fix tutorials

docs/background/plot_01_1D_basis_function.md

@@ -38,6 +38,7 @@ warnings.filterwarnings(
     ),
     category=RuntimeWarning,
 )
+
 ```
 
 (simple_basis_function)=
@@ -58,6 +59,9 @@ import pynapple as nap
 
 import nemos as nmo
 
+# configure plots some
+plt.style.use(nmo.styles.plot_style)
+
 # Initialize hyperparameters
 order = 4
 n_basis = 10
@@ -66,6 +70,39 @@ n_basis = 10
 bspline = nmo.basis.BSplineEval(n_basis_funcs=n_basis, order=order)
 ```
 
+We provide the convenience method `evaluate_on_grid` for evaluating the basis on an equi-spaced grid of points that makes it easier to plot and visualize all basis elements.
+
+```{code-cell} ipython3
+# evaluate the basis on 100 sample points
+x, y = bspline.evaluate_on_grid(100)
+
+fig = plt.figure(figsize=(5, 3))
+plt.plot(x, y, lw=2)
+plt.title("B-Spline Basis")
+```
+
+```{code-cell} ipython3
+:tags: [hide-input]
+
+# save image for thumbnail
+from pathlib import Path
+import os
+
+root = os.environ.get("READTHEDOCS_OUTPUT")
+if root:
+   path = Path(root) / "html/_static/thumbnails/background"
+# if local store in ../_build/html/...
+else:
+   path = Path("../_build/html/_static/thumbnails/background")
+ 
+# make sure the folder exists if run from build
+if root or Path("../_build/html/_static").exists():
+   path.mkdir(parents=True, exist_ok=True)
+
+if path.exists():
+  fig.savefig(path / "plot_01_1D_basis_function.svg")
+```
+
 ## Feature Computation
 The bases in the `nemos.basis` module can be grouped into two categories:
 
@@ -75,7 +112,6 @@ The bases in the `nemos.basis` module can be grouped into two categories:
 
 Let's see how this two modalities operate.
 
-
 ```{code-cell} ipython3
 eval_mode = nmo.basis.MSplineEval(n_basis_funcs=n_basis)
 conv_mode = nmo.basis.MSplineConv(n_basis_funcs=n_basis, window_size=100)
@@ -165,6 +201,7 @@ the fixed range basis.
 
 
 ```{code-cell} ipython3
+samples = np.linspace(0, 1, 200)
 fig, axs = plt.subplots(2,1, sharex=True)
 plt.suptitle("B-spline basis ")
 axs[0].plot(samples, bspline.compute_features(samples), color="k")

docs/background/plot_02_ND_basis_function.md

@@ -150,7 +150,7 @@ x_coord = np.linspace(0, 1, 1000)
 y_coord = np.linspace(0, 1, 1000)
 
 # Evaluate the basis functions for the given trajectory.
-eval_basis = additive_basis(x_coord, y_coord)
+eval_basis = additive_basis.compute_features(x_coord, y_coord)
 
 print(f"Sum of two 1D splines with {eval_basis.shape[1]} "
       f"basis element and {eval_basis.shape[0]} samples:\n"
@@ -169,13 +169,13 @@ basis_b_element = 1
 fig, axs = plt.subplots(1, 2, figsize=(6, 3))
 
 axs[0].set_title(f"$a_{{{basis_a_element}}}(x)$", color="b")
-axs[0].plot(x_coord, a_basis(x_coord), "grey", alpha=.3)
-axs[0].plot(x_coord, a_basis(x_coord)[:, basis_a_element], "b")
+axs[0].plot(x_coord, a_basis.compute_features(x_coord), "grey", alpha=.3)
+axs[0].plot(x_coord, a_basis.compute_features(x_coord)[:, basis_a_element], "b")
 axs[0].set_xlabel("x-coord")
 
 axs[1].set_title(f"$b_{{{basis_b_element}}}(x)$", color="b")
-axs[1].plot(y_coord, b_basis(x_coord), "grey", alpha=.3)
-axs[1].plot(y_coord, b_basis(x_coord)[:, basis_b_element], "b")
+axs[1].plot(y_coord, b_basis.compute_features(x_coord), "grey", alpha=.3)
+axs[1].plot(y_coord, b_basis.compute_features(x_coord)[:, basis_b_element], "b")
 axs[1].set_xlabel("y-coord")
 plt.tight_layout()
 ```
@@ -242,7 +242,7 @@ The number of elements of the product basis will be the product of the elements
 
 ```{code-cell} ipython3
 # Evaluate the product basis at the x and y coordinates
-eval_basis = prod_basis(x_coord, y_coord)
+eval_basis = prod_basis.compute_features(x_coord, y_coord)
 
 # Output the number of elements and samples of the evaluated basis, 
 # as well as the number of elements in the original 1D basis objects
@@ -268,13 +268,13 @@ fig, axs = plt.subplots(3,3,figsize=(8, 6))
 cc = 0
 for i, j in element_pairs:
     # plot the element form a_basis
-    axs[cc, 0].plot(x_coord, a_basis(x_coord), "grey", alpha=.3)
-    axs[cc, 0].plot(x_coord, a_basis(x_coord)[:, i], "b")
+    axs[cc, 0].plot(x_coord, a_basis.compute_features(x_coord), "grey", alpha=.3)
+    axs[cc, 0].plot(x_coord, a_basis.compute_features(x_coord)[:, i], "b")
     axs[cc, 0].set_title(f"$a_{{{i}}}(x)$",color='b')
 
     # plot the element form b_basis
-    axs[cc, 1].plot(y_coord, b_basis(y_coord), "grey", alpha=.3)
-    axs[cc, 1].plot(y_coord, b_basis(y_coord)[:, j], "b")
+    axs[cc, 1].plot(y_coord, b_basis.compute_features(y_coord), "grey", alpha=.3)
+    axs[cc, 1].plot(y_coord, b_basis.compute_features(y_coord)[:, j], "b")
     axs[cc, 1].set_title(f"$b_{{{j}}}(y)$",color='b')
 
     # select & plot the corresponding product basis element
@@ -322,7 +322,6 @@ in a linear maze and the LFP phase angle.
 :::
 
 
-
 N-Dimensional Basis
 -------------------
 Sometimes it may be useful to model even higher dimensional interactions, for example between the heding direction of
@@ -346,7 +345,7 @@ c_basis = nmo.basis.RaisedCosineLinearEval(n_basis_funcs=n_basis)
 
 prod_basis_3 = a_basis * b_basis * c_basis
 samples = np.linspace(0, 1, T)
-eval_basis = prod_basis_3(samples, samples, samples)
+eval_basis = prod_basis_3.compute_features(samples, samples, samples)
 
 print(f"Product of three 1D splines results in {prod_basis_3.n_basis_funcs} "
       f"basis elements.\nEvaluation output of shape {eval_basis.shape}")