I always use the 'ImpulseResponse','iir' name-value pair when using bandpass or its friends. That forces it to design a vewry efficient elliptic filter, that is probably the most computationally-efficient discrete filter available (at least in the MATLAB Signal Processing Toolbox). Otherwise, its default is (frequently) a FIR filter. FIR filters have their strengths (such as being able to have multiple passbands and stopbands, or being able to have specific characteristics such as provided by firls), however are usually not the best option.
The Butterworth design as an IIR filter, so similar to the elliptic filter bandpass designs if required to do so.
I would simply use your existing code, adding the 'ImpulseResponse','iir' name-value pair to create a more efficient IIR filter.
Also, you are re-designing the filter with every iteration of the loop. The bandpass function and its friends have a second ‘digital filter’ output, so it is probably better to do something like this:
[olr_real,df] = bandpass(squeeze(olr_1(1,1,:)),[frecuencia_min frecuencia_max],1, 'ImpulseResponse','iir'); % data is daily so fs=1
then in the loop, something like this:
olr_real(i,j,:) = filtfilt(df, squeeze(olr_1(i,j,:)));
I cannot test that, however it should be more efficient.
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