The old version was interpreting a PyObject_RichCompareBool return of 0
as success when it actually means failure. The fix is to replace 0 by 1.

Update version hash in numpy/core/code_generators/cversions.txt.
Update API version in numpy/core/setup_common.py
Update API version in numpy/core/include/numpy/numpyconfig.h

These functions npy_PyFile_Dup and npy_PyFile_DupClose do not work
correctly in Python 3. Deprecation messages have been raised in
npy_PyFile_Dup since 1.9 and it is probably best to smoke out any
remaining uses by simply removing the function.

The _PyArray_ITER_NEXT3 macro is not used in PyArray_ITER_NEXT,
and has syntax errors that would fail compilation if used.

Computing the type of a dhash can be slow for complex (e.g. structured)
dtypes.  Hashing dtypes can be useful in some applications, such as
when doing type-based dispatching, and speed can be critical in those
cases.  This enhancement caches the once-computed hash value in the
dtype structure, so as to save time on further lookups.  The cached
value is invalidated in the rare cases where the dtype is mutated.

Benchmarks numbers:

python3.4 -m timeit -s "import numpy as np; t=np.dtype('uint64')" "hash(t)"
* before patch: 1000000 loops, best of 3: 0.498 usec per loop
* after patch: 10000000 loops, best of 3: 0.0616 usec per loop

python3.4 -m timeit -s "import numpy as np; t=np.dtype([(s, 'f') for s in 'abcdefghij'])" "hash(t)"
* before patch: 100000 loops, best of 3: 4.43 usec per loop
* after patch: 10000000 loops, best of 3: 0.0603 usec per loop

Closes #5339.

BUG: Simplified fix, Overflow in tan and tanh for large complex values.

The new functions added:

    npy_ctan,
    npy_cacos, npy_casin, npy_catan
    npy_ccosh, npy_csinh, npy_ctanh
    npy_cacosh, npy_casinh, npy_catanh

Closes #5532.