文章目录
0.泰勒图介绍1.准备步骤2.生成泰勒图3.后处理4.参考文献5 allstats.m代码
0.泰勒图介绍
见参考文献[4]
1.准备步骤
（1）taylordiag.m。 在参考文献【2】下载泰勒图的函数脚本。
表1.函数需要的输入变量如下表
输入变量术语解释
STDs标准差（Standard deviations）RMSs去中心的均方根误差（Centered Root Mean Square Difference 就是减去平均值再求RMS）CORs皮尔逊相关系数（Correlation）
（2）allstats.m 在参考文献【1】下载计算输入变量的函数脚本。 为了计算表1.中的参数，可直接利用allstats.m函数计算。如表2，随意设计一组原始数据格式，记为a，第一行为参考序列，第二行为模式1，第二行模式2，依次类推。
表2 原始数据
模式值1值2值3值4值5值6值7
参考序列26475152284732模式132374052464521模式249585152384735模式326475152284732模式426475122284732模式526475152384735
下列代码计算表1中的变量
for iserie
= 2 : size(a
,1)
S
= allstats(a(1,:),a(iserie
,:));
MYSTATS(iserie
,:) = S(:,2); % We get stats versus reference
end
%for iserie
MYSTATS(1,:) = S(:,1); % We assign reference stats to the first row
2.生成泰勒图
预处理后，直接带入taylordiag函数即可，生成图片如下所示。
taylordiag(MYSTATS(:,2),MYSTATS(:,3),MYSTATS(:,4));
3.后处理
（1）一般泰勒图都要标准化处理。标准差与均方根误差同时除以参考序列的标准差。
（2）根据数值范围，设置坐标轴参数。具体参数设置参考taylordiag.m的492-523行。
（3）不想看代码，可保存后导入Ai、PS后处理。完毕。
%%计算输入参数
for iserie
= 2 : size(a
,1)
S
= allstats(a(1,:),a(iserie
,:));
MYSTATS(iserie
,:) = S(:,2); % We get stats versus reference
end
%for iserie
MYSTATS(1,:) = S(:,1); % We assign reference stats to the first row
% taylordiag(MYSTATS(:,2),MYSTATS(:,3),MYSTATS(:,4));
stdev
=MYSTATS(1,2);
MYSTATS(:,2)=MYSTATS(:,2)/stdev
;% 标准化处理
MYSTATS(:,3)=MYSTATS(:,3)/stdev
;%标准化处理
[hp ht axl
] =taylordiag(MYSTATS(:,2),MYSTATS(:,3),MYSTATS(:,4), ...
'tickrms',[0:.2:1],'titleRMS', 0 ,'showlabelsRMS',1,...
'widthRMS',1,'colRMS','r',...
'tickSTD',[0:.25:1.25],'limSTD',1.25,'styleSTD','-',...
'tickCOR',[.1:.1:.9 .95 .99],'showlabelsCOR',1,'titleCOR',1);
注： ①对下面报错
错误使用 taylordiag
(line
108)
taylordiag
.m
: Something's wrong with ALL the datas
You must have
:
RMSs
- sqrt(STDs
.^2 + STDs(1)^2 - 2*STDs
*STDs(1).*CORs
) = 0 !
不要自己创建输入变量STDs、RMSs、CORs这些，一定要自己利用原始值算出。 ②R语言创建泰勒图 ？ TaylorDiagram {openair}（强烈推荐） ？ taylor.diagram {plotrix} ③PeterRochford/SkillMetricsToolbox PeterRochford/SkillMetricsToolbox也可创建泰勒图，但此时注意到的是MATLAB对用的版本要在2016b及以上。否则会报错，"xticks"函数不存在。
4.参考文献
[1]allstats.m下载 [2]taylordiag.m下载 [3]参数设置 [4]Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192.
5 allstats.m代码
% STATM Compute statistics from
2 series
%
% STATM
= allstats(Cr
,Cf
)
%
% Compute statistics from
2 series considering Cr as the reference
.
%
% Inputs
:
% Cr and Cf are of same length and uni
-dimensional
. They may contain NaNs
.
%
% Outputs
:
% STATM(1,:) => Mean
% STATM(2,:) => Standard Deviation
(scaled by N
)
% STATM(3,:) => Centered Root Mean Square Difference
(scaled by N
)
% STATM(4,:) => Correlation
%
% Notes
:
% - N is the number of points where BOTH Cr and Cf are defined
%
% - NaN are handled in the following way
: because this function
% aims to compair
2 series
, statistics are computed with indices
% where both Cr and Cf are defined
.
%
% - STATM(:,1) are from Cr
(ie with C
=Cr hereafter
)
% STATM(:,2) are from Cf versus Cr
(ie with C
=Cf hereafter
)
%
% - The MEAN is computed using the Matlab mean function
.
%
% - The STANDARD DEVIATION is computed as
:
% / sum
[ {C
-mean(C
)} .^2] \
% STD
= sqrt
| --------------------- |
% \ N
/
%
% - The CENTERED ROOT MEAN SQUARE DIFFERENCE is computed as
:
% / sum
[ { [C
-mean(C
)] - [Cr
-mean(Cr
)] }.^2 ] \
% RMSD
= sqrt
| ------------------------------------------- |
% \ N
/
%
% - The CORRELATION is computed as
:
% sum( [C
-mean(C
)].*[Cr
-mean(Cr
)] )
% COR
= ---------------------------------
% N
*STD(C
)*STD(Cr
)
%
% - STATM(3,1) = 0 and
STATM(4,1) = 1 by definition
!
%
% Created by Guillaume Maze on
2008-10-28.
% Rev
. by Guillaume Maze on
2010-02-10: Add NaN values handling
, some checking
% in the inputs and a more complete help
% Copyright
(c
) 2008 Guillaume Maze
.
% http
://codes
.guillaumemaze
.org
%
% This program is free software
: you can redistribute it and
/or modify it under the terms of the GNU General Public License as published by
% the Free Software Foundation
, either version
3 of the License
, or any later version
.
% This program is distributed in the hope that it will be useful
, but WITHOUT ANY WARRANTY
; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE
. See the GNU General Public License
for more details
.
% You should have received a
copy of the GNU General Public License along with this program
. If not
, see
<http
://www
.gnu
.org
/licenses
/>.
%
function STATM
= allstats(varargin
)
Cr
= varargin
{1}; Cr
= Cr(:);
Cf
= varargin
{2}; Cf
= Cf(:);
%%% Check size
:
if length(Cr
) ~
= length(Cf
)
error('Cr and Cf must be of same length');
end
%%% Check NaNs
:
iok
= find(isnan(Cr
)==0 & isnan(Cf
)==0);
if length(iok
) ~
= length(Cr
)
warning('Found NaNs in inputs, removed them to compute statistics');
end
Cr
= Cr(iok
);
Cf
= Cf(iok
);
N
= length(Cr
);
%%% STD
:
st(1) = sqrt(sum( (Cr
-mean(Cr
) ).^2) / N
);
st(2) = sqrt(sum( (Cf
-mean(Cf
) ).^2) / N
);
%st(1) = sqrt(sum( (Cr
-mean(Cr
) ).^2) / (N
-1) );
%st(2) = sqrt(sum( (Cf
-mean(Cf
) ).^2) / (N
-1) );
%%% MEAN
:
me(1) = mean(Cr
);
me(2) = mean(Cf
);
%%% RMSD
:
rms(1) = sqrt(sum( ( ( Cr
-mean(Cr
) )-( Cr
-mean(Cr
) )).^2) /N
);
rms(2) = sqrt(sum( ( ( Cf
-mean(Cf
) )-( Cr
-mean(Cr
) )).^2) /N
);
%%% CORRELATIONS
:
co(1) = sum( ( ( Cr
-mean(Cr
) ).*( Cr
-mean(Cr
) )))/N
/st(1)/st(1);
co(2) = sum( ( ( Cf
-mean(Cf
) ).*( Cr
-mean(Cr
) )))/N
/st(2)/st(1);
%%% OUTPUT
STATM(1,:) = me
;
STATM(2,:) = st
;
STATM(3,:) = rms
;
STATM(4,:) = co
;
end
%function
