MATLAB & Practical Application on Climate Variability Studies EXERCISES

Transcription

1 B.Aires, 20-24/02/06 - Centro de Investigaciones del Mar y la Atmosfera & Department of Atmospheric and Oceanic Sciences (UBA) DAY2 Exercise n. 5 Aim: Read nino3 SSTA series in binary format, plot and save the image. Data:..esercizi\day_1/ex2/output/ssta_nino3_30y.dat Solution: Exe-5.m cd C:\ENRICO\tutorials\esercizi\day_2\ex5; fid=fopen('../../day_1/ex2/output/ssta_nino3_30y.dat'... %... Continue on the next line,'r','b'); % Read nino3 ssta 30y time series ssta_series=fread(fid,'float32'); % in binary format stored % at the end of the exe-2. close(fid); % Close the file. plot(ssta_series); grid on; settings=get(gca) % Struct array of axes properties. %look and change some values: set(gca,'xlim',[0 360]); % Set limits, set(gca,'ylim',[ ]); % set(gca,'xtick',[0:12.*4:360]); % Ticks and set(gca,'xticklabel',[1970:1.*4:2000]); % tick labels step: 4y. line([0 360],[0 0],'color','r') tit=title('nino3 SSTA 30 y','fontweight','bold'); get(tit) ylabel('[ ^oc ]'); xlabel('[ years ]'); ssta_series_smooth=runavg(ssta_series,5); hold on; plot(ssta_series_smooth,'k'); print -djpeg90./output/ssta_series_30y.jpg % Plot zero line. % Title. % Get title properties. % Set axes labels. % Smooth the series with 3 months % window. % Hold previous plots on figure. % Plot the smoothed series on the % same graphic. % Print out the figure in % jpeg format. 1

2 Exercise n. 6 Aim: Vectorizing loops: 6a. vectorizing a double FOR loop. 6b. vectorizing code that finds the cumulative sum of a vector every fifth element. (need cumsum function) 6c. Create a code that repeats a vector value when the following value is 0. Data: No data. Solutions: Exe-6a.m A = magic(100); % Create 2 matrices 100x100 B = pascal(100); % tic for j = 1:100 for k = 1:100; X(j,k) = sqrt(a(j,k)) * (B(j,k) - 1); end end toc % Elapsed time is seconds. % Start stopwatch timer. % L % O % O % P % to vectorize % Stop stopwatch timer. %%%%%%%%%%%% SOLUTION %%%%%%%%%%%%% X tic % Start stopwatch timer. X = sqrt(a).*(b-1); % Vectorized loop. toc % Stop stopwatch timer. % Elapsed time is seconds. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2

3 Exe-6b.m x = 1:10000; y = []; tic for n = 5:5:length(x) y = [y sum(x(1:n))]; end toc % Elapsed time is seconds. % Create a vector. % Start stopwatch timer. % LOOP % to vectorize. % Stop stopwatch timer. %%%%%%%%%%%% SOLUTION %%%%%%%%%%%%% tic % Start stopwatch timer. cums = cumsum(x); % Vectorized y = cums(5:5:length(x)); % loop. toc % Stop stopwatch timer. % Elapsed time is seconds. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3

4 Exe-6c.m a=2; b=3; c=5; d=15; e=11; x = [a b 0 0 c d 0 e ]; % and convert x in % x = [a a a a b b b c c c c c d d e e e e e e]; % Create a vector. %%%%%%%%%%% SOLUTION 1 %%%%%%%%%%%% aa=find(x==a); bb=find(x==b); cc=find(x==c); dd=find(x==d); ee=find(x==e); x(aa:bb-1)=a; x(bb:cc-1)=b; x(cc:dd-1)=c; x(dd:ee-1)=d; x(dd:end)=e; % x = % a a a a b b b c c c c c d d e e e e e e %%%%%%%%%% END SOLUTION 1 %%%%%%%%%% follow a 2 nd solution %%%%%%%%%%%% SOLUTION 2 %%%%%%%%%%%% x = [a b 0 0 c d 0 e ]; % Create a vector. valind = find(x); % valind = > indexes % x = x(valind(2:end)) = diff(x(valind)); %diff(x(valind)) is [ ] % x = x = cumsum(x); % x = % a a a a b b b c c c c c d d e e e e e e %%%%%%%%%% END SOLUTION 2 %%%%%%%%%% % N.B. Despite the high vectorization of the 2nd solution, % the performance is lower! % Verify with TIC and TOC functions or PROFILE function. 4

5 Exercise n. 7 Aim: Read an SST field and plot global mapping in different projections. (starting from the Exe 3 [day 1]) Data:..esercizi\day_1\data\skt.mon.mean.nc.. esercizi\day_1\data\lsmask nc Solution: Exe-7.m %%% part of Exe-3 %%% ncload../../day_1/data/skt.mon.mean.nc ncload../../day_1/data/lsmask nc pcolor(flipud(lsmask));colorbar; lsm=lsmask+1; lsm(lsm==0)=nan; skt_y1=squeeze(mean(skt(1:12,:,:))); lsmask skt sst_y1=skt_y1.*lsm; figure; pcolor(flipud(sst_y1)); %%% end part of Exe-3 %%% %%% Geo-referencing... [x,y]=meshgrid(lon,lat); figure axesm(... 'MapProjection','eqdcylin',... 'MapLatLimit',[-90 90],... 'Maplonlimit',[ ],... 'parallellabel','on',... 'meridianlabel','on',... 'labelformat','compass',... 'grid','on',... 'fontsize',8,... 'mlabelparallel','south',... 'origin',[ ]); % Load skin temperature % field time series and % land-sea mask. % view the mask field % N.B. not geo-referenced. % Set "1" on the sea and "nan" on the land. % 94x192 % Subsample the T time series in time: % get the first year of mm. % 94x192 % 94x192 % Create figure windows. % View the sst field created masking % the skin temperature annual field % (skt_y1). % N.B. not geo-referenced. % Used for the evaluation % of functions of two variables % Create a new map axes/ % define a map projection. pcolorm(y,x,sst_y1); % Projected matrix map. tightmap % Removes whitespace around % the map. 5

6 caxis([-32 32]); colorbar('horizon'); [c,h]=contourm(y,x,sst_y1,[-32:4:32],'k'); ht=clabelm(c,h,'fontsize',8,'fontweight','bold'); tit=title('sst year 1','fontweight','bold'); % Set color axis. % Contouring. % Title. exercise 7. % PLOT THE FIELD IN ORTOGRAPHIC PROJECTION: figure ax=axesm(... 'MapProjection','ortho',... 'parallellabel','on',... 'meridianlabel','on',... 'MapLatLimit',[-20-90],... 'Maplonlimit',[ ],... 'flatlimit',[-inf 70],... 'labelformat','compass',... 'grid','on',... 'origin',[ ],... 'fontsize',8); framem on; pcolorm(y,x,sst_y1); % tightmap caxis([-32 32]); colorbar('horizon'); [c,h]=contourm(y,x,sst_y1,[-32:4:32],'k'); ht=clabelm(c,h,'fontsize',8,'fontweight','bold'); tit=title('sst year 1','fontsize',12,'fontweight','bold'); % FILL THE WHITE SECTOR AND MASK THE LAND: lon(end)=lon(1); [x,y]=meshgrid(lon,lat); figure ax=axesm(... 'MapProjection','ortho',... 'parallellabel','on',... 'meridianlabel','on',... 'MapLatLimit',[-20-90],... 'Maplonlimit',[ ],... 'flatlimit',[-inf 70],... 'labelformat','compass',... 'grid','on',... 'origin',[ ],... 'fontsize',8); framem on; pcolorm(y,x,sst_y1); tightmap caxis([-32 32]); colorbar('horizon'); [c,h]=contourm(y,x,sst_y1,[-32:4:32],'k'); ht=clabelm(c,h,'fontsize',8,'fontweight','bold'); tit=title('sst year 1','fontsize',12,'fontweight','bold'); load coast; patchm(lat,long,[ ]); % Close the boundaries. % Load coast values. % Mask the land. (graphic mask) 6

7 Exercise n. 8 Aim: Create Scatter Plots. Some info about subplotting and page layout settings. Data:..esercizi\day_1\data\h_scatter.mat..esercizi\day_1\ data\ctrl_h_shear_ts_pdi.mat Solution: Exe-8.m load../../day_1/data/h_scatter load../../day_1/data/ctrl_h_shear_ts_pdi % >> whos *atl* % Name Size Bytes Class % % pdi_atl 1x double array % shear_atl 1x double array % temp_atl 1x double array % % Load a pre-built colormap. % % >> whos *wnp* % Name Size Bytes Class % % pdi_wnp 1x double array % shear_wnp 1x double array % temp_wnp 1x double array a4 subplot(2,1,1) scatter(temp_atl,shear_atl,10,pdi_atl); caxis([0 8e+9]); colormap(h_scatter); set(gca,'ylim',[0 22],'xlim',[26 34]); title(' ATL T eye value & local shear',... 'fontweight','bold');grid on; %xlabel('t surface [^o C]'); ylabel('wind shear [m/s]'); % Set figure dimensions. % Subplot 1 of 2 in the figure. % Scatter plot. (10 is the area % of each marker). % Set color axis % Use the pre-built colormap. % Set plot limits. subplot(2,1,2) scatter(temp_wnp,shear_wnp,10,pdi_wnp); caxis([0 8e+9]) colormap(h_scatter) set(gca,'ylim',[0 22],'xlim',[26 34]); xlabel('t surface [^o C]');ylabel('wind shear [m/s]'); title(' WNP T eye value & local shear',... 'fontweight','bold');grid on; 7

8 % Create a new subplot outside the figure, to get only the colorbar. subplot('position',[ ]);scatter(temp_wnp,shear_wnp,10,pdi_wnp); caxis([0 8e+9]) colormap(h_scatter) h=colorbar('horiz'); set(h,'position',[ ]); print -djpeg./output/scatter_ctrl_atl_wnp.jpg % Positioning the colorbar % between the two subplots. exercise 8. 8