pyplot 画多个图时搅合到了一起_matplotlib.pyplot.plot()参数详解

59 阅读 0 评论 39 点赞

我是靠谱客的博主重要芝麻，最近开发中收集的这篇文章主要介绍pyplot 画多个图时搅合到了一起_matplotlib.pyplot.plot()参数详解，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

https://matplotlib.org/api/pyplot_summary.html

在交互环境中查看帮助文档：

import matplotlib.pyplot as plt
help(plt.plot)

以下是对帮助文档重要部分的翻译：
plot函数的一般的调用形式：

#单条线：
plot([x], y, [fmt], data=None, **kwargs)
#多条线一起画
plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)

可选参数[fmt] 是一个字符串来定义图的基本属性如：颜色（color），点型（marker），线型（linestyle），
具体形式 fmt = '[color][marker][line]'
fmt接收的是每个属性的单个字母缩写，例如：

plot(x, y, 'bo-')  # 蓝色圆点实线

若属性用的是全名则不能用*fmt*参数来组合赋值，应该用关键字参数对单个属性赋值如：
plot(x,y2,color='green', marker='o', linestyle='dashed', linewidth=1, markersize=6)
plot(x,y3,color='#900302',marker='+',linestyle='-')
常见的颜色参数：**Colors**
也可以对关键字参数color赋十六进制的RGB字符串如 color='#900302'

    =============    ===============================
    character        color
    =============    ===============================
    ``'b'``          blue 蓝
    ``'g'``          green 绿
    ``'r'``          red 红
    ``'c'``          cyan 蓝绿
    ``'m'``          magenta 洋红
    ``'y'``          yellow 黄
    ``'k'``          black 黑
    ``'w'``          white 白
    =============    ===============================

点型参数**Markers**,如：marker='+' 这个只有简写，英文描述不被识别

    =============    ===============================
    character        description
    =============    ===============================
    ``'.'``          point marker
    ``','``          pixel marker
    ``'o'``          circle marker
    ``'v'``          triangle_down marker
    ``'^'``          triangle_up marker
    ``'<'``          triangle_left marker
    ``'>'``          triangle_right marker
    ``'1'``          tri_down marker
    ``'2'``          tri_up marker
    ``'3'``          tri_left marker
    ``'4'``          tri_right marker
    ``'s'``          square marker
    ``'p'``          pentagon marker
    ``'*'``          star marker
    ``'h'``          hexagon1 marker
    ``'H'``          hexagon2 marker
    ``'+'``          plus marker
    ``'x'``          x marker
    ``'D'``          diamond marker
    ``'d'``          thin_diamond marker
    ``'|'``          vline marker
    ``'_'``          hline marker
    =============    ===============================
线型参数**Line Styles**，linestyle='-'

    =============    ===============================
    character        description
    =============    ===============================
    ``'-'``          solid line style 实线
    ``'--'``         dashed line style 虚线
    ``'-.'``         dash-dot line style 点画线
    ``':'``          dotted line style 点线
    =============    ===============================

线型参数**Line Styles**，linestyle='-'

    =============    ===============================
    character        description
    =============    ===============================
    ``'-'``          solid line style 实线
    ``'--'``         dashed line style 虚线
    ``'-.'``         dash-dot line style 点画线
    ``':'``          dotted line style 点线
    =============    ===============================

样例1

函数原型：matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)
>>> plot('xlabel', 'ylabel', data=obj)
解释：All indexable objects are supported. This could e.g. be a dict, a pandas.DataFame or a structured numpy array.
data 参数接受一个对象数据类型，所有可被索引的对象都支持，如 dict 等

import matplotlib.pyplot as plt  
import numpy as np
'''read file 
fin=open("para.txt")
a=[]
for i in fin:
  a.append(float(i.strip()))
a=np.array(a)
a=a.reshape(9,3)
'''
a=np.random.random((9,3))*2 #随机生成y
 
y1=a[0:,0]
y2=a[0:,1]
y3=a[0:,2]
 
x=np.arange(1,10)
 
ax = plt.subplot(111)
width=10
hight=3
ax.arrow(0,0,0,hight,width=0.01,head_width=0.1, head_length=0.3,length_includes_head=True,fc='k',ec='k')
ax.arrow(0,0,width,0,width=0.01,head_width=0.1, head_length=0.3,length_includes_head=True,fc='k',ec='k')
 
ax.axes.set_xlim(-0.5,width+0.2)
ax.axes.set_ylim(-0.5,hight+0.2)
 
plotdict = { 'dx': x, 'dy': y1 }
ax.plot('dx','dy','bD-',data=plotdict)
 
ax.plot(x,y2,'r^-')
ax.plot(x,y3,color='#900302',marker='*',linestyle='-')
 
plt.show()

样例2，

import matplotlib.pyplot as plt  
import numpy as np  
  
x = np.arange(0, 2*np.pi, 0.02)  
y = np.sin(x)  
y1 = np.sin(2*x)  
y2 = np.sin(3*x)  
ym1 = np.ma.masked_where(y1 > 0.5, y1)  
ym2 = np.ma.masked_where(y2 < -0.5, y2)  
  
lines = plt.plot(x, y, x, ym1, x, ym2, 'o')  
#设置线的属性
plt.setp(lines[0], linewidth=1)  
plt.setp(lines[1], linewidth=2)  
plt.setp(lines[2], linestyle='-',marker='^',markersize=4)  
#线的标签
plt.legend(('No mask', 'Masked if > 0.5', 'Masked if < -0.5'), loc='upper right')  
plt.title('Masked line demo')  
plt.show()

例3 ：圆

import numpy as np
import matplotlib.pyplot as plt
 
theta = np.arange(0, 2*np.pi, 0.01)
xx = [1,2,3,10,15,8]
yy = [1,-1,0,0,7,0]
rr = [7,7,3,6,9,9]
 
fig = plt.figure()
axes = fig.add_subplot(111)
 
i = 0
while i < len(xx):
    x = xx[i] + rr[i] *np.cos(theta)
    y = yy[i] + rr[i] *np.sin(theta)
    axes.plot(x,y)
    axes.plot(xx[i], yy[i], color='#900302', marker='*')
    i = i+1
width = 20
hight = 20
axes.arrow(0,0,0,hight,width=0.01,head_width=0.1,head_length=0.3,fc='k',ec='k')
axes.arrow(0,0,width,0,width=0.01,head_width=0.1,head_length=0.3,fc='k',ec='k')
plt.show()