Ruff fixes

.ci/make_env_file.py

@@ -4,13 +4,13 @@
 from os.path import expanduser
 from shlex import quote
 
-home = expanduser("~")
+home = expanduser('~')
 
-prefixes = os.environ.get("ENV_PREFIXES", "TRAVIS CI encrypt TOKEN TESTS").split(" ")
-blacklist = ["TRAVIS_COMMIT_MESSAGE"]
-env_file = os.environ.get("ENV_FILE", os.path.join(home, "env"))
-with open(env_file, "wt") as env:
+prefixes = os.environ.get('ENV_PREFIXES', 'TRAVIS CI encrypt TOKEN TESTS').split(' ')
+blacklist = ['TRAVIS_COMMIT_MESSAGE']
+env_file = os.environ.get('ENV_FILE', os.path.join(home, 'env'))
+with open(env_file, 'w') as env:
     for k, v in os.environ.items():
         for pref in prefixes:
             if k.startswith(pref) and k not in blacklist:
-                env.write("{}={}\n".format(k, quote(v)))
+                env.write(f'{k}={quote(v)}\n')

src/pymor_dealii/pymor/demo.py

@@ -2,12 +2,21 @@
 # Copyright 2013-2018 pyMOR developers and contributors. All rights reserved.
 # License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
 
-from pymor.basic import *
+from dealii_elasticity import ElasticityExample
+from pymor.basic import (
+    CoerciveRBReductor,
+    ExpressionParameterFunctional,
+    LincombOperator,
+    ProjectionParameterFunctional,
+    StationaryModel,
+    VectorOperator,
+    rb_greedy,
+    reduction_error_analysis,
+)
 
+from pymor_dealii.pymor.gui import DealIIVisualizer
 from pymor_dealii.pymor.operator import DealIIMatrixOperator
 from pymor_dealii.pymor.vectorarray import DealIIVectorSpace
-from pymor_dealii.pymor.gui import DealIIVisualizer
-from dealii_elasticity import ElasticityExample
 
 cpp_disc = ElasticityExample(refine_steps=7)
 
@@ -20,12 +29,12 @@ def run(plot_error=True):
                 DealIIMatrixOperator(cpp_disc.mu_mat()),
             ],
             [
-                ProjectionParameterFunctional("lambda"),
-                ProjectionParameterFunctional("mu"),
+                ProjectionParameterFunctional('lambda'),
+                ProjectionParameterFunctional('mu'),
             ],
         ),
         rhs=VectorOperator(DealIIVectorSpace.make_array([cpp_disc.rhs()])),
-        products={"energy": DealIIMatrixOperator(cpp_disc.mu_mat())},
+        products={'energy': DealIIMatrixOperator(cpp_disc.mu_mat())},
         visualizer=DealIIVisualizer(cpp_disc),
     )
     parameter_space = d.parameters.space((1, 10))
@@ -34,46 +43,46 @@ def run(plot_error=True):
     reductor = CoerciveRBReductor(
         d,
         product=d.energy_product,
-        coercivity_estimator=ExpressionParameterFunctional("max(mu)", d.parameters),
+        coercivity_estimator=ExpressionParameterFunctional('max(mu)', d.parameters),
     )
 
     # greedy basis generation
     greedy_data = rb_greedy(
         d,
         reductor,
         parameter_space.sample_uniformly(3),
-        extension_params={"method": "gram_schmidt"},
+        extension_params={'method': 'gram_schmidt'},
         max_extensions=5,
     )
 
     # get reduced order model
-    rd = greedy_data["rom"]
+    rd = greedy_data['rom']
 
     # validate reduced order model
     result = reduction_error_analysis(
         rd,
         d,
         reductor,
         test_mus=parameter_space.sample_randomly(10),
-        basis_sizes=reductor.bases["RB"].dim + 1,
+        basis_sizes=reductor.bases['RB'].dim + 1,
         condition=True,
         error_norms=[d.energy_norm],
         plot=plot_error,
     )
 
     # visualize solution for parameter with maximum reduction error
-    mu_max = result["max_error_mus"][0, -1]
+    mu_max = result['max_error_mus'][0, -1]
     U = d.solve(mu_max)
     U_rb = reductor.reconstruct(rd.solve(mu_max))
     return result, U, U_rb, d
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     # print/plot results of validation
     from matplotlib import pyplot as plt
 
     result, U, U_rb, d = run()
-    print(result["summary"])
+    print(result['summary'])
     ERR = U - U_rb
-    d.visualize([U, U_rb, ERR], legend=["fom", "rom", "error"])
+    d.visualize([U, U_rb, ERR], legend=['fom', 'rom', 'error'])
     plt.show()

src/pymor_dealii/pymor/gui.py

@@ -27,15 +27,15 @@ def visualize(
         assert title is None or filename is None
         assert legend is None or len(legend) == len(U)
 
-        base_name = title or filename or "out"
+        base_name = title or filename or 'out'
         if len(U) == 1 and not legend:
             filenames = [base_name]
         else:
             legend = legend or list(map(str, list(range(len(U)))))
-            filenames = ["_".join((base_name, l)) for l in legend]
+            filenames = ['_'.join((base_name, l)) for l in legend]
 
         for u, n in zip(U, filenames):
             uu = u.vectors[0]
             if uu.imag_part is not None:
-                self.logger.warning("Imaginary part ignored.")
-            self.impl.visualize(uu.real_part.impl, n + ".vtk")
+                self.logger.warning('Imaginary part ignored.')
+            self.impl.visualize(uu.real_part.impl, n + '.vtk')

src/pymor_dealii/pymor/operator.py

@@ -2,10 +2,10 @@
 # Copyright 2013-2018 pyMOR developers and contributors. All rights reserved.
 # License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
 
+import pymor_dealii_bindings as pd2
 from pymor.operators.list import LinearComplexifiedListVectorArrayOperatorBase
 
 from pymor_dealii.pymor.vectorarray import DealIIVectorSpace
-import pymor_dealii_bindings as pd2
 
 
 class DealIIMatrixOperator(LinearComplexifiedListVectorArrayOperatorBase):

src/pymor_dealii/pymor/vectorarray.py

@@ -10,7 +10,6 @@
     HAVE_DEALII = False
 
 import numpy as np
-
 from pymor.vectorarrays.list import ComplexifiedListVectorSpace, CopyOnWriteVector
 
 

test/test_bindings.py

@@ -43,5 +43,5 @@ def test_vector():
     assert np.allclose(npdd, ddones)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     test_vector()

test/test_demo.py

@@ -3,5 +3,5 @@ def test_demo_results(ndarrays_regression):
 
     result, _, _, _ = run(plot_error=False)
 
-    compare = ["errors", "basis_sizes", "rel_errors"]
+    compare = ['errors', 'basis_sizes', 'rel_errors']
     ndarrays_regression.check({k: v for k, v in result.items() if k in compare})