Merge remote-tracking branch 'origin/master' into 207-pep8-imports

# Conflicts:
#	bilby/core/prior.py
#	bilby/core/result.py
#	bilby/core/sampler/cpnest.py
#	bilby/core/sampler/emcee.py
#	bilby/core/sampler/pymc3.py
#	bilby/gw/prior.py

.gitlab-ci.yml

 # This script is an edited version of the example found at
 # https://git.ligo.org/lscsoft/example-ci-project/blob/python/.gitlab-ci.yml
-# Each 0th-indendation level is a job that will be run within GitLab CI
+# Each 0th-indentation level is a job that will be run within GitLab CI
 # The only exception are a short list of reserved keywords
 #
 # https://docs.gitlab.com/ee/ci/yaml/#gitlab-ci-yml
@@ -18,18 +18,22 @@ python-2:
   stage: test
   image: bilbydev/test-suite-py2
   before_script:
+    # Source the .bashrc for MultiNest
+    - source /root/.bashrc
     # Install the dependencies specified in the Pipfile
     - pipenv install --two --python=/opt/conda/bin/python2 --system --deploy
   script:
     - python setup.py install
     # Run tests without finding coverage
-    - pytest
+    - pytest --ignore=test/utils_py3_test.py
 
 # test example on python 3
 python-3:
   stage: test
   image: bilbydev/test-suite-py3
   before_script:
+    # Source the .bashrc for MultiNest
+    - source /root/.bashrc
     # Install the dependencies specified in the Pipfile
     - pipenv install --three --python=/opt/conda/bin/python --system --deploy
   script:
@@ -63,7 +67,7 @@ pages:
   script:
     - mkdir public/
     - mv htmlcov/ public/
-    - mv /builds/Monash/bilby/coverage_badge.svg public/
+    - mv coverage_badge.svg public/
     - mv docs/_build/html/* public/
   artifacts:
     paths:

CHANGELOG.md

@@ -3,6 +3,12 @@
 ## Unreleased
 
 Changes currently on master, but not under a tag.
+- Renamed `PriorSet` to `PriorDict`
+- Renamed `BBHPriorSet` to `BBHPriorDict`
+- Renamed `BNSPriorSet` to `BNSPriorDict`
+- Renamed `CalibrationPriorSet` to `CalibrationPriorDict`
+
+- Fixed a bug which caused `Interferometer.detector_tensor` not to update when `latitude`, `longitude`, `xarm_azimuth`, `yarm_azimuth`, `xarm_tilt`, `yarm_tilt` were updated.
 
 ## [0.3.1] 2018-11-06
 
@@ -88,7 +94,7 @@ 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/bilby/transient-gw-data.html)
+- Adds documentation on setting data (https://lscsoft.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 `bilby.core.result.plot_multiple()`.
 - Likelihood evaluations are now saved along with the posteriors.
@@ -115,9 +121,8 @@ First `pip` installable version https://pypi.org/project/BILBY/ .
 - 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/bilby/prior.html).
+- A `PriorSet` to handle sets of priors and allows reading in from a standardised prior file (see https://lscsoft.docs.ligo.org/bilby/prior.html).
 - A standardised file for storing detector data.
 
 ### Removed
 - All chainconsumer dependency as this was causing issues.
-

CONTRIBUTING.md

@@ -9,7 +9,7 @@ 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/bilby.  If you haven't
+which is hosted here: https://git.ligo.org/lscsoft/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
@@ -28,7 +28,7 @@ $ python setup.py develop
 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
 
@@ -62,7 +62,7 @@ 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/bilby/issues). This is the right
+tracker](https://git.ligo.org/lscsoft/bilby/issues). This is the right
 place to start a discussion of any of the above or most any other
 topic concerning the project.
 
@@ -157,84 +157,10 @@ you should:
 
 ## Code overview
 
-In this section, we'll give an overview of how the code is structured. This is intended to help orient users and make it easier to contribute. The layout is intended to define the logic of the code and new merge requests should aim to fit within this logic (unless there is a good argument to change it). For example, code which adds a new sampler should not effect the gravitational-wave specific parts of the code. Note that this document is not programatically generated and so may get out of date with time. If you notice something wrong, please open an issue. 
+In this section, we'll give an overview of how the code is structured. This is intended to help orient users and make it easier to contribute. The layout is intended to define the logic of the code and new merge requests should aim to fit within this logic (unless there is a good argument to change it). For example, code which adds a new sampler should not effect the gravitational-wave specific parts of the code. Note that this document is not programatically generated and so may get out of date with time. If you notice something wrong, please open an issue.
 
-### Top level
+### Bilby Code Layout
 
-At the top level, the code is split into three modules containing the core, gravitational-wave specific, and hyper-parameter specific functionality. New changes should always respect this  
-
-```mermaid
-graph TD
-bilby[bilby] --> core[.core]
-bilby --> gw[.gw]
-bilby --> hyper[.hyper]
-```
-
-### Core
-The core module contains the core inference logic - methods to define a prior, likelihood, and run Bayesian inference.
-
-```mermaid
-graph TD
-core[bilby.core] --> prior[.prior]
-core --> likelihood[.likelihood] 
-core --> result[.result] 
-core --> sampler[.sampler] 
-core --> utils[.utils]
-
-prior --> Prior[".Prior()"]
-style Prior fill:#D7BDE2
-prior --> Normal[".Normal(Prior)"]
-style Normal fill:#D7BDE2
-prior --> Uniform[".Uniform(Prior)"]
-style Uniform fill:#D7BDE2
-
-likelihood --> Likelihood(".Likelihood()")
-style Likelihood fill:#D7BDE2
-likelihood --> GaussianLikelihood(".GaussianLikelihood()")
-style GaussianLikelihood fill:#D7BDE2
-
-result --> Result(".Result()")
-style Result fill:#D7BDE2
-
-sampler --> run_sampler("run_sampler()")
-style run_sampler fill:#5499C7
-```
+![bilby overview](docs/images/bilby_layout.png)
 
 Note this layout is not comprehensive, for example only a few example "Priors" are shown.
-
-### Gravitational-wave specific
-
-```mermaid
-graph TD
-gw[bilby.gw] --> gw_likelihood[.likelihood]
-gw --> gw_prior[.prior]
-gw --> gw_source[.source]
-gw --> gw_waveform_generator[.waveform_generator]
-gw --> gw_utils[.utils]
-gw --> gw_conversion[.conversion]
-
-gw_likelihood --> GravitationalWaveTransient(".GravitationalWaveTransient()")
-style GravitationalWaveTransient fill:#D7BDE2
-gw_source --> lal_bbh(".lal_binary_black_hole()")
-style lal_bbh fill:#5499C7
-gw_waveform_generator--> WaveformGenerator(".WaveformGenerator()")
-style WaveformGenerator fill:#D7BDE2
-```
-### Legend of flow diagrams
-To show a module we use a grey box. A nested module is indicated as such
-```mermaid
-graph LR
-module[module] --> A[.other_module </br>]
-```
-i.e., in python, `module.other_module`. Meanwhile, a class,  `MyClass` living in module `my_module`  is indicated by
-```mermaid
-graph LR 
- my_module[my_module] --> Class[".MyClass(parent)"]
- style Class fill:#D7BDE2
-```
-where the `(Parent)` indicates the parent class from which this class inherits. Finally, we define a function living in a module as
-```mermaid
-graph LR 
- my_module[my_module] --> function[".function()"]
- style function fill:#5499C7
-```
\ No newline at end of file

README.rst

@@ -6,20 +6,20 @@ Bilby
 Fulfilling all your Bayesian inference dreams.
 
 -  `Installation
-   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>`__
+   instructions <https://lscsoft.docs.ligo.org/bilby/installation.html>`__
+-  `Contributing <https://git.ligo.org/lscsoft/bilby/blob/master/CONTRIBUTING.md>`__
+-  `Documentation <https://lscsoft.docs.ligo.org/bilby/index.html>`__
+-  `Issue tracker <https://git.ligo.org/lscsoft/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/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/
+.. |pipeline status| image:: https://git.ligo.org/lscsoft/bilby/badges/master/pipeline.svg
+   :target: https://git.ligo.org/lscsoft/bilby/commits/master
+.. |coverage report| image:: https://lscsoft.docs.ligo.org/bilby/coverage_badge.svg
+   :target: https://lscsoft.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

bilby/__init__.py

@@ -9,9 +9,9 @@ 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/bilby.
+The code, and many examples are hosted at https://git.ligo.org/lscsoft/bilby.
 For installation instructions see
-https://monash.docs.ligo.org/bilby/installation.html.
+https://lscsoft.docs.ligo.org/bilby/installation.html.
 
 """
 
@@ -23,3 +23,5 @@ from . import core, gw, hyper
 from .core import utils, likelihood, prior, result, sampler
 from .core.sampler import run_sampler
 from .core.likelihood import Likelihood
+
+__version__ = utils.get_version_information()

bilby/core/prior.py

@@ -13,11 +13,11 @@ from scipy.special import erf, erfinv
 
 # Keep import bilby statement, it is necessary for some eval() statements
 import bilby  # noqa
-from . import utils
+from . import utils import logger, infer_args_from_method
 from .utils import logger
 
 
-class PriorSet(OrderedDict):
+class PriorDict(OrderedDict):
     def __init__(self, dictionary=None, filename=None):
         """ A set of priors
 
@@ -38,7 +38,7 @@ class PriorSet(OrderedDict):
         elif type(filename) is str:
             self.from_file(filename)
         elif dictionary is not None:
-            raise ValueError("PriorSet input dictionary not understood")
+            raise ValueError("PriorDict input dictionary not understood")
 
     def to_file(self, outdir, label):
         """ Write the prior distribution to file.
@@ -189,20 +189,22 @@ class PriorSet(OrderedDict):
                 logger.debug('{} not a known prior.'.format(key))
         return samples
 
-    def prob(self, sample):
+    def prob(self, sample, **kwargs):
         """
 
         Parameters
         ----------
         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`
 
         Returns
         -------
         float: Joint probability of all individual sample probabilities
 
         """
-        return np.product([self[key].prob(sample[key]) for key in sample])
+        return np.product([self[key].prob(sample[key]) for key in sample], **kwargs)
 
     def ln_prob(self, sample):
         """
@@ -240,6 +242,14 @@ class PriorSet(OrderedDict):
         return False
 
 
+class PriorSet(PriorDict):
+
+    def __init__(self, dictionary=None, filename=None):
+        """ DEPRECATED: USE PriorDict INSTEAD"""
+        logger.warning("The name 'PriorSet' is deprecated use 'PriorDict' instead")
+        super(PriorSet, self).__init__(dictionary, filename)
+
+
 def create_default_prior(name, default_priors_file=None):
     """Make a default prior for a parameter with a known name.
 
@@ -263,7 +273,7 @@ def create_default_prior(name, default_priors_file=None):
             "No prior file given.")
         prior = None
     else:
-        default_priors = PriorSet(filename=default_priors_file)
+        default_priors = PriorDict(filename=default_priors_file)
         if name in default_priors.keys():
             prior = default_priors[name]
         else:
@@ -426,8 +436,7 @@ class Prior(object):
         str: A string representation of this instance
 
         """
-        subclass_args = inspect.getargspec(self.__init__).args
-        subclass_args.pop(0)
+        subclass_args = infer_args_from_method(self.__init__)
         prior_name = self.__class__.__name__
 
         property_names = [p for p in dir(self.__class__) if isinstance(getattr(self.__class__, p), property)]

bilby/core/result.py

 import os
 from distutils.version import LooseVersion
-from collections import OrderedDict
+from collections import OrderedDict, namedtuple
 
 import numpy as np
 import deepdish
@@ -11,7 +11,7 @@ import matplotlib.pyplot as plt
 
 from . import utils
 from .utils import logger, infer_parameters_from_function
-from .prior import PriorSet, DeltaFunction
+from .prior import PriorDict, DeltaFunction
 
 
 def result_file_name(outdir, label):
@@ -82,7 +82,7 @@ class Result(dict):
                 setattr(self, key, val)
 
         if getattr(self, 'priors', None) is not None:
-            self.priors = PriorSet(self.priors)
+            self.priors = PriorDict(self.priors)
 
     def __add__(self, other):
         matches = ['sampler', 'search_parameter_keys']
@@ -286,22 +286,112 @@ class Result(dict):
 
         Returns
         -------
-        string: str
-            A string of latex-formatted text of the mean and 1-sigma quantiles
+        summary: namedtuple
+            An object with attributes, median, lower, upper and string
 
         """
+        summary = namedtuple('summary', ['median', 'lower', 'upper', 'string'])
+
         if len(quantiles) != 2:
             raise ValueError("quantiles must be of length 2")
 
         quants_to_compute = np.array([quantiles[0], 0.5, quantiles[1]])
         quants = np.percentile(self.posterior[key], quants_to_compute * 100)
-        median = quants[1]
-        upper = quants[2] - median
-        lower = median - quants[0]
+        summary.median = quants[1]
+        summary.plus = quants[2] - summary.median
+        summary.minus = summary.median - quants[0]
 
         fmt = "{{0:{0}}}".format(fmt).format
-        string = r"${{{0}}}_{{-{1}}}^{{+{2}}}$"
-        return string.format(fmt(median), fmt(lower), fmt(upper))
+        string_template = r"${{{0}}}_{{-{1}}}^{{+{2}}}$"
+        summary.string = string_template.format(
+            fmt(summary.median), fmt(summary.minus), fmt(summary.plus))
+        return summary
+
+    def plot_marginals(self, parameters=None, priors=None, titles=True,
+                       file_base_name=None, bins=50, label_fontsize=16,
+                       title_fontsize=16, quantiles=[0.16, 0.84], dpi=300):
+        """ Plot 1D marginal distributions
+
+        Parameters
+        ----------
+        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), 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.
+        titles: bool
+            If true, add 1D titles of the median and (by default 1-sigma)
+            error bars. To change the error bars, pass in the quantiles kwarg.
+            See method `get_one_dimensional_median_and_error_bar` for further
+            details). If `quantiles=None` is passed in, no title is added.
+        file_base_name: str, optional
+            If given, the base file name to use (by default `outdir/label_` is
+            used)
+        bins: int
+            The number of histogram bins
+        label_fontsize, title_fontsize: int
+            The fontsizes for the labels and titles
+        quantiles: list
+            A length-2 list of the lower and upper-quantiles to calculate
+            the errors bars for.
+        dpi: int
+            Dots per inch resolution of the plot
+
+        Returns
+        -------
+        figures: dictionary
+            A dictionary of the matplotlib figures
+
+        """
+
+        if isinstance(parameters, dict):
+            plot_parameter_keys = list(parameters.keys())
+            truths = list(parameters.values())
+        elif parameters is None:
+            plot_parameter_keys = self.search_parameter_keys
+            truths = None
+        else:
+            plot_parameter_keys = list(parameters)
+            truths = None
+
+        labels = self.get_latex_labels_from_parameter_keys(plot_parameter_keys)
+        if file_base_name is None:
+            file_base_name = '{}/{}_'.format(self.outdir, self.label)
+
+        if priors is True:
+            priors = getattr(self, 'priors', False)
+        elif isinstance(priors, (dict)) or priors in [False, None]:
+            pass
+        else:
+            raise ValueError('Input priors={} not understood'.format(priors))
+
+        figures = dict()
+        for i, key in enumerate(plot_parameter_keys):
+            fig, ax = plt.subplots()
+            ax.hist(self.posterior[key].values, bins=bins, density=True,
+                    histtype='step')
+            ax.set_xlabel(labels[i], fontsize=label_fontsize)
+            if truths is not None:
+                ax.axvline(truths[i], ls='--', color='orange')
+
+            summary = self.get_one_dimensional_median_and_error_bar(
+                key, quantiles=quantiles)
+            ax.axvline(summary.median - summary.minus, ls='--', color='C0')
+            ax.axvline(summary.median + summary.plus, ls='--', color='C0')
+            if titles:
+                ax.set_title(summary.string, fontsize=title_fontsize)
+
+            if isinstance(priors, dict):
+                theta = np.linspace(ax.get_xlim()[0], ax.get_xlim()[1], 300)
+                ax.plot(theta, priors[key].prob(theta), color='C2')
+
+            fig.tight_layout()
+            fig.savefig(file_base_name + key)
+            figures[key] = fig
+
+        return figures
 
     def plot_corner(self, parameters=None, priors=None, titles=True, save=True,
                     filename=None, dpi=300, **kwargs):
@@ -312,9 +402,9 @@ 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), bilby.core.prior.PriorSet}
+        priors: {bool (False), bilby.core.prior.PriorDict}
             If true, add the stored prior probability density functions to the
-            one-dimensional marginal distributions. If instead a PriorSet
+            one-dimensional marginal distributions. If instead a PriorDict
             is provided, this will be plotted.
         titles: bool
             If true, add 1D titles of the median and (by default 1-sigma)
@@ -419,7 +509,7 @@ class Result(dict):
                 ax = axes[i + i * len(plot_parameter_keys)]
                 if ax.title.get_text() == '':
                     ax.set_title(self.get_one_dimensional_median_and_error_bar(
-                        par, quantiles=kwargs['quantiles']),
+                        par, quantiles=kwargs['quantiles']).string,
                         **kwargs['title_kwargs'])
 
         #  Add priors to the 1D plots
@@ -578,7 +668,7 @@ class Result(dict):
 
         Parameters
         ----------
-        priors: dict, PriorSet
+        priors: dict, PriorDict
             Prior distributions
         """
         self.prior_values = pd.DataFrame()

bilby/core/sampler/__init__.py

@@ -4,7 +4,7 @@ import datetime
 from collections import OrderedDict
 
 from ..utils import command_line_args, logger
-from ..prior import PriorSet
+from ..prior import PriorDict
 
 from .base_sampler import Sampler
 from .cpnest import Cpnest
@@ -47,8 +47,8 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
     ----------
     likelihood: `bilby.Likelihood`
         A `Likelihood` instance
-    priors: `bilby.PriorSet`
-        A PriorSet/dictionary of the priors for each parameter - missing
+    priors: `bilby.PriorDict`
+        A PriorDict/dictionary of the priors for each parameter - missing
         parameters will use default priors, if None, all priors will be default
     label: str
         Name for the run, used in output files
@@ -92,6 +92,8 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
 
     if clean:
         command_line_args.clean = clean
+    if command_line_args.clean:
+        kwargs['resume'] = False
 
     from . import implemented_samplers
 
@@ -99,8 +101,8 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
         priors = dict()
 
     if type(priors) in [dict, OrderedDict]:
-        priors = PriorSet(priors)
-    elif isinstance(priors, PriorSet):
+        priors = PriorDict(priors)
+    elif isinstance(priors, PriorDict):
         pass
     else:
         raise ValueError("Input priors not understood")

bilby/core/sampler/base_sampler.py

+from __future__ import absolute_import
 import datetime
 import numpy as np
 
 from pandas import DataFrame
 
 from ..utils import logger, command_line_args
-from ..prior import Prior, PriorSet
+from ..prior import Prior, PriorDict
 from ..result import Result, read_in_result
 
 
@@ -15,7 +16,7 @@ class Sampler(object):
     ----------
     likelihood: likelihood.Likelihood
         A  object with a log_l method
-    priors: bilby.core.prior.PriorSet, dict
+    priors: bilby.core.prior.PriorDict, dict
         Priors to be used in the search.
         This has attributes for each parameter to be sampled.
     external_sampler: str, Sampler, optional
@@ -36,7 +37,7 @@ class Sampler(object):
     -------
     likelihood: likelihood.Likelihood
         A  object with a log_l method
-    priors: bilby.core.prior.PriorSet
+    priors: bilby.core.prior.PriorDict
         Priors to be used in the search.
         This has attributes for each parameter to be sampled.
     external_sampler: Module
@@ -75,10 +76,10 @@ class Sampler(object):
             self, likelihood, priors, outdir='outdir', label='label',
             use_ratio=False, plot=False, skip_import_verification=False, **kwargs):
         self.likelihood = likelihood
-        if isinstance(priors, PriorSet):
+        if isinstance(priors, PriorDict):
             self.priors = priors
         else:
-            self.priors = PriorSet(priors)
+            self.priors = PriorDict(priors)
         self.label = label
         self.outdir = outdir
         self.use_ratio = use_ratio
@@ -441,7 +442,7 @@ class MCMCSampler(Sampler):
 
     def print_nburn_logging_info(self):
         """ Prints logging info as to how nburn was calculated """
-        if type(self.kwargs['nburn']) in [float, int]:
+        if type(self.nburn) in [float, int]:
             logger.info("Discarding {} steps for burn-in".format(self.nburn))
         elif self.result.max_autocorrelation_time is None:
             logger.info("Autocorrelation time not calculated, discarding {} "

bilby/core/sampler/cpnest.py

@@ -4,7 +4,7 @@ import numpy as np
 from pandas import DataFrame
 
 from .base_sampler import NestedSampler
-from ..utils import logger
+from ..utils import logger, check_directory_exists_and_if_not_mkdir
 
 try:
     from cpnest import model as cpmodel, CPNest
@@ -23,27 +23,33 @@ class Cpnest(NestedSampler):
 
     Keyword Arguments
     -----------------
-    npoints: int
+    nlive: int
         The number of live points, note this can also equivalently be given as
-        one of [nlive, nlives, n_live_points]
+        one of [npoints, nlives, n_live_points]
     seed: int (1234)
         Initialised random seed
-    Nthreads: int, (1)
+    nthreads: int, (1)
         Number of threads to use
     maxmcmc: int (1000)
         The maximum number of MCMC steps to take
-    verbose: Bool
+    verbose: Bool (True)
         If true, print information information about the convergence during
+    resume: Bool (False)
+        Whether or not to resume from a previous run
+    output: str
+        Where to write the CPNest, by default this is
+        {self.outdir}/cpnest_{self.label}/
 
     """
-    default_kwargs = dict(verbose=1, Nthreads=1, Nlive=500, maxmcmc=1000,
-                          Poolsize=100, seed=None, balance_samplers=True)
+    default_kwargs = dict(verbose=1, nthreads=1, nlive=500, maxmcmc=1000,
+                          seed=None, poolsize=100, nhamiltonian=0, resume=False,