Merge branch 'development' into document_basis

src/nemos/basis/_basis.py

@@ -683,6 +683,7 @@ def split_by_feature(
         -------
         dict
             A dictionary where:
+
             - **Key**: Label of the basis.
             - **Value**: the array reshaped to: ``(..., n_inputs, n_basis_funcs, ...)``
         """
@@ -1039,6 +1040,7 @@ def split_by_feature(
         -------
         dict
             A dictionary where:
+
             - **Keys**: Labels of the additive basis components.
             - **Values**: Sub-arrays corresponding to each component. Each sub-array has the shape:
 

src/nemos/basis/_basis_mixin.py

@@ -21,11 +21,17 @@ def __init__(self, bounds: Optional[Tuple[float, float]] = None):
         self.bounds = bounds
 
     def _compute_features(self, *xi: ArrayLike | Tsd | TsdFrame | TsdTensor):
-        """
-        Evaluate basis at sample points.
+        """Evaluate basis at sample points.
+
+        The basis is evaluated at the locations specified in the inputs. For example,
+        ``compute_features(np.array([0, .5]))`` would return the array:
+
+        .. code-block:: text
 
-        The basis evaluated at the samples, or :math:`b_i(*xi)`, where :math:`b_i` is a
-        basis element. xi[k] must be a one-dimensional array or a pynapple Tsd/TsdFrame/TsdTensor.
+           b_1(0) ... b_n(0)
+           b_1(.5) ... b_n(.5)
+
+        where ``b_i`` is the i-th basis.
 
         Parameters
         ----------
@@ -89,20 +95,15 @@ def __init__(self, window_size: int, conv_kwargs: Optional[dict] = None):
         self.conv_kwargs = {} if conv_kwargs is None else conv_kwargs
 
     def _compute_features(self, *xi: NDArray | Tsd | TsdFrame | TsdTensor):
-        """
-        Convolve basis functions with input time series.
+        """Convolve basis functions with input time series.
 
-        A bank of basis filters (created by calling fit) is convolved with the
-        input data. Inputs can be a NDArray, or a pynapple Tsd/TsdFrame/TsdTensor. All the dimensions
-        except for the sample-axis are flattened, so that the method always returns a matrix.
+        A bank of basis filters is convolved with the input data. All the dimensions
+        except for the sample-axis are flattened, so that the method always returns a
+        matrix.
 
         For example, if inputs are of shape (num_samples, 2, 3), the output will be
         ``(num_samples, num_basis_funcs * 2 * 3)``.
 
-        The time-axis can be specified at basis initialization by setting the keyword argument ``axis``.
-        For example, if ``axis == 1`` your input should be of shape ``(N1, num_samples N3, ...)``, the output of
-        transform will be of shape ``(num_samples, num_basis_funcs * N1 * N3 *...)``.
-
         Parameters
         ----------
         *xi: