The built-in function hash
Generating the hash temporarily realizes the entire array in memory, which may not be possible for large arrays.
The hash value is dependent on the data type, shape of the data array. If the array is a masked array then the hash value is independent of the fill value and of data array values underlying any masked elements.
If the data array has been changed in place then the hash value may be different if regenerated.
The hash value is not guaranteed to be portable across versions of Python, numpy and cf.
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Examples
>>> print d.array
[[0 1 2 3]]
>>> d.hash()
-8125230271916303273
>>> d[1, 0] = numpy.ma.masked
>>> print d.array
[[0 -- 2 3]]
>>> hash(d)
791917586613573563
>>> d.hardmask = False
>>> d[0, 1] = 999
>>> d[0, 1] = numpy.ma.masked
>>> d.hash()
791917586613573563
>>> d.squeeze()
>>> print d.array
[0 -- 2 3]
>>> hash(d)
-7007538450787927902
>>> d.dtype = float
>>> print d.array
[0.0 -- 2.0 3.0]
>>> hash(d)
-4816859207969696442