[ot][spam][random][crazy][random][crazy]

[Undescribed Horrific Abuse, One Victim & Survivor of Many]

(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])