Refactor signal visualizations to use Matplotlib, removing Altair dependencies and updating figure configurations for clarity
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2 changed files with 73 additions and 88 deletions
cours/SIN
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@ -43,7 +43,7 @@ t = np.arange(n+1)
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s = rng.choice([0, 1], n)
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fig, ax = plt.subplots()
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ax.stairs(s, t, lw=3)
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ax.stairs(s, t, lw=3, baseline=None)
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ax.set(
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xlim=(0, n),
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ylim=(-.5, 1.5),
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@ -88,7 +88,6 @@ lhs = (qmc.LatinHypercube(d=n-2, rng=rng).random(1)[0] - .5) * t_max/n
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t = t_base + np.concatenate(([0], lhs, [0]))
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t = t_base
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s = 5 * rng.random(n)
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s[-1] = s[-2]
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t_interp = np.linspace(0, t_max, 1024)
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s_interp = np.clip(CubicSpline(t, s)(t_interp), 0, 5)
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@ -127,7 +126,7 @@ t = np.arange(n+1)
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s = rng.integers(0, 16, n)
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fig, ax = plt.subplots()
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ax.stairs(s, t, lw=3)
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ax.stairs(s, t, lw=3, baseline=None)
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ax.set(
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xlim=(0, n),
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ylim=(-.5, 16.5),
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@ -136,18 +135,6 @@ ax.set(
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)
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ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
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ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
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# alt.Chart(
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# data
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# ).mark_line(
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# interpolate="step-after",
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# strokeWidth=3,
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# ).encode(
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# alt.X("t:Q").axis(title="Temps (s)").scale(domain=(0,n)),
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# alt.Y("s:Q", axis=alt.Axis(title="Signal numérique", values=np.arange(0, 16))).# scale(domain=(0,15)),
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# ).properties(
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# width="container",
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# height=200,
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# )
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```
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Exemple de signal numérique
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````
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@ -37,69 +37,64 @@ La **caractéristique** du CAN est la courbe représentant la valeur numérique
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:label: fig:exemple-can
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```{code-cell} python
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:tags: [remove-input]
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import altair as alt
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import matplotlib.pyplot as plt
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from matplotlib import ticker
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import numpy as np
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import pandas as pd
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from scipy import interpolate
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from scipy.interpolate import CubicSpline
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from scipy.stats import qmc
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rng = np.random.default_rng(25)
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rng = np.random.default_rng(50)
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n = 20
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t_max = 16
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t_max = 8
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n_interp = t_max * 100 + 1
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T = np.linspace(0, t_max, 1601)
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y = np.clip(
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interpolate.BSpline(np.linspace(0, t_max, n), 5 * rng.random(n), 2)(T),
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0,
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5,
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t_base = np.linspace(0, t_max, n)
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lhs = (qmc.LatinHypercube(d=n-2, rng=rng).random(1)[0] - .5) * t_max/n
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t = t_base + np.concatenate(([0], lhs, [0]))
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t = t_base
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s = 5 * rng.random(n)
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t_interp = np.linspace(0, t_max, n_interp)
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s_interp = np.clip(CubicSpline(t, s)(t_interp), 0, 5)
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s_n = np.full_like(t_interp[::50], np.nan)
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s_n = np.floor(s_interp[::50] * 8 / 5)
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s_n[s_n == 8] = 7
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fig, ax = plt.subplots()
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ax2 = ax.twinx()
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ax.plot(t_interp, s_interp, lw=3)
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ax2.scatter(t_interp[::50], s_n, color="C1")
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ax.grid(False, axis="y")
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ax.grid(True, axis="x", which="both")
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ax2.grid(True)
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ax.set(
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xlim=(0, t_max),
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ylim=(-.5, 5.5),
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xlabel="Temps (s)",
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)
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y_n = np.full([1601], np.nan)
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y_n[::50] = np.floor(y[::50] * 8 / 5)
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y_n[y_n == 8] = 7
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data = pd.DataFrame({
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"t": T,
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"s": y,
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"s_n": y_n,
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})
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base = alt.Chart(
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data
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).encode(
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alt.X("t:Q").axis(title="Temps (s)").scale(domain=(0,t_max)),
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ax.set_ylabel("Signal analogique (V)", color="C0")
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ax2.set(
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ylim=(-8/5*.5, 8/5*5.5),
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)
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ax2.set_ylabel("Signal numérique", color="C1")
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ch = base.mark_line(
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interpolate="basis",
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strokeWidth=3,
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color="#6666cc",
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).encode(
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alt.Y("s:Q", axis=alt.Axis(title="Signal analogique", titleColor="#6666cc")).scale(domain=(0,5)),
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)
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ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
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ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
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ax.xaxis.set_minor_locator(ticker.MultipleLocator(.5))
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ax2.set_yticks(np.arange(9), np.concatenate((np.arange(8), [""])))
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ch_n = base.mark_point(
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filled=True,
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color="#ff6600",
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).encode(
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alt.Y(
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"s_n:Q",
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axis=alt.Axis(
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title="Signal numérisé",
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titleColor="#ff6600",
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values=np.arange(8),
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)
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).scale(domain=(0,8)),
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)
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alt.layer(ch_n, ch).resolve_scale(
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y="independent",
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).properties(
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width="container",
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height=200,
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)
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arr = ax2.annotate("", xy=(0, 0), xytext=(0.5, 0), arrowprops=dict(arrowstyle="<->"))
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ax2.annotate("$T_e$", (0.5, 1), xycoords=arr, ha="center", va="bottom")
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arr2 = ax2.annotate("", xy=(0.5, 0), xytext=(0.5, 1), arrowprops=dict(arrowstyle="<->"))
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ax2.annotate("$q$", (1, 0.5), xycoords=arr2, ha="left", va="center")
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arr3 = ax2.annotate("", xy=(1, 0), xytext=(1, 8), arrowprops=dict(arrowstyle="<->"))
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ax2.annotate("$V_{pe}$", (1, 0.5), xycoords=arr3, ha="left", va="center")
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```
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Signal analogique et signal numérisé.
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````
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@ -108,34 +103,37 @@ Signal analogique et signal numérisé.
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:label: fig:carac-can
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```{code-cell} python
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:tags: [remove-input]
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import altair as alt
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import matplotlib.pyplot as plt
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from matplotlib import ticker
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import numpy as np
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import pandas as pd
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from scipy import interpolate
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N = 8
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s_n = np.arange(N+1)
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s_n[-1] = s_n[-2]
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data = pd.DataFrame({
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"s_n": s_n,
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"s_a": np.linspace(0, 5, N+1),
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})
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s_n = np.arange(N)
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s_a = np.linspace(0, 5, N+1)
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alt.Chart(
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data
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).mark_line(
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interpolate="step-after",
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strokeWidth=3,
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color="#ff6600",
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).encode(
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alt.X("s_a:Q").axis(title="Signal Analogique").scale(domain=(0,5)),
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alt.Y("s_n:Q", axis=alt.Axis(title="Signal numérique", values=np.arange(N))).scale(domain=(0,N)),
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).properties(
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width=200,
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height=200,
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fig, ax = plt.subplots()
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ax.stairs(s_n, s_a, color="C1", lw=3, baseline=None)
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ax.set(
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xlim=(0, 5),
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ylim=(-1, N),
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yticks=s_n,
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xlabel="Signal analogique (V)",
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ylabel="Signal numérique",
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)
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ax.set_xticks(s_a, [f"{v:.3f}" for v in s_a], rotation=45, ha="right", rotation_mode="anchor")
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ax.set_aspect(5/8, 'box')
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arr4 = ax.annotate(
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"", xy=(s_a[0], 0), xytext=(s_a[1], 0), arrowprops=dict(arrowstyle="<->")
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)
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ax.annotate("$q$", (0.5, 1), xycoords=arr4, ha="center", va="bottom")
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arr5 = ax.annotate(
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"", xy=(s_a[0], 1.5), xytext=(s_a[-1], 1.5), arrowprops=dict(arrowstyle="<->")
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)
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ax.annotate("$V_{pe}$", (0.5, 1), xycoords=arr5, ha="center", va="bottom")
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```
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Caractéristique du CAN.
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````
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