fix import order and style

adaptive/__init__.py

@@ -2,26 +2,19 @@
 
 from contextlib import suppress
 
-from adaptive.notebook_integration import (notebook_extension, live_plot,
-                                   active_plotting_tasks)
-
-from adaptive import learner
-from adaptive import runner
-from adaptive import utils
-
-from adaptive.learner import (
-	BaseLearner, Learner1D, Learner2D, LearnerND,
-    AverageLearner, BalancingLearner, make_datasaver,
-    DataSaver, IntegratorLearner
-)
+from adaptive import learner, runner, utils
+from adaptive._version import __version__
+from adaptive.learner import (AverageLearner, BalancingLearner, BaseLearner,
+                              DataSaver, IntegratorLearner, Learner1D,
+                              Learner2D, LearnerND, make_datasaver)
+from adaptive.notebook_integration import (active_plotting_tasks, live_plot,
+                                           notebook_extension)
+from adaptive.runner import AsyncRunner, BlockingRunner, Runner
 
 with suppress(ImportError):
     # Only available if 'scikit-optimize' is installed
     from adaptive.learner import SKOptLearner
 
-from adaptive.runner import Runner, AsyncRunner, BlockingRunner
 
-from adaptive._version import __version__
 del _version
-
 del notebook_integration  # to avoid confusion with `notebook_extension`

adaptive/_version.py

@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 # This file is part of 'miniver': https://github.com/jbweston/miniver
 #
-from collections import namedtuple
 import os
 import subprocess
-
+from collections import namedtuple
 from distutils.command.build_py import build_py as build_py_orig
+
 from setuptools.command.sdist import sdist as sdist_orig
 
 Version = namedtuple('Version', ('release', 'dev', 'labels'))
@@ -53,7 +53,7 @@ def pep440_format(version_info):
         if release.endswith('-dev') or release.endswith('.dev'):
             version_parts.append(dev)
         else:  # prefer PEP440 over strict adhesion to semver
-            version_parts.append('.dev{}'.format(dev))
+            version_parts.append(f'.dev{dev}')
 
     if labels:
         version_parts.append('+')
@@ -146,13 +146,13 @@ def get_version_from_git_archive(version_info):
         return None
 
     VTAG = 'tag: v'
-    refs = set(r.strip() for r in refnames.split(","))
-    version_tags = set(r[len(VTAG):] for r in refs if r.startswith(VTAG))
+    refs = {r.strip() for r in refnames.split(",")}
+    version_tags = {r[len(VTAG):] for r in refs if r.startswith(VTAG)}
     if version_tags:
         release, *_ = sorted(version_tags)  # prefer e.g. "2.0" over "2.0rc1"
         return Version(release, dev=None, labels=None)
     else:
-        return Version('unknown', dev=None, labels=['g{}'.format(git_hash)])
+        return Version('unknown', dev=None, labels=[f'g{git_hash}'])
 
 
 __version__ = get_version()

adaptive/learner/__init__.py

@@ -3,13 +3,13 @@
 from contextlib import suppress
 
 from adaptive.learner.average_learner import AverageLearner
-from adaptive.learner.base_learner import BaseLearner
 from adaptive.learner.balancing_learner import BalancingLearner
+from adaptive.learner.base_learner import BaseLearner
+from adaptive.learner.data_saver import DataSaver, make_datasaver
+from adaptive.learner.integrator_learner import IntegratorLearner
 from adaptive.learner.learner1D import Learner1D
 from adaptive.learner.learner2D import Learner2D
 from adaptive.learner.learnerND import LearnerND
-from adaptive.learner.integrator_learner import IntegratorLearner
-from adaptive.learner.data_saver import DataSaver, make_datasaver
 
 with suppress(ImportError):
     # Only available if 'scikit-optimize' is installed

adaptive/learner/balancing_learner.py

@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 
+import os.path
 from collections import defaultdict
 from collections.abc import Iterable
 from contextlib import suppress
 from functools import partial
 from operator import itemgetter
-import os.path
 
 import numpy as np
 
@@ -82,7 +82,7 @@ def __init__(self, learners, *, cdims=None, strategy='loss_improvements'):
         self._pending_loss = {}
         self._cdims_default = cdims
 
-        if len(set(learner.__class__ for learner in self.learners)) > 1:
+        if len({learner.__class__ for learner in self.learners}) > 1:
             raise TypeError('A BalacingLearner can handle only one type'
                             ' of learners.')
 

adaptive/learner/base_learner.py

@@ -4,7 +4,7 @@
 from contextlib import suppress
 from copy import deepcopy
 
-from adaptive.utils import save, load
+from adaptive.utils import load, save
 
 
 def uses_nth_neighbors(n):

adaptive/learner/data_saver.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 
-from collections import OrderedDict
 import functools
+from collections import OrderedDict
 
 from adaptive.learner.base_learner import BaseLearner
 from adaptive.utils import copy_docstring_from

adaptive/learner/integrator_coeffs.py

@@ -1,8 +1,9 @@
 # -*- coding: utf-8 -*-
 # Based on an adaptive quadrature algorithm by Pedro Gonnet
 
-from fractions import Fraction
 from collections import defaultdict
+from fractions import Fraction
+
 import numpy as np
 import scipy.linalg
 

adaptive/learner/integrator_learner.py

@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
 # Based on an adaptive quadrature algorithm by Pedro Gonnet
 
+import sys
 from collections import defaultdict
 from math import sqrt
 from operator import attrgetter
-import sys
 
 import numpy as np
 from scipy.linalg import norm
@@ -14,9 +14,8 @@
 from adaptive.notebook_integration import ensure_holoviews
 from adaptive.utils import cache_latest, restore
 
-from .integrator_coeffs import (b_def, T_left, T_right, ns, hint,
-                                ndiv_max, min_sep, eps, xi, V_inv,
-                                Vcond, alpha, gamma)
+from .integrator_coeffs import (T_left, T_right, V_inv, Vcond, alpha, b_def,
+                                eps, gamma, hint, min_sep, ndiv_max, ns, xi)
 
 
 def _downdate(c, nans, depth):
@@ -298,10 +297,10 @@ def complete_process(self, depth):
 
     def __repr__(self):
         lst = [
-            '(a, b)=({:.5f}, {:.5f})'.format(self.a, self.b),
-            'depth={}'.format(self.depth),
-            'rdepth={}'.format(self.rdepth),
-            'err={:.5E}'.format(self.err),
+            f'(a, b)=({self.a:.5f}, {self.b:.5f})',
+            f'depth={self.depth}',
+            f'rdepth={self.rdepth}',
+            f'err={self.err:.5E}',
             'igral={:.5E}'.format(self.igral if hasattr(self, 'igral') else np.inf),
         ]
         return ' '.join(lst)

adaptive/learner/learner1D.py

@@ -1,14 +1,14 @@
 # -*- coding: utf-8 -*-
 
-from copy import deepcopy
 import heapq
 import itertools
 import math
 from collections.abc import Iterable
+from copy import deepcopy
 
 import numpy as np
-import sortedcontainers
 import sortedcollections
+import sortedcontainers
 
 from adaptive.learner.base_learner import BaseLearner, uses_nth_neighbors
 from adaptive.learner.learnerND import volume

adaptive/learner/learner2D.py

@@ -1,8 +1,8 @@
 # -*- coding: utf-8 -*-
 
+import itertools
 from collections import OrderedDict
 from copy import copy
-import itertools
 from math import sqrt
 
 import numpy as np
@@ -12,7 +12,6 @@
 from adaptive.notebook_integration import ensure_holoviews
 from adaptive.utils import cache_latest
 
-
 # Learner2D and helper functions.
 
 def deviations(ip):

adaptive/learner/learnerND.py

@@ -1,23 +1,23 @@
 # -*- coding: utf-8 -*-
 
-from collections import OrderedDict
-from collections.abc import Iterable
 import functools
 import heapq
 import itertools
 import random
+from collections import OrderedDict
+from collections.abc import Iterable
 
 import numpy as np
-from scipy import interpolate
 import scipy.spatial
+from scipy import interpolate
 from sortedcontainers import SortedKeyList
 
 from adaptive.learner.base_learner import BaseLearner, uses_nth_neighbors
+from adaptive.learner.triangulation import (Triangulation, circumsphere,
+                                            fast_det, point_in_simplex,
+                                            simplex_volume_in_embedding)
 from adaptive.notebook_integration import ensure_holoviews, ensure_plotly
-from adaptive.learner.triangulation import (
-    Triangulation, point_in_simplex, circumsphere,
-    simplex_volume_in_embedding, fast_det)
-from adaptive.utils import restore, cache_latest
+from adaptive.utils import cache_latest, restore
 
 
 def to_list(inp):
@@ -573,8 +573,8 @@ def _update_losses(self, to_delete: set, to_add: set):
             if subtri is not None:
                 pending_points_unbound.update(subtri.vertices)
 
-        pending_points_unbound = set(p for p in pending_points_unbound
-                                     if p not in self.data)
+        pending_points_unbound = {p for p in pending_points_unbound
+                                  if p not in self.data}
         for simplex in to_add:
             loss = self._compute_loss(simplex)
             self._losses[simplex] = loss

adaptive/learner/skopt_learner.py

@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-from skopt import Optimizer
 
 from adaptive.learner.base_learner import BaseLearner
 from adaptive.notebook_integration import ensure_holoviews
 from adaptive.utils import cache_latest
+from skopt import Optimizer
 
 
 class SKOptLearner(Optimizer, BaseLearner):

adaptive/learner/triangulation.py

@@ -1,11 +1,11 @@
+import math
 from collections import Counter
-from collections.abc import Sized, Iterable
-from itertools import combinations, chain
+from collections.abc import Iterable, Sized
+from itertools import chain, combinations
+from math import factorial
 
 import numpy as np
-import math
 import scipy.spatial
-from math import factorial
 
 
 def fast_norm(v):
@@ -624,8 +624,8 @@ def get_neighbors_from_vertices(self, simplex):
 
     def get_face_sharing_neighbors(self, neighbors, simplex):
         """Keep only the simplices sharing a whole face with simplex."""
-        return set(simpl for simpl in neighbors
-            if len(set(simpl) & set(simplex)) == self.dim)  # they share a face
+        return {simpl for simpl in neighbors
+                if len(set(simpl) & set(simplex)) == self.dim}  # they share a face
 
     def get_simplices_attached_to_points(self, indices):
         # Get all simplices that share at least a point with the simplex
@@ -667,8 +667,8 @@ def hull(self):
             raise RuntimeError("Broken triangulation, a (N-1)-dimensional"
                                " appears in more than 2 simplices.")
 
-        hull = set(point for face, count in counts.items() if count == 1
-                   for point in face)
+        hull = {point for face, count in counts.items() if count == 1
+                for point in face}
         return hull
 
     def convex_invariant(self, vertex):

adaptive/notebook_integration.py

@@ -1,12 +1,11 @@
 # -*- coding: utf-8 -*-
 
 import asyncio
-from contextlib import suppress
 import datetime
 import importlib
 import random
 import warnings
-
+from contextlib import suppress
 
 _async_enabled = False
 _holoviews_enabled = False

adaptive/runner.py

@@ -3,16 +3,16 @@
 import abc
 import asyncio
 import concurrent.futures as concurrent
-from contextlib import suppress
 import functools
 import inspect
 import os
 import sys
 import time
 import traceback
 import warnings
+from contextlib import suppress
 
-from adaptive.notebook_integration import live_plot, live_info, in_ipynb
+from adaptive.notebook_integration import in_ipynb, live_info, live_plot
 
 try:
     import ipyparallel

adaptive/tests/algorithm_4.py

@@ -2,12 +2,12 @@
 # Copyright 2017 Christoph Groth
 
 import warnings
-from fractions import Fraction as Frac
 from collections import defaultdict
+from fractions import Fraction as Frac
+
 import numpy as np
 from numpy.testing import assert_allclose
-from scipy.linalg import norm, inv
-
+from scipy.linalg import inv, norm
 
 eps = np.spacing(1)
 

adaptive/tests/test_balancing_learner.py

@@ -2,7 +2,7 @@
 
 import pytest
 
-from adaptive.learner import Learner1D, BalancingLearner
+from adaptive.learner import BalancingLearner, Learner1D
 from adaptive.runner import simple