matplotlib.pyplot.plot()参数使用详解
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
============= ===============================
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 = flg.add_subplot(111)
i = 0
while i < len(xx):
x = xx[i] + rr[i] *np.cos(theta)
x = xx[i] + rr[i] *np.cos(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()
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