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

% 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’;
Index exceeds the number of array elements. Index must not exceed 12784.
Error in event_line (line 84)
y1=m90_plot(x1-datenum(data_start,1,1)+1);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in an_example2 (line 53)
event_line(sst_full,MHW,mclim,m90,[1 2],1982,[2015 9 1],[2016 5 1]);% 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);

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)];

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);’])

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’;
Index exceeds the number of array elements. Index must not exceed 12784.
Error in event_line (line 84)
y1=m90_plot(x1-datenum(data_start,1,1)+1);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in an_example2 (line 53)
event_line(sst_full,MHW,mclim,m90,[1 2],1982,[2015 9 1],[2016 5 1]); % 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);

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)];

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);’])

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);

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