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科研画图:散点连接并平滑(基于Matlab和Python)

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

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,网上关于科研画图相关的代码比较少,就自己鼓捣了下。

 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

 码字不易,如果您觉得有帮助,麻烦点个赞再走呗~

 


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