remove calls to basis from tutorial

docs/api_reference.rst

@@ -4,6 +4,7 @@ API Reference
 =============
 
 .. _nemos_glm:
+
 The ``nemos.glm`` module
 ------------------------
 Classes for creating Generalized Linear Models (GLMs) for both single neurons and neural populations.
@@ -19,6 +20,7 @@ Classes for creating Generalized Linear Models (GLMs) for both single neurons an
     PopulationGLM
 
 .. _nemos_basis:
+
 The ``nemos.basis`` module
 --------------------------
 Provides basis function classes to construct and transform features for model inputs.
@@ -107,8 +109,9 @@ These classes are the building blocks for the concrete basis classes.
     TransformerBasis
 
 .. _observation_models:
+
 The ``nemos.observation_models`` module
---------------------------------------
+---------------------------------------
 Statistical models to describe the distribution of neural responses or other predicted variables, given inputs.
 
 .. currentmodule:: nemos.observation_models
@@ -123,6 +126,7 @@ Statistical models to describe the distribution of neural responses or other pre
     GammaObservations
 
 .. _regularizers:
+
 The ``nemos.regularizer`` module
 --------------------------------
 Implements various regularization techniques to constrain model parameters, which helps prevent overfitting.

docs/background/basis/plot_01_1D_basis_function.md

@@ -168,7 +168,7 @@ For N-dimensional input, with $N>1$, the method assumes that first axis is the s
 For "Eval" basis, `compute_features` is equivalent to "calling" the basis and then reshaping the input into a 2-dimensional feature matrix.
 
 ```{code-cell} ipython3
-basis = nmo.basis.EvalRaisedCosineLinear(n_basis_funcs=5)
+basis = nmo.basis.RaisedCosineLinearEval(n_basis_funcs=5)
 
 # generate a 3D array
 inp = np.random.randn(50, 2, 3)
@@ -185,7 +185,7 @@ For each of the `3 * 2 = 6` inputs, `n_basis_funcs = 5` features are computed. T
 For "Conv" type basis, `compute_features` is equivalent to convolving each input with `n_basis_funcs` kernels, and concatenate the output into a 2D design matrix.
 
 ```{code-cell} ipython3
-basis = nmo.basis.ConvRaisedCosineLinear(n_basis_funcs=5, window_size=6)
+basis = nmo.basis.RaisedCosineLinearConv(n_basis_funcs=5, window_size=6)
 
 # compute_features to perform the convolution and concatenate
 out = basis.compute_features(inp)

docs/conf.py

@@ -168,4 +168,7 @@
 viewcode_follow_imported_members = True
 
 # option for mpl extension
-plot_html_show_formats = False
+plot_html_show_formats = False
+
+# raise an error if exec error in notebooks
+nb_execution_raise_on_error = True

docs/how_to_guide/plot_06_glm_pytree.md

@@ -286,7 +286,7 @@ spikes = nwb['units'][unit_no]
 basis = nmo.basis.CyclicBSplineEval(10, order=5)
 x = np.linspace(-np.pi, np.pi, 100)
 plt.figure()
-plt.plot(x, basis(x))
+plt.plot(x, basis.compute_features(x))
 
 # Find the interval on which head_dir has no NaNs
 head_dir = head_dir.dropna()
@@ -300,7 +300,7 @@ spikes = spikes.count(bin_size=1/head_dir.rate, ep=valid_data)
 # center.
 head_dir = head_dir.interpolate(spikes)
 
-X = nmo.pytrees.FeaturePytree(head_direction=basis(head_dir))
+X = nmo.pytrees.FeaturePytree(head_direction=basis.compute_features(head_dir))
 ```
 
 Now we'll fit our GLM and then see what our head direction tuning looks like:
@@ -311,7 +311,7 @@ model = nmo.glm.GLM(regularizer="Ridge", regularizer_strength=0.001)
 model.fit(X, spikes)
 print(model.coef_['head_direction'])
 
-bs_vis = basis(x)
+bs_vis = basis.compute_features(x)
 tuning = jnp.einsum('b, tb->t', model.coef_['head_direction'], bs_vis)
 plt.figure()
 plt.polar(x, tuning)
@@ -354,7 +354,7 @@ our data similarly.
 pos_basis = nmo.basis.RaisedCosineLinearEval(10) * nmo.basis.RaisedCosineLinearEval(10)
 spatial_pos = nwb['SpatialSeriesLED1'].restrict(valid_data)
 
-X['spatial_position'] = pos_basis(*spatial_pos.values.T)
+X['spatial_position'] = pos_basis.compute_features(*spatial_pos.values.T)
 ```
 
 Running the GLM is identical to before, but we can see that our coef_
@@ -373,7 +373,7 @@ coefficients).
 
 
 ```{code-cell} ipython3
-bs_vis = basis(x)
+bs_vis = basis.compute_features(x)
 tuning = jnp.einsum('b,nb->n', model.coef_['head_direction'], bs_vis)
 print(model.coef_['head_direction'])
 plt.figure()

src/nemos/basis/_basis_mixin.py

@@ -24,7 +24,7 @@ def _compute_features(self, *xi: NDArray):
         Apply the basis transformation to the input data.
 
         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.
+        basis element. xi[k] must be a N-dimensional array (N >= 1) or a pynapple Tsd/TsdFrame/TsdTensor.
 
         Parameters
         ----------
@@ -36,8 +36,7 @@ def _compute_features(self, *xi: NDArray):
         -------
         :
             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.
+            or :math:`b_i(*xi)`, where :math:`b_i` is a basis element.
 
         """
         out = self._evaluate(*(np.reshape(x, (x.shape[0], -1)) for x in xi))