NestedSampler):
         self.result.nested_samples['weights'] = np.exp(log_weights)
         self.result.log_evidence = out.NS.state.logZ
         self.result.log_evidence_err = np.sqrt(out.NS.state.info / out.NS.state.nlive)
+        self.result.information_gain = out.NS.state.info
         return self.result
 
     def _verify_kwargs_against_default_kwargs(self):

bilby/core/sampler/dynamic_dynesty.py

-from __future__ import absolute_import
 
 import os
 import dill as pickle

bilby/core/sampler/dynesty.py

@@ -6,7 +6,7 @@ import pickle
 import signal
 import time
 
-import tqdm
+from tqdm.auto import tqdm
 import matplotlib.pyplot as plt
 import numpy as np
 from pandas import DataFrame
@@ -224,7 +224,7 @@ class Dynesty(NestedSampler):
             self.kwargs['update_interval'] = int(0.6 * self.kwargs['nlive'])
         if self.kwargs['print_func'] is None:
             self.kwargs['print_func'] = self._print_func
-            self.pbar = tqdm.tqdm(file=sys.stdout)
+            self.pbar = tqdm(file=sys.stdout)
         Sampler._verify_kwargs_against_default_kwargs(self)
 
     def _print_func(self, results, niter, ncall=None, dlogz=None, *args, **kwargs):
@@ -401,6 +401,7 @@ class Dynesty(NestedSampler):
             sorted_samples=self.result.samples)
         self.result.log_evidence = out.logz[-1]
         self.result.log_evidence_err = out.logzerr[-1]
+        self.result.information_gain = out.information[-1]
 
     def _run_nested_wrapper(self, kwargs):
         """ Wrapper function to run_nested
@@ -612,7 +613,7 @@ class Dynesty(NestedSampler):
                 fig = dyplot.traceplot(self.sampler.results, labels=labels)[0]
                 fig.tight_layout()
                 fig.savefig(filename)
-            except (RuntimeError, np.linalg.linalg.LinAlgError, ValueError) as e:
+            except (RuntimeError, np.linalg.linalg.LinAlgError, ValueError, OverflowError, Exception) as e:
                 logger.warning(e)
                 logger.warning('Failed to create dynesty state plot at checkpoint')
             finally:
@@ -690,6 +691,16 @@ class Dynesty(NestedSampler):
         """
         return self.priors.rescale(self._search_parameter_keys, theta)
 
+    def calc_likelihood_count(self):
+        if self.likelihood_benchmark:
+            if hasattr(self, 'sampler'):
+                self.result.num_likelihood_evaluations = \
+                    getattr(self.sampler, 'ncall', 0)
+            else:
+                self.result.num_likelihood_evaluations = 0
+        else:
+            return None
+
 
 def sample_rwalk_bilby(args):
     """ Modified bilby-implemented version of dynesty.sampling.sample_rwalk """

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
 
 
@@ -353,7 +350,7 @@ 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

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
@@ -141,7 +140,7 @@ 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/nestle.py

-from __future__ import absolute_import
 
 import numpy as np
 from pandas import DataFrame
@@ -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
 

bilby/core/sampler/polychord.py

-from __future__ import absolute_import
 
 import numpy as np
 

bilby/core/sampler/ptmcmc.py

-from __future__ import absolute_import, print_function
 
 import glob
 import shutil

bilby/core/sampler/pymc3.py

-from __future__ import absolute_import, print_function
 
 from collections import OrderedDict
 from distutils.version import StrictVersion

bilby/core/sampler/ultranest.py

-from __future__ import absolute_import
 
 import datetime
 import distutils.dir_util
@@ -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/utils.py

-from __future__ import division
 
 from distutils.spawn import find_executable
 import logging
@@ -508,26 +507,6 @@ def get_version_information():
         print("No version information file '.version' found")
 
 
-def get_progress_bar(module='tqdm'):
-    """
-    TODO: Write proper docstring
-    """
-    if module in ['tqdm']:
-        try:
-            from tqdm import tqdm
-        except ImportError:
-            def tqdm(x, *args, **kwargs):
-                return x
-        return tqdm
-    elif module in ['tqdm_notebook']:
-        try:
-            from tqdm import tqdm_notebook as tqdm
-        except ImportError:
-            def tqdm(x, *args, **kwargs):
-                return x
-        return tqdm
-
-
 def spherical_to_cartesian(radius, theta, phi):
     """ Convert from spherical coordinates to cartesian.
 

bilby/gw/conversion.py

-from __future__ import division
 import sys
 import multiprocessing
 
-from tqdm import tqdm
+from tqdm.auto import tqdm
 import numpy as np
 from pandas import DataFrame
 

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

bilby/gw/detector/strain_data.py

@@ -31,7 +31,7 @@ class InterferometerStrainData(object):
     time_array = PropertyAccessor('_times_and_frequencies', 'time_array')
 
     def __init__(self, minimum_frequency=0, maximum_frequency=np.inf,
-                 roll_off=0.2):
+                 roll_off=0.2, notch_list=None):
         """ Initiate an InterferometerStrainData object
 
         The initialised object contains no data, this should be added using one
@@ -46,11 +46,14 @@ class InterferometerStrainData(object):
         roll_off: float
             The roll-off (in seconds) used in the Tukey window, default=0.2s.
             This corresponds to alpha * duration / 2 for scipy tukey window.
+        notch_list: bilby.gw.detector.strain_data.NotchList
+            A list of notches
 
         """
 
         self.minimum_frequency = minimum_frequency
         self.maximum_frequency = maximum_frequency
+        self.notch_list = notch_list
         self.roll_off = roll_off
         self.window_factor = 1
 
@@ -122,18 +125,46 @@ class InterferometerStrainData(object):
         self._maximum_frequency = maximum_frequency
         self._frequency_mask_updated = False
 
+    @property
+    def notch_list(self):
+        return self._notch_list
+
+    @notch_list.setter
+    def notch_list(self, notch_list):
+        """ Set the notch_list
+
+        Parameters
+        ----------
+        notch_list: list, bilby.gw.detector.strain_data.NotchList
+            A list of length-2 tuples of the (max, min) frequency for the
+            notches or a pre-made bilby NotchList.
+
+        """
+        if notch_list is None:
+            self._notch_list = NotchList(None)
+        elif isinstance(notch_list, list):
+            self._notch_list = NotchList(notch_list)
+        elif isinstance(notch_list, NotchList):
+            self._notch_list = notch_list
+        else:
+            raise ValueError("notch_list {} not understood".format(notch_list))
+        self._frequency_mask_updated = False
+
     @property
     def frequency_mask(self):
-        """Masking array for limiting the frequency band.
+        """ Masking array for limiting the frequency band.
 
         Returns
         -------
-        array_like: An array of boolean values
+        mask: np.ndarray
+            An array of boolean values
         """
         if not self._frequency_mask_updated:
             frequency_array = self._times_and_frequencies.frequency_array
             mask = ((frequency_array >= self.minimum_frequency) &
                     (frequency_array <= self.maximum_frequency))
+            for notch in self.notch_list:
+                mask[notch.get_idxs(frequency_array)] = False
             self._frequency_mask = mask
             self._frequency_mask_updated = True
         return self._frequency_mask
@@ -683,3 +714,104 @@ class InterferometerStrainData(object):
         strain = strain.resample(sampling_frequency)
 
         self.set_from_gwpy_timeseries(strain)
+
+
+class Notch(object):
+    def __init__(self, minimum_frequency, maximum_frequency):
+        """ A notch object storing the maximum and minimum frequency of the notch
+
+        Parameters
+        ----------
+        minimum_frequency, maximum_frequency: float
+            The minimum and maximum frequency of the notch
+
+        """
+
+        if 0 < minimum_frequency < maximum_frequency < np.inf:
+            self.minimum_frequency = minimum_frequency
+            self.maximum_frequency = maximum_frequency
+        else:
+            msg = ("Your notch minimum_frequency {} and maximum_frequency {} are invalid"
+                   .format(minimum_frequency, maximum_frequency))
+            raise ValueError(msg)
+
+    def get_idxs(self, frequency_array):
+        """ Get a boolean mask for the frequencies in frequency_array in the notch
+
+        Parameters
+        ----------
+        frequency_array: np.ndarray
+            An array of frequencies
+
+        Returns
+        -------
+        idxs: np.ndarray
+            An array of booleans which are True for frequencies in the notch
+
+        """
+        lower = (frequency_array > self.minimum_frequency)
+        upper = (frequency_array < self.maximum_frequency)
+        return lower & upper
+
+    def check_frequency(self, freq):
+        """ Check if freq is inside the notch
+
+        Parameters
+        ----------
+        freq: float
+            The frequency to check
+
+        Returns
+        -------
+        True/False:
+            If freq inside the notch, return True, else False
+        """
+
+        if self.minimum_frequency < freq < self.maximum_frequency:
+            return True
+        else:
+            return False
+
+
+class NotchList(list):
+    def __init__(self, notch_list):
+        """ A list of notches
+