Matplotlib自定义坐标刻度的使用示例

import matplotlib.pyplot as plt
import numpy as np
ax = plt.axes(xscale='log', yscale='log')
plt.show()

ax = plt.axes()
ax.plot(np.random.rand(50))
ax.yaxis.set_major_locator(plt.NullLocator())
ax.xaxis.set_major_formatter(plt.NullFormatter())
plt.show()

fig, ax = plt.subplots(5, 5, figsize=(5, 5))
fig.subplots_adjust(hspace=0, wspace=0)
# 从scikit-learn获取一些人脸照片数据,(这个scikit-learn库在之前手写数字文章有讲)
from sklearn.datasets import fetch_olivetti_faces
faces = fetch_olivetti_faces().images
for i in range(5):
    for j in range(5):
        ax[i, j].xaxis.set_major_locator(plt.NullLocator())
        ax[i, j].yaxis.set_major_locator(plt.NullLocator())
        ax[i, j].imshow(faces[10 * i + j], cmap="bone")
plt.show()

fig, ax = plt.subplots()
x = np.linspace(0, 3 * np.pi, 1000)
ax.plot(x, np.sin(x), lw=3, label='Sine')
ax.plot(x, np.cos(x), lw=3, label='Cosine')
# 设置网格、图例和坐标轴上下限
ax.grid(True)
ax.legend(frameon=False)
ax.axis('equal')
ax.set_xlim(0, 3 * np.pi)
ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))
ax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi / 4))
plt.show()

def format_func(value, tick_number):
# 找到π/2的倍数刻度
    N = int(np.round(2 * value / np.pi))
    if N == 0:
        return "0"
    elif N == 1:
        return r"$\pi/2$"
    elif N == 2:
        return r"$\pi$"
    elif N % 2 > 0:
        return r"${0}\pi/2$".format(N)
    else:
        return r"${0}\pi$".format(N // 2)
ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))