Fast way to Hash Numpy objects for Caching

Implementing a system where, when it comes to the heavy mathematical lifting, I want to do as little as possible.

I'm aware that there are issues with memoisation with numpy objects, and as such i开发者_高级运维mplemented a lazy-key cache to avoid the whole "Premature optimisation" argument.

def magic(numpyarg,intarg):
    key = str(numpyarg)+str(intarg)

    try:
        ret = self._cache[key]
        return ret
    except:
        pass

    ... here be dragons ...
    self._cache[key]=value
    return value

but since string conversion takes quite a while...

t=timeit.Timer("str(a)","import numpy;a=numpy.random.rand(10,10)")
t.timeit(number=100000)/100000 = 0.00132s/call

What do people suggest as being 'the better way' to do it?

Borrowed from this answer... so really I guess this is a duplicate:

>>> import hashlib
>>> import numpy
>>> a = numpy.random.rand(10, 100)
>>> b = a.view(numpy.uint8)
>>> hashlib.sha1(b).hexdigest()
'15c61fba5c969e5ed12cee619551881be908f11b'
>>> t=timeit.Timer("hashlib.sha1(a.view(numpy.uint8)).hexdigest()", 
                   "import hashlib;import numpy;a=numpy.random.rand(10,10)") 
>>> t.timeit(number=10000)/10000
2.5790500640869139e-05

There is a package for this called joblib. Found from this question.

from joblib import Memory
location = './cachedir'
memory = Memory(location)

# Create caching version of magic
magic_cached = memory.cache(magic)
result = magic_cached(...)

# Or (for one-time use)
result = memory.eval(magic, ...)

For small numpy arrays also this might be suitable:

tuple(map(float, a))

if a is the numpy array.