Performance and stability improvements to wcs.all_world2pix - 2nd iteration

This PR is a second iteration of PR #2373 with the main difference that
now the changes are on a branch (instead of master), hopefully does not
have (yet) merge conflicts, and addresses the issue of failing doctests
from PR #2373 by using data file name relative to the package as done
in PR #2807 and with updated output of the examples.

This (and #2373) provides significant improvements to the performance
(typically 1000-2000 times faster) and stability of `wcs.all_world2pix`
function. The used algorithm is a completely vectorized implementation
of the method of consecutive approximations (fixed point iterations).
While this method is a low-order method, it is more than adequate for
geometric distortions that are typical in astrophysical images (smooth,
slowly varying polynomials).

Compared to the original implementation of `all_world2pix`, this
implementation does not require users to have SciPy installed in order
to run.

While there is a check to see if WCS contains higher-order distortions
(and return the exact solution if it does not), the method itself does
not *require* this check and will converge to the exact solution after
one iteration if WCS does not contain higher order distortions. This
check is mostly for performance purposes to avoid unnecessary iterative
solution for images that do not contain distortions (and pix->sky
requires only WCS-based, i.e., CD-only transformations).

The proposed replacement for the current implementation of
`all_world2pix` avoids unnecessary use of `wcs_world2pix` and
`all_pix2world` such as `wcs_world2pix(all_pix2world(pix))` by
replacing this with `pix2foc`. This avoids unnecessary CD-matrix based
transformation CD(CD^{-1})=I which in itself results in about 50%
performance improvement over `wcs_world2pix(all_pix2world(pix))` and
likely 25% over `all_pix2world` alone.

The function has additional code to check for *divergence* condition
(as opposite to *slow convergence*) when consecutive approximations
*diverge*. This can happen when the pixel coordinates are *far* away
from the reference pixel (distortions are "severely" extrapolated), or
for poorly-defined geometric distortions (at least for this numerical
method).

A new and improved `NoConvergence` exception has attributes that allows
users to retrieve best solution reached by the function, and indices of
*slowly converging* solutions (points) that *did not converge* in the
specified maximum number of iterations as well as indices of
*diverging* solutions (likely an indication of poorly described
distortions or very large distortions).

Finally, the new test function `test_all_world2pix_direct` (proposed as
a replacement to the old test) provides a much more improved
(versatile) and *thorough* test of the `all_world2pix` function.

These improvements address the issues raised in #1977,
https://github.com/scipy/scipy/issues/3227 that plagued
`all_world2pix`, and #2294 (in addition to performance improvements).