Merge branch 'master' into docstring_update

# Conflicts:
#	tupak/gw/conversion.py
#	tupak/gw/detector.py
#	tupak/gw/likelihood.py
#	tupak/gw/utils.py
#	tupak/gw/waveform_generator.py

.gitlab-ci.yml

@@ -22,21 +22,24 @@ exitcode-jessie:
     # CI is too old and fails when trying to install lalsuite
     - sed -i '/lalsuite/d' requirements.txt
     - pip install -r requirements.txt
-    - pip install coverage
+    - pip install 'coverage>=4.5'
     - pip install coverage-badge
   script:
     - python setup.py install
+    # Run tests and collect coverage data
+    - coverage --version
     - coverage erase
     - coverage run --source=tupak/ -a test/conversion_tests.py
     - coverage run --source=tupak/ -a test/detector_tests.py
     - coverage run --source=tupak/ -a test/prior_tests.py
     - coverage run --source=tupak/ -a test/sampler_tests.py
     - coverage run --source=tupak/ -a test/waveform_generator_tests.py
-    - coverage run --omit=* -a test/example_tests.py
-    - coverage run --omit=* -a test/noise_realisation_tests.py
-    - coverage run --omit=* -a test/tests.py
     - coverage html
     - coverage-badge -o coverage_badge.svg -f
+    # Run all other tests (no coverage data collected)
+    - python test/example_tests.py
+    - python test/noise_realisation_tests.py
+    - python test/other_tests.py
 
     # Make the documentation
     - pip install -r docs/requirements.txt

MANIFEST.in

+include README.rst

README.md

-[![pipeline status](https://git.ligo.org/Monash/tupak/badges/master/pipeline.svg)](https://git.ligo.org/Monash/tupak/commits/master)
-[![coverage report](https://monash.docs.ligo.org/tupak/coverage_badge.svg)](
-https://monash.docs.ligo.org/tupak/htmlcov/)
-
-# Tupak
-
-Fulfilling all your gravitational wave dreams.
-
-Documentation can be found
-[here](https://monash.docs.ligo.org/tupak/index.html). This and the code in
-general are a work in progress. We encourage you to submit issues via are
-[issue tracker](https://git.ligo.org/Monash/tupak/issues) and 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.
-
-## Example
-
-To get started with `tupak`, we have a few examples and tutorials:
-
-1. [Examples of injecting and recovering data](https://git.ligo.org/Monash/tupak/tree/master/examples/injection_examples)
-    * [A basic tutorial](https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/basic_tutorial.py)
-    * [Create your own source model](https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/create_your_own_source_model.py)
-    * [Create your own time-domain source model](https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/create_your_own_time_domain_source_model.py)
-    * [How to specify the prior](https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/how_to_specify_the_prior.py)
-    * [Using a partially marginalized likelihood](https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/marginalized_likelihood.py)
-
-2. [Examples using open data](https://git.ligo.org/Monash/tupak/tree/master/examples/open_data_examples)
-    * [Analysing the first Binary Black hole detection, GW150914](https://git.ligo.org/Monash/tupak/blob/master/examples/open_data_examples/GW150914.py)
-
-3. [Notebook-style tutorials](https://git.ligo.org/Monash/tupak/tree/master/examples/tutorials)
-    * [Comparing different samplers](https://git.ligo.org/Monash/tupak/blob/master/examples/tutorials/compare_samplers.ipynb)
-    * [Visualising the output](https://git.ligo.org/Monash/tupak/blob/master/examples/tutorials/visualising_the_results.ipynb)
-
-
-## Installation
-
-`tupak` is developed to work with both Python 2.7+ and Python 3+. In the
-following, we assume you have a working python installation, [python
-pip](https://packaging.python.org/tutorials/installing-packages/#use-pip-for-installing),
-and [git](https://git-scm.com/).
-
-### Install tupak
-Clone the repository, install the requirements, and then install `tupak`.
-```bash
-$ git clone git@git.ligo.org:Monash/tupak.git
-$ cd tupak/
-$ pip install -r requirements.txt
-$ python setup.py install
-```
-
-Once you have run these steps, you have `tupak` installed. You can now try to
-run the examples. However, for many gravitational-wave related examples you'll
-also need to install `lalsuite`.
-
-### Install lalsuite
-We recommend you install `lalsuite` the simple way:
-
-```bash
-$ pip install lalsuite.
-```
-
-If this doesn't work, or you prefer to, you can also install from source.
-Head to
-[https://git.ligo.org/lscsoft/lalsuite](https://git.ligo.org/lscsoft/lalsuite)
-to check you have an account and SSH keys set up. Then,
-
-```bash
-$ git lfs install # you may need to install git-lfs first
-$ git clone git@git.ligo.org:lscsoft/lalsuite.git
-$ cd lalsuite
-$ ./00boot
-$ ./configure --prefix=${HOME}/lalsuite-install --disable-all-lal --enable-swig  --enable-lalsimulation
-$ make; make install
-```
-
-Warning: in the configure line here, we have disabled everything except
-lalsimulation. If you need other modules, see `./configure --help`.
-
-### Install pymultinest
-
-If you want to use the `pymultinest` sampler, you first need the
-MultiNest library to be installed to work properly. The full instructions can
-be found [here](https://johannesbuchner.github.io/PyMultiNest/install.html). We
-have also written [a shortened tl;dr here](./TLDR_MULTINEST.md).
-
-
-## Tests and coverage
-
-We have a variety of tests which can be found in the `tests` directory.  You
-can find a [current coverage report for master
-here.](https://monash.docs.ligo.org/tupak/htmlcov/).
-
-

README.rst

+|pipeline status| |coverage report|
+
+Tupak
+=====
+
+Fulfilling all your gravitational wave dreams.
+
+-  `Installation
+   instructions <https://monash.docs.ligo.org/tupak/installation.html>`__
+-  `Documentation <https://monash.docs.ligo.org/tupak/index.html>`__.
+-  `Issue tracker <https://git.ligo.org/Monash/tupak/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.
+
+Examples
+--------
+
+To get started with ``tupak``, we have a few examples and tutorials:
+
+1. `Examples of injecting and recovering
+   data <https://git.ligo.org/Monash/tupak/tree/master/examples/injection_examples>`__
+
+   -  `A basic
+      tutorial <https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/basic_tutorial.py>`__
+   -  `Create your own source
+      model <https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/create_your_own_source_model.py>`__
+   -  `Create your own time-domain source
+      model <https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/create_your_own_time_domain_source_model.py>`__
+   -  `How to specify the
+      prior <https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/how_to_specify_the_prior.py>`__
+   -  `Using a partially marginalized
+      likelihood <https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/marginalized_likelihood.py>`__
+
+2. `Examples using open
+   data <https://git.ligo.org/Monash/tupak/tree/master/examples/open_data_examples>`__
+
+   -  `Analysing the first Binary Black hole detection,
+      GW150914 <https://git.ligo.org/Monash/tupak/blob/master/examples/open_data_examples/GW150914.py>`__
+
+3. `Notebook-style
+   tutorials <https://git.ligo.org/Monash/tupak/tree/master/examples/tutorials>`__
+
+   -  `Comparing different
+      samplers <https://git.ligo.org/Monash/tupak/blob/master/examples/tutorials/compare_samplers.ipynb>`__
+   -  `Visualising the
+      output <https://git.ligo.org/Monash/tupak/blob/master/examples/tutorials/visualising_the_results.ipynb>`__
+
+.. |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/

TLDR_MULTINEST.md

-Here is a short version, if this fails refer to the full instructions.
-
-First, install the dependencies (for Ubuntu/Linux):
-
-```bash
-$ sudo apt-get install python-{scipy,numpy,matplotlib,progressbar} ipython libblas{3,-dev} liblapack{3,-dev} libatlas{3-base,-dev} cmake build-essential git gfortran
-```
-
-For Mac, the advice in the instructions are "If you google for “MultiNest Mac OSX” or “PyMultiNest Mac OSX” you will find installation instructions".
-
-The following will place a directory `MultiNest` in your `$HOME` directory, if you want
-to place it somewhere, adjust the instructions as such.
-
-```bash
-git clone https://github.com/JohannesBuchner/MultiNest $HOME
-cd $HOME/MultiNest/build
-cmake ..
-make
-```
-
-Finally, add the libraries to you path. Add this to the `.bashrc` file
-```
-export LD_LIBRARY_PATH=$HOME/Downloads/MultiNest/lib:
-```
-
-(you'll need to resource your `.bashrc` after this, i.e. run `bash`.
-

docs/index.txt

@@ -8,6 +8,7 @@ Welcome to tupak's documentation!
    :maxdepth: 3
    :caption: Contents:
 
+   installation
    basics-of-parameter-estimation
    compact-binary-coalescence-parameter-estimation
    prior

docs/installation.txt

+============
+Installation
+============
+
+The lastest stable release of Tupak can be `pip-installed
+<https://pypi.org/project/TUPAK/>`_ from
+
+.. code-block:: console
+
+   $ pip install tupak
+
+If you would like to install the development version, see the instructions
+below.
+
+Install tupak from source
+-------------------------
+
+:code:`tupak` is developed to work with both Python 2.7+ and Python 3+. In the
+following, we assume you have a working python installation, `python pip
+<https://packaging.python.org/tutorials/installing-packages/#use-pip-for-installing)>`_,
+and `git <https://git-scm.com/>`_.
+
+Clone the repository, install the requirements, and then install the software:
+
+.. code-block:: console
+
+   $ git clone git@git.ligo.org:Monash/tupak.git
+   $ cd tupak/
+   $ pip install -r requirements.txt
+   $ python setup.py install
+
+Once you have run these steps, you have `tupak` installed. You can now try to
+run the examples.
+
+.. note::
+   If you do not have a git.ligo account, and recieve and error message:
+
+   .. code-block:: console
+
+      git@git.ligo.org: Permission denied (publickey,gssapi-keyex,gssapi-with-mic).
+      fatal: Could not read from remote repository.
+
+   Then you need to use the HTTPS URL, e.g. replace the first line above with
+
+   .. code-block:: console
+
+      $ git clone https://git.ligo.org/Monash/tupak.git
+
+
+Installing lalsuite
+-------------------
+
+For many gravitational-wave related examples you'll need access to the
+`lalsuite <https://wiki.ligo.org/DASWG/LALSuite>`_ tool set. These should be
+installed by default in the requirements. However, if you have problems with
+your installation, you can first try to install the simple way:
+
+.. code-block:: console
+
+   $ pip install lalsuite.
+
+If this doesn't work, or you prefer to, you can also install from source.
+
+First, head to https://git.ligo.org/lscsoft/lalsuite to check you have an
+account and SSH keys set up. Then,
+
+.. code-block:: console
+
+   $ git lfs install # you may need to install git-lfs first
+   $ git clone git@git.ligo.org:lscsoft/lalsuite.git
+   $ cd lalsuite
+   $ ./00boot
+   $ ./configure --prefix=${HOME}/lalsuite-install --disable-all-lal --enable-swig  --enable-lalsimulation
+   $ make; make install
+
+Warning: in the configure line here, we have disabled everything except
+lalsimulation. If you need other modules, see `./configure --help`.
+
+Installing pymultinest
+----------------------
+
+If you want to use the `pymultinest` sampler, you first need the
+MultiNest library to be installed to work properly. The full instructions can
+be found here: https://johannesbuchner.github.io/PyMultiNest/install.html. Here
+is a shortened version:
+
+First, install the dependencies (for Ubuntu/Linux):
+
+.. code-block:: console
+
+   $ sudo apt-get install python-{scipy,numpy,matplotlib,progressbar} ipython libblas{3,-dev} liblapack{3,-dev} libatlas{3-base,-dev} cmake build-essential git gfortran
+
+For Mac, the advice in the instructions are "If you google for “MultiNest Mac OSX” or “PyMultiNest Mac OSX” you will find installation instructions".
+
+The following will place a directory `MultiNest` in your :code:`$HOME` directory, if you want
+to place it somewhere, adjust the instructions as such.
+
+.. code-block:: console
+
+   $ git clone https://github.com/JohannesBuchner/MultiNest $HOME
+   $ cd $HOME/MultiNest/build
+   $ cmake ..
+   $ make
+
+Finally, add the libraries to you path. Add this to the `.bashrc` file
+
+.. code-block:: console
+
+   $ export LD_LIBRARY_PATH=$HOME/Downloads/MultiNest/lib:
+
+(you'll need to resource your `.bashrc` after this, i.e. run `bash`).
+

docs/likelihood.txt

@@ -167,11 +167,55 @@ In the last example, we considered only cases with known noise (e.g., a
 prespecified standard deviation. We now present a general function which can
 handle unknown noise (in which case you need to specify a prior for
 :math:`\sigma`, or known noise (in which case you pass the known noise in when
-instatiating the likelihood
-
-.. literalinclude:: ../examples/other_examples/linear_regression_unknown_noise.py
-   :lines: 52-94
-
+instatiating the likelihood::
+
+  class GaussianLikelihood(tupak.Likelihood):
+      def __init__(self, x, y, function, sigma=None):
+          """
+          A general Gaussian likelihood for known or unknown noise - the model
+          parameters are inferred from the arguments of function
+
+          Parameters
+          ----------
+          x, y: array_like
+              The data to analyse
+          function:
+              The python function to fit to the data. Note, this must take the
+              dependent variable as its first argument. The other arguments are
+              will require a prior and will be sampled over (unless a fixed
+              value is given).
+          sigma: None, float, array_like
+              If None, the standard deviation of the noise is unknown and will be
+              estimated (note: this requires a prior to be given for sigma). If
+              not None, this defined the standard-deviation of the data points.
+              This can either be a single float, or an array with length equal
+              to that for `x` and `y`.
+          """
+          self.x = x
+          self.y = y
+          self.N = len(x)
+          self.sigma = sigma
+          self.function = function
+
+          # These lines of code infer the parameters from the provided function
+          parameters = inspect.getargspec(function).args
+          parameters.pop(0)
+          self.parameters = dict.fromkeys(parameters)
+          self.function_keys = self.parameters.keys()
+          if self.sigma is None:
+              self.parameters['sigma'] = None
+
+      def log_likelihood(self):
+          sigma = self.parameters.get('sigma', self.sigma)
+          model_parameters = {k: self.parameters[k] for k in self.function_keys}
+          res = self.y - self.function(self.x, **model_parameters)
+          return -0.5 * (np.sum((res / sigma)**2)
+                         + self.N*np.log(2*np.pi*sigma**2))
+
+We provide this general-purpose class as part of tupak:
+
+.. autoclass:: tupak.core.likelihood.GaussianLikelihood
+   :members:
 
 An example using this likelihood can be found `on this page <https://git.ligo.org/Monash/tupak/blob/master/examples/other_examples/linear_regression_unknown_noise.py>`_.
 

docs/prior.txt

@@ -39,7 +39,7 @@ If any model parameter required by the :ref:`likelihood` are not defined in the
 will try to use a default prior. By default, these are setup for a binary black
 hole and defined in a file like this
 
-.. literalinclude:: /../tupak/core/prior_files/binary_black_holes.prior
+.. literalinclude:: /../tupak/gw/prior_files/binary_black_holes.prior
 
 You can define your own default prior and pass a string pointing to that file
 to :ref:`run_sampler <run_sampler>`.

examples/injection_examples/basic_tutorial.py

@@ -29,13 +29,18 @@ np.random.seed(88170235)
 # spins of both black holes (a, tilt, phi), etc.
 injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
                             luminosity_distance=2000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
-                            waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
+                            ra=1.375, dec=-1.2108)
+
+# Fixed arguments passed into the source model
+waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
+                          reference_frequency=50.)
 
 # Create the waveform_generator using a LAL BinaryBlackHole source function
 waveform_generator = tupak.WaveformGenerator(time_duration=time_duration,
                                              sampling_frequency=sampling_frequency,
                                              frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
-                                             parameters=injection_parameters)
+                                             parameters=injection_parameters,
+                                             waveform_arguments=waveform_arguments)
 hf_signal = waveform_generator.frequency_domain_strain()
 
 # Set up interferometers.  In this case we'll use three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1),
@@ -69,4 +74,4 @@ result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynest
 
 # make some plots of the outputs
 result.plot_corner()
-print(result)
+

examples/injection_examples/basic_tutorial_dist_time_phase_marg.py

@@ -27,13 +27,18 @@ np.random.seed(88170235)
 # spins of both black holes (a, tilt, phi), etc.
 injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
                             luminosity_distance=2000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
-                            waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
+                            ra=1.375, dec=-1.2108)
+
+# Fixed arguments passed into the source model
+waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
+                          reference_frequency=50.)
 
 # Create the waveform_generator using a LAL BinaryBlackHole source function
 waveform_generator = tupak.WaveformGenerator(time_duration=time_duration,
                                              sampling_frequency=sampling_frequency,
                                              frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
-                                             parameters=injection_parameters)
+                                             parameters=injection_parameters,
+                                             waveform_arguments=waveform_arguments)
 hf_signal = waveform_generator.frequency_domain_strain()
 
 # Set up interferometers.  In this case we'll use three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1),
@@ -66,4 +71,4 @@ result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynest
 
 # make some plots of the outputs
 result.plot_corner()
-print(result)
+

examples/injection_examples/basic_tutorial_time_phase_marg.py

@@ -27,13 +27,17 @@ np.random.seed(88170235)
 # spins of both black holes (a, tilt, phi), etc.
 injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
                             luminosity_distance=2000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
-                            waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
+                            ra=1.375, dec=-1.2108)
+
+waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
+                          reference_frequency=50.)
 
 # Create the waveform_generator using a LAL BinaryBlackHole source function
 waveform_generator = tupak.WaveformGenerator(time_duration=time_duration,
                                              sampling_frequency=sampling_frequency,
                                              frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
-                                             parameters=injection_parameters)
+                                             parameters=injection_parameters,
+                                             waveform_arguments=waveform_arguments)
 hf_signal = waveform_generator.frequency_domain_strain()
 
 # Set up interferometers.  In this case we'll use three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1),
@@ -66,4 +70,4 @@ result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynest
 
 # make some plots of the outputs
 result.plot_corner()
-print(result)
+

examples/injection_examples/change_sampled_parameters.py

@@ -20,7 +20,10 @@ np.random.seed(151226)
 
 injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
                             luminosity_distance=3000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
-                            waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
+                            ra=1.375, dec=-1.2108)
+
+waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
+                          reference_frequency=50.)
 
 # Create the waveform_generator using a LAL BinaryBlackHole source function
 waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
@@ -28,7 +31,7 @@ waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
     frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
     parameter_conversion=tupak.gw.conversion.convert_to_lal_binary_black_hole_parameters,
     non_standard_sampling_parameter_keys=['chirp_mass', 'mass_ratio'],
-    parameters=injection_parameters)
+    parameters=injection_parameters, waveform_arguments=waveform_arguments)
 hf_signal = waveform_generator.frequency_domain_strain()
 
 # Set up interferometers.
@@ -55,4 +58,4 @@ result = tupak.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sa
                                         injection_parameters=injection_parameters, label='DifferentParameters',
                                         outdir=outdir, conversion_function=tupak.gw.conversion.generate_all_bbh_parameters)
 result.plot_corner()
-print(result)
+

examples/injection_examples/create_your_own_source_model.py

@@ -51,4 +51,4 @@ result = tupak.core.sampler.run_sampler(
     likelihood, prior, sampler='dynesty', outdir=outdir, label=label,
     resume=False, sample='unif', injection_parameters=injection_parameters)
 result.plot_corner()
-print(result)
+

examples/injection_examples/create_your_own_time_domain_source_model.py

@@ -72,4 +72,4 @@ result = tupak.core.sampler.run_sampler(likelihood, prior, sampler='dynesty', np
                                         outdir=outdir, label=label)
 
 result.plot_corner()
-print(result)
+

examples/injection_examples/how_to_specify_the_prior.py

@@ -18,13 +18,17 @@ np.random.seed(151012)
 
 injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
                             luminosity_distance=4000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
-                            waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
+                            ra=1.375, dec=-1.2108)
+
+waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
+                          reference_frequency=50.)
 
 # Create the waveform_generator using a LAL BinaryBlackHole source function
 waveform_generator = tupak.WaveformGenerator(time_duration=time_duration,
                                              sampling_frequency=sampling_frequency,
                                              frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
-                                             parameters=injection_parameters)
+                                             parameters=injection_parameters,
+                                             waveform_arguments=waveform_arguments)
 hf_signal = waveform_generator.frequency_domain_strain()
 
 # Set up interferometers.
@@ -60,4 +64,4 @@ likelihood = tupak.GravitationalWaveTransient(interferometers=IFOs, waveform_gen
 result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
                            injection_parameters=injection_parameters, outdir=outdir, label='specify_prior')
 result.plot_corner()
-print(result)
+

examples/injection_examples/marginalized_likelihood.py

@@ -17,12 +17,16 @@ np.random.seed(170608)
 
 injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
                             luminosity_distance=4000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
-                            waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
+                            ra=1.375, dec=-1.2108)
+
+waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
+                          reference_frequency=50.)
 
 # Create the waveform_generator using a LAL BinaryBlackHole source function
 waveform_generator = tupak.WaveformGenerator(
     time_duration=time_duration, sampling_frequency=sampling_frequency,
-    frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole, parameters=injection_parameters)
+    frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole, parameters=injection_parameters,
+    waveform_arguments=waveform_arguments)
 hf_signal = waveform_generator.frequency_domain_strain()
 
 # Set up interferometers.
@@ -46,4 +50,4 @@ likelihood = tupak.GravitationalWaveTransient(
 result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
                            injection_parameters=injection_parameters, outdir=outdir, label='BasicTutorial')
 result.plot_corner()
-print(result)
+

examples/other_examples/sine_gaussian_example.py → examples/injection_examples/sine_gaussian_example.py

@@ -63,7 +63,7 @@ result = tupak.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sa
 
 # make some plots of the outputs
 result.plot_corner()
-print(result)
+
 
 
 

examples/open_data_examples/GW150914.py

@@ -39,8 +39,8 @@ prior = tupak.gw.prior.BBHPriorSet(filename='GW150914.prior')
 waveform_generator = tupak.WaveformGenerator(time_duration=interferometers[0].duration,
                                              sampling_frequency=interferometers[0].sampling_frequency,
                                              frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
-                                             parameters={'waveform_approximant': 'IMRPhenomPv2',
-                                                         'reference_frequency': 50})
+                                             waveform_arguments={'waveform_approximant': 'IMRPhenomPv2',
+                                                                 'reference_frequency': 50})
 
 # In this step, we define the likelihood. Here we use the standard likelihood
 # function, passing it the data and the waveform generator.
@@ -51,4 +51,4 @@ likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers, wav
 result = tupak.run_sampler(likelihood, prior, sampler='dynesty',
                            outdir=outdir, label=label)
 result.plot_corner()
-print(result)
+