code review: rename dimensions as 'newshape'

3 files changed, 11 insertions, 11 deletions

diff --git a/src/python/doc/cubical_complex_sklearn_itf_ref.rst b/src/python/doc/cubical_complex_sklearn_itf_ref.rst
index a57e5fbb..8248343b 100644
--- a/src/python/doc/cubical_complex_sklearn_itf_ref.rst
+++ b/src/python/doc/cubical_complex_sklearn_itf_ref.rst
@@ -54,9 +54,9 @@ two holes in :math:`\mathbf{H}_1`, or, like in this example, three connected com
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
     pipe = Pipeline(
         [
-            ("cub_pers", CubicalPersistence(persistence_dimension=0, dimensions=[28, 28], n_jobs=-2)),
+            ("cub_pers", CubicalPersistence(persistence_dimension=0, newshape=[28, 28], n_jobs=-2)),
             # Or for multiple persistence dimension computation
-            # ("cub_pers", CubicalPersistence(persistence_dimension=[0, 1], dimensions=[28, 28], n_jobs=-2)),
+            # ("cub_pers", CubicalPersistence(persistence_dimension=[0, 1], newshape=[28, 28], n_jobs=-2)),
             # ("H0_diags", DimensionSelector(index=0), # where index is the index in persistence_dimension array
             ("finite_diags", DiagramSelector(use=True, point_type="finite")),
             (
@@ -78,7 +78,7 @@ two holes in :math:`\mathbf{H}_1`, or, like in this example, three connected com
 
     There are 6825 eights out of 70000 numbers.
     Classification report for TDA pipeline Pipeline(steps=[('cub_pers',
-                     CubicalPersistence(dimensions=[28, 28], n_jobs=-2)),
+                     CubicalPersistence(newshape=[28, 28], n_jobs=-2)),
                     ('finite_diags', DiagramSelector(use=True)),
                     ('pers_img',
                      PersistenceImage(bandwidth=50, im_range=[0, 256, 0, 256],
diff --git a/src/python/gudhi/sklearn/cubical_persistence.py b/src/python/gudhi/sklearn/cubical_persistence.py
index 3997bc8a..ed56d2dd 100644
--- a/src/python/gudhi/sklearn/cubical_persistence.py
+++ b/src/python/gudhi/sklearn/cubical_persistence.py
@@ -32,7 +32,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
 
     def __init__(
         self,
-        dimensions=None,
+        newshape=None,
         persistence_dimension=-1,
         homology_coeff_field=11,
         min_persistence=0.0,
@@ -42,7 +42,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
         Constructor for the CubicalPersistence class.
 
         Parameters:
-            dimensions (list of int): A list of number of top dimensional cells if cells filtration values will require
+            newshape (list of int): A list of number of top dimensional cells if cells filtration values will require
                 to be reshaped (cf. :func:`~gudhi.sklearn.cubical_persistence.CubicalPersistence.transform`)
             persistence_dimension (int or list of int): The returned persistence diagrams dimension(s).
                 Short circuit the use of :class:`~gudhi.representations.preprocessing.DimensionSelector` when only one
@@ -52,7 +52,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
                 `min_persistence`). Default value is `0.0`. Set `min_persistence` to `-1.0` to see all values.
             n_jobs (int): cf. https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html
         """
-        self.dimensions = dimensions
+        self.newshape = newshape
         self.persistence_dimension = persistence_dimension
         self.homology_coeff_field = homology_coeff_field
         self.min_persistence = min_persistence
@@ -65,7 +65,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
         return self
 
     def __transform(self, cells):
-        cubical_complex = CubicalComplex(top_dimensional_cells=cells, dimensions=self.dimensions)
+        cubical_complex = CubicalComplex(top_dimensional_cells=cells, dimensions=self.newshape)
         cubical_complex.compute_persistence(
             homology_coeff_field=self.homology_coeff_field, min_persistence=self.min_persistence
         )
@@ -74,7 +74,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
         ]
 
     def __transform_only_this_dim(self, cells):
-        cubical_complex = CubicalComplex(top_dimensional_cells=cells, dimensions=self.dimensions)
+        cubical_complex = CubicalComplex(top_dimensional_cells=cells, dimensions=self.newshape)
         cubical_complex.compute_persistence(
             homology_coeff_field=self.homology_coeff_field, min_persistence=self.min_persistence
         )
@@ -83,8 +83,8 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
     def transform(self, X, Y=None):
         """Compute all the cubical complexes and their associated persistence diagrams.
 
-        :param X: List of cells filtration values that should be flatten if `dimensions` is set in the constructor, or
-            already with the correct shape in a numpy.ndarray (and `dimensions` must not be set).
+        :param X: List of cells filtration values that should be flatten if `newshape` is set in the constructor, or
+            already with the correct shape in a numpy.ndarray (and `newshape` must not be set).
         :type X: list of list of float OR list of numpy.ndarray
 
         :return: Persistence diagrams in the format:
diff --git a/src/python/test/test_sklearn_cubical_persistence.py b/src/python/test/test_sklearn_cubical_persistence.py
index 56c44db0..69b65dde 100644
--- a/src/python/test/test_sklearn_cubical_persistence.py
+++ b/src/python/test/test_sklearn_cubical_persistence.py
@@ -44,7 +44,7 @@ def test_simple_constructor_from_flattened_cells():
     # Not squared (extended) flatten cells
     cells = np.hstack((cells, np.zeros((cells.shape[0], 2)))).flatten()
     
-    cp = CubicalPersistence(persistence_dimension=0, dimensions=[10, 8])
+    cp = CubicalPersistence(persistence_dimension=0, newshape=[10, 8])
     diags = cp.fit_transform([cells])
 
     np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0)