matplotlib实现矩阵和图像的可视化表示

import matplotlib.pyplot as plt
import numpy as np

x = np.random.rand(5,5)

fig,axes = plt.subplots(1,2,figsize=(8,4))

axes[0].imshow(x)
plt.title("imshow")
axes[1].matshow(x)
plt.title("matshow")

plt.tight_layout()
plt.show()

def drawMat(x, ax=None):
    M, N = x.shape
    if not ax:
        ax = plt.subplot()
    arrM, arrN = np.arange(M), np.arange(N)
    plt.yticks(arrM+0.5, arrM)
    plt.xticks(arrN+0.5, arrN)
    ax.pcolormesh(x)
    ax.invert_yaxis()
    for i,j in product(range(M),range(N)):
        ax.text(j+0.2, i+0.55, f"{x[i,j]:.2}")
    plt.show()

x = np.random.rand(5,5)
drawMat(x)

X, Y = np.indices([100,100])/30 - 1.5
Z = (1 - X/2 + X**5 + Y**3) * np.exp(-X**2 - Y**2)

fDct = {"contour": plt.contour, "contourf":plt.contourf,
    "pcolormesh" : plt.pcolormesh, "imshow":plt.imshow}

fig = plt.figure(figsize=(9,6))
for i,key in enumerate(fDct, 1):
    ax = fig.add_subplot(2,2,i)
    fDct[key](Z)
    plt.title(key)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
for i in range(2):
    ax = fig.add_subplot(2,1,i+1)
    ax = plt.pcolormesh(np.random.rand(10,20))
    plt.colorbar()

plt.show()

fig = plt.figure()
for i in range(2):
    ax = fig.add_subplot(2,1,i+1)
    im = plt.pcolormesh(np.random.rand(10,20))
    plt.axis('off')

# 指定colorbar的位置和大小
cax = fig.add_axes([0.92, 0.1, 0.02, 0.8])
fig.colorbar(im, cax=cax)
plt.show()

fig = plt.figure()

gs = plt.GridSpec(2, 2, width_ratios=[15, 1], height_ratios=[1, 1])

plt.subplot(gs[0, 0])
plt.title("A")
im = plt.pcolormesh(np.random.rand(10,20))
plt.axis('off')

plt.subplot(gs[1, 0])
plt.title("B")
im = plt.pcolormesh(np.random.rand(10,20))
plt.axis('off')

ax = plt.subplot(gs[:, 1])
fig.colorbar(im, cax=ax)
plt.show()