导师要求参照别人论文中的图(下图)，将其论文中的图画美观些

附上自己整合验证过的代码：

功能：将散点连接并平滑

1)Matlab

效果图：

x1=[431.50032,759.5552,1335.3736,2530.388] %输入以下三组数据

y1=[34.06366,35.73132,37.2244,38.61294]

x2=[263.8656,458.7952,839.6584,1740.9088]

y2=[33.5318074,35.1415668,36.8603528,38.244926]

x3=[253.91296,441.854,803.4116,1625.2548]

y3=[34.3625,35.88912,37.5403,38.45364]

a=linspace(min(x1),max(x1)); %插值后将散点连线平滑化

b=interp1(x1,y1,a,'cubic');

c=linspace(min(x2),max(x2));

d=interp1(x2,y2,c,'cubic');

e=linspace(min(x3),max(x3));

f=interp1(x3,y3,e,'cubic');

plot(a,b, 'LineWidth',2, 'LineSmoothing', 'on'); %画ab对应曲线，粗细，平滑

hold on

plot(c,d, 'LineWidth',2, 'LineSmoothing', 'on'); %画cd对应曲线，粗细，平滑

hold on

plot(e,f, 'LineWidth',2, 'LineSmoothing', 'on'); %画ef对应曲线，粗细，平滑

axis([0,3000,33,39]) %确定x轴与y轴框图大小

legend({'MRMV','MVDM','MVLL'},'FontSize',13,'Location','southeast','Orientation','vertical') %题注设置：名称，字号，位置，方向

xlabel('Bit rates(kbps)','FontSize',13,'FontWeight','bold') %x轴设置：标题，字号，字体粗细

ylabel('PSNR(dB)','FontSize',13,'FontWeight','bold') %y轴设置：名称，字号，字体粗细

title('Balloons','FontSize',15,'FontWeight','bold') %标题描述，名称，字号，字体粗细

set(gca,'ygrid','on','gridlinestyle','--','Gridalpha',0.3) %网格设置

grid on; %网格

print(gcf, '-dpng', '-r800', 'C:\Users\Administrator\Desktop\test.png') %保存图片，格式为png，分辨率800，保存路径

2)Python

小问题：翘尾问题需要解决

# author: Kobay time:2019/10/18

import matplotlib.pyplot as plt

import numpy as np

from scipy.interpolate import spline

x1 = np.array([431.50032,759.5552,1335.3736,2530.388])

y1 = np.array([34.06366,35.73132,37.2244,38.61294])

x2 = np.array([263.8656,458.7952,839.6584,1740.9088])

y2 = np.array([33.5318074,35.1415668,36.8603528,38.244926])

x3 = np.array([253.91296,441.854,803.4116,1625.2548])

y3 = np.array([34.3625,35.88912,37.5403,38.45364])

x1_new = np.linspace(x1.min(), x1.max()) # 300 represents number of points to make between T.min and T.max

y1_smooth = spline(x1, y1, x1_new)

x2_new = np.linspace(x2.min(), x2.max(), 3000) # 300 represents number of points to make between T.min and T.max

y2_smooth = spline(x2, y2, x2_new)

x3_new = np.linspace(x3.min(), x3.max(), 3000) # 300 represents number of points to make between T.min and T.max

y3_smooth = spline(x3, y3, x3_new)

# 散点图

plt.scatter(x1, y1, c='black', alpha=0.5) # alpha:透明度) c:颜色

# 折线图

plt.plot(x1, y1, linewidth=1) # 线宽linewidth=1matl

# 平滑后的折线图

plt.plot(x1_new, y1_smooth, c='blue',label='MRMV')

plt.plot(x2_new, y2_smooth, c='orange',label='MVDM')

plt.plot(x3_new, y3_smooth, c='gray',label='MVLL')

# 解决中文显示问题

# plt.rcParams['font.sans-serif'] = ['SimHei'] # SimHei黑体

# plt.rcParams['axes.unicode_minus'] = False

plt.title("Balloons", fontdict={'family' : 'Calibri', 'size': 16,'weight':'bold'}) # 标题及字号

plt.xlabel("Bit rates(kbps)", fontdict={'family' : 'Calibri', 'size': 14,'weight':'bold'}) # X轴标题及字号

plt.ylabel("PSNR(dB)", fontdict={'family' : 'Calibri', 'size': 14,'weight':'bold'}) # Y轴标题及字号

plt.tick_params(axis='both', labelsize=14) # 刻度大小

plt.axis([0, 3000, 33, 39])#设置坐标轴的取值范围

plt.grid(linestyle='-.')

plt.legend(loc=4)

plt.show()

# plt.save('squares_plot.png'(文件名), bbox_inches='tight'(将图表多余的空白部分剪掉))

# 用它替换plt.show实现自动保存图表

标签：plt,min,Python,np,散点,Matlab,x2,x3,x1

来源： https://www.cnblogs.com/Kobaayyy/p/11788002.html