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
ENH: accept non-int definitions of cluster groups
ENH: use np.unique for indexing clusters
ENH: using human-readable group names instead of integer ids in MultiComparison
added file that got left behind
Put back an import that my IDE incorrectly flagged as unused
Added unit test for the group_order parameter of MultiComparison. It also tests the human-readable labels in TukeyHSDResults are in the right order
Expanded docstring of MultiComparison
Added b prefix to test strings
DOC: Fix link to wrong notebook [skip ci]
Merge pull request #1546 from jseabold/fix-example-links

DOC: Fix link to wrong notebook
MAINT: Fix missed usage of deprecated tools.rank
DOC: Add links for build machines. Closes #1153 [skip ci]
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