Why Would Scipy's Interp1d Take Over A Minute To Build An Interpolator?

I'd like to quad or cube interpolate a long series of floats (or vectors) in 1d, where long could be 1E+05 or 1E+06 (or more). For some reason SciPi's handy interp1d()'s time overh

Solution 1:

Short answer: update your scipy installation.

Longer answer: pre-0.19, interp1d was based on splmake which is using linear algebra with full matrices. In scipy 0.19, it was refactored to use banded linear algebra. As a result, (below is with scipy 0.19.1)

In [14]: rndm = np.random.RandomState(1234) 

In [15]: for n in [100, 1000, 10000, int(1e6)]:
    ...:     x = np.sort(rndm.uniform(size=n))
    ...:     y = np.sin(x)
    ...:     %timeit interp1d(x, y, kind='quadratic', assume_sorted=True)
    ...:     
    ...:     

244 µs ± 4.6 µs per loop (mean ± std. dev. of7 runs, 1000 loops each)
422 µs ± 4.21 µs per loop (mean ± std. dev. of7 runs, 1000 loops each)
2.17 ms ± 50.2 µs per loop (mean ± std. dev. of7 runs, 100 loops each)
227 ms ± 4.45 ms per loop (mean ± std. dev. of7 runs, 1 loop each)

In [16]: 

In [16]: for n in [100, 1000, 10000, int(1e6)]:
    ...:     x = np.sort(rndm.uniform(size=n))
    ...:     y = np.sin(x)
    ...:     %timeit interp1d(x, y, kind='cubic', assume_sorted=True)
    ...:     
    ...:     

241 µs ± 4.67 µs per loop (mean ± std. dev. of7 runs, 1000 loops each)
462 µs ± 4.92 µs per loop (mean ± std. dev. of7 runs, 1000 loops each)
2.64 ms ± 37.6 µs per loop (mean ± std. dev. of7 runs, 100 loops each)
276 ms ± 1.91 ms per loop (mean ± std. dev. of7 runs, 1 loop each)

Your other options are CubicSpline if you want cubic splines specifically (this is new in scipy 0.18) or make_interp_spline if you want also quadratic splines (new in scipy 0.19; this is what interp1d is using under the hood).