FDataIrregular personal proposed reviews

skfda/misc/covariances.py

@@ -31,7 +31,7 @@ def _transform_to_2d(t: ArrayLike) -> NDArrayFloat:
     """Transform 1d arrays in column vectors."""
     t = np.asfarray(t)
 
-    dim = len(t.shape)
+    dim = t.ndim
     assert dim <= 2
 
     if dim < 2:

skfda/ml/regression/_linear_regression.py

@@ -607,7 +607,7 @@ def _check_and_convert(
             np.ndarray: numpy 2D array.
         """
         new_X = np.asarray(X)
-        if len(new_X.shape) == 1:
+        if new_X.ndim == 1:
             new_X = new_X[:, np.newaxis]
         return new_X
 

skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py

@@ -46,7 +46,7 @@
 def _transform_to_2d(t: ArrayLike) -> NDArrayFloat:
     t = np.asfarray(t)
 
-    dim = len(t.shape)
+    dim = t.ndim
     assert dim <= 2
 
     if dim < 2:

skfda/representation/basis/_fdatabasis.py

@@ -687,8 +687,8 @@ def _array_to_R(  # noqa: N802
         coefficients: NDArrayFloat,
         transpose: bool = False,
     ) -> str:
-        if len(coefficients.shape) == 1:
-            coefficients = coefficients.reshape((1, coefficients.shape[0]))
+        if coefficients.ndim == 1:
+            coefficients = coefficients[None]
 
         if transpose is True:
             coefficients = np.transpose(coefficients)