This improves the documentation and error messages of the testing
functions and extends `numpy.testing.assert_array_compare` to
more data types.

Fixed maximum relative error reporting in assert_allclose:

In cases where the two arrays have zeros at the same positions it will
no longer report nan as the max relative error

Nose is outdated and causes a DeprecationWarning during import,
a change in pytest seems to now trip over the warning, so ignore
it (in a slightly ugly manner)

Example behavior:

>>> x = np.array([1, 2, 3])
>>> y = np.array([1, 2, 3.0001])
>>> np.testing.assert_allclose(x, y)
AssertionError:
Not equal to tolerance rtol=1e-07, atol=0

Mismatch: 33.3%
Max absolute difference: 0.0001
Max relative difference: 3.33322223e-05
 x: array([1, 2, 3])
 y: array([1.    , 2.    , 3.0001])

Motivation: when writing numerical algorithms, I frequently find myself
experimenting to pick the right value of `atol` and `rtol` for
`np.testing.assert_allclose()`. If I make the tolerance too generous, I risk
missing regressions in accuracy, so I usually try to pick the smallest values
for which tests pass. This change immediately reveals appropriate values to
use for these parameters, so I don't need to guess and check.

* Review F401,F841,F842 flake8 errors (unused variables, imports)

* Review comments

* More tests in test_installed_npymath_ini

* Review comments