Add the code spell pre-commit and fix all existing spelling errors

.dictionary.txt

@@ -1,2 +1,5 @@
 hist
 livetime
+iff
+amin
+amax

.pre-commit-config.yaml

@@ -15,7 +15,6 @@ repos:
     hooks:
     -   id: codespell
         args: [--ignore-words=.dictionary.txt]
-        files: ^bilby/bilby_mcmc/
 -   repo: https://github.com/asottile/seed-isort-config
     rev: v1.3.0
     hooks:

CHANGELOG.md

@@ -99,7 +99,7 @@ Version 1.1.0 release of bilby
 - Fixed `ultranest` from failing
 - Fixed issues with plotting failing in tests (!904)
 - Changed the CI to run on auto-built images (!899)
-- Resolved a `matplotlib` error occuring at `dynesty` checkpoint plots (!902)
+- Resolved a `matplotlib` error occurring at `dynesty` checkpoint plots (!902)
 - Fixed the multidimensional Gaussian example (!901)
 - Now allow any lal dictionary option and added a numerical relativity file (!896)
 - Fixed the likelihood count in `dynesty` (!853)
@@ -171,7 +171,7 @@ Version 1.0.1 release of bilby
 - Fixed a minor issue with conditional priors that could cause unexpected behaviour in edge cases (!838)
 - Fixed `__repr__` method in the `FromFile` prior (!836)
 - Fixed an issue that caused problems for some users when plotting with a latex backend (!816)
-- Fixed bug that occured when min/max of interpolated priors was changed (!815)
+- Fixed bug that occurred when min/max of interpolated priors was changed (!815)
 - Fixed time domain waveform epoch (!736)
 - Fixed time keeping in multinest (!830)
 - Now checks if marginalised priors were defined before marginalising (!829)
@@ -209,7 +209,7 @@ for details
 - Updated the default PSD to O4 (!757)
 - Make multinest allow long file names, optional and work with MPI (!764 !785)
 - Add min/max to aligned spin prior (!787)
-- Reduce redudant code (!703)
+- Reduce redundant code (!703)
 - Added testing for python 3.8 (!762)
 - Improvements to the waveform plot (!769)
 
@@ -310,7 +310,7 @@ parameters.
 
 ## Changes
 - Speed up the prior evaluations by implementing directly with checks to scipy !627
-- Soft initalisation option for the Sampler class !620
+- Soft initialisation option for the Sampler class !620
 - Improvements to JSON reading and writing for functions !621
 - Fixed bug in prior reading !618 !617
 - Fixes to the examples !619 !614 !626 !616
@@ -371,7 +371,7 @@ parameters.
 - Removed the sqrt(2) normalisation from the scalar longitudinal mode
 - Improve PSD filename reading (no longer required "/" to read local files)
 - Fix bug in emcee chains
-- Added a try/except cluase for building the lookup table
+- Added a try/except clause for building the lookup table
 
 ## [0.5.4] 2019-07-30
 
@@ -412,7 +412,7 @@ shown (!564) to provide better convergence for the long-duration high-spin tests
 
 ### Changed
 - Updated and fixed bugs in examples
-- Resolve sampling time persistence for runs which are interupted
+- Resolve sampling time persistence for runs which are interrupted
 - Improvements to the PP plot
 - Speed up of the distance calculation
 - Fixed a bug in the inteference of bilby command line arguments with user specified command lines
@@ -708,7 +708,7 @@ re-instantiate the Prior in most cases
 - Changed to using `setuptools` for installation.
 - Clean up of real data handling: all data is now windowed with a 0.4s roll off (unless set otherwise) and low-pass filtered.
 - Add explicit method to create a power spectral density from time-domain data
-- Clean up of `PowerSpectralDensity()` - addds `set_from` methods to handle various ways to define the PSD.
+- Clean up of `PowerSpectralDensity()` - adds `set_from` methods to handle various ways to define the PSD.
 - Clean up of `detectors.py`: adds an `InterferometerStrainData` to handle strain data and `InterferometerSet` to handle multiple interferometers. All data setting should primarily be done through the `Interferometer.set_strain_data..` methods.
 - Fix the comments and units of `nfft` and `infft` and general improvement to documentation of data.
 - Fixed a bug in create_time_series

CONTRIBUTING.md

@@ -275,7 +275,7 @@ 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
+parts of the code. Note that this document is not programmatically generated and
 so may get out of date with time. If you notice something wrong, please open an
 issue.
 

bilby/core/prior/analytical.py

@@ -270,7 +270,7 @@ class SymmetricLogUniform(Prior):
 
     def __init__(self, minimum, maximum, name=None, latex_label=None,
                  unit=None, boundary=None):
-        """Symmetric Log-Uniform distribtions with bounds
+        """Symmetric Log-Uniform distributions with bounds
 
         This is identical to a Log-Uniform distribution, but mirrored about
         the zero-axis and subsequently normalized. As such, the distribution

bilby/core/prior/base.py

@@ -413,7 +413,7 @@ def _parse_argument_string(cls, val):
         Parameters
         ==========
         val: str
-            The string version of the agument
+            The string version of the argument
 
         Returns
         =======

bilby/core/prior/joint.py

@@ -11,7 +11,7 @@ class BaseJointPriorDist(object):
     def __init__(self, names, bounds=None):
         """
         A class defining JointPriorDist that will be overwritten with child
-        classes defining the joint prior distribtuions between given parameters,
+        classes defining the joint prior distributions between given parameters,
 
 
         Parameters
@@ -210,7 +210,7 @@ def _ln_prob(self, samp, lnprob, outbounds):
         samp: vector
             sample to evaluate the ln_prob at
         lnprob: vector
-            of -inf pased in with the same shape as the number of samples
+            of -inf passed in with the same shape as the number of samples
         outbounds: array_like
             boolean array showing which samples in lnprob vector are out of the given bounds
 
@@ -231,7 +231,7 @@ def sample(self, size=1, **kwargs):
         Parameters
         ==========
         size: int
-            number of samples to generate, defualts to 1
+            number of samples to generate, defaults to 1
         """
 
         if size is None:
@@ -250,7 +250,7 @@ def _sample(self, size, **kwargs):
         Parameters
         ==========
         size: int
-            number of samples to generate, defualts to 1
+            number of samples to generate, defaults to 1
         """
         samps = np.zeros((size, len(self)))
         """
@@ -299,7 +299,7 @@ def _rescale(self, samp, **kwargs):
         Parameters
         ==========
         samp: numpy array
-            this is a vector sample drawn from a uniform distribtuion to be rescaled to the distribution
+            this is a vector sample drawn from a uniform distribution to be rescaled to the distribution
         """
         """
         Here is where the subclass where overwrite rescale method
@@ -707,7 +707,7 @@ def rescale(self, val, **kwargs):
         Returns
         =======
         float:
-            A sample from the prior paramter.
+            A sample from the prior parameter.
         """
 
         self.dist.rescale_parameters[self.name] = val
@@ -733,7 +733,7 @@ def sample(self, size=1, **kwargs):
         Returns
         =======
         float:
-            A sample from the prior paramter.
+            A sample from the prior parameter.
         """
 
         if self.name in self.dist.sampled_parameters:

bilby/core/result.py

@@ -801,7 +801,7 @@ def save_to_file(self, filename=None, overwrite=False, outdir=None,
     def save_posterior_samples(self, filename=None, outdir=None, label=None):
         """ Saves posterior samples to a file
 
-        Generates a .dat file containing the posterior samples and auxillary
+        Generates a .dat file containing the posterior samples and auxiliary
         data saved in the posterior. Note, strings in the posterior are
         removed while complex numbers will be given as absolute values with
         abs appended to the column name
@@ -1724,7 +1724,7 @@ class ResultList(list):
     def __init__(self, results=None):
         """ A class to store a list of :class:`bilby.core.result.Result` objects
         from equivalent runs on the same data. This provides methods for
-        outputing combined results.
+        outputting combined results.
 
         Parameters
         ==========
@@ -1820,7 +1820,7 @@ def _combine_nested_sampled_runs(self, result):
         result.log_evidence = logsumexp(log_evidences, b=1. / len(self))
         result.log_bayes_factor = result.log_evidence - result.log_noise_evidence
 
-        # Propogate uncertainty in combined evidence
+        # Propagate uncertainty in combined evidence
         log_errs = [res.log_evidence_err for res in self if np.isfinite(res.log_evidence_err)]
         if len(log_errs) > 0:
             result.log_evidence_err = 0.5 * logsumexp(2 * np.array(log_errs), b=1. / len(self))
@@ -1866,7 +1866,7 @@ def _combine_mcmc_sampled_runs(self, result):
         result.log_evidence = logsumexp(log_evidences, b=1. / len(self))
         result.log_bayes_factor = result.log_evidence - result.log_noise_evidence
 
-        # Propogate uncertainty in combined evidence
+        # Propagate uncertainty in combined evidence
         log_errs = [res.log_evidence_err for res in self if np.isfinite(res.log_evidence_err)]
         if len(log_errs) > 0:
             result.log_evidence_err = 0.5 * logsumexp(2 * np.array(log_errs), b=1. / len(self))
@@ -2136,7 +2136,7 @@ class ResultError(Exception):
 
 
 class ResultListError(ResultError):
-    """ For Errors occuring during combining results. """
+    """ For Errors occurring during combining results. """
 
 
 class FileMovedError(ResultError):

bilby/core/sampler/dynesty.py

@@ -831,7 +831,7 @@ def sample_rwalk_bilby(args):
 def estimate_nmcmc(accept_ratio, old_act, maxmcmc, safety=5, tau=None):
     """ Estimate autocorrelation length of chain using acceptance fraction
 
-    Using ACL = (2/acc) - 1 multiplied by a safety margin. Code adapated from CPNest:
+    Using ACL = (2/acc) - 1 multiplied by a safety margin. Code adapted from CPNest:
 
     - https://github.com/johnveitch/cpnest/blob/master/cpnest/sampler.py
     - http://github.com/farr/Ensemble.jl

bilby/core/sampler/emcee.py

@@ -263,7 +263,7 @@ def checkpoint(self):
             dill.dump(self._sampler, f)
 
     def checkpoint_and_exit(self, signum, frame):
-        logger.info("Recieved signal {}".format(signum))
+        logger.info("Received signal {}".format(signum))
         self.checkpoint()
         sys.exit()
 
@@ -275,7 +275,7 @@ def _initialise_sampler(self):
     def sampler(self):
         """ Returns the ptemcee sampler object
 
-        If, alrady initialized, returns the stored _sampler value. Otherwise,
+        If, already initialized, returns the stored _sampler value. Otherwise,
         first checks if there is a pickle file from which to load. If there is
         not, then initialize the sampler and set the initial random draw
 

bilby/core/sampler/nessai.py

@@ -8,7 +8,7 @@
 class Nessai(NestedSampler):
     """bilby wrapper of nessai (https://github.com/mj-will/nessai)
 
-    All positional and keyword arguments passed to `run_sampler` are propogated
+    All positional and keyword arguments passed to `run_sampler` are propagated
     to `nessai.flowsampler.FlowSampler`
 
     See the documentation for an explanation of the different kwargs.

bilby/core/sampler/ptemcee.py

@@ -105,7 +105,7 @@ class Ptemcee(MCMCSampler):
         The number of iteration steps to take before checking ACT. This
         effectively pre-thins the chains. Larger values reduce the per-eval
         timing due to improved efficiency. But, if it is made too large the
-        pre-thinning may be overly agressive effectively wasting compute-time.
+        pre-thinning may be overly aggressive effectively wasting compute-time.
         If you see tau=1, then niterations_per_check is likely too large.
 
 
@@ -720,7 +720,7 @@ def check_iteration(
     Returns
     =======
     stop: bool
-        A boolean flag, True if the stoping criteria has been met
+        A boolean flag, True if the stopping criteria has been met
     burn: int
         The number of burn-in steps to discard
     thin: int

bilby/core/utils/calculus.py

@@ -49,7 +49,7 @@ def derivatives(vals, func, releps=1e-3, abseps=None, mineps=1e-9, reltol=1e-3,
         raise ValueError("To many non-fixed values")
 
     if max(nonfixedidx) >= len(vals) or min(nonfixedidx) < 0:
-        raise ValueError("Non-fixed indexes contain non-existant indices")
+        raise ValueError("Non-fixed indexes contain non-existent indices")
 
     grads = np.zeros(len(nonfixedidx))
 

bilby/core/utils/cmd.py

@@ -31,7 +31,7 @@ def set_up_command_line_arguments():
 
     .. code-block:: python
 
-        # Here we import bilby, which initialses and parses the default command-line args
+        # Here we import bilby, which initialises and parses the default command-line args
         >>> import bilby
         # The command line arguments can then be accessed via
         >>> bilby.core.utils.command_line_args

bilby/core/utils/io.py

@@ -210,7 +210,7 @@ def decode_from_hdf5(item):
 
 def encode_for_hdf5(item):
     """
-    Encode an item to a HDF5 savable format.
+    Encode an item to a HDF5 saveable format.
 
     .. versionadded:: 1.1.0
 
@@ -222,7 +222,7 @@ def encode_for_hdf5(item):
     Returns
     -------
     output: object
-        Input item converted into HDF5 savable format
+        Input item converted into HDF5 saveable format
     """
     from ..prior.dict import PriorDict
     if isinstance(item, np.int_):

bilby/gw/conversion.py

@@ -474,7 +474,7 @@ def chirp_mass_and_primary_mass_to_mass_ratio(chirp_mass, mass_1):
 
         (chirp_mass/mass_1)^5 = q^3 / (1 + q)
 
-    Solving for q, we find the releation expressed in python below for q.
+    Solving for q, we find the relation expressed in python below for q.
 
     Parameters
     ==========

bilby/gw/detector/interferometer.py

@@ -427,7 +427,7 @@ def inject_signal_from_waveform_polarizations(self, parameters, injection_polari
 
     @property
     def amplitude_spectral_density_array(self):
-        """ Returns the amplitude spectral density (ASD) given we know a power spectral denstiy (PSD)
+        """ Returns the amplitude spectral density (ASD) given we know a power spectral density (PSD)
 
         Returns
         =======

bilby/gw/eos/eos.py

@@ -668,7 +668,7 @@ def __init__(self, eos, npts=500):
         # If we're not at the end of the array, determine actual maximum mass. Else, assume
         # last point is the maximum mass and proceed.
         if i < (npts - 1):
-            # Now replace ith point with interpolated maximum mass
+            # Now replace with point with interpolated maximum mass
             # This is done by interpolating the last three points and then
             # minimizing the negative of the interpolated function
             x = [energy_density_grid[i - 2], energy_density_grid[i - 1], energy_density_grid[i]]

bilby/gw/likelihood.py

@@ -520,7 +520,7 @@ def generate_time_sample_from_marginalized_likelihood(
         Generate a single sample from the posterior distribution for coalescence
         time when using a likelihood which explicitly marginalises over time.
 
-        In order to resolve the posterior we artifically upsample to 16kHz.
+        In order to resolve the posterior we artificially upsample to 16kHz.
 
         See Eq. (C29-C32) of https://arxiv.org/abs/1809.02293
 
@@ -1605,7 +1605,7 @@ def load_weights(filename, format=None):
         if format is None:
             format = filename.split(".")[-1]
         if format not in ["json", "npz"]:
-            raise IOError("Format {} not recongized.".format(format))
+            raise IOError("Format {} not recognized.".format(format))
         logger.info("Loading ROQ weights from {}".format(filename))
         if format == "json":
             with open(filename, 'r') as file:

bilby/gw/prior.py

@@ -981,7 +981,7 @@ def from_envelope_file(envelope_file, minimum_frequency,
             Number of nodes for the spline.
         label: str
             Label for the names of the parameters, e.g., `recalib_H1_`
-        bounadry: None, 'reflective', 'periodic'
+        boundary: None, 'reflective', 'periodic'
             The type of prior boundary to assign
 
         Returns
@@ -1114,7 +1114,7 @@ class HealPixMapPriorDist(BaseJointPriorDist):
     ==========
 
     hp_file : file path to .fits file
-        .fits file that containes the 2D or 3D Healpix Map
+        .fits file that contains the 2D or 3D Healpix Map
     names : list (optional)
         list of names of parameters included in the JointPriorDist, defaults to ['ra', 'dec']
     bounds : dict or list (optional)
@@ -1238,7 +1238,7 @@ def update_distance(self, pix_idx):
         Parameters
         ==========
         pix_idx : int
-            pixel index value to create the distribtuion for
+            pixel index value to create the distribution for
 
         Returns
         =======

bilby/gw/source.py

@@ -152,7 +152,7 @@ def lal_binary_neutron_star(
           only take positive modes and return the positive and the negative
           mode together, while others need to call both.  e.g.
           waveform_arguments = dict(waveform_approximant='IMRPhenomHM',
-          mode_array=[[2,2],[4,-4]]) returns the 22 a\nd 2-2 of IMRPhenomHM.
+          mode_array=[[2,2],[4,-4]]) returns the 22 and 2-2 of IMRPhenomHM.
           However, waveform_arguments =
           dict(waveform_approximant='IMRPhenomXHM', mode_array=[[2,2],[4,-4]])
           returns the 22 and 4-4 of IMRPhenomXHM.