Exercice Matplotlib
Projet Matplotlib
py
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.mplot3d.axes3d import Axes3D
import numpy as np
from matplotlib import ticker
import matplotlib
# ----------------------------
# Tracé de l'équation d'Einstein E = mc²
# ----------------------------
# Données
c = 3 * 10**8 # Vitesse de la lumière
m = np.arange(0, 11)
e = m*c**2
# Shema
fig, axe = plt.subplots()
axe.plot(m, e, color="#FF8C00", lw=2)
axe.set_xlabel('Masse (g)')
axe.set_ylabel('Energie (Joules)')
axe.set_title('E = mc²')
# ÉCHELLE LOG
plt.yscale("log")
plt.grid(which='both',axis='y')
plt.show()
# ----------------------------
# Création de tracés à partir de points de données
# ----------------------------
# Données
"""
Les données montrent les intérêts payés pour une obligation du Trésor américain pour une certaine durée de contrat. La liste des étiquettes affiche la longueur de contrat correspondante par position d'index.
https://home.treasury.gov/policy-issues/financing-the-government/interest-rate-statistics?data=yield
"""
labels = ['1 Mo','3 Mo','6 Mo','1 Yr','2 Yr','3 Yr','5 Yr','7 Yr','10 Yr','20 Yr','30 Yr']
july16_2007 =[4.75,4.98,5.08,5.01,4.89,4.89,4.95,4.99,5.05,5.21,5.14]
july16_2020 = [0.12,0.11,0.13,0.14,0.16,0.17,0.28,0.46,0.62,1.09,1.31]
# Shema 1
fig, axe = plt.subplots()
axe.plot(labels, july16_2007, color="#FF8C00", lw=2, label="july16_2007")
axe.plot(labels, july16_2020, color="#8B008B", lw=2, label="july16_2020")
axe.legend(loc=(1.04,0.5))
plt.show()
# Shema 2
fig, axes = plt.subplots(2, 1)
axes[0].plot(labels, july16_2007, color="#FF8C00", lw=2, label="july16_2007")
axes[0].set_title('16 juillet 2007')
axes[1].plot(labels, july16_2020, color="#8B008B", lw=2, label="july16_2020")
axes[1].set_title('16 juillet 2020')
plt.show()
# Shema bonus
fig, ax1 = plt.subplots(figsize=(12,8))
ax1.plot(labels,july16_2007, lw=2, color="blue")
ax1.set_ylabel("2007", fontsize=18, color="blue")
ax1.spines['left'].set_color('blue')
ax1.spines['left'].set_linewidth(4)
for label in ax1.get_yticklabels():
label.set_color("blue")
plt.yticks(fontsize=15)
ax2 = ax1.twinx()
ax2.plot(labels,july16_2020, lw=2, color="red")
ax2.set_ylabel("2020", fontsize=18, color="red")
ax2.spines['right'].set_color('red')
ax2.spines['right'].set_linewidth(4)
for label in ax2.get_yticklabels():
label.set_color("red")
ax1.set_title("Courbes de rendement du 16 juillet")
plt.yticks(fontsize=15)
plt.show()Commandes Avancées Matplotlib
py
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.mplot3d.axes3d import Axes3D
import numpy as np
from matplotlib import ticker
import matplotlib
"""
# Commandes Avancées Matplotlib
Ce script est une référence complète pour les commandes avancées de Matplotlib.
Chaque section illustre un concept différent, avec des exemples visuels.
"""
# ----------------------------
# Données de base
# ----------------------------
x = np.linspace(0, 5, 50) # données pour les tracés de base
# ----------------------------
# Échelle logarithmique
# ----------------------------
fig, axes = plt.subplots(1, 2, figsize=(10,4))
axes[0].plot(x, x**2, x, np.exp(x))
axes[0].set_title("Normal scale")
axes[1].plot(x, x**2, x, np.exp(x))
axes[1].set_yscale("log")
axes[1].set_title("Logarithmic scale (y)")
plt.show()
# ----------------------------
# Placement de ticks et étiquettes personnalisées
# ----------------------------
fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(x, x**2, x, x**3, lw=2)
ax.set_xticks([1, 2, 3, 4, 5])
ax.set_xticklabels([r'$\alpha$', r'$\beta$', r'$\gamma$', r'$\delta$', r'$\epsilon$'], fontsize=18)
yticks = [0, 50, 100, 150]
ax.set_yticks(yticks)
ax.set_yticklabels(["$%.1f$" % y for y in yticks], fontsize=18)
plt.show()
# ----------------------------
# Notation scientifique
# ----------------------------
fig, ax = plt.subplots()
ax.plot(x, x**2, x, np.exp(x))
ax.set_title("Scientific notation")
ax.set_yticks([0, 50, 100, 150])
formatter = ticker.ScalarFormatter(useMathText=True)
formatter.set_scientific(True)
formatter.set_powerlimits((-1,1))
ax.yaxis.set_major_formatter(formatter)
plt.show()
# ----------------------------
# Espacement entre axes et labels
# ----------------------------
matplotlib.rcParams['xtick.major.pad'] = 5
matplotlib.rcParams['ytick.major.pad'] = 5
fig, ax = plt.subplots()
ax.plot(x, x**2, x, np.exp(x))
ax.set_yticks([0, 50, 100, 150])
ax.set_title("Label and axis spacing")
ax.xaxis.labelpad = 5
ax.yaxis.labelpad = 5
ax.set_xlabel("x")
ax.set_ylabel("y")
plt.show()
# Restaurer valeurs par défaut
matplotlib.rcParams['xtick.major.pad'] = 3
matplotlib.rcParams['ytick.major.pad'] = 3
# ----------------------------
# Réglages de position des axes
# ----------------------------
fig, ax = plt.subplots()
ax.plot(x, x**2, x, np.exp(x))
ax.set_yticks([0, 50, 100, 150])
ax.set_title("Title")
ax.set_xlabel("x")
ax.set_ylabel("y")
fig.subplots_adjust(left=0.15, right=.9, bottom=0.1, top=0.9)
plt.show()
# ----------------------------
# Grille d'axe
# ----------------------------
fig, axes = plt.subplots(1, 2, figsize=(10,3))
axes[0].plot(x, x**2, x, x**3, lw=2)
axes[0].grid(True)
axes[1].plot(x, x**2, x, x**3, lw=2)
axes[1].grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
plt.show()
# ----------------------------
# Axis spines
# ----------------------------
fig, ax = plt.subplots(figsize=(6,2))
ax.spines['bottom'].set_color('blue')
ax.spines['top'].set_color('blue')
ax.spines['left'].set_color('red')
ax.spines['left'].set_linewidth(2)
ax.spines['right'].set_color("none")
ax.yaxis.tick_left()
plt.show()
# ----------------------------
# Axes jumeaux
# ----------------------------
fig, ax1 = plt.subplots()
ax1.plot(x, x**2, lw=2, color="blue")
ax1.set_ylabel(r"area $(m^2)$", fontsize=18, color="blue")
for label in ax1.get_yticklabels():
label.set_color("blue")
ax2 = ax1.twinx()
ax2.plot(x, x**3, lw=2, color="red")
ax2.set_ylabel(r"volume $(m^3)$", fontsize=18, color="red")
for label in ax2.get_yticklabels():
label.set_color("red")
plt.show()
# ----------------------------
# Axes x et y à zéro
# ----------------------------
fig, ax = plt.subplots()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
xx = np.linspace(-0.75, 1., 100)
ax.plot(xx, xx**3)
plt.show()
# ----------------------------
# Autres styles de tracés 2D
# ----------------------------
n = np.array([0,1,2,3,4,5])
fig, axes = plt.subplots(1, 4, figsize=(12,3))
axes[0].scatter(xx, xx + 0.25*np.random.randn(len(xx))); axes[0].set_title("scatter")
axes[1].step(n, n**2, lw=2); axes[1].set_title("step")
axes[2].bar(n, n**2, align="center", width=0.5, alpha=0.5); axes[2].set_title("bar")
axes[3].fill_between(x, x**2, x**3, color="green", alpha=0.5); axes[3].set_title("fill_between")
plt.show()
# ----------------------------
# Annotation de texte
# ----------------------------
fig, ax = plt.subplots()
ax.plot(xx, xx**2, xx, xx**3)
ax.text(0.15, 0.2, r"$y=x^2$", fontsize=20, color="blue")
ax.text(0.65, 0.1, r"$y=x^3$", fontsize=20, color="green")
plt.show()
# ----------------------------
# Figures avec plusieurs sous-graphiques et encarts
# ----------------------------
# subplots
fig, ax = plt.subplots(2, 3)
fig.tight_layout()
plt.show()
# subplot2grid
fig = plt.figure()
ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)
ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)
ax3 = plt.subplot2grid((3,3), (1,2), rowspan=2)
ax4 = plt.subplot2grid((3,3), (2,0))
ax5 = plt.subplot2grid((3,3), (2,1))
fig.tight_layout()
plt.show()
# gridspec
fig = plt.figure()
gs = gridspec.GridSpec(2, 3, height_ratios=[2,1], width_ratios=[1,2,1])
for g in gs:
ax = fig.add_subplot(g)
fig.tight_layout()
plt.show()
# add_axes et inset
fig, ax = plt.subplots()
ax.plot(xx, xx**2, xx, xx**3)
fig.tight_layout()
inset_ax = fig.add_axes([0.2, 0.55, 0.35, 0.35])
inset_ax.plot(xx, xx**2, xx, xx**3)
inset_ax.set_title('zoom near origin')
inset_ax.set_xlim(-.2, .2)
inset_ax.set_ylim(-.005, .01)
inset_ax.set_yticks([0, 0.005, 0.01])
inset_ax.set_xticks([-0.1,0,.1])
plt.show()
# ----------------------------
# Cartes de couleurs et figures de contour
# ----------------------------
alpha = 0.7
phi_ext = 2 * np.pi * 0.5
def flux_qubit_potential(phi_m, phi_p):
return 2 + alpha - 2 * np.cos(phi_p) * np.cos(phi_m) - alpha * np.cos(phi_ext - 2*phi_p)
phi_m = np.linspace(0, 2*np.pi, 100)
phi_p = np.linspace(0, 2*np.pi, 100)
X,Y = np.meshgrid(phi_p, phi_m)
Z = flux_qubit_potential(X, Y).T
# pcolor
fig, ax = plt.subplots()
p = ax.pcolor(X/(2*np.pi), Y/(2*np.pi), Z, cmap=matplotlib.cm.RdBu,
vmin=abs(Z).min(), vmax=abs(Z).max())
cb = fig.colorbar(p, ax=ax)
plt.show()
# imshow
fig, ax = plt.subplots()
im = ax.imshow(Z, cmap=matplotlib.cm.RdBu,
vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])
im.set_interpolation('bilinear')
cb = fig.colorbar(im, ax=ax)
plt.show()
# contour
fig, ax = plt.subplots()
cnt = ax.contour(Z, cmap=matplotlib.cm.RdBu,
vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])
plt.show()
# ----------------------------
# Figures 3D
# ----------------------------
# Surface plot
fig = plt.figure(figsize=(14,6))
ax = fig.add_subplot(1, 2, 1, projection='3d')
p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)
ax = fig.add_subplot(1, 2, 2, projection='3d')
p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm,
linewidth=0, antialiased=False)
cb = fig.colorbar(p, shrink=0.5)
plt.show()
# Wireframe
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(1, 1, 1, projection='3d')
p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)
plt.show()
# Surface avec projections de contours
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(1,1,1, projection='3d')
ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)
ax.contour(X, Y, Z, zdir='z', offset=-np.pi, cmap=matplotlib.cm.coolwarm)
ax.contour(X, Y, Z, zdir='x', offset=-np.pi, cmap=matplotlib.cm.coolwarm)
ax.contour(X, Y, Z, zdir='y', offset=3*np.pi, cmap=matplotlib.cm.coolwarm)
ax.set_xlim3d(-np.pi, 2*np.pi)
ax.set_ylim3d(0, 3*np.pi)
ax.set_zlim3d(-np.pi, 2*np.pi)
plt.show()