Hello
I want to represent data with 2 variables (latitude and longitude) in 2D format. The value is represented by color and the 2 variables as the 2 axis and I am using the contourf function to plot my data. All the data comes from a xlsx file and I put it in a matrix.
Locations = xlsread('Availability results.xlsx');
column_numberloc = 1; % Column in the locations file containing the number of the locations
column_latitude = 2; % Column in the locations file containing the latitude of the locations
column_longitude = 3; % Column in the locations file containing the longitude of the locations
column_availability = 4; % Column in the locations file containing the availability of the locations
min_latitude = min(Locations(:,column_latitude));
max_latitude = max(Locations(:,column_latitude));
min_longitude = min(Locations(:,column_longitude));
max_longitude = max(Locations(:,column_longitude));
max_availability = max(Locations(:,column_availability));
min_availability = min(Locations(:,column_availability));
longitude = Locations(:,column_longitude);
latitude = Locations(:,column_latitude);
Contour = zeros(23,17);
for numerofile=1:204
[coord_x,coord_y] =transformation(Locations(numerofile,column_latitude),Locations(numerofile,column_longitude));
Contour(coord_x,coord_y) = Locations(numerofile,column_availability);
end
for i=1:23
for j=1:17
if Contour(i,j) == 0
Contour(i,j) = NaN;
end
end
end
cMap=jet(256);
figure(1);
x = linspace(min_longitude,max_longitude,17);
y = linspace(min_latitude,max_latitude,23);
newpoints = 100;
[xq,yq] = meshgrid(linspace(min(x),max(x),newpoints),linspace(min(y),max(y),newpoints ));
Contourq = interp2(x,y,Contour,xq,yq,'linear',max_availability);
[c,h]=contourf(xq,yq,Contourq,100);
%[c,h]=contourf(x,y,Contour,50);
set(h, 'edgecolor','none');
colormap(cMap);
cb=colorbar;
caxis([min_availability max_availability]);
The transformation function allows me to place all the data in the Contour matrix as it associate a longitude and a latitude to a row and a column.
I've putted a NaN for every data equal to zero to have a better look at my data and I obtained this : interpolation_linear
Which is nice but I wanted this data to be close to : Without interpolation
So, I decided to change the linear interpolation to a 'nearest' interpolation and I got this : interpolation_nearest
I can see more data but the contour plot isn't as smooth as with the linear interpolation.
I've seen many posts about how to make smooth contour plot (that's how I found the function 'interp2') but I think that my problem comes from the NaN data which prevent me from having a smooth contour plot at the edge between NaN values and the rest like the first image but with enough data like the third image.
My question is : Do you know how can I get a smooth edge contour plot with enough data thanks to the nearest interpolation but with a nice visual like the first image ?
Thank you very much
Since you are doing an interpolation on a square grid, you could directly display a 2D image with imagesc.
The advantage is that you can access the AlphaData property of image objects, which can be used as a display mask.
r=rand(50); % random 50x50 array
r(11:20,11:20)=NaN; % some hole filled with NaN
imagesc(r) % show the image, with NaN considered as the lowest value in color scale
imagesc(r,'AlphaData',~isnan(r)) % show the image, with NaN values set as fully transparent
You may also then:
interp2, perhaps even with the 'cubic' parameter for improved smoothnessAlphaData.