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@elenyafea
Last active September 16, 2018 08:53
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matlab 绘制极坐标柱形图
在某个点需要表示多维数据时,简单的柱形图很难看,这个时候南丁格尔玫瑰图作用就体现出来了!
南丁格尔就是那个伟大的护士,她在做报表时发明的这种图,所以后人以她的名字命名。简单来说我们可以用极坐标下的柱状图来理解。
实现是用polarhistogram函数实现的,本来包含了统计,虽然用在这里有点大材小用,但是没想到其他方法。
提供了两个参数第一个是一维的纵数组,第二个是是否去坐标,0是去坐标,1是保留坐标,默认为1.
```matlab
function d_nrose(e,bac)
if nargin<2
bac=1;
end
[N,~]=size(e);
c=rand(1,N);
b=rand(1,N);
a=rand(1,N);
%配色随机生成,不满意再来一次
for i=1:N
ra= 0:2*pi/N:2*pi;
ee=zeros(1,N);
ee(i)=e(i);
%绘制柱状图的一个柱子
polarhistogram('BinEdges',ra,'BinCounts',ee,'FaceColor',[c(i) a(i) b(i)],'FaceAlpha',1,'Edgecolor','none');
hold on;
end
%是否关闭背景
if bac==0
hold off;
ax=gca;
ax.Visible='off';
end
end
```
利用随机数来生成的图片
```matlab
e=rand(10,1);
d_nrose(e);
```
![untitled.jpg](data:image/jpeg;base64,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