本文链接： https://blog.csdn.net/u012176591/article/details/44700551

Python绘图精简实例附代码

作者：金良（golden1314521@gmail.com） csdn博客：http://blog.csdn.net/u012176591

Python绘图精简实例附代码

1.引自知乎：如何在论文中画出漂亮的插图

这个例子源于我在知乎上看到的一个名为《如何在论文中画出漂亮的插图》的话题（网址为http://www.zhihu.com/question/21664179），这个例子让我对Python绘制插图的能力感到震撼，于是我研摩了该例子的源码并对其不足做了一些修正。
首先看插图，吃精了吧

这里写图片描述

还有源码，我加了详细的注释，应该不难理解

import matplotlib.pyplot as plt
import numpy as np

# 空心框
Boxcell_x = np.concatenate([np.arange(0,4)*1.5+4.2,np.arange(0,2)*1.5+7.2])#np.array([])
Boxcell_y = np.concatenate([np.ones(4)*2.6,np.ones(2)*4.1])

#空心圆圈
circell_x = np.array([2.7,4.2,5.7,7.2,8.7])
circell_y = np.array([2.6,4.1,5.6,5.6])

#实心灰色圆
dotcell_x = np.array([2.7,2.7,8.7])
dotcell_y = np.array([5.6,7.2])

#阴影区
shodow_x = np.array([4.2,5.8])
shodow_y = np.array([5.6,5.5])

#带加号的小型圈
crosscell_x = np.array([2.5,5.55])
crosscell_y = np.array([4.3,5.89])

#不带加号的小型圈
sdotcell_x = np.array([5.3,5.8])
sdotcell_y = np.array([6.3,5.5])

# plot marks
#空心圆圈，alpha参数是透明度，一共5个
plt.plot(circell_x,circell_y,marker=r'$\bigodot$',markersize=22,linewidth=0,alpha=0.6,color='k',label='Gost-Cell')

# 空心框，一共6个
plt.plot(Boxcell_x,Boxcell_y,marker=r'$\Boxdot$',label='Solid-Cell')

#实心灰色圆，一共7个
plt.plot(dotcell_x,dotcell_y,marker=r'$\bullet$',markersize=12,alpha=0.5,label='Fluid-Cell')

#带加号的小型圈，两个
plt.plot(crosscell_x,crosscell_y,marker=r'$\oplus$',markersize=8,label='Fresh-Cell')

#不带加号的小型圈，两个
plt.plot(sdotcell_x,sdotcell_y,marker=r'$\circ$',linewidth=1,alpha=0.8,color='k')

# plot shodow，一处
plt.fill(shodow_x,shodow_y,alpha=0.2,color='k')

# textcoords属性设置为'offset points'，表示以xytext是以xy为起点的偏移量，偏移量是像素点，呵呵。
plt.annotate('BI',xy=(5.8,5.5),xytext=(+5,+5),textcoords='offset points')
plt.annotate('IP',xy=(5.3,6.3),textcoords='offset points')

# plot curve
# connectionstyle表示曲线的弯曲程度；arrowstyle表示箭头的风格，这里实际上用的是虚线；xycoords和textcoords指示xy和xytext的位置关系
# 二者的参数是字符串'data'，表示使用被注释对象的坐标系统，也就是通常说的坐标。
plt.annotate(r'$n+1$',xy=(9.6,6.5),xycoords='data',xytext=(1.4,2.4),textcoords='data',fontsize=16,arrowprops=dict(arrowstyle="-",connectionstyle="arc3,rad=-.2"))
plt.annotate(r'$n$',xy=(9.4,6.9),3.5),rad=-.2",linestyle='dashed'))
plt.grid(True,linewidth=2)

# set the figure style
ax = plt.gca()

#规定坐标轴的刻度
ax.set_xticks(np.arange(0,10,1.5)+0.4)
ax.set_yticks(np.arange(0,1.5)+0.4)

#设置周边的坐标轴的颜色为空白，如果不设置，图片四周就会有黑色的坐标线。
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['top'].set_color('none')

#调整图框的尺寸（而不是改变坐标轴取值范围），使x、y 轴长度一致
plt.axis('scaled')#没有此项，则图形为长方形

#下面连个参数规定了最终生成的图片的坐标范围
plt.xlim(0.5,10.5)
plt.ylim(-1,9)

#控制各个轴线是否显示刻度标签
#plt.tick_params(labelbottom='off',labelleft='off',left='off',right='off',bottom='off',top='off')

# add the legend
#numberpoints是标签中点的个数设置，用scatterpoints不起作用；ncol表示每行有两个标签；handlelength图例中xiant线条长度；
#handletextpad线条和文字间距；labelspacingtu图例的行距；fancybox图例边框是否花边。
ax.legend(loc='lower center',numpoints=1,handlelength=2.3,handletextpad=1,labelspacing=1,ncol=2,mode="None",borderpad=1,fancybox=True)

plt.savefig('myfig.png',dpi=400,bBox_inches='tight')
plt.show()

2.正态曲线图

这里写图片描述

代码：

from scipy.stats import norm
normobj = norm()
x = np.arange(-4,4,0.01)
y = normobj.pdf(x)#正态曲线的概率密度函数

plot(x,y)
plot([-1,-1],[0,normobj.pdf(-1)],'k')#作一条直线
plot([1,1],normobj.pdf(1)],'k')

plot([-2,-2],normobj.pdf(-2)],'k')
plot([2,2],normobj.pdf(2)],'k')

plot([-3,-3],normobj.pdf(-3)],'k')
plot([3,3],normobj.pdf(3)],'k')

plot([-3.9,3.9],0],'k')

xlim(-3.9,3.9)
ylim(-0.1,0.42)

#写字符串，前两个参数分别是横坐标和纵坐标，是字符串的起始点而不是中点。
text(-1-0.3,-0.02,u'$\mu-\delta$',fontsize=16)
text(1-0.3,u'$\mu+\delta$',fontsize=16)
text(-2-0.3,u'$\mu-2\delta$',fontsize=16)
text(2-0.3,u'$\mu+2\delta$',fontsize=16)
text(-3-0.3,u'$\mu-3\delta$',fontsize=16)
text(3-0.3,u'$\mu+3\delta$',fontsize=16)

plot([-1,[-0.025,-0.035],'k')
plot([1,-0.055],-0.075],'k')

text(-0.3,-0.035,u'$68.26\%$',fontsize=12)
text(-0.3,-0.055,u'$95.44\%$',-0.075,u'$99.74\%$',fontsize=12)

#作箭头，第一个参数为空，表示交投不带标注，参数xy为箭头坐标，xytext为箭尾坐标。
annotate('',xy=(-1,-0.03),xytext=(-0.3,arrowprops=dict(arrowstyle="->",connectionstyle="arc3"))
annotate('',xy=(1,xytext=(0.3,connectionstyle="arc3"))

annotate('',xy=(-2,-0.05),xy=(2,xy=(-3,-0.07),xy=(3,connectionstyle="arc3"))

#不要坐标轴
tick_params(labelbottom='off',labelleft='off',left='off',right='off',bottom='off',top='off')

#使边框不显示
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['top'].set_color('none')
savefig('norm.png',bBox_inches='tight')

3.云模型示意图

这里写图片描述

import numpy as np
import matplotlib.pyplot as plt
%pylab
import random
def backcloud(cloudpara,n=1000):
    Ex = cloudpara[0]
    En = cloudpara[1]
    He = cloudpara[2]

    xd = []
    yd = []

    for i in range(n):
        Enn = random.gauss(0,1)*He + En
        x = random.gauss(0,1)*Enn + Ex
        y = np.exp(-(x-Ex)**2/(2*Enn**2))
        xd.append(x)
        yd.append(y)

    return xd,yd

def expcurve(myex=0,mydelta=1,myshift=4):
    x = np.arange(-myshift,myshift,0.01)
    y = np.exp(-0.5*(x-myex)**2/mydelta**2)
    return x,y

x,y = backcloud((0,1,0.1),3000)
plot(x,y,'.k')
x,y = expcurve(0,1.3,4)
plot(x,'b')
x,0.7,'b')
annotate(u'$\mu_2$',xy=(-1.22,0.63),xytext=(-2.7,connectionstyle="arc3"),fontsize=20)
annotate(u'$\mu_1$',xy=(-0.889,0.47),fontsize=20)

plot([4,4],0.4],'k')
plot([3.2,3.2],0.2],'k')
plot([2.54,2.54],0.3],'k')
plot([0,'k')

text(4-0.5,0.42,u'$3(E_n+3H_e)$',fontsize=14)
text(2.54-0.5,0.32,u'$3(E_n-3H_e)$',fontsize=14)
text(3.2-0.1,0.22,u'$3S$',fontsize=14)
text(0-0.1,u'$E_x$',fontsize=14)

xlim(-4,5)
ax = plt.gca()
xlabel('x')
ylabel(u'$\mu$')
savefig('cloudmodelfeature.png',dpi=700,bBox_inches='tight')

4. scatter-hist图

这里写图片描述

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter

# the random data
x = np.random.randn(3000)
y = np.random.randn(3000)

nullfmt   = NullFormatter()         # no labels

# deFinitions for the axes
left,width = 0.1,0.65
bottom,height = 0.1,0.65
bottom_h = left_h = left+width+0.02

#左下角较大的区域，参数为画布的比例
rect_scatter = [left,bottom,width,height]
#上面的条形图区域
rect_histx = [left,bottom_h,0.2]
#右边的条形图区域
rect_histy = [left_h,0.2,height]

# figsize参数是以inch为单位，在桌面上显示的画布的大小
plt.figure(1,figsize=(8,8))

axScatter = plt.axes(rect_scatter)
axHistx = plt.axes(rect_histx)
axHisty = plt.axes(rect_histy)

# no labels
#axHistx的x坐标无刻度
axHistx.xaxis.set_major_formatter(nullfmt)
#axHisty的y坐标无刻度
axHisty.yaxis.set_major_formatter(nullfmt)

# the scatter plot:
#画散点图
axScatter.scatter(x,y)

# Now determine nice limits by hand:
binwidth = 0.25
xymax = np.max( [np.max(np.fabs(x)),np.max(np.fabs(y))] )
lim = xymax+0.1#( int(xymax/binwidth) + 1) * binwidth

axScatter.set_xlim( (-lim,lim) )
axScatter.set_ylim( (-lim,lim) )

bins = np.arange(-lim,lim + binwidth,binwidth)
axHistx.hist(x,bins=bins)
axHisty.hist(y,bins=bins,orientation='horizontal')

axHistx.set_xlim( axScatter.get_xlim() )
axHistx.set_yticklabels(labels=['0','50','100','150','200','250','300','350'],rotation=47)

axHisty.set_ylim( axScatter.get_ylim() )
axHisty.set_xticklabels(labels=['0',rotation=-47)

#plt.show()
savefig('scatter-hist.png',bBox_inches='tight')

5.若干类型的图例

这里写图片描述

Matplotlib tutorial
http://www.labri.fr/perso/nrougier/teaching/matplotlib/

6.热图

这里写图片描述

import matplotlib.pyplot as plt
import numpy as np

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


xx,yy = np.meshgrid(np.linspace(-3,3,50),np.linspace(-3,50))
zz = f(xx,yy)
plt.imshow(zz,origin='lower',extent=[xx.min(),xx.max(),yy.min(),yy.max()])

If you don’t need an equal aspect you can set aspect to auto :

imshow(random.rand(8,90),interpolation='nearest',aspect='auto')

效果图如下：

这里写图片描述

data2show = np.random.random((242,8))

fig,ax= plt.subplots()

plt.imshow(data2show,aspect='auto')
cb = plt.colorbar()
cb.ax.tick_params(labelsize=25)


cb.set_label(r'mean value of ratings',labelpad=10,y=0.45) #colorbar的标签和位置
text = cb.ax.yaxis.label
font = matplotlib.font_manager.FontProperties(size=30)
text.set_font_properties(font)


plt.yticks([5,50,100,150,200,240],[250,fontsize = 30)
plt.xticks([0.5,1.4,2.59,3.3,4.5,5.5,6.7,7.5],['male-young','male-adult','male-middle-age','male-old','female-young','female-adult','female-middle-age','female-old'],fontsize = 30,rotation=-13)

plt.xlabel('Gender-age groups',{'fontname':'STFangsong','fontsize':52})
plt.ylabel('List of movies','fontsize':52})

ax.spines['right'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['top'].set_color('none')

效果如下：

这里写图片描述

7.为点加标签

这里写图片描述

import numpy as np
import matplotlib.pyplot as plt

N = 10
data = np.random.random((N,4))
labels = ['point{0}'.format(i) for i in range(N)]
plt.subplots_adjust(bottom = 0.1)
plt.scatter(
    data[:,0],data[:,1],marker = 'o',c = data[:,2],s = data[:,3]*1500,
    cmap = plt.get_cmap('Spectral'))
for label,x,y in zip(labels,data[:,1]):
    plt.annotate(
        label,xy = (x,y),xytext = (-20,20),textcoords = 'offset points',ha = 'right',va = 'bottom',bBox = dict(Boxstyle = 'round,pad=0.5',fc = 'yellow',alpha = 0.5),arrowprops = dict(arrowstyle = '->',connectionstyle = 'arc3,rad=0'))

plt.show()

8.导入已有图片并进行修饰

这里写图片描述

fig = plt.figure()
rect=[0.1,0.1,0.8,0.8]
axprops = dict(xticks=[],yticks=[])

ax0=fig.add_axes(rect,label='ax0',**axprops)
imgP = plt.imread('Portland.png')
ax0.imshow(imgP)   

ax1=fig.add_axes(rect,label='ax1',frameon=False)
x = linspace(0,100)
y = sin(x)
ax1.plot(x,y)
ax1.set_xlim([-2,12])
ax1.set_ylim([-5,12])


ax2=fig.add_axes(rect,label='ax2',frameon=False,**axprops)
shodow_x = np.array([4.2,5.8])
shodow_y = np.array([5.6,5.5])
ax2.set_xlim([-2,12])
ax2.set_ylim([-5,12])
ax2.fill(shodow_x,alpha=0.4,color='r')
ax2.annotate('plot shadow area',xy=(5.7,7.2),xytext=(4.7,3.3),textcoords = 'offset points',fontsize=20)



plt.show()

９.热度方格图

这里写图片描述

data = np.array([[ 0.45833279,0.35737994,0.54257486,0.66908835],[ 0.19544843,0.48287855,0.97316904,0.25445816],[ 0.44500619,0.88060579,0.74509425,0.65703952],[ 0.80474809,0.48011234,0.05086501,0.47188907]])
fig,ax = plt.subplots()
heatmap = ax.pcolor(data,cmap=plt.cm.Greys)

# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[0])+0.5,minor=False)
ax.set_yticks(np.arange(data.shape[1])+0.5,minor=False)

# want a more natural,table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()

ax.set_xticklabels(['a','b','c','d'],minor=False)
ax.set_yticklabels(['A','B','C','D'],minor=False)

cbar = plt.colorbar(heatmap)

10. 箱线图

这里写图片描述

np.random.seed(10)
data_to_plot = [np.random.normal(100,200),np.random.normal(80,30,np.random.normal(90,20,np.random.normal(70,25,200)]

fig = plt.figure(1,figsize=(9,6))
# Create an axes instance
ax = fig.add_subplot(111)

## add patch_artist=True option to ax.Boxplot() 
## to get fill color
bp = ax.Boxplot(data_to_plot,patch_artist=True)

## change outline color,fill color and linewidth of the Boxes
for Box in bp['Boxes']:
    # change outline color
    Box.set( color='#7570b3',linewidth=2)
    # change fill color
    Box.set( facecolor = '#1b9e77' )

## change color and linewidth of the whiskers
for whisker in bp['whiskers']:
    whisker.set(color='y',linewidth=2)

## change color and linewidth of the caps
for cap in bp['caps']: # 上下的帽子
    cap.set(color='#7570b3',linewidth=2)

## change color and linewidth of the medians
for median in bp['medians']: #中值
    median.set(color='r',linewidth=2)

## change the style of fliers and their fill
for flier in bp['fliers']: #异常值
    flier.set(marker='o',alpha=0.5)

双纵坐标

legend不在一块

这里写图片描述

x = np.arange(0.,np.e,0.01)
y1 = np.exp(-x)
y2 = np.log(x+1)
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(x,y1,label='bb');
ax1.set_ylabel('Y values for exp(-x)');
ax2 = ax1.twinx() # this is the important function
ax2.plot(x,y2,'r',label = 'aa');
ax2.set_xlim([0,np.e]);
ax2.set_ylabel('Y values for ln(x)');
ax2.set_xlabel('Same X for both exp(-x) and ln(x)');
ax1.legend()
ax2.legend(loc=0)
plt.show()

legend放一块儿

这里写图片描述

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

time = np.arange(10)
data1,data2 = np.random.random(10)*30,np.random.random(10)*100-10

fig = plt.figure()
ax = fig.add_subplot(111)

lns1 = ax.plot(time,data1,'-',label = 'data1')

ax2 = ax.twinx()
lns2 = ax2.plot(time,data2,'-r',label = 'data2')
ax2.tick_params(axis='y',colors='red') # 刻度颜色
ax2.spines['right'].set_color('red') # 纵轴颜色
setp(ax2.get_yticklabels(),color='r') # label颜色

lns = lns1+lns2
labs = [l.get_label() for l in lns]
ax.legend(lns,labs,loc=0)

ax.grid()
ax.set_xlabel("Time")
ax.set_ylabel(r"Radiation ")
ax2.set_ylabel(r"Temperature")

ax2.set_ylim(-20,100)
ax.set_ylim(0,35)

多个子图共用一个legend

这里写图片描述

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.axislines import subplotZero

width = 1 # 两条柱之间的距离
num = 5 #柱的个数
ind = np.arange(num)

fig = plt.figure(figsize=(15,4))
ax = subplotZero(fig,1)
ax1 = fig.add_subplot(ax)

means = [0.6481,0.6215,0.58,0.56,0.442] 
stds = [0.0129,0.0119,0.01,0.009,0.003]

plt.bar(0.2+ind,means,0.6*width,color=['k','y','r'],linewidth = 0.1,yerr=stds,error_kw=dict(elinewidth=1.5,ecolor='green'))
plt.axis([0,5,0,1])
plt.ylabel(u'wPrecision')
plt.grid()
plt.xticks([])



ax = subplotZero(fig,2)
ax2 = fig.add_subplot(ax)
means = [0.341,0.39,0.48,0.582] 
stds = [0.0109,0.0149,0.02,0.011,0.009]
plt.bar(0.2+ind,1])
plt.ylabel(u'wRecall')
plt.grid()
plt.xticks([])

ax = subplotZero(fig,3)
ax3 = fig.add_subplot(ax)
means = [0.68,0.6415,0.6,0.548] 
stds = [0.02,0.0099,0.007]
label = plt.bar(0.2+ind,1])
plt.ylabel(u'wF1')
plt.grid()
plt.xticks([])

fig.legend((label[0],label[1],label[2],label[3],label[4]),('SVD-Single','SVD-Multiple','JNE','SNE','R2P'),'upper center',ncol=5)
savefig('comparison_baseline.png',dpi=250,bBox_inches='tight')

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原文地址：https://blog.csdn.net/pary__for

Python科学绘图实例附代码

Python绘图精简实例附代码

1.引自知乎：如何在论文中画出漂亮的插图

2.正态曲线图

3.云模型示意图

4. scatter-hist图

5.若干类型的图例

6.热图

7.为点加标签

8.导入已有图片并进行修饰

９.热度方格图

10. 箱线图

双纵坐标

legend不在一块

legend放一块儿

多个子图共用一个legend

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