REF: ensure O(N log N) when using fft for acf

The numpy implementation will fall back to a O(N**2) version of the fft
in cases where it cannot factorize the array length, which can be a
increase in runtime of several order of magnitude. This uses the
_next_regular function from scipy to ensure that we always use array
lengths for which fftpack is optimized.
TST: add test for _next_regular

also copy the test for _next_regular from scipy
MAINT: Fix missed usage of deprecated tools.rank
Merge pull request #1154 from jseabold/testing-machine-links

DOC: Add links for build machines.
Merge pull request #1196 from kain88-de/fast_fft_acf

ENH: ensure O(N log N) when using fft for acf
COMPAT: Correct bit_length for 2.6
DOC: Update comment to reflect code. [skip ci]
Merge pull request #1545 from jseabold/rank-cleanup

MAINT: Fix missed usage of deprecated tools.rank
Merge pull request #1547 from jseabold/fix-bit-length-2.6

COMPAT: Correct bit_length for 2.6