black files

3 files changed, 43 insertions, 37 deletions

diff --git a/src/python/gudhi/sklearn/cubical_persistence.py b/src/python/gudhi/sklearn/cubical_persistence.py
index 7b77000d..329c9435 100644
--- a/src/python/gudhi/sklearn/cubical_persistence.py
+++ b/src/python/gudhi/sklearn/cubical_persistence.py
@@ -30,7 +30,15 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
     This is a class for computing the persistence diagrams from a cubical complex.
     """
 
-    def __init__(self, dimensions=None, max_persistence_dimension=0, only_this_dim=-1, homology_coeff_field=11, min_persistence=0., n_jobs=None):
+    def __init__(
+        self,
+        dimensions=None,
+        max_persistence_dimension=0,
+        only_this_dim=-1,
+        homology_coeff_field=11,
+        min_persistence=0.0,
+        n_jobs=None,
+    ):
         """
         Constructor for the CubicalPersistence class.
 
@@ -66,7 +74,9 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
         cubical_complex.compute_persistence(
             homology_coeff_field=self.homology_coeff_field, min_persistence=self.min_persistence
         )
-        return [cubical_complex.persistence_intervals_in_dimension(dim) for dim in range(self.max_persistence_dimension + 1)]
+        return [
+            cubical_complex.persistence_intervals_in_dimension(dim) for dim in range(self.max_persistence_dimension + 1)
+        ]
 
     def __transform_only_this_dim(self, cells):
         cubical_complex = CubicalComplex(top_dimensional_cells=cells, dimensions=self.dimensions)
@@ -95,4 +105,6 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
             return Parallel(n_jobs=self.n_jobs, prefer="threads")(delayed(self.__transform)(cells) for cells in X)
         else:
             # threads is preferred as cubical construction and persistence computation releases the GIL
-            return Parallel(n_jobs=self.n_jobs, prefer="threads")(delayed(self.__transform_only_this_dim)(cells) for cells in X)
+            return Parallel(n_jobs=self.n_jobs, prefer="threads")(
+                delayed(self.__transform_only_this_dim)(cells) for cells in X
+            )
diff --git a/src/python/test/test_sklearn_cubical_persistence.py b/src/python/test/test_sklearn_cubical_persistence.py
index 506985f1..488495d1 100644
--- a/src/python/test/test_sklearn_cubical_persistence.py
+++ b/src/python/test/test_sklearn_cubical_persistence.py
@@ -16,29 +16,28 @@ __author__ = "Vincent Rouvreau"
 __copyright__ = "Copyright (C) 2021 Inria"
 __license__ = "MIT"
 
-CUBICAL_PERSISTENCE_H0_IMG0 = np.array([[0., 6.], [0., 8.], [ 0., np.inf]])
+CUBICAL_PERSISTENCE_H0_IMG0 = np.array([[0.0, 6.0], [0.0, 8.0], [0.0, np.inf]])
+
 
 def test_simple_constructor_from_top_cells():
     cells = datasets.load_digits().images[0]
-    cp = CubicalPersistence(only_this_dim = 0)
-    np.testing.assert_array_equal(cp._CubicalPersistence__transform(cells),
-                                  [CUBICAL_PERSISTENCE_H0_IMG0])
-    cp = CubicalPersistence(max_persistence_dimension = 2)
+    cp = CubicalPersistence(only_this_dim=0)
+    np.testing.assert_array_equal(cp._CubicalPersistence__transform(cells), [CUBICAL_PERSISTENCE_H0_IMG0])
+    cp = CubicalPersistence(max_persistence_dimension=2)
     diags = cp._CubicalPersistence__transform(cells)
     assert len(diags) == 3
-    np.testing.assert_array_equal(diags[0],
-                                  CUBICAL_PERSISTENCE_H0_IMG0)
+    np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0)
+
 
 def test_simple_constructor_from_top_cells_list():
     digits = datasets.load_digits().images[:10]
-    cp = CubicalPersistence(only_this_dim = 0, n_jobs=-2)
+    cp = CubicalPersistence(only_this_dim=0, n_jobs=-2)
 
     diags = cp.fit_transform(digits)
     assert len(diags) == 10
-    np.testing.assert_array_equal(diags[0],
-                                  CUBICAL_PERSISTENCE_H0_IMG0)
+    np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0)
 
-    cp = CubicalPersistence(max_persistence_dimension = 1, n_jobs=-1)
+    cp = CubicalPersistence(max_persistence_dimension=1, n_jobs=-1)
     diagsH0H1 = cp.fit_transform(digits)
     assert len(diagsH0H1) == 10
     for idx in range(10):
diff --git a/src/python/test/test_sklearn_post_processing.py b/src/python/test/test_sklearn_post_processing.py
index 3a251d34..60bf8162 100644
--- a/src/python/test/test_sklearn_post_processing.py
+++ b/src/python/test/test_sklearn_post_processing.py
@@ -16,33 +16,28 @@ __author__ = "Vincent Rouvreau"
 __copyright__ = "Copyright (C) 2021 Inria"
 __license__ = "MIT"
 
-H0_0 = np.array([0., 0.])
-H1_0 = np.array([1., 0.])
-H0_1 = np.array([0., 1.])
-H1_1 = np.array([1., 1.])
-H0_2 = np.array([0., 2.])
-H1_2 = np.array([1., 2.])
+H0_0 = np.array([0.0, 0.0])
+H1_0 = np.array([1.0, 0.0])
+H0_1 = np.array([0.0, 1.0])
+H1_1 = np.array([1.0, 1.0])
+H0_2 = np.array([0.0, 2.0])
+H1_2 = np.array([1.0, 2.0])
+
 
 def test_dimension_selector():
     X = [[H0_0, H1_0], [H0_1, H1_1], [H0_2, H1_2]]
-    ds = DimensionSelector(persistence_dimension = 0, n_jobs=-2)
+    ds = DimensionSelector(persistence_dimension=0, n_jobs=-2)
     h0 = ds.fit_transform(X)
-    np.testing.assert_array_equal(h0[0],
-                                  H0_0)
-    np.testing.assert_array_equal(h0[1],
-                                  H0_1)
-    np.testing.assert_array_equal(h0[2],
-                                  H0_2)
-    
-    ds = DimensionSelector(persistence_dimension = 1, n_jobs=-1)
+    np.testing.assert_array_equal(h0[0], H0_0)
+    np.testing.assert_array_equal(h0[1], H0_1)
+    np.testing.assert_array_equal(h0[2], H0_2)
+
+    ds = DimensionSelector(persistence_dimension=1, n_jobs=-1)
     h1 = ds.fit_transform(X)
-    np.testing.assert_array_equal(h1[0],
-                                  H1_0)
-    np.testing.assert_array_equal(h1[1],
-                                  H1_1)
-    np.testing.assert_array_equal(h1[2],
-                                  H1_2)
-
-    ds = DimensionSelector(persistence_dimension = 2, n_jobs=-2)
+    np.testing.assert_array_equal(h1[0], H1_0)
+    np.testing.assert_array_equal(h1[1], H1_1)
+    np.testing.assert_array_equal(h1[2], H1_2)
+
+    ds = DimensionSelector(persistence_dimension=2, n_jobs=-2)
     with pytest.raises(IndexError):
         h2 = ds.fit_transform([[H0_0, H1_0], [H0_1, H1_1], [H0_2, H1_2]])