Tupak is dead long live bilby

.gitignore

@@ -2,7 +2,7 @@
 build/
 dist/
 docs/_*
-tupak.egg-info/
+bilby.egg-info/
 MANIFEST
 *.pyc
 *.png

.gitlab-ci.yml

@@ -39,7 +39,7 @@ python-3:
     - flake8 .
 
     # Run tests and collect coverage data
-    - pytest --cov=tupak --ignore=test/gw_example_test.py
+    - pytest --cov=bilby --ignore=test/gw_example_test.py
     - coverage html
     - coverage-badge -o coverage_badge.svg -f
 
@@ -63,7 +63,7 @@ pages:
   script:
     - mkdir public/
     - mv htmlcov/ public/
-    - mv /builds/Monash/tupak/coverage_badge.svg public/
+    - mv /builds/Monash/bilby/coverage_badge.svg public/
     - mv docs/_build/html/* public/
   artifacts:
     paths:

CHANGELOG.md

@@ -71,8 +71,8 @@ re-instantiate the Prior in most cases
 - Result print function fixed
 - Add snr functions as methods of `Interferometer`
 - The paths between imports where changed so that calls such as
-  `tupak.WaveformGenerator` no longer work. Instead, we need to use
-  `tupak.gw.WaveformGenerator`. This was done to keep things cleaner going
+  `bilby.WaveformGenerator` no longer work. Instead, we need to use
+  `bilby.gw.WaveformGenerator`. This was done to keep things cleaner going
   forward (when, for example, there may be multiple wfg's).
 - Samplers reorganised into individual files.
 
@@ -80,9 +80,9 @@ re-instantiate the Prior in most cases
 
 ### Added
 - InterferometerStrainData now handles both time-domain and frequencu-domain data
-- Adds documentation on setting data (https://monash.docs.ligo.org/tupak/transient-gw-data.html)
+- Adds documentation on setting data (https://monash.docs.ligo.org/bilby/transient-gw-data.html)
 - Checkpointing for `dynesty`: the sampling will be checkpointed every 10 minutes (approximately) and can be resumed.
-- Add functionality to plot multiple results in a corner plot, see `tupak.core.result.plot_multiple()`.
+- Add functionality to plot multiple results in a corner plot, see `bilby.core.result.plot_multiple()`.
 - Likelihood evaluations are now saved along with the posteriors.
 
 ### Changed
@@ -99,15 +99,15 @@ re-instantiate the Prior in most cases
 
 ## [0.2.0] 2018-06-17
 
-First `pip` installable version https://pypi.org/project/TUPAK/ .
+First `pip` installable version https://pypi.org/project/BILBY/ .
 
 ### Added
-- Reoriganisation of the directory into `tupak.core` and `tupak.gw`.
+- Reoriganisation of the directory into `bilby.core` and `bilby.gw`.
 - Reading of frame files.
 - Major effort to update all docstrings and add some documentation.
 - Marginalized likelihoods.
 - Examples of searches for gravitational waves from a Supernova and using a sine-Gaussian.
-- A `PriorSet` to handle sets of priors and allows reading in from a standardised prior file (see https://monash.docs.ligo.org/tupak/prior.html).
+- A `PriorSet` to handle sets of priors and allows reading in from a standardised prior file (see https://monash.docs.ligo.org/bilby/prior.html).
 - A standardised file for storing detector data.
 
 ### Removed

CONTRIBUTING.md

 
-Contributing to tupak
+Contributing to bilby
 =================
 
-This is a short guide to help get you started contributing to tupak.
+This is a short guide to help get you started contributing to bilby.
 
 Getting started
 -------------------
 
 All the code lives in a git repository (for a short introduction to git, see
 [this tutorial](https://docs.gitlab.com/ee/gitlab-basics/start-using-git.html))
-which is hosted here: https://git.ligo.org/Monash/tupak.  If you haven't
+which is hosted here: https://git.ligo.org/Monash/bilby.  If you haven't
 already, you should
 [fork](https://docs.gitlab.com/ee/gitlab-basics/fork-project.html) the repository
 and clone your fork, i.e., on your local machine run
 
 ```bash
-$ git clone git@git.ligo.org:albert.einstein/tupak.git
+$ git clone git@git.ligo.org:albert.einstein/bilby.git
 ```
 
-replacing the SSH url to that of your fork. This will create a directory `/tupak`
+replacing the SSH url to that of your fork. This will create a directory `/bilby`
 containing a local copy of the code. From this directory, you can run
 
 ```bash
 $ python setup.py develop
 ```
 
-which will install `tupak` and, because we used `develop` instead of `install`
+which will install `bilby` and, because we used `develop` instead of `install`
 when you change the code your installed version will automatically be updated.
 
 --- 
 
 #### Removing previously installed versions
 
-If you have previously installed `tupak` using `pip` (or generally find buggy
+If you have previously installed `bilby` using `pip` (or generally find buggy
 behaviour). It may be worthwhile purging your system and reinstalling. To do
 this, first find out where the module is being imported from: from any
 directory that is *not* the source directory, do the following
 
 ```bash
 $ python
->>> import tupak
->>> print(tupak.__file__)
-/home/user/anaconda2/lib/python2.7/site-packages/tupak-0.2.2-py2.7.egg/tupak/__init__.pyc
+>>> import bilby
+>>> print(bilby.__file__)
+/home/user/anaconda2/lib/python2.7/site-packages/bilby-0.2.2-py2.7.egg/bilby/__init__.pyc
 ```
 In the example output above, we see that the directory that module is installed
 in. To purge our python installation, run
 
 ```bash
-$ rm -r /home/user/anaconda2/lib/python2.7/site-packages/tupak*
+$ rm -r /home/user/anaconda2/lib/python2.7/site-packages/bilby*
 ```
 
-You can then verify this was successful by trying to import tupak in the python
+You can then verify this was successful by trying to import bilby in the python
 interpreter.
 
 Discussion
@@ -62,13 +62,13 @@ you've found a bug or would like a feature it doesn't have, we want to
 hear from you!
 
 Our main forum for discussion is the project's [GitLab issue
-tracker](https://git.ligo.org/Monash/tupak/issues). This is the right
+tracker](https://git.ligo.org/Monash/bilby/issues). This is the right
 place to start a discussion of any of the above or most any other
 topic concerning the project.
 
 #### Code of Conduct
 
-Everyone participating in the tupak community, and in particular in our
+Everyone participating in the bilby community, and in particular in our
 issue tracker, pull requests, and IRC channel, is expected to treat
 other people with respect and more generally to follow the guidelines
 articulated in the [Python Community Code of
@@ -165,9 +165,9 @@ At the top level, the code is split into three modules containing the core, grav
 
 ```mermaid
 graph TD
-tupak[tupak] --> core[.core]
-tupak --> gw[.gw]
-tupak --> hyper[.hyper]
+bilby[bilby] --> core[.core]
+bilby --> gw[.gw]
+bilby --> hyper[.hyper]
 ```
 
 ### Core
@@ -175,7 +175,7 @@ The core module contains the core inference logic - methods to define a prior, l
 
 ```mermaid
 graph TD
-core[tupak.core] --> prior[.prior] 
+core[bilby.core] --> prior[.prior]
 core --> likelihood[.likelihood] 
 core --> result[.result] 
 core --> sampler[.sampler] 
@@ -206,7 +206,7 @@ Note this layout is not comprehensive, for example only a few example "Priors" a
 
 ```mermaid
 graph TD
-gw[tupak.gw] --> gw_likelihood[.likelihood]
+gw[bilby.gw] --> gw_likelihood[.likelihood]
 gw --> gw_prior[.prior]
 gw --> gw_source[.source]
 gw --> gw_waveform_generator[.waveform_generator]

README.rst

 |pipeline status| |coverage report| |pypi| |version|
 
-Tupak
+Bilby
 =====
 
-Fulfilling all your gravitational wave dreams.
+Fulfilling all your Bayesian inference dreams.
 
 -  `Installation
-   instructions <https://monash.docs.ligo.org/tupak/installation.html>`__
--  `Contributing <https://git.ligo.org/Monash/tupak/blob/master/CONTRIBUTING.md>`__
--  `Documentation <https://monash.docs.ligo.org/tupak/index.html>`__
--  `Issue tracker <https://git.ligo.org/Monash/tupak/issues>`__
+   instructions <https://monash.docs.ligo.org/bilby/installation.html>`__
+-  `Contributing <https://git.ligo.org/Monash/bilby/blob/master/CONTRIBUTING.md>`__
+-  `Documentation <https://monash.docs.ligo.org/bilby/index.html>`__
+-  `Issue tracker <https://git.ligo.org/Monash/bilby/issues>`__
 
 We encourage you to contribute to the development via a merge request.  For
 help in creating a merge request, see `this page
 <https://docs.gitlab.com/ee/gitlab-basics/add-merge-request.html>`__ or contact
 us directly.
 
-.. |pipeline status| image:: https://git.ligo.org/Monash/tupak/badges/master/pipeline.svg
-   :target: https://git.ligo.org/Monash/tupak/commits/master
-.. |coverage report| image:: https://monash.docs.ligo.org/tupak/coverage_badge.svg
-   :target: https://monash.docs.ligo.org/tupak/htmlcov/
-.. |pypi| image:: https://badge.fury.io/py/TUPAK.svg
-   :target: https://pypi.org/project/TUPAK/
-.. |version| image:: https://img.shields.io/pypi/pyversions/tupak.svg
-   :target: https://pypi.org/project/TUPAK/
+.. |pipeline status| image:: https://git.ligo.org/Monash/bilby/badges/master/pipeline.svg
+   :target: https://git.ligo.org/Monash/bilby/commits/master
+.. |coverage report| image:: https://monash.docs.ligo.org/bilby/coverage_badge.svg
+   :target: https://monash.docs.ligo.org/bilby/htmlcov/
+.. |pypi| image:: https://badge.fury.io/py/BILBY.svg
+   :target: https://pypi.org/project/BILBY/
+.. |version| image:: https://img.shields.io/pypi/pyversions/bilby.svg
+   :target: https://pypi.org/project/BILBY/

tupak/__init__.py → bilby/__init__.py

 """
-tupak
+Bilby
 =====
 
-Tupak is The User friendly Parameter estimAtion Kode.
+Bilby: a user friendly Bayesian inference library.
 
-The aim of tupak is to provide user friendly interface to perform parameter
+The aim of bilby is to provide user friendly interface to perform parameter
 estimation. It is primarily designed and built for inference of compact
 binary coalescence events in interferometric data, but it can also be used for
 more general problems.
 
-The code, and many examples are hosted at https://git.ligo.org/Monash/tupak.
+The code, and many examples are hosted at https://git.ligo.org/Monash/bilby.
 For installation instructions see
-https://monash.docs.ligo.org/tupak/installation.html.
+https://monash.docs.ligo.org/bilby/installation.html.
 
 """
 

tupak/core/likelihood.py → bilby/core/likelihood.py

@@ -370,7 +370,7 @@ class JointLikelihood(Likelihood):
 
         Parameters
         ----------
-        *likelihoods: tupak.core.likelihood.Likelihood
+        *likelihoods: bilby.core.likelihood.Likelihood
             likelihoods to be combined parsed as arguments
         """
         self.likelihoods = likelihoods

tupak/core/prior.py → bilby/core/prior.py

@@ -11,7 +11,7 @@ from future.utils import iteritems
 
 from .utils import logger
 from . import utils
-import tupak  # noqa
+import bilby  # noqa
 import inspect
 
 
@@ -117,11 +117,11 @@ class PriorSet(OrderedDict):
 
         Parameters
         ----------
-        likelihood: tupak.likelihood.GravitationalWaveTransient instance
+        likelihood: bilby.likelihood.GravitationalWaveTransient instance
             Used to infer the set of parameters to fill the prior with
         default_priors_file: str, optional
             If given, a file containing the default priors; otherwise defaults
-            to the tupak defaults for a binary black hole.
+            to the bilby defaults for a binary black hole.
 
 
         Returns
@@ -247,7 +247,7 @@ def create_default_prior(name, default_priors_file=None):
         Parameter name
     default_priors_file: str, optional
         If given, a file containing the default priors; otherwise defaults to
-        the tupak defaults for a binary black hole.
+        the bilby defaults for a binary black hole.
 
     Return
     ------

tupak/core/result.py → bilby/core/result.py

@@ -42,12 +42,12 @@ def read_in_result(outdir=None, label=None, filename=None):
 
     Returns
     -------
-    result: tupak.core.result.Result
+    result: bilby.core.result.Result
 
     Raises
     -------
     ValueError: If no filename is given and either outdir or label is None
-                If no tupak.core.result.Result is found in the path
+                If no bilby.core.result.Result is found in the path
 
     """
     if filename is None:
@@ -304,7 +304,7 @@ class Result(dict):
         parameters: (list, dict), optional
             If given, either a list of the parameter names to include, or a
             dictionary of parameter names and their "true" values to plot.
-        priors: {bool (False), tupak.core.prior.PriorSet}
+        priors: {bool (False), bilby.core.prior.PriorSet}
             If true, add the stored prior probability density functions to the
             one-dimensional marginal distributions. If instead a PriorSet
             is provided, this will be plotted.
@@ -341,7 +341,7 @@ class Result(dict):
         if utils.command_line_args.test:
             return
 
-        # tupak default corner kwargs. Overwritten by anything passed to kwargs
+        # bilby default corner kwargs. Overwritten by anything passed to kwargs
         defaults_kwargs = dict(
             bins=50, smooth=0.9, label_kwargs=dict(fontsize=16),
             title_kwargs=dict(fontsize=16), color='#0072C1',
@@ -532,7 +532,7 @@ class Result(dict):
 
         Parameters
         ----------
-        likelihood: tupak.likelihood.GravitationalWaveTransient, optional
+        likelihood: bilby.likelihood.GravitationalWaveTransient, optional
             GravitationalWaveTransient likelihood used for sampling.
         priors: dict, optional
             Dictionary of prior object, used to fill in delta function priors.
@@ -646,7 +646,7 @@ def plot_multiple(results, filename=None, labels=None, colours=None,
     Parameters
     ----------
     results: list
-        A list of `tupak.core.result.Result` objects containing the samples to
+        A list of `bilby.core.result.Result` objects containing the samples to
         plot.
     filename: str
         File name to save the figure to. If None (default), a filename is

tupak/core/sampler/__init__.py → bilby/core/sampler/__init__.py

 import inspect
 import sys
-import numpy as np
 import datetime
 from collections import OrderedDict
 
@@ -46,9 +45,9 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
 
     Parameters
     ----------
-    likelihood: `tupak.Likelihood`
+    likelihood: `bilby.Likelihood`
         A `Likelihood` instance
-    priors: `tupak.PriorSet`
+    priors: `bilby.PriorSet`
         A PriorSet/dictionary of the priors for each parameter - missing
         parameters will use default priors, if None, all priors will be default
     label: str
@@ -57,7 +56,7 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
         A string used in defining output files
     sampler: str, Sampler
         The name of the sampler to use - see
-        `tupak.sampler.get_implemented_samplers()` for a list of available
+        `bilby.sampler.get_implemented_samplers()` for a list of available
         samplers.
         Alternatively a Sampler object can be passed
     use_ratio: bool (False)
@@ -72,13 +71,13 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
         Function to apply to posterior to generate additional parameters.
     default_priors_file: str
         If given, a file containing the default priors; otherwise defaults to
-        the tupak defaults for a binary black hole.
+        the bilby defaults for a binary black hole.
     clean: bool
         If given, override the command line interface `clean` option.
     meta_data: dict
         If given, adds the key-value pairs to the 'results' object before
         saving. For example, if `meta_data={dtype: 'signal'}`. Warning: in case
-        of conflict with keys saved by tupak, the meta_data keys will be
+        of conflict with keys saved by bilby, the meta_data keys will be
         overwritten.
     save: bool
         If true, save the priors and results to disk.

tupak/core/sampler/base_sampler.py → bilby/core/sampler/base_sampler.py

@@ -13,7 +13,7 @@ class Sampler(object):
     ----------
     likelihood: likelihood.Likelihood
         A  object with a log_l method
-    priors: tupak.core.prior.PriorSet, dict
+    priors: bilby.core.prior.PriorSet, dict
         Priors to be used in the search.
         This has attributes for each parameter to be sampled.
     external_sampler: str, Sampler, optional
@@ -34,7 +34,7 @@ class Sampler(object):
     -------
     likelihood: likelihood.Likelihood
         A  object with a log_l method
-    priors: tupak.core.prior.PriorSet
+    priors: bilby.core.prior.PriorSet
         Priors to be used in the search.
         This has attributes for each parameter to be sampled.
     external_sampler: Module
@@ -51,7 +51,7 @@ class Sampler(object):
     skip_import_verification: bool
         Skips the check if the sampler is installed if true. This is
         only advisable for testing environments
-    result: tupak.core.result.Result
+    result: bilby.core.result.Result
         Container for the results of the sampling run
     kwargs: dict
         Dictionary of keyword arguments that can be used in the external sampler
@@ -182,7 +182,7 @@ class Sampler(object):
         """
         Returns
         -------
-        tupak.core.result.Result: An initial template for the result
+        bilby.core.result.Result: An initial template for the result
 
         """
         result = Result()

tupak/core/sampler/cpnest.py → bilby/core/sampler/cpnest.py

@@ -6,12 +6,12 @@ from .base_sampler import NestedSampler
 
 
 class Cpnest(NestedSampler):
-    """ tupak wrapper of cpnest (https://github.com/johnveitch/cpnest)
+    """ bilby wrapper of cpnest (https://github.com/johnveitch/cpnest)
 
     All positional and keyword arguments (i.e., the args and kwargs) passed to
     `run_sampler` will be propagated to `cpnest.CPNest`, see documentation
     for that class for further help. Under Keyword Arguments, we list commonly
-    used kwargs and the tupak defaults.
+    used kwargs and the bilby defaults.
 
     Keyword Arguments
     -----------------

tupak/core/sampler/dynesty.py → bilby/core/sampler/dynesty.py

@@ -11,13 +11,13 @@ from .base_sampler import Sampler, NestedSampler
 
 class Dynesty(NestedSampler):
     """
-    tupak wrapper of `dynesty.NestedSampler`
+    bilby wrapper of `dynesty.NestedSampler`
     (https://dynesty.readthedocs.io/en/latest/)
 
     All positional and keyword arguments (i.e., the args and kwargs) passed to
     `run_sampler` will be propagated to `dynesty.NestedSampler`, see
     documentation for that class for further help. Under Keyword Arguments, we
-    list commonly used kwargs and the tupak defaults.
+    list commonly used kwargs and the bilby defaults.
 
     Keyword Arguments
     -----------------

tupak/core/sampler/emcee.py → bilby/core/sampler/emcee.py

@@ -6,12 +6,12 @@ from .base_sampler import MCMCSampler
 
 
 class Emcee(MCMCSampler):
-    """tupak wrapper emcee (https://github.com/dfm/emcee)
+    """bilby wrapper emcee (https://github.com/dfm/emcee)
 
     All positional and keyword arguments (i.e., the args and kwargs) passed to
     `run_sampler` will be propagated to `emcee.EnsembleSampler`, see
     documentation for that class for further help. Under Keyword Arguments, we
-    list commonly used kwargs and the tupak defaults.
+    list commonly used kwargs and the bilby defaults.
 
     Keyword Arguments
     -----------------

tupak/core/sampler/nestle.py → bilby/core/sampler/nestle.py

@@ -6,12 +6,12 @@ from .base_sampler import NestedSampler
 
 
 class Nestle(NestedSampler):
-    """tupak wrapper `nestle.Sampler` (http://kylebarbary.com/nestle/)
+    """bilby wrapper `nestle.Sampler` (http://kylebarbary.com/nestle/)
 
     All positional and keyword arguments (i.e., the args and kwargs) passed to
     `run_sampler` will be propagated to `nestle.sample`, see documentation for
     that function for further help. Under Keyword Arguments, we list commonly
-    used kwargs and the tupak defaults
+    used kwargs and the bilby defaults
 
     Keyword Arguments
    ------------------
@@ -48,7 +48,7 @@ class Nestle(NestedSampler):
 
         Returns
         -------
-        tupak.core.result.Result: Packaged information about the result
+        bilby.core.result.Result: Packaged information about the result
 
         """
         import nestle
@@ -78,7 +78,7 @@ class Nestle(NestedSampler):
 
         Returns
         -------
-        tupak.core.result.Result: Dummy container for sampling results.
+        bilby.core.result.Result: Dummy container for sampling results.
 
         """
         import nestle

tupak/core/sampler/ptemcee.py → bilby/core/sampler/ptemcee.py

@@ -6,12 +6,12 @@ from . import Emcee
 
 
 class Ptemcee(Emcee):
-    """tupak wrapper ptemcee (https://github.com/willvousden/ptemcee)
+    """bilby wrapper ptemcee (https://github.com/willvousden/ptemcee)
 
     All positional and keyword arguments (i.e., the args and kwargs) passed to
     `run_sampler` will be propagated to `ptemcee.Sampler`, see
     documentation for that class for further help. Under Keyword Arguments, we
-    list commonly used kwargs and the tupak defaults.
+    list commonly used kwargs and the bilby defaults.
 
     Keyword Arguments
     -----------------

tupak/core/sampler/pymc3.py → bilby/core/sampler/pymc3.py

@@ -11,12 +11,12 @@ from .base_sampler import Sampler, MCMCSampler
 
 
 class Pymc3(MCMCSampler):
-    """ tupak wrapper of the PyMC3 sampler (https://docs.pymc.io/)
+    """ bilby wrapper of the PyMC3 sampler (https://docs.pymc.io/)
 
     All keyword arguments (i.e., the kwargs) passed to `run_sampler` will be
     propapated to `pymc3.sample` where appropriate, see documentation for that
     class for further help. Under Keyword Arguments, we list commonly used
-    kwargs and the tupak, or where appropriate, PyMC3 defaults.
+    kwargs and the bilby, or where appropriate, PyMC3 defaults.
 
     Keyword Arguments
     -----------------
@@ -137,7 +137,7 @@ class Pymc3(MCMCSampler):
 
     def setup_prior_mapping(self):
         """
-        Set the mapping between predefined tupak priors and the equivalent
+        Set the mapping between predefined bilby priors and the equivalent
         PyMC3 distributions.
         """
 
@@ -208,7 +208,7 @@ class Pymc3(MCMCSampler):
         # GW specific priors
         prior_map['UniformComovingVolume'] = prior_map['Interped']
 
-        # internally defined mappings for tupak priors
+        # internally defined mappings for bilby priors
         prior_map['DeltaFunction'] = {'internal': self._deltafunction_prior}
         prior_map['Sine'] = {'internal': self._sine_prior}
         prior_map['Cosine'] = {'internal': self._cosine_prior}
@@ -217,10 +217,10 @@ class Pymc3(MCMCSampler):
 
     def _deltafunction_prior(self, key, **kwargs):
         """
-        Map the tupak delta function prior to a single value for PyMC3.
+        Map the bilby delta function prior to a single value for PyMC3.
         """
 
-        from tupak.core.prior import DeltaFunction
+        from ..prior import DeltaFunction
 
         # check prior is a DeltaFunction
         if isinstance(self.priors[key], DeltaFunction):
@@ -230,10 +230,10 @@ class Pymc3(MCMCSampler):
 
     def _sine_prior(self, key):
         """
-        Map the tupak Sine prior to a PyMC3 style function
+        Map the bilby Sine prior to a PyMC3 style function
         """
 
-        from tupak.core.prior import Sine
+        from ..prior import Sine
 
         # check prior is a Sine
         if isinstance(self.priors[key], Sine):
@@ -277,10 +277,10 @@ class Pymc3(MCMCSampler):
 
     def _cosine_prior(self, key):
         """
-        Map the tupak Cosine prior to a PyMC3 style function
+        Map the bilby Cosine prior to a PyMC3 style function
         """
 
-        from tupak.core.prior import Cosine
+        from ..prior import Cosine
 
         # check prior is a Cosine
         if isinstance(self.priors[key], Cosine):
@@ -324,10 +324,10 @@ class Pymc3(MCMCSampler):
 
     def _powerlaw_prior(self, key):
         """
-        Map the tupak PowerLaw prior to a PyMC3 style function
+        Map the bilby PowerLaw prior to a PyMC3 style function
         """
 
-        from tupak.core.prior import PowerLaw
+        from ..prior import PowerLaw
 
         # check prior is a PowerLaw
         if isinstance(self.priors[key], PowerLaw):
@@ -533,7 +533,7 @@ class Pymc3(MCMCSampler):
 
     def set_likelihood(self):
         """
-        Convert any tupak likelihoods to PyMC3 distributions.
+        Convert any bilby likelihoods to PyMC3 distributions.
         """
 
         try:
@@ -543,9 +543,9 @@ class Pymc3(MCMCSampler):
         except ImportError:
             raise ImportError("Could not import theano")
 
-        from tupak.core.likelihood import GaussianLikelihood, PoissonLikelihood, ExponentialLikelihood, \
+        from ..likelihood import GaussianLikelihood, PoissonLikelihood, ExponentialLikelihood, \
             StudentTLikelihood
-        from tupak.gw.likelihood import BasicGravitationalWaveTransient, GravitationalWaveTransient
+        from ...gw.likelihood import BasicGravitationalWaveTransient, GravitationalWaveTransient
 
         # create theano Op for the log likelihood if not using a predefined model
         class LogLike(tt.Op):

tupak/core/sampler/pymultinest.py → bilby/core/sampler/pymultinest.py

@@ -2,18 +2,18 @@ from __future__ import absolute_import