Python 透明度混合算法 matplotlib 透明度

Matplotlib库

图像属性设置、坐标轴设置、图例、某点的标注、透明度设置：

import matplotlib.pyplot as plt
import numpy as np

#####################图像属性设置、坐标轴设置、图例、某点的标注、透明度设置##################
x = np.linspace(-3,3,50)
y1 = 2*x+1
y2 = x*x+2

plt.figure()
plt.plot(x,y1)

#######figure图像属性设置
plt.figure(num=3, figsize=(8,5))   #figsize(length,height)
plt.plot(x,y2)
plt.plot(x,y1,color='red',linewidth=1.0,linestyle='--')

#######igure坐标轴设置
plt.xlim((-1,3))
plt.ylim((-2,5))     #x,y的显示范围
plt.xlabel('I am X')
plt.ylabel('I am Y')    #x,y轴注释

new_ticks = np.linspace(0,3,4)
print(new_ticks)
plt.xticks(new_ticks)   #修改x轴的显示范围以及间距
plt.yticks([0,1,2,3,],  #修改y轴的显示，改为文字形式
        [r'$really\ bad$',r'$bad\ \alpha$',r'$good$',r'$really\ good$'])

####### gca = 'get current axis'
ax = plt.gca()
ax.spines['right'].set_color('none')     #脊梁，即四个边框轴
ax.spines['top'].set_color('none')      #让右边和上边的轴消失
ax.xaxis.set_ticks_position('bottom')   #修改默认坐标轴，原先可能是上下左右任二
ax.yaxis.set_ticks_position('left')
ax.spines['bottom'].set_position(('data',2))   #outward,axes
ax.spines['left'].set_position(('data',0))       #设置x轴y轴的定位

#######图例
plt.figure()
x2 = np.linspace(0,2,50)
y4 = x2**0.5
y5 = x2**3
l1, = plt.plot(x2,y4,label='down')
l2, = plt.plot(x2,y5,color='red',linewidth=1.0,linestyle='--',label='up')
plt.legend(handles=[l1,l2,],labels=['aa','bb'],loc='best')

ax = plt.gca()
ax.spines['bottom'].set_position(('data',0))   #outward,axes
ax.spines['left'].set_position(('data',0))

#######作某点的标注
x0 = 1
y0 = x0**3
plt.scatter(x0,y0,s=50,color='b')     #打印点
plt.plot([x0,x0],[y0,0],'k--')  #k就是black

#method 1
#############################
plt.annotate(r'$x**3=%s$' % y0,xy=(x0,y0),xycoords='data',xytext=(+30,-30),textcoords='offset points',
             fontsize=16,arrowprops=dict(arrowstyle='->',connectionstyle='arc3,rad=.2'))

#method 2
#############################
plt.text(0,3,r'$This\ is\ the\ some\ text.\ \mu\ \sigma_i\ \alpha_t$',
         fontdict={'size':16,'color':'r'})

#######透明度
for label in ax.get_xticklabels() + ax.get_yticklabels():
    label.set_fontsize(16)  #变大些
    label.set_bbox(dict(facecolor='white',edgecolor='None',alpha=0.8))  #80%


plt.show()

散点图、柱状图、等高线图

import matplotlib.pyplot as plt
import numpy as np

####################散点图、柱状图、等高线图
#######散点图
plt.figure()
n = 1024
X = np.random.normal(0,1,n)  #平均为0方差为1的n个随机数
Y = np.random.normal(0,1,n)
T = np.arctan2(Y,X)   #for color value

plt.scatter(X,Y,s=75,c=T,alpha=0.5)

plt.xlim((-1.5,1.5))
plt.ylim((-1.5,1.5))
plt.xticks(())
plt.yticks(())



#######柱状图
plt.figure()
n = 12
X = np.arange(n)    # 0~11
Y1 = (1-X/float(n))*np.random.uniform(0.5,1.0,n)
Y2 = (1-X/float(n))*np.random.uniform(0.5,1.0,n)

plt.bar(X,+Y1,facecolor='#66ccff',edgecolor='white')
plt.bar(X,-Y2,facecolor='#ff9999',edgecolor='white')

for x,y in zip(X,Y1):
    # ha:horizontal alignment 横向对齐  va:vertical alignment 纵向对齐
    plt.text(x , y + 0.05, '%.2f' % y, ha='center', va='bottom')

for x,y in zip(X,Y2):
    # ha:horizontal alignment 横向对齐  va:vertical alignment 纵向对齐
    plt.text(x , -y - 0.05, '-%.2f' % y, ha='center', va='top')

plt.xlim(-5,n)
plt.xticks(())  #消掉x轴
plt.ylim(-1.25,1.25)
plt.yticks(())  #消掉y轴


######等高线:
plt.figure()

def f(x,y):
    # the height function
    return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)

n = 256
x = np.linspace(-3,3,n)
y = np.linspace(-3,3,n)
X,Y = np.meshgrid(x,y)  #将xy网格化

# use plt.contourf to fill contours
# X,Y and value for (X,Y) point
plt.contourf(X,Y,f(X,Y),16,alpha=0.75,cmap=plt.cm.hot)   #  2.cmap=plt.cm.cool, 16为等高线区别颜色的数量

# use plt.contour to add contour lines
C = plt.contour(X,Y,f(X,Y),16,colors='black',linewidths=.5) # 16为等高线数量
#adding label
plt.clabel(C,inline=True,fontsize=10)

plt.xticks(())
plt.yticks(())  #去掉xy轴
plt.show()

image data

import matplotlib.pyplot as plt
import numpy as np

# image data
a = np.array([0.3,0.33,0.36,
              0.39,0.45,0.5,
              0.55,0.59,0.65]).reshape(3,3)
"""
for the value of "interpolation",check this:
https://matplotlib.org/examples/images_contours_and_fields/interpolation_methods.html
"""

plt.imshow(a,interpolation='nearest',cmap='bone',origin='upper') #cmap colormap
plt.colorbar(shrink=0.9)

plt.xticks(())
plt.yticks(())
plt.show()

3D绘图

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
##################  3D数据

fig = plt.figure()
ax = Axes3D(fig)    #添加3D的axes
# X,Y value
X = np.arange(-4,4,0.25)
Y = np.arange(-4,4,0.25)
X,Y = np.meshgrid(X,Y)  #网格化
R = np.sqrt(X**2+Y**2)
# height value
Z = np.sin(R)
#   rstride=行跨度     cstride=列跨度  且均为整数
ax.plot_surface(X,Y,Z,rstride=1,cstride=1,edgecolor='black',cmap=plt.get_cmap('rainbow'))
ax.contourf(X,Y,Z,zdir='z',offset=-2,cmap='rainbow')
ax.set_zlim(-2,2)

plt.show()

subplot的多种方法：

import matplotlib.pyplot as plt
import numpy as np

plt.figure()
plt.subplot(2,2,1)
plt.plot([0,1],[0,1])
plt.subplot(2,2,2)
plt.plot([0,1],[0,2])
plt.subplot(223)
plt.plot([0,1],[0,3])
plt.subplot(224)
plt.plot([0,1],[0,4])


plt.figure()
plt.subplot(2,1,1)
plt.plot([0,1],[0,1])
plt.subplot(2,3,4)
plt.plot([0,1],[0,2])
plt.subplot(235)
plt.plot([0,1],[0,3])
plt.subplot(236)
plt.plot([0,1],[0,4])

plt.show()

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

# method 1:subplot2grid 单元格
###########################

plt.figure()
ax1 = plt.subplot2grid((3,3),(0,0),colspan=3,rowspan=1)  #行3列1
ax1.plot([1,3],[2,4])
ax1.set_xlabel('aaa')
ax1.set_title('first')

ax2 = plt.subplot2grid((3,3),(1,0),colspan=2,rowspan=1)  #行2列1
ax3 = plt.subplot2grid((3,3),(1,2),rowspan=2)
ax4 = plt.subplot2grid((3,3),(2,0))
ax5 = plt.subplot2grid((3,3),(2,1))


# method 2:gridspec
#######################################
plt.figure()
gs = gridspec.GridSpec(3,3)
ax1 = plt.subplot(gs[0,:])
ax2 = plt.subplot(gs[1,:2])
ax3 = plt.subplot(gs[1:,2])
ax4 = plt.subplot(gs[-1,0])
ax5 = plt.subplot(gs[-1,-2])


# method 3: easy to define structure
###########################################
f,((ax11,ax12),(ax21,ax22)) = plt.subplots(2,2,sharex=True,sharey=True)
ax11.scatter([1,2],[1,2])

plt.tight_layout()
plt.show()

图中图

import matplotlib.pyplot as plt

fig = plt.figure()
x = [1,2,3,4,5,6,7]
y = [1,3,4,2,5,8,6]

left,bottom,width,height = 0.1,0.1,0.8,0.8  # 大图占据整个屏幕的位置
ax1 = fig.add_axes([left,bottom,width,height])
ax1.plot(x,y,'r')
ax1.set_xlabel('x')
ax1.set_ylabel('y')
ax1.set_title('title')

left,bottom,width,height = 0.2,0.6,0.25,0.25  # 小图占据整个屏幕的位置
ax2 = fig.add_axes([left,bottom,width,height])
ax2.plot(y,x,'b')
ax2.set_xlabel('x')
ax2.set_ylabel('y')
ax2.set_title('title inside 1')

plt.axes([0.6,0.2,0.25,0.25])
plt.plot(y[::-1],x,'g')
plt.xlabel('x')
plt.ylabel('y')
plt.title('title inside 2')

plt.show()

次坐标轴

import matplotlib.pyplot as plt
import numpy as np

#############次坐标轴
x = np.arange(0,10,0.1)
y1 = 0.05*x**2
y2 = -1*y1

fig,ax1 = plt.subplots()
ax2 = ax1.twinx()  # 反向坐标轴
ax1.plot(x,y1,'g')
ax2.plot(x,y2,'b--')

ax1.set_xlabel('X data')
ax2.set_ylabel('Y2',color='g')
ax1.set_ylabel('Y1',color='b')


plt.show()

动画效果

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import animation

fig,ax = plt.subplots()
x = np.arange(0,2*np.pi,0.01)
line, = ax.plot(x,np.sin(x))    #列表第一位加,

def animate(i):
    line.set_ydata(np.sin(x+i/1000))
    return line,

def init():
    line.set_ydata(np.sin(x))
    return line,

ani = animation.FuncAnimation(fig=fig,func=animate,frames=100,init_func=init,interval=20,blit=False)  # frames贞 interval频率

plt.show()

标签的中文设置

# 只需添加这两句即可，亲测有效
# 设置显示中文
plt.rcParams['font.sans-serif']=['SimHei']
# 设置显示正常字符
plt.rcParams['axes.unicode_minus'] = False

横纵坐标出现日期

# -*- encoding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

'''1.读取并预处理数据'''
# 文件地址
io = r'D:\数学建模\Matlab\数学建模\数据集\德州制造业活动.xls'
# 抽取数据
data1 = pd.read_excel(io,sheet_name=0,usecols=[1],squeeze=True)
Unemployment_rate = np.array(data1)

# 39个失业率样本数据 36+4
# 由于数据量较小，整个数据集充当训练集，同时也充当测试集
x = np.arange(0, 40, 1).reshape(-1,1)
y = data1
x_before = x[:36]
y_before = y[:36]
x_virus = x[36:40]
y_virus = y[36:40]

'''2.图像框架绘制'''
# 横坐标修改为日期
dates = ['2017.1','2017.2','2017.3','2017.4','2017.5','2017.6','2017.7','2017.8','2017.9','2017.10','2017.11','2017.12'
         ,'2018.1','2018.2','2018.3','2018.4','2018.5','2018.6','2018.7','2018.8','2018.9','2018.10','2018.11','2018.12'
         ,'2019.1','2019.2','2019.3','2019.4','2019.5','2019.6','2019.7','2019.8','2019.9','2019.10','2019.11','2019.12'
         ,'2020.1','2020.2','2020.3','2020.4','2020.5','2020.6']

x_range = range(len(dates))
# plt.plot(x_range, y, 'ro-')
plt.xticks(x_range, dates, rotation=60)
plt.subplots_adjust(bottom=0.15)
plt.xlabel("year/month")
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.ylabel("德州制造业活动变化率/%")
plt.title("德州制造业活动")
plt.show()

绘制结果如下：

• 添加网格线：plt.grid()