Hi,I tray RUN this code but i get tis error ( below the code) (2025)

% Please note that this section requires the toolbox m_map

% Now we would like to know the mean states and annual trends of MHW

% frequency, i.e. how many MHW events would be detected per year and how it

% changes with time.

[mean_freq,annual_freq,trend_freq,p_freq]=mean_and_trend_new(MHW,mhw_ts,1982,'Metric','Frequency');

% These four outputs separately represent the total mean, annual mean,

% annual trend and associated p value of frequency.

% This function could detect mean states and trends for six different

% variables (Frequency, mean intensity, max intensity, duration and total

% MHW/MCs days).

metric_used={'Frequency','MeanInt','MaxInt','CumInt','Duration','Days'};

for i=1:6;

eval(['[mean_' metric_used{i} ',annual_' metric_used{i} ',trend_' metric_used{i} ',p_' metric_used{i} ']=mean_and_trend_new(MHW,mhw_ts,1982,' '''' 'Metric' '''' ',' 'metric_used{i}' ');'])

end

% plot mean and trend

% It could be detected that, as a global hotspot, the oceanic region off

% eastern Tasmania exhibits significant positive trends of MHW metrics.

figure('pos',[10 10 1500 1500]);

m_proj('mercator','lat',[-45 -37],'lon',[147 155]);

for i=1:6;

subplot(2,6,i);

eval(['mean_here=mean_' metric_used{i} ';']);

eval(['t_here=trend_' metric_used{i} ';']);

m_pcolor(lon_used,lat_used,mean_here');

shading interp

m_coast('patch',[0.7 0.7 0.7]);

m_grid;

colormap(jet);

s=colorbar('location','southoutside');

title(metric_used{i},'fontname','consolas','fontsize',12);

subplot(2,6,i+6);

eval(['mean_here=mean_' metric_used{i} ';']);

eval(['t_here=trend_' metric_used{i} ';']);

m_pcolor(lon_used,lat_used,t_here');

shading interp

m_coast('patch',[0.7 0.7 0.7]);

m_grid;

colormap(jet);

s=colorbar('location','southoutside');

title(['Trend-' metric_used{i}],'fontname','consolas','fontsize',12);

end

%% 5. Applying cluster algoirthm to MHW - A kmeans example.

% We get so many MHWs now.... Could we distinguish them into different

% gropus based on their metrics?

% Change it to matrix;

MHW_m=MHW{:,:};

% Extract mean, max, cumulative intensity and duration.

MHW_m=MHW_m(:,[3 4 5 7]);

[data_for_k,mu,sd]=zscore(MHW_m);

% Determine suitable groups of kmeans cluster.

index_full=[];

cor_full=[];

for i=2:20;

k=kmeans(data_for_k,i,'Distance','cityblock','maxiter',200);

k_full=[];

for j=1:i;

k_full=[k_full;nanmean(data_for_k(k==j,:))];

end

k_cor=k_full(k,:);

k_cor=k_cor(:);

[c,p]=corr([data_for_k(:) k_cor]);

index_full=[index_full;2];

cor_full=[cor_full;c(1,2)];

end

figure('pos',[10 10 1500 1500]);

subplot(1,2,1);

plot(2:20,cor_full,'linewidth',2);

hold on

plot(9*ones(1000,1),linspace(0.6,1,1000),'r--');

xlabel('Number of Groups','fontsize',16,'fontweight','bold');

ylabel('Correlation','fontsize',16,'fontweight','bold');

title('Correlation','fontsize',16);

set(gca,'xtick',[5 9 10 15 20],'fontsize',16);

subplot(1,2,2);

plot(3:20,diff(cor_full),'linewidth',2);

hold on

plot(9*ones(1000,1),linspace(-0.02,0.14,1000),'r--');

xlabel('Number of Groups','fontsize',16,'fontweight','bold');

ylabel('First difference of Correlation','fontsize',16,'fontweight','bold');

title('First Difference of Correlation','fontsize',16);

set(gca,'fontsize',16);

% Use 9 groups.

k=kmeans(data_for_k,9,'Distance','cityblock','maxiter',200);

k_9=[];

prop_9=[];

for i=1:9;

data_here=data_for_k(k==i,:);

data_here=nanmean(data_here);

data_here=data_here.*sd+mu;

k_9=[k_9;data_here];

prop_9=[prop_9;nansum(k==i)./size(data_for_k,1)];

end

loc_x=[1.5 1.5 1.5 2.5 2.5 2.5 3.5 3.5 3.5];

loc_y=[1.5 2.5 3.5 1.5 2.5 3.5 1.5 2.5 3.5];

text_used={['1: ' num2str(round(prop_9(1)*100)) '%'],['2: ' num2str(round(prop_9(2)*100)) '%'],['3: ' num2str(round(prop_9(3)*100)) '%'],...

['4: ' num2str(round(prop_9(4)*100)) '%'],['5: ' num2str(round(prop_9(5)*100)) '%'],['6: ' num2str(round(prop_9(6)*100)) '%'],...

['7: ' num2str(round(prop_9(7)*100)) '%'],['8: ' num2str(round(prop_9(8)*100)) '%'],['9: ' num2str(round(prop_9(9)*100)) '%']};

figure('pos',[10 10 1500 1500]);

h=subplot(2,2,1);

data_here=k_9(:,1);

data_here=reshape(data_here,3,3);

data_here(:,end+1)=data_here(:,end);

data_here(end+1,:)=data_here(end,:);

pcolor(1:4,1:4,data_here);

set(h,'ydir','reverse');

axis off

colormap(jet);

text(loc_x,loc_y,text_used,'fontsize',16,'horiz','center','fontweight','bold');

colorbar;

title('Durations','fontsize',16,'fontweight','bold');

h=subplot(2,2,2);

data_here=k_9(:,2);

data_here=reshape(data_here,3,3);

data_here(:,end+1)=data_here(:,end);

data_here(end+1,:)=data_here(end,:);

pcolor(1:4,1:4,data_here);

axis off

set(h,'ydir','reverse');

colormap(jet);

text(loc_x,loc_y,text_used,'fontsize',16,'horiz','center','fontweight','bold');

colorbar

title('MaxInt','fontsize',16,'fontweight','bold');

h=subplot(2,2,3);

data_here=k_9(:,3);

data_here=reshape(data_here,3,3);

data_here(:,end+1)=data_here(:,end);

data_here(end+1,:)=data_here(end,:);

pcolor(1:4,1:4,data_here);

axis off

set(h,'ydir','reverse');

colormap(jet);

text(loc_x,loc_y,text_used,'fontsize',16,'horiz','center','fontweight','bold');

colorbar

title('MeanInt','fontsize',16,'fontweight','bold');

h=subplot(2,2,4);

data_here=k_9(:,4);

data_here=reshape(data_here,3,3);

data_here(:,end+1)=data_here(:,end);

data_here(end+1,:)=data_here(end,:);

[x,y]=meshgrid(1:4,1:4);

pcolor(1:4,1:4,data_here);

set(h,'ydir','reverse');

axis off

colormap(jet);

text(loc_x,loc_y,text_used,'fontsize',16,'horiz','center','fontweight','bold');

colorbar

title('CumInt','fontsize',16,'fontweight','bold');

% Their associated SSTA patterns

% Calculate SSTA

time_used=datevec(datenum(1982,1,1):datenum(2016,12,31));

m_d_unique=unique(time_used(:,2:3),'rows');

ssta_full=NaN(size(sst_full));

for i=1:size(m_d_unique);

date_here=m_d_unique(i,:);

index_here=find(time_used(:,2)==date_here(1) & time_used(:,3)==date_here(2));

ssta_full(:,:,index_here)=sst_full(:,:,index_here)-nanmean(sst_full(:,:,index_here),3);

end

sst_1993_2016=ssta_full(:,:,(datenum(1993,1,1):datenum(2016,12,31))-datenum(1982,1,1)+1);

time_used=MHW{:,:};

time_used=time_used(:,1:2);

start_full=datenum(num2str(time_used(:,1)),'yyyymmdd')-datenum(1993,1,1)+1;

end_full=datenum(num2str(time_used(:,2)),'yyyymmdd')-datenum(1993,1,1)+1;

for i=1:9;

start_here=start_full(k==i);

end_here=end_full(k==i);

index_here=[];

for j=1:length(start_here);

period_here=start_here(j):end_here(j);

index_here=[index_here;period_here(:)];

end

eval(['sst_' num2str(i) '=nanmean(sst_1993_2016(:,:,index_here),3);'])

end

color_used=hot;

color_used=color_used(end:-1:1,:);

figure('pos',[10 10 1500 1500]);

plot_index=[1 4 7 2 5 8 3 6 9];

for i=1:9;

eval(['plot_here=sst_' num2str(i) ';']);

subplot(3,3,plot_index(i));

eval(['data_here=sst_' num2str(i) ';'])

m_contourf(lon_used,lat_used,data_here',-3:0.1:3,'linestyle','none');

if i~=3;

m_grid('xtick',[],'ytick',[]);

else;

m_grid('linestyle','none');

end

m_gshhs_h('patch',[0 0 0]);

colormap(color_used);

caxis([0 2]);

title(['Group (' num2str(i) '):' num2str(round(prop_9(i)*100)) '%'],'fontsize',16);

end

hp4=get(subplot(3,3,9),'Position');

s=colorbar('Position', [hp4(1)+hp4(3)+0.025 hp4(2) 0.025 0.85],'fontsize',14);

s.Label.String='^{o}C';

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