好的...第一步是构建一个函数，可以检测给定日期所处的季节。幸运的是，我很久以前就开发了一个函数，它也可以处理南半球的季节（这些季节是相反的）。
该函数没有实现任何健全性检查，因为我是在使用已经过消毒的数据集时使用它的，但你最终应该很容易实现一些（除非你决定在使用它之前对数据集进行消毒）。它以向量化的方式工作，以最大化Matlab内的计算性能。
以下是它：
function season = GetSeason(date,southern_hemisphere)
if (nargin == 1)
southern_hemisphere = false;
end
[~,month,day] = datevec(date);
offset = month + (day / 100);
winter = (offset < 3.21) | (offset >= 12.22);
spring = ~winter & (offset < 6.21);
summer = ~winter & ~spring & (offset < 9.23);
autumn = ~winter & ~spring & ~summer;
offset(spring) = 0;
offset(summer) = 1;
offset(autumn) = 2;
offset(winter) = 3;
if (southern_hemisphere)
offset = offset + 2;
end
season = mod(offset,4) + 1;
end
现在，第一步，在你的脚本中，是从数据集文件中提取你的观测结果。为了为您创建一个完全可工作的演示，我创建了一个 Excel 数据集。但你也可以使用几乎没有代码更改的 CSV 数据集或由 Matlab 处理的其他文件格式：
opts = detectImportOptions('data.xlsx');
opts = setvartype(opts,{'datetime' 'double' 'double' 'double'});
data = readtable('data.xlsx',opts);
data = rmmissing(data);
data = sortrows(data);
第二个测试是为观测日期获取相应的季节：
seasons = GetSeason(data.Date);
第三步，假设我们只针对名为
Obs1的第一列观测值执行所有这些过程：
spring_1 = data.Obs1(seasons == 1);
summer_1 = data.Obs1(seasons == 2);
autumn_1 = data.Obs1(seasons == 3);
winter_1 = data.Obs1(seasons == 4);
第四步是在单个图表中为每个季节绘制一个箱线图（必须将分组变量groups作为参数传递给boxplot函数，以便让后者知道它必须绘制多少个箱子以及使用哪些值）：
groups = [
ones(size(spring_1));
2 * ones(size(summer_1));
3 * ones(size(autumn_1));
4 * ones(size(winter_1));
];
figure();
boxplot([spring_1; summer_1; autumn_1; winter_1],groups);
set(gca,'XTickLabel',{'Spring' 'Summer' 'Autumn' 'Winter'});
这是结果：
更新所有观测的完整可工作代码
opts = detectImportOptions('data.xlsx');
opts = setvartype(opts,{'datetime' 'double' 'double' 'double'});
data = readtable('data.xlsx',opts);
data = rmmissing(data);
data = sortrows(data);
seasons = GetSeason(data.Date);
spring_1 = data.Obs1(seasons == 1);
summer_1 = data.Obs1(seasons == 2);
autumn_1 = data.Obs1(seasons == 3);
winter_1 = data.Obs1(seasons == 4);
spring_2 = data.Obs2(seasons == 1);
summer_2 = data.Obs2(seasons == 2);
autumn_2 = data.Obs2(seasons == 3);
winter_2 = data.Obs2(seasons == 4);
spring_3 = data.Obs3(seasons == 1);
summer_3 = data.Obs3(seasons == 2);
autumn_3 = data.Obs3(seasons == 3);
winter_3 = data.Obs3(seasons == 4);
plot_data = [
spring_1;
summer_1;
autumn_1;
winter_1;
spring_2;
summer_2;
autumn_2;
winter_2;
spring_3;
summer_3;
autumn_3;
winter_3
];
plot_groups = [
(1 * ones(size(spring_1))) (1 * ones(size(spring_1)));
(1 * ones(size(summer_1))) (2 * ones(size(summer_1)));
(1 * ones(size(autumn_1))) (3 * ones(size(autumn_1)));
(1 * ones(size(winter_1))) (4 * ones(size(winter_1)));
(2 * ones(size(spring_2))) (5 * ones(size(spring_2)));
(2 * ones(size(summer_2))) (6 * ones(size(summer_2)));
(2 * ones(size(autumn_2))) (7 * ones(size(autumn_2)));
(2 * ones(size(winter_2))) (8 * ones(size(winter_2)));
(3 * ones(size(spring_3))) (9 * ones(size(spring_3)));
(3 * ones(size(summer_3))) (10 * ones(size(summer_3)));
(3 * ones(size(autumn_3))) (11 * ones(size(autumn_3)));
(3 * ones(size(winter_3))) (12 * ones(size(winter_3)))
];
labels_obs = {'' '' '' '' '' '' '' '' '' '' '' ''};
labels_season = repmat({'Spring' 'Summer' 'Autumn' 'Winter'},1,3);
figure('Units','normalized','Position',[0.05 0.1 0.9 0.8]);
boxplot(plot_data,plot_groups, ...
'BoxStyle','outline', ...
'FactorGap',[5 1], ...
'Labels',{labels_obs; labels_season}, ...
'Notch','on');
colors = repmat('wcyg',1,3);
h = findobj(gca,'Tag','Box');
for i = 1:numel(h)
patch(get(h(i),'XData'),get(h(i),'YData'),colors(i),'FaceAlpha',0.5);
end
h = findall(allchild(findall(gca,'Type','hggroup')),'Type','text','String','');
positions = cell2mat(get(h,'pos'));
positions_new = num2cell([mean(reshape(positions(:,1),4,[]))' positions(1:4:end,2:end)],2);
set(h(1:4:end),{'Position'},positions_new,{'String'},{'Observations 3'; 'Observations 2'; 'Observations 1'})
h = findall(allchild(findall(gca,'Type','hggroup')),'Type','text','String','');
delete(h);
结果：
