機器學習（六）：初級matplitlib繪圖-之前的微補充

將一張圖繪製在另一張圖的內部：

fig = plt.figure() # 新建畫板

axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # 大畫布
axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # 小畫布

# 大畫布
axes1.plot(x, y, 'r')

# 小畫布
axes2.plot(y, x, 'g')

設置線的顏色、透明度：

fig, axes = plt.subplots()

axes.plot(x, x+1, color="red", alpha=0.5)
axes.plot(x, x+2, color="#1155dd")
axes.plot(x, x+3, color="#15cc55")

設置線型：

fig, ax = plt.subplots(figsize=(12,6))

# 線寬
ax.plot(x, x+1, color="blue", linewidth=0.25)
ax.plot(x, x+2, color="blue", linewidth=0.50)
ax.plot(x, x+3, color="blue", linewidth=1.00)
ax.plot(x, x+4, color="blue", linewidth=2.00)

# 虛線類型
ax.plot(x, x+5, color="red", lw=2, linestyle='-')
ax.plot(x, x+6, color="red", lw=2, ls='-.')
ax.plot(x, x+7, color="red", lw=2, ls=':')

# 虛線交錯寬度
line, = ax.plot(x, x+8, color="black", lw=1.50)
line.set_dashes([5, 10, 15, 10])

# 符號
ax.plot(x, x+ 9, color="green", lw=2, ls='--', marker='+')
ax.plot(x, x+10, color="green", lw=2, ls='--', marker='o')
ax.plot(x, x+11, color="green", lw=2, ls='--', marker='s')
ax.plot(x, x+12, color="green", lw=2, ls='--', marker='1')

# 符號大小和顏色
ax.plot(x, x+13, color="purple", lw=1, ls='-', marker='o', markersize=2)
ax.plot(x, x+14, color="purple", lw=1, ls='-', marker='o', markersize=4)
ax.plot(x, x+15, color="purple", lw=1, ls='-', marker='o', markersize=8, markerfacecolor="red")
ax.plot(x, x+16, color="purple", lw=1, ls='-', marker='s', markersize=8, 
        markerfacecolor="yellow", markeredgewidth=2, markeredgecolor="blue")

繪製散點圖、梯步圖、條形圖、面積圖：

n = np.array([0,1,2,3,4,5])

fig, axes = plt.subplots(1, 4, figsize=(16,5))

axes[0].scatter(x, x + 0.25*np.random.randn(len(x)))
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")

繪製雷達圖：

fig = plt.figure(figsize=(6,6))
ax = fig.add_axes([0.0, 0.0, .6, .6], polar=True)
t = np.linspace(0, 2 * np.pi, 100)
ax.plot(t, t, color='blue', lw=3)

繪製等高線圖：

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
fig, ax = plt.subplots()

cnt = ax.contour(Z, cmap=plt.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])

繪製 3D 表面圖：

from mpl_toolkits.mplot3d.axes3d import Axes3D

fig = plt.figure(figsize=(14,6))

# 通過 projection='3d' 指定繪製 3D 圖形
ax = fig.add_subplot(1, 2, 1, projection='3d')
ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)

繪製複雜一些的 3D 圖：

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)
cset = ax.contour(X, Y, Z, zdir='z', offset=-np.pi, cmap=plt.cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='x', offset=-np.pi, cmap=plt.cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='y', offset=3*np.pi, cmap=plt.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)