.gitlab-ci.yml

@@ -75,6 +75,10 @@ basic-3.10:
   <<: *test-python
   image: python:3.10
 
+basic-3.11:
+  <<: *test-python
+  image: python:3.11
+
 .test-samplers-import: &test-samplers-import
   stage: initial
   script:
@@ -90,6 +94,10 @@ import-samplers-3.10:
   <<: *test-samplers-import
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
 
+import-samplers-3.11:
+  <<: *test-samplers-import
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+
 .precommits: &precommits
   stage: initial
   script:
@@ -98,17 +106,9 @@ import-samplers-3.10:
     - pip install --upgrade pip
     - pip --cache-dir=$CACHE_DIR install --upgrade bilby
     - pip --cache-dir=$CACHE_DIR install .
-    - pip --cache-dir=$CACHE_DIR install pre-commit
     # Run precommits (flake8, spellcheck, isort, no merge conflicts, etc)
     - pre-commit run --all-files --verbose --show-diff-on-failure
 
-precommits-py3.9:
-  <<: *precommits
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python39
-  variables:
-    CACHE_DIR: ".pip39"
-    PYVERSION: "python39"
-
 precommits-py3.10:
   <<: *precommits
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
@@ -137,7 +137,7 @@ install:
 
 python-3.9:
   <<: *unit-test
-  needs: ["basic-3.9", "precommits-py3.9"]
+  needs: ["basic-3.9"]
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python39
 
 python-3.10:
@@ -157,6 +157,11 @@ python-3.10:
       - htmlcov/
     expire_in: 30 days
 
+python-3.11:
+  <<: *unit-test
+  needs: ["basic-3.11"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+
 .test-sampler: &test-sampler
   stage: test
   script:
@@ -166,7 +171,7 @@ python-3.10:
 
 python-3.9-samplers:
   <<: *test-sampler
-  needs: ["basic-3.9", "precommits-py3.9"]
+  needs: ["basic-3.9"]
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python39
 
 python-3.10-samplers:
@@ -174,6 +179,11 @@ python-3.10-samplers:
   needs: ["basic-3.10", "precommits-py3.10"]
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
 
+python-3.11-samplers:
+  <<: *test-sampler
+  needs: ["basic-3.11"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+
 integration-tests-python-3.10:
   stage: test
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
@@ -198,21 +208,24 @@ integration-tests-python-3.10:
 plotting-python-3.9:
   <<: *plotting
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python39
-  needs: ["basic-3.9", "precommits-py3.9"]
+  needs: ["basic-3.9"]
 
 plotting-python-3.10:
   <<: *plotting
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
   needs: ["basic-3.10", "precommits-py3.10"]
 
+plotting-python-3.11:
+  <<: *plotting
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+  needs: ["basic-3.11"]
+
 # ------------------- Docs stage -------------------------------------------
 
 docs:
   stage: docs
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
   before_script:
-    - conda install -c conda-forge pandoc ipython jupyter nbconvert
-    - python -m pip install ipykernel
     - python -m ipykernel install
   script:
     # Make the documentation
@@ -258,7 +271,8 @@ pages:
   script:
     - cd containers
     - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
-    - docker build --tag v3-bilby-$PYVERSION - < v3-dockerfile-test-suite-$PYVERSION
+    - cp v3-dockerfile-test-suite-$PYVERSION Dockerfile
+    - docker build --tag v3-bilby-$PYVERSION .
     - docker image tag v3-bilby-$PYVERSION containers.ligo.org/lscsoft/bilby/v2-bilby-$PYVERSION:latest
     - docker image push containers.ligo.org/lscsoft/bilby/v2-bilby-$PYVERSION:latest
 
@@ -272,6 +286,11 @@ build-python310-container:
   variables:
     PYVERSION: "python310"
 
+build-python311-container:
+  <<: *build-container
+  variables:
+    PYVERSION: "python311"
+
 pypi-release:
   stage: deploy
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
@@ -279,7 +298,6 @@ pypi-release:
     TWINE_USERNAME: $PYPI_USERNAME
     TWINE_PASSWORD: $PYPI_PASSWORD
   before_script:
-    - python -m pip install twine setuptools_scm build
     - python -m build --sdist --wheel --outdir dist/ .
   script:
     - twine upload dist/*

AUTHORS.md

@@ -57,6 +57,7 @@ Michael Puerrer
 Michael Williams
 Monica Rizzo
 Moritz Huebner
+Nico Gerardo Bers
 Nicola De Lillo
 Nikhil Sarin
 Nirban Bose
@@ -72,6 +73,7 @@ Roberto Cotesta
 Rory Smith
 S. H. Oh
 Sacha Husa
+Samson Leong
 Scott Coughlin
 Serguei Ossokine
 Shanika Galaudage
@@ -79,6 +81,7 @@ Sharan Banagiri
 Shichao Wu
 Simon Stevenson
 Soichiro Morisaki
+Soumen Roy
 Stephen R Green
 Sumeet Kulkarni
 Sylvia Biscoveanu
@@ -94,3 +97,4 @@ Marc Penuliar
 Andrew Fowlie
 Martin White
 Peter Tsun-Ho Pang
+Alexandre Sebastien Goettel

CHANGELOG.md

 # All notable changes will be documented in this file
 
+## [2.2.3] 2024-02-24
+Version 2.2.3 release of Bilby
+
+This is a bugfix release 
+
+There are also a number of testing/infrastructure updates.
+
+### Changes
+- Fix a bug when the specified maximum frequency is too low for the multibanding likelihood (!1279)
+- Allow the `DirichletElement` prior to be pickled (!1312)
+- Add the ability to change the pool size when resuming a `dynesty` job (!1315)
+- Fix how the random seed is passed to `dynesty` (!1319)
+
+## [2.2.2] 2023-11-29
+Version 2.2.2 release of Bilby
+
+This is a bugfix release reverting a change from 2.2.1
+
+### Changes
+- Revert !1284 (!1306)
+
+## [2.2.1] 2023-1111
+Version 2.2.1 release of Bilby
+
+This release is a bugfix release.
+
+### Changes
+- Ensure inteferometer metadata is not empty (!1281)
+- Make interrupted pools exit more quickly (!1284)
+- Fix conditional sampling with DeltaFunction conditions (!1289)
+- The triangular prior raised an error with numpy (!1294)
+- Make sure strain data resampling works (!1295)
+- Dynesty logging (!1296)
+- A bug with saving lists that contain None (!1301)
+- Preparatory fix an upcoming change in dynesty (!1302)
+
 ## [2.2.0] 2023-07-24
 Version 2.2.0 release of Bilby
 

bilby/bilby_mcmc/flows.py

 import torch
-from nflows.distributions.normal import StandardNormal
-from nflows.flows.base import Flow
-from nflows.nn import nets as nets
-from nflows.transforms import (
+from glasflow.nflows.distributions.normal import StandardNormal
+from glasflow.nflows.flows.base import Flow
+from glasflow.nflows.nn import nets as nets
+from glasflow.nflows.transforms import (
     CompositeTransform,
     MaskedAffineAutoregressiveTransform,
     RandomPermutation,
 )
-from nflows.transforms.coupling import (
+from glasflow.nflows.transforms.coupling import (
     AdditiveCouplingTransform,
     AffineCouplingTransform,
 )
-from nflows.transforms.normalization import BatchNorm
+from glasflow.nflows.transforms.normalization import BatchNorm
 from torch.nn import functional as F
 
 # Turn off parallelism

bilby/bilby_mcmc/proposals.py

@@ -754,10 +754,10 @@ class NormalizingFlowProposal(DensityEstimateProposal):
 
     @staticmethod
     def check_dependencies(warn=True):
-        if importlib.util.find_spec("nflows") is None:
+        if importlib.util.find_spec("glasflow") is None:
             if warn:
                 logger.warning(
-                    "Unable to utilise NormalizingFlowProposal as nflows is not installed"
+                    "Unable to utilise NormalizingFlowProposal as glasflow is not installed"
                 )
             return False
         else:

bilby/core/prior/analytical.py

@@ -28,6 +28,7 @@ class DeltaFunction(Prior):
                                             minimum=peak, maximum=peak, check_range_nonzero=False)
         self.peak = peak
         self._is_fixed = True
+        self.least_recently_sampled = peak
 
     def rescale(self, val):
         """Rescale everything to the peak with the correct shape.
@@ -1608,7 +1609,7 @@ class Triangular(Prior):
 
         """
         return (
-            + (val > self.mode)
+            (val > self.mode)
             + (val > self.minimum)
             * (val <= self.maximum)
             / (self.scale)

bilby/core/prior/base.py

@@ -168,14 +168,14 @@ class Prior(object):
 
     def cdf(self, val):
         """ Generic method to calculate CDF, can be overwritten in subclass """
-        from scipy.integrate import cumtrapz
+        from scipy.integrate import cumulative_trapezoid
         if np.any(np.isinf([self.minimum, self.maximum])):
             raise ValueError(
                 "Unable to use the generic CDF calculation for priors with"
                 "infinite support")
         x = np.linspace(self.minimum, self.maximum, 1000)
         pdf = self.prob(x)
-        cdf = cumtrapz(pdf, x, initial=0)
+        cdf = cumulative_trapezoid(pdf, x, initial=0)
         interp = interp1d(x, cdf, assume_sorted=True, bounds_error=False,
                           fill_value=(0, 1))
         return interp(val)

bilby/core/prior/conditional.py

@@ -372,6 +372,7 @@ class DirichletElement(ConditionalBeta):
             label + str(ii) for ii in range(order)
         ]
         self.__class__.__name__ = 'DirichletElement'
+        self.__class__.__qualname__ = 'DirichletElement'
 
     def dirichlet_condition(self, reference_parms, **kwargs):
         remaining = 1 - sum(

bilby/core/prior/dict.py

@@ -510,14 +510,14 @@ class PriorDict(dict):
         sample: dict
             Dictionary of the samples of which we want to have the probability of
         kwargs:
-            The keyword arguments are passed directly to `np.product`
+            The keyword arguments are passed directly to `np.prod`
 
         Returns
         =======
         float: Joint probability of all individual sample probabilities
 
         """
-        prob = np.product([self[key].prob(sample[key]) for key in sample], **kwargs)
+        prob = np.prod([self[key].prob(sample[key]) for key in sample], **kwargs)
 
         return self.check_prob(sample, prob)
 
@@ -711,16 +711,22 @@ class ConditionalPriorDict(PriorDict):
 
     def sample_subset(self, keys=iter([]), size=None):
         self.convert_floats_to_delta_functions()
-        subset_dict = ConditionalPriorDict({key: self[key] for key in keys})
+        add_delta_keys = [
+            key
+            for key in self.keys()
+            if key not in keys and isinstance(self[key], DeltaFunction)
+        ]
+        use_keys = add_delta_keys + list(keys)
+        subset_dict = ConditionalPriorDict({key: self[key] for key in use_keys})
         if not subset_dict._resolved:
             raise IllegalConditionsException(
                 "The current set of priors contains unresolvable conditions."
             )
         samples = dict()
         for key in subset_dict.sorted_keys:
-            if isinstance(self[key], Constraint):
+            if key not in keys or isinstance(self[key], Constraint):
                 continue
-            elif isinstance(self[key], Prior):
+            if isinstance(self[key], Prior):
                 try:
                     samples[key] = subset_dict[key].sample(
                         size=size, **subset_dict.get_required_variables(key)
@@ -764,7 +770,7 @@ class ConditionalPriorDict(PriorDict):
         sample: dict
             Dictionary of the samples of which we want to have the probability of
         kwargs:
-            The keyword arguments are passed directly to `np.product`
+            The keyword arguments are passed directly to `np.prod`
 
         Returns
         =======
@@ -776,7 +782,7 @@ class ConditionalPriorDict(PriorDict):
             self[key].prob(sample[key], **self.get_required_variables(key))
             for key in sample
         ]
-        prob = np.product(res, **kwargs)
+        prob = np.prod(res, **kwargs)
         return self.check_prob(sample, prob)
 
     def ln_prob(self, sample, axis=None):

bilby/core/prior/interpolated.py

@@ -162,11 +162,11 @@ class Interped(Prior):
         self._initialize_attributes()
 
     def _initialize_attributes(self):
-        from scipy.integrate import cumtrapz
+        from scipy.integrate import cumulative_trapezoid
         if np.trapz(self._yy, self.xx) != 1:
             logger.debug('Supplied PDF for {} is not normalised, normalising.'.format(self.name))
         self._yy /= np.trapz(self._yy, self.xx)
-        self.YY = cumtrapz(self._yy, self.xx, initial=0)
+        self.YY = cumulative_trapezoid(self._yy, self.xx, initial=0)
         # Need last element of cumulative distribution to be exactly one.
         self.YY[-1] = 1
         self.probability_density = interp1d(x=self.xx, y=self._yy, bounds_error=False, fill_value=0)

bilby/core/sampler/dynesty.py

@@ -123,6 +123,9 @@ class Dynesty(NestedSampler):
         The proposal methods to use during MCMC. This can be some combination
         of :code:`"diff", "volumetric"`. See the dynesty guide in the Bilby docs
         for more details. default=:code:`["diff"]`.
+    rstate: numpy.random.Generator (None)
+        Instance of a numpy random generator for generating random numbers.
+        Also see :code:`seed` in 'Other Parameters'.
 
     Other Parameters
     ================
@@ -143,8 +146,13 @@ class Dynesty(NestedSampler):
         has no impact on the sampling.
     dlogz: float, (0.1)
         Stopping criteria
+    seed: int (None)
+        Use to seed the random number generator if :code:`rstate` is not
+        specified.
     """
 
+    sampling_seed_key = "seed"
+
     @property
     def _dynesty_init_kwargs(self):
         params = inspect.signature(self.sampler_init).parameters
@@ -176,6 +184,7 @@ class Dynesty(NestedSampler):
     def default_kwargs(self):
         kwargs = self._dynesty_init_kwargs
         kwargs.update(self._dynesty_sampler_kwargs)
+        kwargs["seed"] = None
         return kwargs
 
     def __init__(
@@ -265,6 +274,14 @@ class Dynesty(NestedSampler):
             for equiv in self.npool_equiv_kwargs:
                 if equiv in kwargs:
                     kwargs["queue_size"] = kwargs.pop(equiv)
+        if "seed" in kwargs:
+            seed = kwargs.get("seed")
+            if "rstate" not in kwargs:
+                kwargs["rstate"] = np.random.default_rng(seed)
+            else:
+                logger.warning(
+                    "Kwargs contain both 'rstate' and 'seed', ignoring 'seed'."
+                )
 
     def _verify_kwargs_against_default_kwargs(self):
         if not self.kwargs["walks"]:
@@ -446,7 +463,7 @@ class Dynesty(NestedSampler):
 
         if sample == "rwalk":
             logger.info(
-                "Using the bilby-implemented rwalk sample method with ACT estimated walks. "
+                f"Using the bilby-implemented {sample} sample method with ACT estimated walks. "
                 f"An average of {2 * self.nact} steps will be accepted up to chain length "
                 f"{self.maxmcmc}."
             )
@@ -460,7 +477,7 @@ class Dynesty(NestedSampler):
             dynesty.nestedsamplers._SAMPLING["rwalk"] = AcceptanceTrackingRWalk()
         elif sample == "acceptance-walk":
             logger.info(
-                "Using the bilby-implemented rwalk sampling with an average of "
+                f"Using the bilby-implemented {sample} sampling with an average of "
                 f"{self.naccept} accepted steps per MCMC and maximum length {self.maxmcmc}"
             )
             from .dynesty_utils import FixedRWalk
@@ -468,7 +485,7 @@ class Dynesty(NestedSampler):
             dynesty.nestedsamplers._SAMPLING["acceptance-walk"] = FixedRWalk()
         elif sample == "act-walk":
             logger.info(
-                "Using the bilby-implemented rwalk sampling tracking the "
+                f"Using the bilby-implemented {sample} sampling tracking the "
                 f"autocorrelation function and thinning by "
                 f"{self.nact} with maximum length {self.nact * self.maxmcmc}"
             )
@@ -604,6 +621,7 @@ class Dynesty(NestedSampler):
             sampling_time_s=self.sampling_time.seconds,
             ncores=self.kwargs.get("queue_size", 1),
         )
+        self.kwargs["rstate"] = None
 
     def _update_sampling_time(self):
         end_time = datetime.datetime.now()
@@ -717,6 +735,7 @@ class Dynesty(NestedSampler):
                 self.sampler.nqueue = -1
                 self.start_time = self.sampler.kwargs.pop("start_time")
                 self.sampling_time = self.sampler.kwargs.pop("sampling_time")
+                self.sampler.queue_size = self.kwargs["queue_size"]
                 self.sampler.pool = self.pool
                 if self.pool is not None:
                     self.sampler.M = self.pool.map

bilby/core/sampler/dynesty_utils.py

@@ -68,7 +68,7 @@ class LivePointSampler(UnitCubeSampler):
         self.kwargs["live"] = self.live_u
         i = self.rstate.integers(self.nlive)
         u = self.live_u[i, :]
-        return u, np.identity(self.npdim)
+        return u, np.identity(self.ncdim)
 
 
 class MultiEllipsoidLivePointSampler(MultiEllipsoidSampler):

bilby/core/utils/io.py

@@ -283,7 +283,7 @@ def encode_for_hdf5(key, item):
             output = item
         elif np.issubdtype(item_array.dtype, np.number):
             output = np.array(item)
-        elif issubclass(item_array.dtype.type, str) or item[0] is None:
+        elif issubclass(item_array.dtype.type, str) or None in item:
             output = list()
             for value in item:
                 if isinstance(value, str):

bilby/gw/detector/interferometer.py

@@ -90,7 +90,7 @@ class Interferometer(object):
         self.strain_data = InterferometerStrainData(
             minimum_frequency=minimum_frequency,
             maximum_frequency=maximum_frequency)
-        self.meta_data = dict()
+        self.meta_data = dict(name=name)
 
     def __eq__(self, other):
         if self.name == other.name and \
@@ -607,7 +607,7 @@ class Interferometer(object):
 
         Returns
         =======
-        float: The matched filter signal to noise ratio squared
+        complex: The matched filter signal to noise ratio
 
         """
         return gwutils.matched_filter_snr(

bilby/gw/eos/eos.py

@@ -55,7 +55,7 @@ class TabularEOS(object):
     """
 
     def __init__(self, eos, sampling_flag=False, warning_flag=False):
-        from scipy.integrate import cumtrapz
+        from scipy.integrate import cumulative_trapezoid
 
         self.sampling_flag = sampling_flag
         self.warning_flag = warning_flag
@@ -83,7 +83,7 @@ class TabularEOS(object):
             self.warning_flag = True
         else:
             integrand = self.pressure / (self.energy_density + self.pressure)
-            self.pseudo_enthalpy = cumtrapz(integrand, np.log(self.pressure), initial=0) + integrand[0]
+            self.pseudo_enthalpy = cumulative_trapezoid(integrand, np.log(self.pressure), initial=0) + integrand[0]
 
             self.interp_energy_density_from_pressure = CubicSpline(np.log10(self.pressure),
                                                                    np.log10(self.energy_density),

bilby/gw/likelihood/multiband.py

@@ -517,7 +517,7 @@ class MBGravitationalWaveTransient(GravitationalWaveTransient):
         for ifo in self.interferometers:
             logger.info("Pre-computing linear coefficients for {}".format(ifo.name))
             fddata = np.zeros(N // 2 + 1, dtype=complex)
-            fddata[:len(ifo.frequency_domain_strain)][ifo.frequency_mask] += \
+            fddata[:len(ifo.frequency_domain_strain)][ifo.frequency_mask[:len(fddata)]] += \
                 ifo.frequency_domain_strain[ifo.frequency_mask] / ifo.power_spectral_density_array[ifo.frequency_mask]
             for b in range(self.number_of_bands):
                 Ks, Ke = self.Ks_Ke[b]
@@ -606,7 +606,7 @@ class MBGravitationalWaveTransient(GravitationalWaveTransient):
         for ifo in self.interferometers:
             logger.info("Pre-computing quadratic coefficients for {}".format(ifo.name))
             full_inv_psds = np.zeros(N // 2 + 1)
-            full_inv_psds[:len(ifo.power_spectral_density_array)][ifo.frequency_mask] = (
+            full_inv_psds[:len(ifo.power_spectral_density_array)][ifo.frequency_mask[:len(full_inv_psds)]] = (
                 1 / ifo.power_spectral_density_array[ifo.frequency_mask]
             )
             for b in range(self.number_of_bands):

bilby/gw/likelihood/relative.py

@@ -142,10 +142,6 @@ class RelativeBinningGravitationalWaveTransient(GravitationalWaveTransient):
         self.fiducial_polarizations = None
         self.per_detector_fiducial_waveforms = dict()
         self.per_detector_fiducial_waveform_points = dict()
-        self.bin_freqs = dict()
-        self.bin_inds = dict()
-        self.bin_widths = dict()
-        self.bin_centers = dict()
         self.set_fiducial_waveforms(self.fiducial_parameters)
         logger.info("Initial fiducial waveforms set up")
         self.setup_bins()
@@ -219,6 +215,8 @@ class RelativeBinningGravitationalWaveTransient(GravitationalWaveTransient):
             bin_inds.append(bin_index)
             bin_freqs.append(bin_freq)
         self.bin_inds = np.array(bin_inds, dtype=int)
+        self.bin_sizes = np.diff(bin_inds)
+        self.bin_sizes[-1] += 1
         self.bin_freqs = np.array(bin_freqs)
         self.number_of_bins = len(self.bin_inds) - 1
         logger.debug(
@@ -319,6 +317,10 @@ class RelativeBinningGravitationalWaveTransient(GravitationalWaveTransient):
             for edge in self.bin_freqs:
                 index = np.where(masked_frequency_array == edge)[0][0]
                 masked_bin_inds.append(index)
+            # For the last bin, make sure to include
+            # the last point in the frequency array
+            masked_bin_inds[-1] += 1
+
             masked_strain = interferometer.frequency_domain_strain[mask]
             masked_h0 = self.per_detector_fiducial_waveforms[interferometer.name][mask]
             masked_psd = interferometer.power_spectral_density_array[mask]
@@ -370,16 +372,15 @@ class RelativeBinningGravitationalWaveTransient(GravitationalWaveTransient):
             waveform_polarizations=signal_polarizations,
             interferometer=interferometer,
         )
-        f = interferometer.frequency_array
-        duplicated_r0, duplicated_r1, duplicated_fm = np.zeros((3, f.shape[0]), dtype=complex)
 
-        for i in range(self.number_of_bins):
-            idxs = slice(self.bin_inds[i], self.bin_inds[i + 1])
-            duplicated_fm[idxs] = self.bin_centers[i]
-            duplicated_r0[idxs] = r0[i]
-            duplicated_r1[idxs] = r1[i]
+        idxs = slice(self.bin_inds[0], self.bin_inds[-1] + 1)
+        duplicated_r0 = np.repeat(r0, self.bin_sizes)
+        duplicated_r1 = np.repeat(r1, self.bin_sizes)
+        duplicated_fm = np.repeat(self.bin_centers, self.bin_sizes)
 
-        full_waveform_ratio = duplicated_r0 + duplicated_r1 * (f - duplicated_fm)
+        f = interferometer.frequency_array
+        full_waveform_ratio = np.zeros(f.shape[0], dtype=complex)
+        full_waveform_ratio[idxs] = duplicated_r0 + duplicated_r1 * (f[idxs] - duplicated_fm)
         return fiducial_waveform * full_waveform_ratio
 
     def calculate_snrs(self, waveform_polarizations, interferometer, return_array=True):

bilby/gw/prior.py

@@ -1385,10 +1385,10 @@ class HealPixMapPriorDist(BaseJointPriorDist):
         """
         Method that builds the inverse cdf of the P(pixel) distribution for rescaling
         """
-        from scipy.integrate import cumtrapz
+        from scipy.integrate import cumulative_trapezoid
         yy = self._all_interped(self.pix_xx)
         yy /= np.trapz(yy, self.pix_xx)
-        YY = cumtrapz(yy, self.pix_xx, initial=0)
+        YY = cumulative_trapezoid(yy, self.pix_xx, initial=0)
         YY[-1] = 1
         self.inverse_cdf = interp1d(x=YY, y=self.pix_xx, bounds_error=True)
 

bilby/gw/source.py

@@ -205,7 +205,7 @@ def gwsignal_binary_black_hole(frequency_array, mass_1, mass_2, luminosity_dista
             ]
             if EDOM:
                 failed_parameters = dict(mass_1=mass_1, mass_2=mass_2,
-                                         spin_1=(spin_1x, spin_2y, spin_1z),
+                                         spin_1=(spin_1x, spin_1y, spin_1z),
                                          spin_2=(spin_2x, spin_2y, spin_2z),
                                          luminosity_distance=luminosity_distance,
                                          iota=iota, phase=phase,
@@ -614,7 +614,7 @@ def _base_lal_cbc_fd_waveform(
             EDOM = (e.args[0] == 'Internal function call failed: Input domain error')
             if EDOM:
                 failed_parameters = dict(mass_1=mass_1, mass_2=mass_2,
-                                         spin_1=(spin_1x, spin_2y, spin_1z),
+                                         spin_1=(spin_1x, spin_1y, spin_1z),
                                          spin_2=(spin_2x, spin_2y, spin_2z),
                                          luminosity_distance=luminosity_distance,
                                          iota=iota, phase=phase,
@@ -1124,7 +1124,7 @@ def _base_waveform_frequency_sequence(
             EDOM = (e.args[0] == 'Internal function call failed: Input domain error')
             if EDOM:
                 failed_parameters = dict(mass_1=mass_1, mass_2=mass_2,
-                                         spin_1=(spin_1x, spin_2y, spin_1z),
+                                         spin_1=(spin_1x, spin_1y, spin_1z),
                                          spin_2=(spin_2x, spin_2y, spin_2z),
                                          luminosity_distance=luminosity_distance,
                                          iota=iota, phase=phase)

bilby/gw/utils.py

@@ -398,7 +398,7 @@ def read_frame_file(file_name, start_time, end_time, resample=None, channel=None
             strain = TimeSeries.read(source=file_name, channel=channel, start=start_time, end=end_time, **kwargs)
             loaded = True
             logger.info('Successfully loaded {}.'.format(channel))
-        except RuntimeError:
+        except (RuntimeError, ValueError):
             logger.warning('Channel {} not found. Trying preset channel names'.format(channel))
 
     if loaded is False:
@@ -418,12 +418,12 @@ def read_frame_file(file_name, start_time, end_time, resample=None, channel=None
                                              **kwargs)
                     loaded = True
                     logger.info('Successfully read strain data for channel {}.'.format(channel))
-                except RuntimeError:
+                except (RuntimeError, ValueError):
                     pass
 
     if loaded:
         if resample and (strain.sample_rate.value != resample):
-            strain.resample(resample)
+            strain = strain.resample(resample)
         return strain
     else:
         logger.warning('No data loaded.')