matplotlib 基本功能
matplotlib 是 Python 的一个绘图库，使用它可以很方便地绘制出版质量级别的图形图片。本节主要介绍的是 matplotlib 的基本绘图功能，即在二维平面坐标系中绘制连续的曲线。
- 设置线型、线宽和颜色；
- 设置坐标轴的范围；
- 设置坐标刻度；
- 设置坐标轴；
- 图例；
- 特殊点；
- 备注；
matplotlib 基本功能详解
matplotlib.pyplot 绘图核心API
import numpy as np
import matplotlib.pyplot as plt
plt.plot(x_array, y_array)
plt.show()
案例：绘制一条正弦曲线
import numpy as np
import matplotlib.pyplot as plt
x_array = np.linspace(-2 * np.pi, 2 * np.pi, 100)
y_array = np.sin(x_array)
plt.plot(x_array, y_array)
plt.savefig("sinx.jpg")
plt.show()
1、绘制水平线与垂直线
import numpy as np
import matplotlib.pyplot as plt
plt.vlines(xval, ymin, ymax, ...)
plt.hlines(yval, xmin, xmax, ...)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 1000)
y = np.sin(x)
plt.plot(x, y)
plt.vlines([-2, -1, 0, 1, 2], -1, 1, colors='blue')
plt.hlines(0, -3, 3, color='red')
plt.show()
图中绘制了 5 条垂直线( x = [-2, -1, 0, 1, 1], ymin = -1, ymax = 1 )和 1 条水平线( y = 0, xmin = -3, xmax = 3 ).
2、线型、线宽和颜色
- 线型：linestyle = [ ‘ – ‘, ‘ – ‘, ‘ -. ‘, ‘ : ‘ ]；
- 线宽：linewidth = 数字；
- 颜色：color = 英文颜色单词 或 常用颜色的英文首字母 或 #495434 或 (56,54,20)；
- 透明度：alpha = 浮点数值；
比如： plt.plot(x_array, y_array, linestyle='--', linewidth=2, color='r', alpha=0.5)
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 1000)
sinx = np.sin(x)
cosx = np.cos(x)
plt.plot(x, sinx, linestyle="-.", linewidth=2, color="dodgerblue", alpha=0.8)
plt.plot(x, cosx, linestyle='--', linewidth=2, color='orangered', alpha=1)
plt.savefig("cosx.png")
plt.show()
3、设置坐标轴范围
案例：以 sigmoid 函数为例，设置 x x x 轴和 y y y 轴的坐标轴范围
plt.xlim(x_limt_min, x_limt_max)
plt.ylim(y_limt_min, y_limt_max)
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-10, 10, 1000)
y = 1/(1 + np.exp(-x))
plt.xlim(-8, 8)
plt.ylim(-0.01, 1.01)
plt.plot(x, y, linestyle='-', linewidth=2, color='red', alpha=0.8)
plt.show()
4、设置坐标刻度
案例：把正弦函数 y = s i n x y=sinx y =s i n x 的横坐标刻度设置为：0、π 2 \dfrac{\pi}{2}2 π、π \pi π、3 π 2 \dfrac{3\pi}{2}2 3 π 和 2 π 2\pi 2 π.
plt.xticks(x_val_list, x_text_list )
plt.yticks(y_val_list, y_text_list)
【推荐】：设置刻度值的时候，数值序列与文本序列的长度要一一对应且相同。
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 1000)
sinx = np.sin(x)
x_val_list = [-np.pi, -1/2*np.pi, 0, 1/2*np.pi, np.pi]
x_text_list = [r'-$\pi$', r'-$\dfrac{\pi}{2}$', r'$0$', r'$\dfrac{\pi}{2}$', r'-$\pi$']
plt.xticks(x_val_list, x_text_list)
plt.plot(x, sinx, linestyle="-", linewidth=2, color="orangered", alpha=0.9)
plt.savefig("ysinx.jpg")
plt.show()
5、设置坐标轴
从上面绘制的曲线可以看到，坐标轴有上下左右，而实际绘制数学函数的图象时，我们的直角坐标系都是通过原点且相互垂直的。因此，matplotlib 设置有四个坐标轴名：left / right / top / bottom。
ax = plt.gca()
axis = ax.spines['坐标轴名']
axis.set_position((type, val))
axis.set_color(color)
例如： ax.spines['left'].set_position(('data', 0))，先获取到当前的坐标轴，然后设置坐标轴的位置或颜色值。
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 1000)
sinx = np.sin(x)
plt.plot(x, sinx, linestyle='-.',linewidth=2, color='red', alpha=0.9)
x_val_list = [-np.pi, -1/2*np.pi, 0, 1/2*np.pi, np.pi]
x_text_list = [r'-$\pi$', r'-$\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'-$\pi$']
plt.xticks(x_val_list, x_text_list)
plt.yticks([-1, -0.5, 0.5, 1])
ax = plt.gca()
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['left'].set_position(('data', 0))
ax.spines['bottom'].set_position(('data', 0))
plt.show()
6、图例
案例：显示两条曲线的图例，并测试 loc 属性；
设置图例的位置：loc :
plt.plot(x, y, ... label='', ...)
plt.legend(loc='')
Location StringLocation Codebest0upper right1upper left2lower left3lower right4right5center left6center right7lower center8upper center9center10
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 1000)
sinx = np.sin(x)
cosx = 1/2 * np.cos(x)
plt.plot(x, sinx, linestyle='-.', linewidth=2, color='dodgerblue', label=r'$y=sin(x)$')
plt.plot(x, cosx, linestyle='--', linewidth=2, color='orangered', label=r'$y=\frac{1}{2}cos(x)$')
x_val_list = [-np.pi, -1/2*np.pi, 0, 1/2*np.pi, np.pi]
x_text_list = [r'-$\pi$', r'-$\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'-$\pi$']
plt.xticks(x_val_list, x_text_list)
plt.yticks([-1, -0.5, 0.5, 1])
ax = plt.gca()
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['left'].set_position(('data',0))
ax.spines['bottom'].set_position(('data',0))
plt.legend(loc='upper left')
plt.show()
7、特殊点
案例：绘制激活函数 sigmoid 曲线上的特殊点；
plt.scatter(xarray, yarray,
marker='',
s=70,
edgecolor='',
facecolor='',
zorder=3
)
说明：标注特殊点用到了 pyplot 的散点图绘制方法
Matplotlib Point 样式（marker 属性）
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-20, 20, 1000)
y = 1/(1+np.exp(-x))
plt.plot(x, y, linestyle='-',linewidth=2, color='red', alpha=0.9, label=r'$\frac{1}{1+e^{-x}}$')
plt.yticks([0.5, 1])
ax = plt.gca()
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['left'].set_position(('data',0))
ax.spines['bottom'].set_position(('data',0))
pointx = [0]
pointy = [0.5]
plt.scatter(pointx, pointy, marker='o', s=50, color='red', label='sample points',zorder=3)
plt.title(r"Function : $y=\dfrac{1}{1+e^{-x}}$")
plt.legend()
plt.show()
8、备注
案例：在某条曲线上的点添加备注，指明函数方程与值。
plt.annotate(
r'($\frac{\pi}{2}, 0)$',
xycoords='data',
xy=(x, y),
textcoords='offset points',
xytext=(x, y),
fontsize=14,
arrowprops=dict()
)
arrowprops = dict(
arrowstyle='->',
connectionstyle=''
)
个人觉得， arrowstyle 的参数很多，只需要记住 -> 就足够了。
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 1000)
sinx = np.sin(x)
cosx = 1/2 * np.cos(x)
plt.plot(x, sinx, linestyle='-.', linewidth=2, color='dodgerblue', label=r'$y=sin(x)$')
plt.plot(x, cosx, linestyle='--', linewidth=2, color='orangered', label=r'$y=\frac{1}{2}cos(x)$')
x_val_list = [-np.pi, -1/2*np.pi, 0, 1/2*np.pi, np.pi]
x_text_list = [r'-$\pi$', r'-$\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'-$\pi$']
plt.xticks(x_val_list, x_text_list)
plt.yticks([-1, -0.5, 0.5, 1])
ax = plt.gca()
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['left'].set_position(('data',0))
ax.spines['bottom'].set_position(('data',0))
plt.legend(loc='upper left')
pointx = [np.pi / 2, np.pi / 2]
pointy = [1, 0]
plt.scatter(pointx, pointy, marker='o', s=50, color='red', label='sample points',zorder=3)
plt.annotate(
r'$(\frac{\pi}{2}, 1)$',
xycoords='data',
xy=(np.pi / 2, 1),
textcoords='offset points',
xytext=(50, 30),
fontsize=14,
arrowprops=dict(
arrowstyle='->',
connectionstyle='angle3'
)
)
plt.annotate(
r'$(\frac{\pi}{2}, 0)$',
xycoords='data',
xy=(np.pi / 2, 0),
textcoords='offset points',
xytext=(-50, -30),
fontsize=14,
arrowprops=dict(
arrowstyle='->',
connectionstyle='arc3'
)
)
plt.show()
Original: https://blog.csdn.net/qq_41775769/article/details/121460775
Author: Training.L
Title: 1. 科研绘图之 matplotlib 基本语法
原创文章受到原创版权保护。转载请注明出处：https://www.johngo689.com/767843/
转载文章受原作者版权保护。转载请注明原作者出处！