Refactor signal visualizations to use Matplotlib, removing Altair dependencies and updating figure configurations for clarity

cours/SIN/02-signaux.md

@@ -43,7 +43,7 @@ t = np.arange(n+1)
 s = rng.choice([0, 1], n)
 
 fig, ax = plt.subplots()
-ax.stairs(s, t, lw=3)
+ax.stairs(s, t, lw=3, baseline=None)
 ax.set(
   xlim=(0, n),
   ylim=(-.5, 1.5),
@@ -88,7 +88,6 @@ lhs = (qmc.LatinHypercube(d=n-2, rng=rng).random(1)[0] - .5) * t_max/n
 t = t_base + np.concatenate(([0], lhs, [0]))
 t = t_base
 s = 5 * rng.random(n)
-s[-1] = s[-2]
 
 t_interp = np.linspace(0, t_max, 1024)
 s_interp = np.clip(CubicSpline(t, s)(t_interp), 0, 5)
@@ -127,7 +126,7 @@ t = np.arange(n+1)
 s = rng.integers(0, 16, n)
 
 fig, ax = plt.subplots()
-ax.stairs(s, t, lw=3)
+ax.stairs(s, t, lw=3, baseline=None)
 ax.set(
   xlim=(0, n),
   ylim=(-.5, 16.5),
@@ -136,18 +135,6 @@ ax.set(
 )
 ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
 ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
-# alt.Chart(
-#   data
-# ).mark_line(
-#   interpolate="step-after",
-#   strokeWidth=3,
-# ).encode(
-#   alt.X("t:Q").axis(title="Temps (s)").scale(domain=(0,n)),
-#   alt.Y("s:Q", axis=alt.Axis(title="Signal numérique", values=np.arange(0, 16))).# scale(domain=(0,15)),
-# ).properties(
-#   width="container",
-#   height=200,
-# )
 ```
 Exemple de signal numérique
 ````