python线型图

时间:2022-06-20 Vergil_Zsh 人气:0

用法：

matplot.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)

参数解释：

x,y

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0.2, 2.0, 0.01)
y1 = np.sin(2*np.pi*x)
y2 = np.sin(4*np.pi*x)

plt.figure(1)
plt.subplot(211)
plt.plot(x,y1)

plt.subplot(212)
plt.plot(x,y2)
plt.show()

color

Colors的值：

import numpy as np
import matplotlib.pyplot as plt

# 需要解释下,下面两行代码是防止出现中文时,会报警告
# 因为我们的title里面写的是中文
plt.rcParams['font.family'] = 'SimHei'
plt.rcParams['axes.unicode_minus']=False
x = np.arange(0.2, 2.0, 0.01)
y1 = np.sin(2*np.pi*x)
y2 = np.sin(4*np.pi*x)

plt.figure(1)
plt.subplot(211)
plt.title('不添加颜色')
plt.plot(x,y1)

plt.subplot(212)
plt.title('添加颜色')
plt.plot(x,y2,color='c')
plt.show()

linstyle

'b'    # blue markers with default shape
'or'   # red circles
'-g'   # green solid line
'--'   # dashed line with default color
'^k:'  # black triangle_up markers connected by a dotted line

import numpy as np
import matplotlib.pyplot as plt

plt.figsize=((10,8))
plt.rcParams['font.family'] = 'SimHei'
plt.rcParams['axes.unicode_minus']=False

x = [1, 2, 3, 4]
y = [1, 4, 9, 16]

plt.subplot(221)
plt.title('样式: -')
plt.plot(x,y,'-')

plt.subplot(222)
plt.title('样式: --')
plt.plot(x,y,'--')

plt.subplot(223)
plt.title('样式: -.')
plt.plot(x, y, '-.')

plt.subplot(224)
plt.title('样式: :')
plt.plot(x, y, ':')
plt.show()

缩写方式

import numpy as np
import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
plt.subplot()
# 线形状 '-',颜色'g'
plt.plot(x, y, '-g')
plt.show()

marker, markersize

marker在scatter里面我已经有所解释过了，有好多种情况，可以在scatter散点图这里会将颜色和marker连接起来,可以有个很清楚的了解，并且较为清楚，也是缩写

import matplotlib.pyplot as plt
plt.figsize=((12,6))
plt.rcParams['font.family'] = 'SimHei'
plt.rcParams['axes.unicode_minus']=False
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
plt.subplot(131)
plt.title('默认情况')
plt.plot(x, y)

plt.subplot(132)
plt.title('红色圆圈')
# marker为o 颜色r
plt.plot(x, y, 'or')

plt.subplot(133)
plt.title('正三角黑色')
# marker为^ 颜色k->black
plt.plot(x, y, '^k')
plt.show()

label

标签,这个在所有图形中都可以使用,在这里展示下,包括之前的alpha也是,都所属**kwargs里面，在任何绘图中都可以添加，legend为图例

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-np.pi/2, np.pi/2, 31)
y = np.cos(x)**3

# 1) remove points where y > 0.7
x2 = x[y <= 0.7]
y2 = y[y <= 0.7]

# 2) mask points where y > 0.7
y3 = np.ma.masked_where(y > 0.7, y)

# 3) set to NaN where y > 0.7
y4 = y.copy()
y4[y3 > 0.7] = np.nan

plt.plot(x*0.1, y, 'o-', color='lightgrey', label='No mask')
plt.plot(x2*0.4, y2, 'o-', label='Points removed')
plt.plot(x*0.7, y3, 'o-', label='Masked values')
plt.plot(x*1.0, y4, 'o-', label='NaN values')
plt.legend()
plt.show()

下面就是一些案例

一次性绘制三个线条图

import numpy as np
import matplotlib.pyplot as plt
t = np.arange(0., 5., 0.2)
# 红色虚线,蓝色方块,浅蓝六边形
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'cH')
plt.show()

import numpy as np
import matplotlib.pyplot as plt
x1 = np.linspace(0.0, 5.0)
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
x2 = np.linspace(0.0, 2.0)
y2 = np.cos(2 * np.pi * x2)
plt.subplot(211)
plt.plot(x1, y1, 'o-')
plt.subplot(212)
plt.plot(x1, y1, '.-')
plt.show()

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