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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