.coveragerc

 [run]
 omit =
-    test/core/example_test.py
-    test/gw/example_test.py
-    test/gw/noise_realisation_test.py
-    test/gw/other_test.py
+    test/integration/example_test.py
+    test/integration/noise_realisation_test.py
+    test/integration/other_test.py

.gitlab-ci.yml

@@ -91,8 +91,8 @@ python-3.7-samplers:
   script:
     - python -m pip install .
 
-    - pytest test/core/sampler/sampler_run_test.py --durations 10
-    - pytest test/gw/sample_from_the_prior_test.py
+    - pytest test/integration/sampler_run_test.py --durations 10
+    - pytest test/integration/sample_from_the_prior_test.py
 
 # test samplers on python 3.6
 python-3.6-samplers:
@@ -101,7 +101,18 @@ python-3.6-samplers:
   script:
     - python -m pip install .
 
-    - pytest test/core/sampler/sampler_run_test.py
+    - pytest test/integration/sampler_run_test.py
+
+# Test containers are up to date
+containers:
+  stage: test
+  image: bilbydev/v2-dockerfile-test-suite-python37
+  script:
+    - cd containers
+    - python write_dockerfiles.py
+    # Fail if differences exist. If this fails, you may need to run
+    # write_dockerfiles.py and commit the changes.
+    - git diff --exit-code
 
 # Tests run at a fixed schedule rather than on push
 scheduled-python-3.7:
@@ -113,9 +124,8 @@ scheduled-python-3.7:
     - python -m pip install .
 
     # Run tests which are only done on schedule
-    - pytest test/core/example_test.py
-    - pytest test/gw/example_test.py
-    - pytest test/gw/sample_from_the_prior_test.py
+    - pytest test/integration/example_test.py
+    - pytest test/integration/sample_from_the_prior_test.py
 
 plotting:
   stage: test

CHANGELOG.md

 # All notable changes will be documented in this file
 
+## [1.0.2] 2020-09-14
+
+Version 1.0.2 release of bilby
+
+### Added
+- Template for the docker files (!783)
+- New delta_phase parameter (!850)
+- Normalization factor to time-domain waveform plot (!867)
+- JSON encoding for int and float types (!866) 
+- Various minor formatting additions (!870)
+
+### Changes
+- Switched to the conda-forge version of multinest and ultranest (!783)
+- Updates KAGRA - K1 interferometer information (!861)
+- Restructures to tests to be uniform across project (!834)
+- Fix to distance and phase marginalization method (!875)
+- Fixed roundoff of in-plane spins samples with vectorisation (!864)
+- Fix to reference distance and interpolant behavior (!858)
+- Fix to constraint prior sampling method (!863)
+- Clean up of code (!854)
+- Various minor bug, test and plotting fixes (!859, !874, !872, !865)
+
 ## [1.0.1] 2020-08-29
 
 Version 1.0.1 release of bilby

bilby/core/prior/base.py

@@ -444,9 +444,6 @@ class Constraint(Prior):
     def prob(self, val):
         return (val > self.minimum) & (val < self.maximum)
 
-    def ln_prob(self, val):
-        return np.log((val > self.minimum) & (val < self.maximum))
-
 
 class PriorException(Exception):
     """ General base class for all prior exceptions """

bilby/core/prior/dict.py

@@ -365,23 +365,21 @@ class PriorDict(dict):
                     return sample
         else:
             needed = np.prod(size)
-            constraint_keys = list()
-            for ii, key in enumerate(keys[-1::-1]):
+            for key in keys.copy():
                 if isinstance(self[key], Constraint):
-                    constraint_keys.append(-ii - 1)
-            for ii in constraint_keys[-1::-1]:
-                del keys[ii]
+                    del keys[keys.index(key)]
             all_samples = {key: np.array([]) for key in keys}
             _first_key = list(all_samples.keys())[0]
             while len(all_samples[_first_key]) < needed:
                 samples = self.sample_subset(keys=keys, size=needed)
                 keep = np.array(self.evaluate_constraints(samples), dtype=bool)
-                for key in samples:
-                    all_samples[key] = np.hstack(
-                        [all_samples[key], samples[key][keep].flatten()])
-            all_samples = {key: np.reshape(all_samples[key][:needed], size)
-                           for key in all_samples
-                           if not isinstance(self[key], Constraint)}
+                for key in keys:
+                    all_samples[key] = np.hstack([
+                        all_samples[key], samples[key][keep].flatten()
+                    ])
+            all_samples = {
+                key: np.reshape(all_samples[key][:needed], size) for key in keys
+            }
             return all_samples
 
     def normalize_constraint_factor(self, keys):

bilby/core/result.py

@@ -705,16 +705,20 @@ class Result(object):
 
         """
         latex_labels = []
-        for k in keys:
-            if k in self.search_parameter_keys:
-                idx = self.search_parameter_keys.index(k)
-                latex_labels.append(self.parameter_labels_with_unit[idx])
-            elif k in self.parameter_labels:
-                latex_labels.append(k)
+        for key in keys:
+            if key in self.search_parameter_keys:
+                idx = self.search_parameter_keys.index(key)
+                label = self.parameter_labels_with_unit[idx]
+            elif key in self.parameter_labels:
+                label = key
             else:
+                label = None
                 logger.debug(
-                    'key {} not a parameter label or latex label'.format(k))
-                latex_labels.append(' '.join(k.split('_')))
+                    'key {} not a parameter label or latex label'.format(key)
+                )
+            if label is None:
+                label = key.replace("_", " ")
+            latex_labels.append(label)
         return latex_labels
 
     @property

bilby/core/sampler/__init__.py

@@ -3,6 +3,7 @@ import sys
 import datetime
 from collections import OrderedDict
 
+import bilby
 from ..utils import command_line_args, logger
 from ..prior import PriorDict, DeltaFunction
 
@@ -107,7 +108,7 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
 
     Returns
     -------
-    result
+    result: bilby.core.result.Result
         An object containing the results
     """
 

bilby/core/sampler/dynesty.py

-from __future__ import absolute_import
-
 import datetime
 import dill
 import os
@@ -326,10 +324,6 @@ class Dynesty(NestedSampler):
     def run_sampler(self):
         import dynesty
         logger.info("Using dynesty version {}".format(dynesty.__version__))
-        if self.kwargs['live_points'] is None:
-            self.kwargs['live_points'] = (
-                self.get_initial_points_from_prior(
-                    self.kwargs['nlive']))
 
         if self.kwargs.get("sample", "rwalk") == "rwalk":
             logger.info(
@@ -351,10 +345,21 @@ class Dynesty(NestedSampler):
 
         self._setup_pool()
 
-        self.sampler = dynesty.NestedSampler(
-            loglikelihood=_log_likelihood_wrapper,
-            prior_transform=_prior_transform_wrapper,
-            ndim=self.ndim, **self.sampler_init_kwargs)
+        if self.resume:
+            self.resume = self.read_saved_state(continuing=True)
+
+        if self.resume:
+            logger.info('Resume file successfully loaded.')
+        else:
+            if self.kwargs['live_points'] is None:
+                self.kwargs['live_points'] = (
+                    self.get_initial_points_from_prior(self.kwargs['nlive'])
+                )
+            self.sampler = dynesty.NestedSampler(
+                loglikelihood=_log_likelihood_wrapper,
+                prior_transform=_prior_transform_wrapper,
+                ndim=self.ndim, **self.sampler_init_kwargs
+            )
 
         if self.check_point:
             out = self._run_external_sampler_with_checkpointing()
@@ -424,10 +429,6 @@ class Dynesty(NestedSampler):
 
     def _run_external_sampler_with_checkpointing(self):
         logger.debug("Running sampler with checkpointing")
-        if self.resume:
-            resume_file_loaded = self.read_saved_state(continuing=True)
-            if resume_file_loaded:
-                logger.info('Resume file successfully loaded.')
 
         old_ncall = self.sampler.ncall
         sampler_kwargs = self.sampler_function_kwargs.copy()

bilby/core/utils.py

@@ -977,6 +977,10 @@ class BilbyJsonEncoder(json.JSONEncoder):
     def default(self, obj):
         from .prior import MultivariateGaussianDist, Prior, PriorDict
         from ..gw.prior import HealPixMapPriorDist
+        if isinstance(obj, np.integer):
+            return int(obj)
+        if isinstance(obj, np.floating):
+            return float(obj)
         if isinstance(obj, PriorDict):
             return {'__prior_dict__': True, 'content': obj._get_json_dict()}
         if isinstance(obj, (MultivariateGaussianDist, HealPixMapPriorDist, Prior)):

bilby/gw/conversion.py

@@ -248,6 +248,14 @@ def convert_to_lal_binary_black_hole_parameters(parameters):
             converted_parameters[angle] =\
                 np.arccos(converted_parameters[cos_angle])
 
+    if "delta_phase" in original_keys:
+        converted_parameters["phase"] = np.mod(
+            converted_parameters["delta_phase"]
+            - np.sign(np.cos(converted_parameters["theta_jn"]))
+            * converted_parameters["psi"],
+            2 * np.pi
+        )
+
     added_keys = [key for key in converted_parameters.keys()
                   if key not in original_keys]
 
@@ -1009,18 +1017,19 @@ def generate_component_spins(sample):
         ['theta_jn', 'phi_jl', 'tilt_1', 'tilt_2', 'phi_12', 'a_1', 'a_2',
          'mass_1', 'mass_2', 'reference_frequency', 'phase']
     if all(key in output_sample.keys() for key in spin_conversion_parameters):
-        output_sample['iota'], output_sample['spin_1x'],\
-            output_sample['spin_1y'], output_sample['spin_1z'], \
-            output_sample['spin_2x'], output_sample['spin_2y'],\
-            output_sample['spin_2z'] =\
-            transform_precessing_spins(
-                output_sample['theta_jn'], output_sample['phi_jl'],
-                output_sample['tilt_1'], output_sample['tilt_2'],
-                output_sample['phi_12'], output_sample['a_1'],
-                output_sample['a_2'],
-                output_sample['mass_1'] * solar_mass,
-                output_sample['mass_2'] * solar_mass,
-                output_sample['reference_frequency'], output_sample['phase'])
+        (
+            output_sample['iota'], output_sample['spin_1x'],
+            output_sample['spin_1y'], output_sample['spin_1z'],
+            output_sample['spin_2x'], output_sample['spin_2y'],
+            output_sample['spin_2z']
+        ) = np.vectorize(bilby_to_lalsimulation_spins)(
+            output_sample['theta_jn'], output_sample['phi_jl'],
+            output_sample['tilt_1'], output_sample['tilt_2'],
+            output_sample['phi_12'], output_sample['a_1'], output_sample['a_2'],
+            output_sample['mass_1'] * solar_mass,
+            output_sample['mass_2'] * solar_mass,
+            output_sample['reference_frequency'], output_sample['phase']
+        )
 
         output_sample['phi_1'] =\
             np.fmod(2 * np.pi + np.arctan2(

bilby/gw/detector/detectors/K1.interferometer

 # KAGRA at design sensitvity.
-# https://arxiv.org/pdf/1102.5421.pdf
-# WARNING: I think there's a typo in that reference for the orientation.
 # https://dcc.ligo.org/LIGO-P1200087-v42/public
+
+#Kagra detector location: https://gwdoc.icrr.u-tokyo.ac.jp/cgi-bin/DocDB/ShowDocument?docid=3824
+#The detector physical location needs to be converted into detector constants according to the BILBY convention.
+#check the detector constant LAL value (careful about the convention) (https://lscsoft.docs.ligo.org/lalsuite/lal/group___detector_constants.html) 
+
 name = 'K1'
 power_spectral_density = PowerSpectralDensity(psd_file='KAGRA_design_psd.txt')
 length = 3
 minimum_frequency = 20
 maximum_frequency = 2048
-latitude = 36 + 15. / 60 + 00. / 3600
-longitude = 137 + 10. / 60 + 48. / 3600
-elevation = 414.0
-xarm_azimuth = 25.0
-yarm_azimuth = 115.0
+latitude = 36 + 24 / 60 + 42.69722 / 3600
+longitude = 137 + 18 / 60 + 21.44171 / 3600 
+elevation = 414.181
+xarm_azimuth = 90 - 60.39623489157727
+yarm_azimuth = 90 + 29.60357629670688

bilby/gw/detector/interferometer.py

@@ -38,6 +38,9 @@ class Interferometer(object):
     vertex = PropertyAccessor('geometry', 'vertex')
     detector_tensor = PropertyAccessor('geometry', 'detector_tensor')
 
+    duration = PropertyAccessor('strain_data', 'duration')
+    sampling_frequency = PropertyAccessor('strain_data', 'sampling_frequency')
+    start_time = PropertyAccessor('strain_data', 'start_time')
     frequency_array = PropertyAccessor('strain_data', 'frequency_array')
     time_array = PropertyAccessor('strain_data', 'time_array')
     minimum_frequency = PropertyAccessor('strain_data', 'minimum_frequency')

bilby/gw/detector/networks.py

@@ -398,7 +398,7 @@ def load_interferometer(filename):
     with open(filename, 'r') as parameter_file:
         lines = parameter_file.readlines()
         for line in lines:
-            if line[0] == '#':
+            if line[0] == '#' or line[0] == '\n':
                 continue
             split_line = line.split('=')
             key = split_line[0].strip()

bilby/gw/eos/eos.py

@@ -29,7 +29,7 @@ path_to_eos_tables = os.path.join(os.path.dirname(__file__), 'eos_tables')
 list_of_eos_tables = os.listdir(path_to_eos_tables)
 valid_eos_files = [i for i in list_of_eos_tables if 'LAL' in i]
 valid_eos_file_paths = [os.path.join(path_to_eos_tables, filename) for filename in valid_eos_files]
-valid_eos_names = [i.split('_')[-1].strip('.dat') for i in valid_eos_files]
+valid_eos_names = [i.split('_', maxsplit=1)[-1].strip('.dat') for i in valid_eos_files]
 valid_eos_dict = dict(zip(valid_eos_names, valid_eos_file_paths))
 
 

bilby/gw/likelihood.py

@@ -167,6 +167,9 @@ class GravitationalWaveTransient(Likelihood):
             self.distance_prior_array = np.array(
                 [self.priors['luminosity_distance'].prob(distance)
                  for distance in self._distance_array])
+            if self.phase_marginalization:
+                max_bound = np.ceil(10 + np.log10(self._dist_multiplier))
+                self._setup_phase_marginalization(max_bound=max_bound)
             self._setup_distance_marginalization(
                 distance_marginalization_lookup_table)
             for key in ['redshift', 'comoving_distance']:
@@ -597,8 +600,13 @@ class GravitationalWaveTransient(Likelihood):
 
     @property
     def _ref_dist(self):
-        """ Smallest distance contained in priors """
-        return self._distance_array[0]
+        """ Median distance in priors """
+        return self.priors['luminosity_distance'].rescale(0.5)
+
+    @property
+    def _dist_multiplier(self):
+        ''' Maximum value of ref_dist/dist_array '''
+        return self._ref_dist / self._distance_array[0]
 
     @property
     def _optimal_snr_squared_ref_array(self):
@@ -632,7 +640,7 @@ class GravitationalWaveTransient(Likelihood):
             self._create_lookup_table()
         self._interp_dist_margd_loglikelihood = UnsortedInterp2d(
             self._d_inner_h_ref_array, self._optimal_snr_squared_ref_array,
-            self._dist_margd_loglikelihood_array, kind='cubic')
+            self._dist_margd_loglikelihood_array, kind='cubic', fill_value=-np.inf)
 
     @property
     def cached_lookup_table_filename(self):
@@ -714,10 +722,10 @@ class GravitationalWaveTransient(Likelihood):
         self._dist_margd_loglikelihood_array -= log_norm
         self.cache_lookup_table()
 
-    def _setup_phase_marginalization(self):
+    def _setup_phase_marginalization(self, min_bound=-5, max_bound=10):
         self._bessel_function_interped = interp1d(
-            np.logspace(-5, 10, int(1e6)), np.logspace(-5, 10, int(1e6)) +
-            np.log([i0e(snr) for snr in np.logspace(-5, 10, int(1e6))]),
+            np.logspace(-5, max_bound, int(1e6)), np.logspace(-5, max_bound, int(1e6)) +
+            np.log([i0e(snr) for snr in np.logspace(-5, max_bound, int(1e6))]),
             bounds_error=False, fill_value=(0, np.nan))
 
     def _setup_time_marginalization(self):

bilby/gw/result.py

@@ -436,7 +436,8 @@ class CompactBinaryCoalescenceResult(CoreResult):
                     go.Scatter(
                         x=plot_times,
                         y=infft(
-                            interferometer.whitened_frequency_domain_strain,
+                            interferometer.whitened_frequency_domain_strain *
+                            np.sqrt(2. / interferometer.sampling_frequency),
                             sampling_frequency=interferometer.strain_data.sampling_frequency)[time_idxs],
                         fill=None,
                         mode='lines', line_color=DATA_COLOR,
@@ -461,7 +462,8 @@ class CompactBinaryCoalescenceResult(CoreResult):
                     color=DATA_COLOR, label='ASD')
                 axs[1].plot(
                     plot_times, infft(
-                        interferometer.whitened_frequency_domain_strain,
+                        interferometer.whitened_frequency_domain_strain *
+                        np.sqrt(2. / interferometer.sampling_frequency),
                         sampling_frequency=interferometer.strain_data.sampling_frequency)[time_idxs],
                     color=DATA_COLOR, alpha=0.3)
             logger.debug('Plotted interferometer data.')
@@ -474,7 +476,8 @@ class CompactBinaryCoalescenceResult(CoreResult):
             fd_waveform = interferometer.get_detector_response(wf_pols, params)
             fd_waveforms.append(fd_waveform[frequency_idxs])
             td_waveform = infft(
-                fd_waveform / interferometer.amplitude_spectral_density_array,
+                fd_waveform * np.sqrt(2. / interferometer.sampling_frequency) /
+                interferometer.amplitude_spectral_density_array,
                 self.sampling_frequency)[time_idxs]
             td_waveforms.append(td_waveform)
         fd_waveforms = asd_from_freq_series(
@@ -601,7 +604,7 @@ class CompactBinaryCoalescenceResult(CoreResult):
                 hf_inj_det = interferometer.get_detector_response(
                     hf_inj, self.injection_parameters)
                 ht_inj_det = infft(
-                    hf_inj_det /
+                    hf_inj_det * np.sqrt(2. / interferometer.sampling_frequency) /
                     interferometer.amplitude_spectral_density_array,
                     self.sampling_frequency)[time_idxs]
                 if format == "html":

bilby/gw/utils.py

@@ -279,6 +279,16 @@ def optimal_snr_squared(signal, power_spectral_density, duration):
     return noise_weighted_inner_product(signal, signal, power_spectral_density, duration)
 
 
+def overlap(signal_a, signal_b, power_spectral_density=None, delta_frequency=None,
+            lower_cut_off=None, upper_cut_off=None, norm_a=None, norm_b=None):
+    low_index = int(lower_cut_off / delta_frequency)
+    up_index = int(upper_cut_off / delta_frequency)
+    integrand = np.conj(signal_a) * signal_b
+    integrand = integrand[low_index:up_index] / power_spectral_density[low_index:up_index]
+    integral = (4 * delta_frequency * integrand) / norm_a / norm_b
+    return sum(integral).real
+
+
 __cached_euler_matrix = None
 __cached_delta_x = None
 

containers/Dockerfile-bilby

-FROM bilbydev/bilby-test-suite-python37
-
-LABEL name="bilby" \
-maintainer="Gregory Ashton <gregory.ashton@ligo.org>" \
-date="20190130"
-
-RUN pip install bilby

containers/Dockerfile-test-suite-python27

-FROM ubuntu:18.04
-
-LABEL name="bilby Base Enterprise Linux 7" \
-maintainer="Gregory Ashton <gregory.ashton@ligo.org>" \
-date="20190104"
-
-ENV PATH /opt/conda/bin:$PATH
-
-# Install backend
-RUN apt-get update --fix-missing \
-&& apt-get install -y libglib2.0-0 libxext6 libsm6 libxrender1 libgl1-mesa-glx \
-dh-autoreconf build-essential libarchive-dev wget curl git libhdf5-serial-dev
-
-# Install python2.7
-RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda2-4.5.11-Linux-x86_64.sh -O ~/miniconda.sh && \
-/bin/bash ~/miniconda.sh -b -p /opt/conda && \
-rm ~/miniconda.sh && \
-ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
-echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
-echo "conda activate base" >> ~/.bashrc
-
-# Install conda-installable programs
-RUN conda install -y numpy scipy matplotlib pandas==0.23
-
-RUN conda install -c conda-forge deepdish arviz
-
-# Install requirements
-RUN pip install --upgrade pip \
-&& pip install --upgrade setuptools \
-&& pip install future \
-pycondor>=0.5 \
-configargparse \
-flake8 \
-mock \
-pipenv \
-coverage \
-pytest-cov \
-coverage-badge
-
-# Install additional bilby requirements
-RUN pip install corner lalsuite astropy gwpy theano tables
-
-# Install samplers
-RUN pip install cpnest dynesty emcee nestle ptemcee pymc3 pymultinest kombine ultranest dnest4
-
-# Install pymultinest requirements
-RUN apt-get install -y libblas3 libblas-dev liblapack3 liblapack-dev \
-libatlas3-base libatlas-base-dev cmake build-essential gfortran
-
-# Install pymultinest
-RUN git clone https://github.com/farhanferoz/MultiNest.git \
-&& (cd MultiNest/MultiNest_v3.11_CMake/multinest && mkdir build && cd build && cmake .. && make)
-
-ENV LD_LIBRARY_PATH $HOME/MultiNest/MultiNest_v3.11_CMake/multinest/lib:
-
-# Install Polychord
-RUN git clone https://github.com/PolyChord/PolyChordLite.git \
-&& (cd PolyChordLite && python setup.py --no-mpi install)
-
-# Install PTMCMCSampler
-RUN git clone https://github.com/jellis18/PTMCMCSampler.git \
-&& (cd PTMCMCSampler && python setup.py install)
-
-# Add the ROQ data to the image
-RUN mkdir roq_basis \
-    && cd roq_basis \
-    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/B_linear.npy \
-    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/B_quadratic.npy \
-    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/fnodes_linear.npy \
-    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/fnodes_quadratic.npy \
-    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/params.dat \

containers/Dockerfile-test-suite-python37 → containers/dockerfile-template

-FROM ubuntu:18.04
+FROM continuumio/miniconda3
+LABEL name="bilby Base miniconda3" \
+maintainer="Gregory Ashton <gregory.ashton@ligo.org>"
 
-LABEL name="bilby Base Enterprise Linux 7" \
-maintainer="Gregory Ashton <gregory.ashton@ligo.org>" \
-date="20190104"
+RUN conda update -n base -c defaults conda
 
-ENV PATH /opt/conda/bin:$PATH
+ENV conda_env python{python_major_version}{python_minor_version}
 
-# Install backend
-RUN apt-get update --fix-missing \
-&& apt-get install -y libglib2.0-0 libxext6 libsm6 libxrender1 libgl1-mesa-glx \
-dh-autoreconf build-essential libarchive-dev wget curl git libhdf5-serial-dev
+RUN conda create -n ${{conda_env}} python={python_major_version}.{python_minor_version}
+RUN echo "source activate ${{conda_env}}" > ~/.bashrc
+ENV PATH /opt/conda/envs/${{conda_env}}/bin:$PATH
+RUN /bin/bash -c "source activate ${{conda_env}}"
+RUN conda info
+RUN python --version
 
-# Install python3.7
-RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.5.11-Linux-x86_64.sh -O ~/miniconda.sh && \
-/bin/bash ~/miniconda.sh -b -p /opt/conda && \
-rm ~/miniconda.sh && \
-/opt/conda/bin/conda clean -tipsy && \
-ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
-echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
-echo "conda activate base" >> ~/.bashrc
-
-# Update pip and setuptools
-RUN pip install --upgrade pip \
-&& LC_ALL=C pip install --upgrade setuptools
+# Install pymultinest requirements
+RUN apt-get update
+RUN apt-get install -y libblas3 libblas-dev liblapack3 liblapack-dev \
+libatlas3-base libatlas-base-dev cmake build-essential gfortran
 
 # Install conda-installable programs
-RUN conda install -y numpy scipy matplotlib pandas
+RUN conda install -n ${{conda_env}} -y matplotlib numpy scipy pandas astropy flake8 mock
+RUN conda install -n ${{conda_env}} -c anaconda coverage configargparse future
 
 # Install conda-forge-installable programs
-RUN conda install -c conda-forge deepdish arviz
+RUN conda install -n ${{conda_env}} -c conda-forge black ligo-gracedb gwpy lalsuite ligo.skymap pytest-cov deepdish arviz
 
-# Install requirements
-RUN pip install future \
-pycondor>=0.5 \
-configargparse \
-flake8 \
-mock \
-pipenv \
-coverage \
-pytest-cov \
-coverage-badge
+# Install pip-requirements
+RUN pip install --upgrade pip
+RUN pip install --upgrade setuptools coverage-badge
 
 # Install documentation requirements
-RUN pip install sphinx numpydoc nbsphinx sphinx_rtd_theme sphinx-tabs
-
-# Install additional bilby requirements
-RUN pip install corner lalsuite astropy gwpy theano healpy tables
-
-# Install samplers
-RUN pip install cpnest dynesty emcee nestle ptemcee pymc3 pymultinest kombine ultranest dnest4
-
-# Install pymultinest requirements
-RUN apt-get install -y libblas3 libblas-dev liblapack3 liblapack-dev \
-libatlas3-base libatlas-base-dev cmake build-essential gfortran
-
-# Install pymultinest
-RUN git clone https://github.com/farhanferoz/MultiNest.git \
-&& (cd MultiNest/MultiNest_v3.11_CMake/multinest && mkdir build && cd build && cmake .. && make)
+RUN pip install sphinx numpydoc nbsphinx sphinx_rtd_theme sphinx-tabs autodoc
 
-ENV LD_LIBRARY_PATH $HOME/MultiNest/MultiNest_v3.11_CMake/multinest/lib:
+# Install dependencies and samplers
+RUN pip install corner lalsuite theano healpy cython tables
+RUN pip install cpnest dynesty emcee nestle ptemcee pymc3 kombine dnest4
+RUN conda install -n ${{conda_env}} -c conda-forge pymultinest ultranest
 
 # Install Polychord
 RUN git clone https://github.com/PolyChord/PolyChordLite.git \
@@ -75,4 +52,4 @@ RUN mkdir roq_basis \
     && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/B_quadratic.npy \
     && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/fnodes_linear.npy \
     && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/fnodes_quadratic.npy \
-    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/params.dat \
+    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/params.dat