Python矩阵作图库matplotlib的初级使用(2)

基础介绍

matplotlib图形对象层级结构:

图形对象(figure) → 子图对象(axes) → 坐标轴对象(axis) → 定位器对象-刻度线(locator)/格式化器对象-刻度线标签(formatter)

绘图对象创建

from matplotlib import pyplot as plt

# 创建绘图对象

fig = plt.figure()

# 创建网格子图

ax1 = fig.add_subplot(rows, cols, idx)

# 创建手动子图

ax1 = fig.add_axes([left, bottom, width, height])

# 获取坐标轴对象

ax1.xaxis

ax1.yaxis

# 设置定位器对象

ax1.xaxis.set_major_locator(plt.NullLocator)

ax1.xaxis.set_minor_locator(plt.NullLocator)

# 设置格式化器对象

ax1.xaxis.set_major_formatter(plt.NullFormatter)

ax1.xaxis.set_minor_formatter(plt.NullFormatter)

部分细节控制
- 颜色支持
  
  ①. 标准颜色名称, 如: "red"
  
  ②. 范围在0～1的灰度值, 如: "0.75"
  
  ③. RGB十六进制, 如: "#FFDD44"
  
  ④. RGB元组, 范围在0～1, 如: (1.0, 0.2, 0.3)
- 配色方案支持
  
  ①. 获取配色方案: plt.cm
  
  ②. 常见配色方案: "jet", "viridis", "RdBu"
  
  ③. 离散化配色方案: plt.cm.get_cmap("Blues", 6)
- 颜色条控制支持
  
  plt.colorbar(mappable, ticks=range(6), label="digit value", extend="both")
  
  注意, colorbar本身也是一个子图对象, 创建时依赖于mappable对象
- linestyle线条风格支持
  
  ①. "solid"; ②. "dashed"; ③. "dashdot"; ④. "dotted"
- 散点控制支持
  
  ①. markersize; ②. markerfacecolor; ③. markeredgecolor; ④. markeredgewidth
  
  注意, 绘制散点时, plt.plot较plt.scatter性能更好, plt.scatter较plt.plot更加灵活
- 标签支持
  
  ①. title; ②. xlabel; ③. ylabel; ④. label; ⑤. legend
- 图例控制支持
  
  ax1.legend([labels,] loc="upper left", frameon=False, ncol=2, fontsize=10, title="Area")
- 文本注释支持
  
  ax1.text(x, y, text, size, color, ha, va, transform=ax1.transData|ax1.transAxes|fig.transFigure)
- 坐标轴控制支持
  
  ax1.set(xlabel, xlim, xticks, xscale)

部分绘图实例

误差绘制

code

import numpy

from matplotlib import pyplot as plt

X = numpy.linspace(-1, 1, 30)

Y = X ** 3 + X ** 2 + X

YErr = numpy.random.uniform(0.3, 0.7, Y.shape)

fig = plt.figure(figsize=(4, 4))

ax1 = fig.add_subplot(2, 1, 1)

ax2 = fig.add_subplot(2, 1, 2)

ax1.errorbar(X, Y, YErr, fmt="o", color="black", ecolor="lightgray", elinewidth=3, capsize=0)

ax2.plot(X, Y, "-", color="gray")

ax2.fill_between(X, Y-YErr, Y+YErr, color="gray", alpha=0.2)

fig.savefig("plot.png", dpi=300)

等高线绘制

code

import numpy

from matplotlib import pyplot as plt

X = numpy.linspace(-1, 1, 30)

Y = numpy.linspace(-2, 2, 50)

X, Y = numpy.meshgrid(X, Y)

Z = numpy.sin(X) + numpy.cos(Y ** 2)

fig = plt.figure(figsize=(10, 3))

ax1 = fig.add_axes([0.1, 0.1, 0.3, 0.9])

ax2 = fig.add_axes([0.5, 0.1, 0.3, 0.9])

ax1.contourf(X, Y, Z, 20, cmap="RdGy")

ax2.imshow(Z, extent=[-1, 1, -2, 2], origin="lower", cmap="RdGy", aspect="auto")

fig.savefig("plot.png", dpi=300)

3维曲面绘制

code

import numpy

from matplotlib import pyplot as plt

from mpl_toolkits import mplot3d

X = numpy.linspace(-1, 1, 30)

Y = numpy.linspace(-2, 2, 50)

X, Y = numpy.meshgrid(X, Y)

Z = numpy.sin(X) + numpy.cos(Y ** 2)

fig = plt.figure(figsize=(10, 3))

ax1 = fig.add_subplot(1, 2, 1, projection="3d")

ax2 = fig.add_subplot(1, 2, 2, projection="3d")

ax1.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap="viridis", edgecolor="none")

ax2.plot_trisurf(X.flatten(), Y.flatten(), Z.flatten(), cmap="viridis", edgecolor="none")

fig.tight_layout()

fig.savefig("plot.png", dpi=300)

直方图与密度图

code

import numpy

import seaborn as sns

from matplotlib import pyplot as plt

X = numpy.random.normal(0, 1, (10000,))

fig = plt.figure(figsize=(10, 3))

ax1 = fig.add_subplot(1, 2, 1)

ax2 = fig.add_subplot(1, 2, 2)

sns.histplot(X, kde=False, ax=ax1)

sns.kdeplot(data=X, ax=ax2)

fig.savefig("plot.png", dpi=300)

多维矩阵图

code

import numpy

import pandas

import seaborn as sns

X1 = numpy.random.normal(0, 1, (300,))

X2 = numpy.sin(X1)

X3 = X1 ** 2

data = numpy.vstack((X1, X2, X3)).T

df = pandas.DataFrame(data, columns=["X1", "X2", "X3"])

sns_plot = sns.pairplot(df)

sns_plot.savefig("plot.png", dpi=300)

参考文档

①. Python Data Science Handbook by Jake VanderPlas (O’Reilly). Copyright 2017 Jake VanderPlas, 978-1-491-91205-8

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Python矩阵作图库matplotlib的初级使用(2)

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