last fixes

docs/background/plot_01_1D_basis_function.md

@@ -86,7 +86,7 @@ print(f"Evaluated B-spline of order {order} with {eval_basis.shape[1]} "
 
 fig = plt.figure()
 plt.title("B-spline basis")
-_ = plt.plot(eval_basis)
+plt.plot(samples, eval_basis);
 ```
 
 ```{code-cell} ipython3
@@ -133,9 +133,9 @@ the fixed range basis.
 ```{code-cell} ipython3
 fig, axs = plt.subplots(2,1, sharex=True)
 plt.suptitle("B-spline basis ")
-axs[0].plot(bspline(samples), color="k")
+axs[0].plot(samples, bspline(samples), color="k")
 axs[0].set_title("default")
-axs[1].plot(bspline_range(samples), color="tomato")
+axs[1].plot(samples, bspline_range(samples), color="tomato")
 axs[1].set_title("bounds=[0.2, 0.8]")
 plt.tight_layout()
 ```

docs/how_to_guide/plot_05_sklearn_pipeline_cv_demo.md

@@ -438,7 +438,7 @@ if path.exists():
   fig.savefig(path / "plot_05_sklearn_pipeline_cv_demo.svg")
 ```
 
-:rocket::rocket::rocket: **Success!** :rocket::rocket::rocket:
+_U+1F680 _U+1F680 _U+1F680 **Success!** _U+1F680 _U+1F680 _U+1F680
 We are now able to capture the distribution of the firing rate appropriately: both peaks and valleys in the spiking activity are matched by our model predicitons.
 
 ### Evaluating different bases directly

docs/tutorials/plot_01_current_injection.md

@@ -404,7 +404,7 @@ current). We can easily plot the tuning curve of the neuron:
 
 
 ```{code-cell} ipython3
-doc_plots.tuning_curve_plot(tuning_curve)
+doc_plots.tuning_curve_plot(tuning_curve);
 ```
 
 We can see that, while the firing rate mostly increases with the current,