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

.forgejo/workflows/serve.yaml

@@ -9,12 +9,12 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@v4
       - name: Initialize virtual environment
-        run: /usr/bin/python -m venv .env
+        run: /usr/bin/python -m venv .venv
       - name: Install dependencies
-        run: ./.env/bin/pip install mystmd jupyter jupyterlab_myst ipykernel altair pandas
+        run: ./.venv/bin/pip install -r requirements.txt
       - name: Build static HTML
         run: |
-          . .env/bin/activate
+          . .venv/bin/activate
           myst build --execute --html
       - name: Copy files
         run: |

cours/SIN/02-signaux.md

@@ -32,32 +32,26 @@ et un niveau **bas** ("Low").
 :label: fig:sig-logique
 ```{code-cell} python
 :tags: [remove-input]
-import altair as alt
+import matplotlib.pyplot as plt
+from matplotlib import ticker
 import numpy as np
-import pandas as pd
 
 rng = np.random.default_rng(25)
 
 n = 16
 t = np.arange(n+1)
-s = rng.choice([0, 1], n+1)
+s = rng.choice([0, 1], n)
-s[-1] = s[-2]
+
-data = pd.DataFrame({
+fig, ax = plt.subplots()
-  "t": t,
+ax.stairs(s, t, lw=3, baseline=None)
-  "s": s,
+ax.set(
-})
+  xlim=(0, n),
-alt.Chart(
+  ylim=(-.5, 1.5),
-  data
+  xlabel="Temps (s)",
-).mark_line(
+  ylabel="Signal logique",
-  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 logique", values=[0, 1], format=".0f")).scale(domain=(0,1)),
-).properties(
-  width="container",
-  height=100,
 )
+ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
 ```
 Exemple de signal logique
 ````
@@ -77,35 +71,37 @@ Un exemple de signal analogique est donné en @fig:sig-analogique.
 :label: fig:sig-analogique
 ```{code-cell} python
 :tags: [remove-input]
-import altair as alt
+import matplotlib.pyplot as plt
+from matplotlib import ticker
 import numpy as np
-import pandas as pd
+from scipy.interpolate import CubicSpline
+from scipy.stats import qmc
+
 
 rng = np.random.default_rng(25)
 
 n = 20
 t_max = 16
 
-t = np.linspace(0, t_max, n)
+t_base = np.linspace(0, t_max, n)
-
+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]
+
-data = pd.DataFrame({
+t_interp = np.linspace(0, t_max, 1024)
-  "t": t,
+s_interp = np.clip(CubicSpline(t, s)(t_interp), 0, 5)
-  "s": s,
+
-})
+fig, ax = plt.subplots()
-alt.Chart(
+ax.plot(t_interp, s_interp, lw=3)
-  data
+ax.set(
-).mark_line(
+  xlim=(0, t_max),
-  interpolate="basis",
+  ylim=(-.5, 5.5),
-  strokeWidth=3,
+  xlabel="Temps (s)",
-).encode(
+  ylabel="Signal analogique",
-  alt.X("t:Q").axis(title="Temps (s)").scale(domain=(0,t_max)),
-  alt.Y("s:Q", axis=alt.Axis(title="Signal analogique")).scale(domain=(0,5)),
-).properties(
-  width="container",
-  height=200,
 )
+ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
 ```
 Exemple de signal analogique
 ````
@@ -119,32 +115,26 @@ Un exemple de signal analogique est donné en @fig:sig-numerique.
 :label: fig:sig-numerique
 ```{code-cell} python
 :tags: [remove-input]
-import altair as alt
+import matplotlib.pyplot as plt
+from matplotlib import ticker
 import numpy as np
-import pandas as pd
 
 rng = np.random.default_rng(25)
 
 n = 16
 t = np.arange(n+1)
-s = rng.integers(0, 16, n+1)
+s = rng.integers(0, 16, n)
-s[-1] = s[-2]
+
-data = pd.DataFrame({
+fig, ax = plt.subplots()
-  "t": t,
+ax.stairs(s, t, lw=3, baseline=None)
-  "s": s,
+ax.set(
-})
+  xlim=(0, n),
-alt.Chart(
+  ylim=(-.5, 16.5),
-  data
+  xlabel="Temps (s)",
-).mark_line(
+  ylabel="Signal numérique",
-  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,
 )
+ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
 ```
 Exemple de signal numérique
 ````

cours/SIN/03-can.md

@@ -37,69 +37,64 @@ La **caractéristique** du CAN est la courbe représentant la valeur numérique
 :label: fig:exemple-can
 ```{code-cell} python
 :tags: [remove-input]
-import altair as alt
+import matplotlib.pyplot as plt
+from matplotlib import ticker
 import numpy as np
-import pandas as pd
+from scipy.interpolate import CubicSpline
-from scipy import interpolate
+from scipy.stats import qmc
 
-rng = np.random.default_rng(25)
+rng = np.random.default_rng(50)
 
 n = 20
-t_max = 16
+t_max = 8
+n_interp = t_max * 100 + 1
 
-T = np.linspace(0, t_max, 1601)
+t_base = np.linspace(0, t_max, n)
-y = np.clip(
+lhs = (qmc.LatinHypercube(d=n-2, rng=rng).random(1)[0] - .5) * t_max/n
-    interpolate.BSpline(np.linspace(0, t_max, n), 5 * rng.random(n), 2)(T),
+t = t_base + np.concatenate(([0], lhs, [0]))
-    0,
+t = t_base
-    5,
+s = 5 * rng.random(n)
+
+t_interp = np.linspace(0, t_max, n_interp)
+s_interp = np.clip(CubicSpline(t, s)(t_interp), 0, 5)
+s_n = np.full_like(t_interp[::50], np.nan)
+s_n = np.floor(s_interp[::50] * 8 / 5)
+s_n[s_n == 8] = 7
+
+fig, ax = plt.subplots()
+ax2 = ax.twinx()
+
+ax.plot(t_interp, s_interp, lw=3)
+ax2.scatter(t_interp[::50], s_n, color="C1")
+
+ax.grid(False, axis="y")
+ax.grid(True, axis="x", which="both")
+ax2.grid(True)
+
+ax.set(
+  xlim=(0, t_max),
+  ylim=(-.5, 5.5),
+  xlabel="Temps (s)",
 )
-y_n = np.full([1601], np.nan)
+ax.set_ylabel("Signal analogique (V)", color="C0")
-y_n[::50] = np.floor(y[::50] * 8 / 5)
+ax2.set(
-y_n[y_n == 8] = 7
+  ylim=(-8/5*.5, 8/5*5.5),
-
-
-data = pd.DataFrame({
-  "t": T,
-  "s": y,
-  "s_n": y_n,
-})
-
-base = alt.Chart(
-  data
-).encode(
-  alt.X("t:Q").axis(title="Temps (s)").scale(domain=(0,t_max)),
 )
+ax2.set_ylabel("Signal numérique", color="C1")
 
-ch = base.mark_line(
+ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
-  interpolate="basis",
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
-  strokeWidth=3,
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(.5))
-  color="#6666cc",
+ax2.set_yticks(np.arange(9), np.concatenate((np.arange(8), [""])))
-).encode(
-  alt.Y("s:Q", axis=alt.Axis(title="Signal analogique", titleColor="#6666cc")).scale(domain=(0,5)),
-)
 
-ch_n = base.mark_point(
+arr = ax2.annotate("", xy=(0, 0), xytext=(0.5, 0), arrowprops=dict(arrowstyle="<->"))
-  filled=True,
+ax2.annotate("$T_e$", (0.5, 1), xycoords=arr, ha="center", va="bottom")
-  color="#ff6600",
-).encode(
-  alt.Y(
-    "s_n:Q",
-    axis=alt.Axis(
-      title="Signal numérisé",
-      titleColor="#ff6600",
-      values=np.arange(8),
-    )
-  ).scale(domain=(0,8)),
-)
-
-alt.layer(ch_n, ch).resolve_scale(
-  y="independent",
-).properties(
-  width="container",
-  height=200,
-)
 
+arr2 = ax2.annotate("", xy=(0.5, 0), xytext=(0.5, 1), arrowprops=dict(arrowstyle="<->"))
+ax2.annotate("$q$", (1, 0.5), xycoords=arr2, ha="left", va="center")
 
+arr3 = ax2.annotate("", xy=(1, 0), xytext=(1, 8), arrowprops=dict(arrowstyle="<->"))
+ax2.annotate("$V_{pe}$", (1, 0.5), xycoords=arr3, ha="left", va="center")
 ```
 Signal analogique et signal numérisé.
 ````
@@ -108,34 +103,37 @@ Signal analogique et signal numérisé.
 :label: fig:carac-can
 ```{code-cell} python
 :tags: [remove-input]
-import altair as alt
+import matplotlib.pyplot as plt
+from matplotlib import ticker
 import numpy as np
-import pandas as pd
-from scipy import interpolate
 
 N = 8
-s_n = np.arange(N+1)
+s_n = np.arange(N)
-s_n[-1] = s_n[-2]
+s_a = np.linspace(0, 5, N+1)
-data = pd.DataFrame({
-  "s_n": s_n,
-  "s_a": np.linspace(0, 5, N+1),
-})
 
-alt.Chart(
+fig, ax = plt.subplots()
-  data
+
-).mark_line(
+ax.stairs(s_n, s_a, color="C1", lw=3, baseline=None)
-  interpolate="step-after",
+
-  strokeWidth=3,
+ax.set(
-  color="#ff6600",
+  xlim=(0, 5),
-).encode(
+  ylim=(-1, N),
-  alt.X("s_a:Q").axis(title="Signal Analogique").scale(domain=(0,5)),
+  yticks=s_n,
-  alt.Y("s_n:Q", axis=alt.Axis(title="Signal numérique", values=np.arange(N))).scale(domain=(0,N)),
+  xlabel="Signal analogique (V)",
-).properties(
+  ylabel="Signal numérique",
-  width=200,
-  height=200,
 )
+ax.set_xticks(s_a, [f"{v:.3f}" for v in s_a], rotation=45, ha="right", rotation_mode="anchor")
+ax.set_aspect(5/8, 'box')
 
+arr4 = ax.annotate(
+    "", xy=(s_a[0], 0), xytext=(s_a[1], 0), arrowprops=dict(arrowstyle="<->")
+)
+ax.annotate("$q$", (0.5, 1), xycoords=arr4, ha="center", va="bottom")
 
+arr5 = ax.annotate(
+    "", xy=(s_a[0], 1.5), xytext=(s_a[-1], 1.5), arrowprops=dict(arrowstyle="<->")
+)
+ax.annotate("$V_{pe}$", (0.5, 1), xycoords=arr5, ha="center", va="bottom")
 ```
 Caractéristique du CAN.
 ````

cours/SIN/matplotlibrc

@@ -0,0 +1 @@
+../../matplotlibrc

matplotlibrc

@@ -0,0 +1,43 @@
+lines.linewidth: 3
+
+font.family: Fira Code
+
+image.cmap: inferno
+
+axes.linewidth: 1
+axes.grid: True
+axes.grid.which: major
+axes.titlelocation: right
+axes.titleweight: 700
+axes.axisbelow: True
+
+axes.prop_cycle: cycler(color=["#4269d0","#efb118","#ff725c","#6cc5b0","#3ca951","#ff8ab7","#a463f2","#97bbf5","#9c6b4e","#9498a0"])
+
+axes.formatter.use_locale: True
+
+grid.color: "#bebebe"
+grid.linewidth: 1
+grid.alpha: 1
+
+hatch.linewidth: 8
+hatch.color: "#00000013"
+
+boxplot.showmeans: true
+boxplot.meanprops.markeredgecolor: "k"
+boxplot.meanprops.marker: "+"
+boxplot.flierprops.markerfacecolor: C0
+boxplot.medianprops.color: C0
+
+figure.figsize: 8, 4.5
+figure.dpi: 96
+figure.constrained_layout.use: True
+
+xtick.direction: in
+xtick.major.size: 4
+xtick.minor.size: 2
+
+ytick.direction: in
+ytick.major.size: 4
+ytick.minor.size: 2
+
+savefig.format: pdf

requirements.txt

@@ -0,0 +1,7 @@
+mystmd
+jupyter-server
+ipykernel
+matplotlib
+numpy
+pandas
+scipy