Histogramdd returns negative values after bins were not checked for monotonic increase

A call to np.histogram(2, bins=[1, 3, 1]) will raise a ValueError: bins must increase monotonically. exception. However, arrays generated with a datatype of uint64 or np.uint64 will not be checked (correctly, at least) for monotonicity and will execute without a problem, generating a histogram with a negative value:

hist, edges = np.histogram(2, bins=np.array([1, 3, 1], dtype='uint64'))
# hist == np.array([1, -1]) 

The two problems here are obvious:

1. numpy doesn’t check the monotonicity of the bins, or at least does so incorrectly.
2. A histogram with a negative value is returned.

I’m using numpy version 1.12.1 with Python 3.6.1 on Windows 64 bit. Thanks!

[eric-wieser]

Hey look, a float-integer overflow bug:

def test_large_integers():
    big = 2**60  # Too large to represent with a full precision float

    x = np.array([0], np.int64)
    x_edges = np.array([-1, +1], np.int64)
    y = big + x
    y_edges = big + x_edges

    hist, edges = histogramdd((x, y), bins=(x_edges, y_edges))

gives, on 1.13.3:

ValueError: Found bin edge of size <= 0. Did you specify `bins` withnon-monotonic sequence?

This one is definitely strictly increasing

[mattip]

#11023 has been merged, this no longer raises on master