(Pdb) p data array([0.24436139, 0.0199289 , 0.47162206, 0.0475059 ]) (Pdb) p micro_ift(micro_ft(quaddata, 4), 4, upsample=True) array([ 0.24436139+0.00000000e+00j, 0.00810581-1.82059549e-02j, -0.44397862-1.59092263e-01j, 0.01057106+4.63148326e-02j, 0.06852563-2.34556449e-01j, -0.00587415+1.90435136e-02j, -0.08421137-4.64042897e-01j, 0.0433988 +1.93223922e-02j, -0.1221807 +2.11623172e-01j, -0.01974854-2.67512115e-03j, -0.32078447-3.45723432e-01j, -0.02497308-4.04123262e-02j, -0.18402159-1.60774822e-01j, -0.01987317+1.48928846e-03j, -0.09805574+4.61315984e-01j, 0.0475059 +4.65541255e-16j]) the first sample and last sample match, but all the middle are interpolated. here, N=16, and max_period=4 . i'd like it if there were 3 interpolated samples after each matching sample. so, I would want a linspace that, instead of (0, 4, 16), is more (0, 5, 17)[:-1]
np.linspace(0,4,16) array([0. , 0.26666667, 0.53333333, 0.8 , 1.06666667, 1.33333333, 1.6 , 1.86666667, 2.13333333, 2.4 , 2.66666667, 2.93333333, 3.2 , 3.46666667, 3.73333333, 4. ]) np.linspace(0,5,17)[:-1] array([0. , 0.3125, 0.625 , 0.9375, 1.25 , 1.5625, 1.875 , 2.1875, 2.5 , 2.8125, 3.125 , 3.4375, 3.75 , 4.0625, 4.375 , 4.6875])