STY: apply ruff formatting

.git-blame-ignore-revs

@@ -0,0 +1,2 @@
+# apply ruff formatting
+3920071bac94e2c4b20bcf0ce9911a7b7656d0ac

.github/workflows/pre-commit.yml

@@ -18,11 +18,14 @@ jobs:
   pre-commit:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3
-    - uses: actions/setup-python@v4
+    - uses: actions/checkout@v5
+    - uses: actions/setup-python@v6
+      with:
+        python-version: '3.13'
+        cache: 'pip'
     - name: Install jupyter
       run: |
         python -m pip install jupyterlab
     - uses: pre-commit/action@v3.0.1
-    - uses: pre-commit-ci/lite-action@v1.0.2
+    - uses: pre-commit-ci/lite-action@v1.1.0
       if: always()

.pre-commit-config.yaml

@@ -3,18 +3,12 @@ repos:
     rev: v2.3.0
     hooks:
     -   id: check-merge-conflict # prevent committing files with merge conflicts
--   repo: https://github.com/pycqa/flake8
-    rev: 6.1.0
-    hooks:
-      - id: flake8
-        additional_dependencies:
-          - Flake8-pyproject
--   repo: https://github.com/psf/black
-    rev: 22.3.0
-    hooks:
-      - id: black
-        language_version: python3
-        files: '^(bilby/bilby_mcmc/|bilby/core/sampler/|examples/)'
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: v0.14.0 # Use the latest stable Ruff version
+  hooks:
+    - id: ruff-check # Runs the Ruff linter
+      args: [ --fix ] # Optionally enable automatic fixes
+    - id: ruff-format # Runs the Ruff formatter
 -   repo: https://github.com/codespell-project/codespell
     rev: v2.1.0
     hooks:

CHANGELOG.md

@@ -5,6 +5,8 @@ The original MRs are only visible on the [LIGO GitLab repository](https://git.li
 
 ## [Unreleased]
 
+* MAINT: switch to ruff for automated formatting
+
 ## [2.7.1]
 
 ### Fixes

CONTRIBUTING.md

@@ -24,14 +24,12 @@ Code of Conduct](https://www.python.org/psf/codeofconduct/). Furthermore, member
 
 ## Code style
 
-During a code review (when you want to contribute changes to the code base),
-you may be asked to change your code to fit with the bilby style. This is based
-on a few python conventions and is generally maintained to ensure the code base
-remains consistent and readable to new users. Here we list some typical things
-to keep in mind ensuring the code review is as smooth as possible
-
-1. We follow the [standard python PEP8](https://www.python.org/dev/peps/pep-0008/) conventions for style. While the testing of this is largely automated (the C.I. pipeline tests check using [flake8](http://flake8.pycqa.org/en/latest/)), some more subjective things might slip the net. 
-2. New classes/functions/methods should have a docstring and following the [numpy docstring guide](https://numpydoc.readthedocs.io/en/latest/format.html), for example
+We apply [ruff](https://docs.astral.sh/ruff/) automated formatting to the
+entire codebase. This ensures that we have an objectively enforced and
+consistent style across the project. In addition to the automated test below
+are a few useful guidelines when writing code for Bilby.
+
+1. New classes/functions/methods should have a docstring and following the [numpy docstring guide](https://numpydoc.readthedocs.io/en/latest/format.html), for example
 ```python
 def my_new_function(x, y, print=False):
     """ A function to calculate the sum of two numbers
@@ -47,15 +45,17 @@ def my_new_function(x, y, print=False):
         print("Message!")
     return x + y
 ```
+2. Changes to existing functions and classes that change the functionality should generally be accompanied by a change in the docstring describing the change in behaviour and version the change was introduced.
 3. Avoid inline comments unless necessary. Ideally, the code should make it obvious what is going on, if not the docstring, only in subtle cases use comments
 4. Name variables sensibly. Avoid using single-letter variables, it is better to name something `power_spectral_density_array` than `psda`.
 5. Don't repeat yourself. If code is repeated in multiple places, wrap it up into a function. This also helps with the writing of robust unit tests (see below).
 
 
 ## Automated code checking
 
-In order to automate checking of the code quality, we use
-[pre-commit](https://pre-commit.com/). For more details, see the documentation,
+We use [pre-commit](https://pre-commit.com/) to apply the automated code checking.
+To maximize smoothness of the contributing process, we recommend that you install
+the pre-commit tests on your development machine. For more details, see the documentation,
 here we will give a quick-start guide:
 1. Install and configure:
 ```console
@@ -78,6 +78,14 @@ $ pre-commit install
 If you experience any issues with pre-commit, please ask for support on the
 usual help channels.
 
+You can additionally run the individual checks manually, e.g.,
+
+```console
+$ pip install ruff
+$ cd bilby
+$ ruff format
+$ ruff check --fix
+```
 
 ## Unit Testing
 

bilby/__init__.py

@@ -15,39 +15,29 @@
 
 """
 
-
-import sys
-
 from . import core, gw, hyper
-
-from .core import utils, likelihood, prior, result, sampler
-from .core.sampler import run_sampler
+from .core import likelihood, prior, result, sampler, utils
 from .core.likelihood import Likelihood
 from .core.result import read_in_result, read_in_result_list
+from .core.sampler import run_sampler
 
 try:
     from ._version import version as __version__
 except ModuleNotFoundError:  # development mode
-    __version__ = 'unknown'
-
-
-if sys.version_info < (3,):
-    raise ImportError(
-"""You are running bilby >= 0.6.4 on Python 2
-
-Bilby 0.6.4 and above are no longer compatible with Python 2, and you still
-ended up with this version installed. That's unfortunate; sorry about that.
-It should not have happened. Make sure you have pip >= 9.0 to avoid this kind
-of issue, as well as setuptools >= 24.2:
-
- $ pip install pip setuptools --upgrade
-
-Your choices:
-
-- Upgrade to Python 3.
-
-- Install an older version of bilby:
-
- $ pip install 'bilby<0.6.4'
-
-""")
+    __version__ = "unknown"
+
+__all__ = [
+    core,
+    gw,
+    hyper,
+    likelihood,
+    prior,
+    result,
+    sampler,
+    utils,
+    Likelihood,
+    read_in_result,
+    read_in_result_list,
+    run_sampler,
+    __version__,
+]

bilby/bilby_mcmc/__init__.py

@@ -1 +1,3 @@
 from .sampler import Bilby_MCMC
+
+__all__ = [Bilby_MCMC]

bilby/bilby_mcmc/chain.py

@@ -7,7 +7,7 @@
 from .utils import LOGLKEY, LOGLLATEXKEY, LOGPKEY, LOGPLATEXKEY
 
 
-class Chain(object):
+class Chain:
     def __init__(
         self,
         initial_sample,
@@ -84,9 +84,7 @@ def _get_zero_chain_array(self):
         return np.zeros((self.block_length, self.ndim + 2), dtype=np.float64)
 
     def _extend_chain_array(self):
-        self._chain_array = np.concatenate(
-            (self._chain_array, self._get_zero_chain_array()), axis=0
-        )
+        self._chain_array = np.concatenate((self._chain_array, self._get_zero_chain_array()), axis=0)
         self._chain_array_length = len(self._chain_array)
 
     @property
@@ -259,11 +257,7 @@ def tau(self):
         if self.position in self.max_tau_dict:
             # If we have the ACT at the current position, return it
             return self.max_tau_dict[self.position]
-        elif (
-            self.tau_last < np.inf
-            and self.cached_tau_count < 50
-            and self.nsamples_last > 50
-        ):
+        elif self.tau_last < np.inf and self.cached_tau_count < 50 and self.nsamples_last > 50:
             # If we have a recent ACT return it
             self.cached_tau_count += 1
             return self.tau_last
@@ -312,9 +306,7 @@ def _calculate_tau_dict(self, minimum_index):
         # Choose minimimum index for the ACT calculation
         last_tau = self.tau_last
         if self.tau_window is not None and last_tau < np.inf:
-            minimum_index_for_act = max(
-                minimum_index, int(self.position - self.tau_window * last_tau)
-            )
+            minimum_index_for_act = max(minimum_index, int(self.position - self.tau_window * last_tau))
         else:
             minimum_index_for_act = minimum_index
 
@@ -364,9 +356,7 @@ def samples(self):
     def plot(self, outdir=".", label="label", priors=None, all_samples=None):
         import matplotlib.pyplot as plt
 
-        fig, axes = plt.subplots(
-            nrows=self.ndim + 3, ncols=2, figsize=(8, 9 + 3 * (self.ndim))
-        )
+        fig, axes = plt.subplots(nrows=self.ndim + 3, ncols=2, figsize=(8, 9 + 3 * (self.ndim)))
         scatter_kwargs = dict(
             lw=0,
             marker="o",
@@ -386,7 +376,7 @@ def plot(self, outdir=".", label="label", priors=None, all_samples=None):
         position_indexes = np.arange(self.position + 1)
 
         # Plot the traceplots
-        for (start, stop, thin, color, alpha, ms) in plot_setups:
+        for start, stop, thin, color, alpha, ms in plot_setups:
             for ax, key in zip(axes[:, 0], self.keys):
                 xx = position_indexes[start:stop:thin] / K
                 yy = self.get_1d_array(key)[start:stop:thin]
@@ -415,16 +405,12 @@ def plot(self, outdir=".", label="label", priors=None, all_samples=None):
             if all_samples is not None:
                 yy_all = all_samples[key]
                 if np.any(np.isinf(yy_all)):
-                    logger.warning(
-                        f"Could not plot histogram for parameter {key} due to infinite values"
-                    )
+                    logger.warning(f"Could not plot histogram for parameter {key} due to infinite values")
                 else:
                     ax.hist(yy_all, bins=50, alpha=0.6, density=True, color="k")
             yy = self.get_1d_array(key)[nburn : self.position : self.thin]
             if np.any(np.isinf(yy)):
-                logger.warning(
-                    f"Could not plot histogram for parameter {key} due to infinite values"
-                )
+                logger.warning(f"Could not plot histogram for parameter {key} due to infinite values")
             else:
                 ax.hist(yy, bins=50, alpha=0.8, density=True)
                 ax.set_xlabel(self._get_plot_label_by_key(key, priors))
@@ -441,16 +427,13 @@ def plot(self, outdir=".", label="label", priors=None, all_samples=None):
 
         axes[-1, 1].set_axis_off()
 
-        filename = "{}/{}_checkpoint_trace.png".format(outdir, label)
+        filename = f"{outdir}/{label}_checkpoint_trace.png"
         msg = [
             r"Maximum $\tau$" + f"={self.tau:0.1f} ",
             r"$n_{\rm samples}=$" + f"{self.nsamples} ",
         ]
         if self.thin_by_nact != 1:
-            msg += [
-                r"$n_{\rm samples}^{\rm eff}=$"
-                + f"{int(self.nsamples * self.thin_by_nact)} "
-            ]
+            msg += [r"$n_{\rm samples}^{\rm eff}=$" + f"{int(self.nsamples * self.thin_by_nact)} "]
         fig.suptitle(
             "| ".join(msg),
             y=1,
@@ -471,7 +454,7 @@ def _get_plot_label_by_key(key, priors=None):
             return key
 
 
-class Sample(object):
+class Sample:
     def __init__(self, sample_dict):
         """A single sample
 

bilby/bilby_mcmc/flows.py

@@ -44,7 +44,6 @@ def __init__(
         batch_norm_between_layers=False,
         random_permutation=True,
     ):
-
         if use_volume_preserving:
             coupling_constructor = AdditiveCouplingTransform
         else:
@@ -66,9 +65,7 @@ def create_resnet(in_features, out_features):
 
         layers = []
         for _ in range(num_layers):
-            transform = coupling_constructor(
-                mask=mask, transform_net_create_fn=create_resnet
-            )
+            transform = coupling_constructor(mask=mask, transform_net_create_fn=create_resnet)
             layers.append(transform)
             mask *= -1
             if batch_norm_between_layers:
@@ -87,9 +84,7 @@ class BasicFlow(Flow):
     def __init__(self, features):
         transform = CompositeTransform(
             [
-                MaskedAffineAutoregressiveTransform(
-                    features=features, hidden_features=2 * features
-                ),
+                MaskedAffineAutoregressiveTransform(features=features, hidden_features=2 * features),
                 RandomPermutation(features=features),
             ]
         )