@Christoph Try this a quick overview:
Step 1: Create a Regular Grid
First, determine the range of your x and y data and create a regular grid using meshgrid. For example:
% Determine the range and create a regular grid
x_range = linspace(min(x(:)), max(x(:)), 100); % Adjust 100 to your desired resolution
y_range = linspace(min(y(:)), max(y(:)), 100); % Adjust 100 to your desired resolution
[xq, yq] = meshgrid(x_range, y_range);
Step 2: Use griddata for Interpolation
Now, use griddata to interpolate your z values onto the regular grid:
% Interpolate z onto the regular grid
zq = griddata(x, y, z, xq, yq, 'linear'); % You can also try 'cubic' or 'nearest'
Step 3: Use interp2 if Further Interpolation is Needed
After you have zq defined on a regular grid, you can use interp2 for any further interpolation, as xq, yq, and zq now have the required structure.
% Further interpolation (if needed)
z_interp = interp2(xq, yq, zq, x_interp, y_interp);
Additional Note
- If your data contains NaNs or if the interpolation results in unexpected values, consider using different interpolation methods with griddata or cleaning your data beforehand.
- The choice between linear, cubic, and nearest in griddata can significantly affect your results, especially near the boundaries of your data set. Experiment with these to see which gives the best results for your particular data.
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