formatting

Author: segasai

py/dynesty/dynamicsampler.py

@@ -712,7 +712,8 @@ def __init__(self,
         # internal sampler object
         self.sampler = None
 
-        self.bound_enlarge, self.bound_bootstrap = bound_enlarge, bound_bootstrap
+        self.bound_enlarge, self.bound_bootstrap = (bound_enlarge,
+                                                    bound_bootstrap)
 
         # TODO FIX
         # self.cite = self.kwargs.get('cite')
@@ -909,7 +910,7 @@ def citations(self):
         """
 
         # TODO REMOVE
-        return []  ## self.cite
+        return []  # self.cite
 
     def sample_initial(self,
                        nlive=None,

py/dynesty/dynesty.py

@@ -12,8 +12,7 @@
 import traceback
 import numpy as np
 from .sampling import (INTERNAL_SAMPLER_LIST, InternalSampler, RSliceSampler,
-                       UniformBoundSampler, UnitCubeSampler, RWalkSampler,
-                       SliceSampler)
+                       UniformBoundSampler, RWalkSampler, SliceSampler)
 from .sampler import Sampler, _initialize_live_points
 from .bounding import BOUND_LIST
 from . import bounding
@@ -246,8 +245,8 @@ def _get_update_interval_ratio(update_interval, sample, nlive):
         else:
             update_interval_ratio = 1
             warnings.warn(
-                "No update_interval set with unknown sampling method: "
-                f"'. Defaulting to no 1 update per nlive points.")
+                "No update_interval set with unknown sampling method."
+                ". Defaulting to no 1 update per nlive points.")
     elif isinstance(update_interval, float):
         update_interval_ratio = update_interval
     elif isinstance(update_interval, int):

py/dynesty/pool.py

@@ -90,8 +90,8 @@ class Pool:
     To use the dynesty pool you have to use it with the context manager::
 
         with dynesty.pool.Pool(16, loglike, prior_transform) as pool:
-            dns = DynamicNestedSampler(pool.loglike, pool.prior_transform, ndim,
-                                     pool=pool)
+            dns = DynamicNestedSampler(pool.loglike, pool.prior_transform,
+                                       ndim, pool=pool)
 
     Also note that you have to provide the .loglike/.prior_transform attributes
     from the pool object to the Nested samper rather than your original
@@ -100,14 +100,14 @@ class Pool:
     If your likelihood function takes additional arguments, it is better to
     pass them when creating the pool, rather then to nested sampler::
 
-        with dynesty.pool.Pool(16, loglike, prior_transform, 
+        with dynesty.pool.Pool(16, loglike, prior_transform,
                                             logl_args=(...) ) as pool:
-            dns = DynamicNestedSampler(pool.loglike, pool.prior_transform, ndim,
-                                     pool=pool)
+            dns = DynamicNestedSampler(pool.loglike, pool.prior_transform,
+                                       ndim, pool=pool)
 
     as this way they will not need to be pickled and unpickled every function
     call.
-    
+
     Note though that if you specify logl_args, and ptform_args when  creating
     the Pool *AND* in the sampler those will be concatenated
     """

py/dynesty/sampler.py

@@ -270,7 +270,8 @@ class Sampler:
 
     live_points : list of 3 or 4 `~numpy.ndarray`
         Each with shape (nlive, ndim) for the first three arrays.
-        If `blob=True`, a fourth array of blobs (arbitrary shape) may be included.
+        If `blob=True`, a fourth array of blobs (arbitrary shape) may be
+        included.
 
     sampling : {`'unif'`, `'rwalk'`, `'slice'`, `'rslice'`}
         Sampling Method used to sample uniformly within the likelihood

py/dynesty/sampling.py

@@ -97,7 +97,9 @@ def _new_from_template(self, template_kwargs):
                 template_kwargs1[k] = v
             else:
                 if template_kwargs1[k] != self.input_kwargs[k]:
-                    warnings.warn(f"Incompatible sampler parameters: {template_kwargs1[k]} vs {self.input_kwargs[k]}")
+                    warnings.warn(
+                        "Incompatible sampler parameters: "
+                        f"{template_kwargs1[k]} vs {self.input_kwargs[k]}")
         return self.__class__(**template_kwargs1)
 
     def prepare_sampler(self,

py/dynesty/utils.py

@@ -32,8 +32,7 @@
 __all__ = [
     "unitcheck", "resample_equal", "mean_and_cov", "quantile", "jitter_run",
     "resample_run", "reweight_run", "unravel_run", "merge_runs", "kld_error",
-    "get_enlarge_bootstrap", "LoglOutput", "LogLikelihood", "RunRecord",
-    "DelayTimer"
+    "LoglOutput", "LogLikelihood", "RunRecord", "DelayTimer"
 ]
 
 SQRTEPS = math.sqrt(float(np.finfo(np.float64).eps))