Initializing numpy array from np.empty

Question

How are the sign bits determined when initializing an ndarray from empty memory?

>>> np.random.randn(3,3)
array([[-0.35557367, -0.0561576 , -1.84722985],
       [ 0.89342124, -0.50871646,  1.31368413],
       [ 0.0062188 ,  1.62968789,  0.72367089]])
>>> np.empty((3,3))
array([[0.35557367, 0.0561576 , 1.84722985],
       [0.89342124, 0.50871646, 1.31368413],
       [0.0062188 , 1.62968789, 0.72367089]])

These float values initialized from empty memory have lost their signs^†. Why is that?

^†_{Note: this result relies on implementation detail of memory re-use. The question asks what the implementation is doing.}

Answer 1

numpy.empty isn't clearing the sign bits manually or anything. The sign bits are just whatever garbage happens to be left in those bits of the malloc return value. The effect you're seeing is due to a numpy.absolute call somewhere else.

The thing is, numpy.empty isn't reusing the randn return value's buffer. After all, the randn return value is still alive when empty creates its array, due to the _ variable.

numpy.empty is reusing the buffer of an array created in the process of stringifying the first array. I believe it's this one:

def fillFormat(self, data):
    # only the finite values are used to compute the number of digits
    finite_vals = data[isfinite(data)]

    # choose exponential mode based on the non-zero finite values:
    abs_non_zero = absolute(finite_vals[finite_vals != 0])
    ...

See that absolute call? That's the one.

Here's some additional testing that supports that conclusion:

>>> a = numpy.random.randn(3, 3)
>>> b = numpy.arange(-5, 4, dtype=float)
>>> c = numpy.arange(-5, 13, 2, dtype=float)
>>> a
array([[-0.96810932,  0.86091026, -0.32675013],
       [-1.23458136,  0.56151178, -0.37409982],
       [-1.71348979,  0.64170792, -0.20679512]])
>>> numpy.empty((3, 3))
array([[ 0.96810932,  0.86091026,  0.32675013],
       [ 1.23458136,  0.56151178,  0.37409982],
       [ 1.71348979,  0.64170792,  0.20679512]])
>>> b
array([-5., -4., -3., -2., -1.,  0.,  1.,  2.,  3.])
>>> numpy.empty((3, 3))
array([[ 0.96810932,  0.86091026,  0.32675013],
       [ 1.23458136,  0.56151178,  0.37409982],
       [ 1.71348979,  0.64170792,  0.20679512]])
>>> c
array([ -5.,  -3.,  -1.,   1.,   3.,   5.,   7.,   9.,  11.])
>>> numpy.empty((3, 3))
array([[  5.,   3.,   1.],
       [  1.,   3.,   5.],
       [  7.,   9.,  11.]])
>>> numpy.array([1.0, 0, 2, 3, 4, 5, 6, 7, 8, 9])
array([ 1.,  0.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])
>>> numpy.empty((3, 3))
array([[ 1.,  2.,  3.],
       [ 4.,  5.,  6.],
       [ 7.,  8.,  9.]])

The numpy.empty results are affected by printing a and c, rather than by the process of creating those arrays. b has no effect, because it has 8 nonzero elements. The final array([1.0, 0, 2, ...]) has an effect, because even though it has 10 elements, exactly 9 of them are nonzero.