[BUG] fix scipy.linalg.hessenberg

do not permute the input matrix. fixes #3364.
[TST] linalg.hessenberg: add unit test

add the example from #3364 as unit test.
PEP8: add ignore E126 to tox.ini.  Changed from E121 in pep8 1.5

There were 910 errors for E126, which is too many to fix now.
PEP8: fix E111 and E113 (indentation unexpected / not a multiple of 4) errors.
PEP8: fix E112 ("expected indented block") errors.
PEP8: fix E129 ("visal indent same as next logical line") errors.
PEP8: fix E713/E714 ("not in", "is not") errors.
PEP8: fix E131 ("continuation line unaligned") errors.
DOC: libatlas3-base-dev seems to have never been a thing
MAINT: fix pyflakes issues.
MAINT: address review comments on gh-3512 by Warren and Denis.
Merge pull request #3514 from argriffing/libatlas3-base-dev-is-not-a-thing

DOC: libatlas3-base-dev seems to have never been a thing
Merge pull request #3512 from rgommers/travis-pep8

Fix PEP8 errors showing up on TravisCI after pep8 1.5 release
ENH: speed-up ndimage.filters.min(max)imum_filter1d

The current implementation of min(max)imum_filter1d rescans the full filter
window at every step, leading to an O(n*m) complexity, n being the size of
the image, and m of the filter.
This PR changes the computation algorithm, keeping track of the min(max)imum
value, and its offset from the current position. This allows to compute the
min(max)imum by making a single compare of the next value in the image against
the min(max)imum, and checking that the min(max)imum is still within the filter
window. This leads to an expected O(n) average performance, although the worst
case is still O(n*m).

The worst case happens for strictly inc(dec)reasing sequences of values, and in
this case the performance is actually worse than the original, because of the
overhead of computing the min(max)imum offset.
Merge pull request #3517 from jaimefrio/minmax-filter

ENH: speed-up ndimage.filters.min(max)imum_filter1d
DOC: sparse: LIL is *not* efficient at all

... unless the user takes care to pre-sort the items. Naively
constructing, say, a term-document matrix can easily take
quadratic time using LIL.
DOC: sparse: demonstrate toarray, not todense

Also pep8 fixes.
DOC: sparse: example of incremental CSR construction
Merge pull request #3516 from larsmans/sparse-docs

DOC improve scipy.sparse docstrings
Merge pull request #3497 from andrenarchy/fix-hessenberg

[BUG] fix scipy.linalg.hessenberg