merged conflicts

docs/background/plot_01_1D_basis_function.md

@@ -106,9 +106,9 @@ if path.exists():
 ## Feature Computation
 The bases in the `nemos.basis` module can be grouped into two categories:
 
-1. **Evaluation Bases**: These bases use the [`compute_features`](nemos.basis._basis.Basis.compute_features) method to evaluate the basis directly, applying a non-linear transformation to the input. Classes in this category have names starting with "Eval," such as `BSplineEval`.
+1. **Evaluation Bases**: These bases use the [`compute_features`](nemos.basis._basis.Basis.compute_features) method to evaluate the basis directly, applying a non-linear transformation to the input. Classes in this category have names ending with "Eval," such as `BSplineEval`.
 
-2. **Convolution Bases**: These bases use the [`compute_features`](nemos.basis._basis.Basis.compute_features) method to convolve the input with a kernel of basis elements, using a `window_size` specified by the user. Classes in this category have names starting with "Conv," such as `BSplineConv`.
+2. **Convolution Bases**: These bases use the [`compute_features`](nemos.basis._basis.Basis.compute_features) method to convolve the input with a kernel of basis elements, using a `window_size` specified by the user. Classes in this category have names ending with "Conv," such as `BSplineConv`.
 
 Let's see how this two modalities operate.
 

docs/background/plot_03_1D_convolution.md

@@ -188,7 +188,7 @@ if path.exists():
 
 ## Convolve using [`Basis.compute_features`](nemos.basis._basis.Basis.compute_features)
 
-Every basis in the `nemos.basis` module whose class name starts with "Conv" will perform a 1D convolution over the 
+Every basis in the `nemos.basis` module whose class name ends with "Conv" will perform a 1D convolution over the 
 provided input when the `compute_features` method is called. The basis elements will be used as filters for the
 convolution.
 

docs/developers_notes/04-basis_module.md

@@ -28,7 +28,7 @@ Abstract Class Basis
 
 The super-class [`Basis`](nemos.basis._basis.Basis) provides two public methods, [`compute_features`](the-public-method-compute_features) and [`evaluate_on_grid`](the-public-method-evaluate_on_grid). These methods perform checks on both the input provided by the user and the output of the evaluation to ensure correctness, and are thus considered "safe". They both make use of the abstract method `_evaluate` that is specific for each concrete class. See below for more details.
 
-## The Class `nemos.basis._basis.Basis`
+## The Abstract Super-class [`Basis`](nemos.basis._basis.Basis)
 
 (the-public-method-compute_features)=
 ### The Public Method `compute_features`
@@ -42,7 +42,7 @@ It accepts one or more NumPy array or pynapple `Tsd` object as input, and perfor
 
 1. Checks that the inputs all have the same sample size `M`, and raises a `ValueError` if this is not the case.
 2. Checks that the number of inputs matches what the basis being evaluated expects (e.g., one input for a 1-D basis, N inputs for an N-D basis, or the sum of N 1-D bases), and raises a `ValueError` if this is not the case.
-3. In `"eval"` mode, calls the `_evaluate` method on the input, which is the subclass-specific implementation of the basis set evaluation. In `"conv"` mode, generates a filter bank using [`compute_features`](nemos.basis._basis.Basis.evaluate_on_grid) and then applies the convolution to the input with [`nemos.convolve.create_convolutional_predictor`](nemos.convolve.create_convolutional_predictor).
+3. In `"eval"` mode, calls the [`_evaluate`](nemos.basis._basis.Basis._evaluate) method on the input, which is the subclass-specific implementation of the basis set evaluation. In `"conv"` mode, generates a filter bank using [`compute_features`](nemos.basis._basis.Basis.evaluate_on_grid) and then applies the convolution to the input with [`nemos.convolve.create_convolutional_predictor`](nemos.convolve.create_convolutional_predictor).
 4. Returns a NumPy array or  pynapple `TsdFrame` of shape `(M, n_basis_funcs)`, with each basis element evaluated at the samples.
 
 :::{admonition} Multiple epochs
@@ -61,14 +61,14 @@ This method performs the following steps:
 
 1. Checks that the number of inputs matches what the basis being evaluated expects (e.g., one input for a 1-D basis, N inputs for an N-D basis, or the sum of N 1-D bases), and raises a `ValueError` if this is not the case.
 2. Calls `_get_samples` method, which returns equidistant samples over the domain of the basis function. The domain may depend on the type of basis.
-3. Calls the `_evaluate` method.
+3. Calls the [`_evaluate`](nemos.basis._basis.Basis._evaluate) method on these samples.
 4. Returns both the sample grid points of shape `(m1, ..., mN)`, and the evaluation output at each grid point of shape `(m1, ..., mN, n_basis_funcs)`, where `mi` is the number of sample points for the i-th axis of the grid.
 
 ### Abstract Methods
 
 The [`nemos.basis._basis.Basis`](nemos.basis._basis.Basis) class has the following abstract methods, which every concrete subclass must implement:
 
-1. `_evaluate`: Evaluates a basis over some specified samples.
+1. [`_evaluate`](nemos.basis._basis.Basis._evaluate) : Evaluates a basis over some specified samples.
 2. `_check_n_basis_min`: Checks the minimum number of basis functions required. This requirement can be specific to the type of basis.
 
 ## Contributors Guidelines

src/nemos/basis/_basis_mixin.py

@@ -36,9 +36,7 @@ def _compute_features(self, *xi: ArrayLike | Tsd | TsdFrame | TsdTensor):
         Returns
         -------
         :
-            A matrix with the transformed features. 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.
+            A matrix with the transformed features. 
 
         """
         return self._evaluate(*xi)
@@ -144,15 +142,14 @@ def _set_kernel(self) -> "ConvBasisMixin":
         -----
         Subclasses implementing this method should detail the specifics of how the kernel is
         computed and how the input parameters are utilized.
+
         """
         self.kernel_ = self._evaluate(np.linspace(0, 1, self.window_size))
         return self
 
     @property
     def window_size(self):
-        """Window size as number of samples.
-
-        Duration of the convolutional kernel in number of samples.
+        """Duration of the convolutional kernel in number of samples.
         """
         return self._window_size
 
@@ -161,7 +158,7 @@ def window_size(self, window_size):
         """Setter for the window size parameter."""
         if window_size is None:
             raise ValueError(
-                "If the basis is in `conv` mode, you must provide a window_size!"
+                "You must provide a window_size!"
             )
 
         elif not (isinstance(window_size, int) and window_size > 0):

src/nemos/basis/_transformer_basis.py

@@ -64,7 +64,7 @@ def __init__(self, basis: Basis):
 
     @staticmethod
     def _unpack_inputs(X: FeatureMatrix):
-        """Unpack impute without using transpose.
+        """Unpack inputs without using transpose.
 
         Unpack horizontally stacked inputs using slicing. This works gracefully with ``pynapple``,
         returning a list of Tsd objects. Attempt to unpack using *X.T will raise a ``pynapple``