switched from flake8 to ruff

.github/workflows/tests.yml

@@ -31,13 +31,11 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install flake8 pytest pytest-cov
-    - name: Lint with flake8
+        python -m pip install ruff pytest pytest-cov
+    - name: Lint with ruff
       run: |
-        # stop the test if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+        ruff check nautilus
+        ruff check --exit-zero --select ALL nautilus
     - name: Test with pytest
       run: |
         python -m pip install .[tests]

nautilus/__init__.py

@@ -1,8 +1,9 @@
-"""Neural Network-Boosted Importance Sampling for Bayesian Statistics"""
+"""Neural Network-Boosted Importance Sampling for Bayesian Statistics."""
 
 from .sampler import Sampler
 from .prior import Prior
 
 __author__ = 'Johannes U. Lange'
 __email__ = '[email protected]'
 __version__ = '1.0.5'
+__all__ = ['Prior', 'Sampler']

nautilus/bounds/nautilus.py

@@ -33,6 +33,7 @@ class NautilusBound():
     n_reject : int
         Number of points rejected due to not falling into the neural
         network-based bounds.
+
     """
 
     @classmethod
@@ -267,6 +268,7 @@ def n_ell(self):
         -------
         n_ell : int
             The number of ellipsoids.
+
         """
         return np.sum([np.any(~bound.dim_cube) for bound in
                        self.outer_bound.bounds])
@@ -279,6 +281,7 @@ def n_net(self):
         -------
         n_net : int
             The number of neural networks.
+
         """
         if self.neural_bounds[0].emulator is not None:
             return len(self.neural_bounds) * len(

nautilus/bounds/neural.py

@@ -22,6 +22,7 @@ class NeuralBound():
     score_predict_min : float
         Minimum score predicted by the emulator to be considered part of the
         bound.
+
     """
 
     @classmethod

nautilus/bounds/periodic.py

@@ -92,6 +92,8 @@ def read(cls, group, rng=None):
         ----------
         group : h5py.Group
             HDF5 group to write to.
+        rng : None or numpy.random.Generator, optional
+            Determines random number generation. Default is None.
 
         Returns
         -------

nautilus/pool.py

@@ -4,21 +4,20 @@
 
 
 def initialize_worker(likelihood):
-    """
-    Initialize a worker for likelihood evaluations.
+    """Initialize a worker for likelihood evaluations.
 
     Parameters
     ----------
     likelihood : function
         Likelihood function that each worker will evaluate.
+
     """
     global LIKELIHOOD
     LIKELIHOOD = likelihood
 
 
 def likelihood_worker(*args):
-    """
-    Have the worker evaluate the likelihood.
+    """Have the worker evaluate the likelihood.
 
     Parameters
     ----------
@@ -29,13 +28,13 @@ def likelihood_worker(*args):
     -------
     object
         Return value of the likelihood function.
+
     """
     return LIKELIHOOD(*args)
 
 
 class NautilusPool:
-    """
-    Wrapper for avoiding implementation-specific details elsewhere.
+    """Wrapper for avoiding implementation-specific details elsewhere.
 
     Attributes
     ----------
@@ -45,15 +44,16 @@ class NautilusPool:
     """
 
     def __init__(self, pool, likelihood=None):
-        """
-        Initialize a pool.
+        """Initialize a pool.
 
         Parameters
         ----------
         pool : object
             Pool used for parallelization. If a number, initialize a pool
             from the `multiprocessing` library with the specified number of
             workers.
+        likelihood : function, optional
+            Likelihood function to cache. Defaul is None.
 
         """
         if isinstance(pool, int):
@@ -63,8 +63,7 @@ def __init__(self, pool, likelihood=None):
             self.pool = pool
 
     def map(self, func, iterable):
-        """
-        Loop a function over an iterable, similar to the built-in map function.
+        """Loop a function over an iterable like the built-in map function.
 
         Parameters
         ----------

nautilus/prior.py

@@ -45,6 +45,7 @@ def add_parameter(self, key=None, dist=(0, 1)):
         ValueError
             If a new key already exists in the key list or if `dist` is a
             string but does not refer to a previously defined key.
+
         """
         if key is None:
             self.keys.append('x_{}'.format(len(self.keys)))
@@ -76,6 +77,7 @@ def dimensionality(self):
         -------
         n_dim : int
             The number of free model parameters.
+
         """
         return sum(not isinstance(dist, (numbers.Number, str)) for dist in
                    self.dists)

nautilus/sampler.py

@@ -19,8 +19,7 @@
 
 
 class Sampler():
-    """
-    A dynamic sampler built upon the framework of importance nested sampling.
+    """A dynamic sampler built upon the framework of importance nested sampling.
 
     Attributes
     ----------
@@ -128,8 +127,7 @@ def __init__(self, prior, likelihood, n_dim=None, n_live=2000,
                  n_like_new_bound=None, vectorized=False, pass_dict=None,
                  pool=None, seed=None, blobs_dtype=None, filepath=None,
                  resume=True):
-        r"""
-        Initialize the sampler.
+        r"""Initialize the sampler.
 
         Parameters
         ----------
@@ -601,7 +599,7 @@ def posterior(self, return_as_dict=None, equal_weight=False,
         points = np.concatenate([p[s:] for p, s in zip(self.points, start)])
         log_v = np.repeat(self.shell_log_v -
                           np.log(np.maximum(self.shell_n, 1)), self.shell_n)
-        log_l = np.concatenate([l[s:] for l, s in zip(self.log_l, start)])
+        log_l = np.concatenate([ll[s:] for ll, s in zip(self.log_l, start)])
         log_w = log_v + log_l
         if return_blobs:
             if self.blobs is None: