python - How to avoid bug in numpy.place while calculating inverse cumulative distribution function of large numpy array? -


i have come across bug in scipy or numpy, has seen following issue or has workaround?

from scipy.stats import distributions import numpy np distributions.norm.ppf(np.ones((30000, 10000)) / 2.0)

results in

array([[  0.,   0.,   0., ...,   0.,   0.,   0.],        [  0.,   0.,   0., ...,   0.,   0.,   0.],        [  0.,   0.,   0., ...,   0.,   0.,   0.],        ...,         [ nan,  nan,  nan, ...,  nan,  nan,  nan],        [ nan,  nan,  nan, ...,  nan,  nan,  nan],        [ nan,  nan,  nan, ...,  nan,  nan,  nan]])

smaller runs (like 20000 rows) work fine.

using numpy 1.10.4.

edit

problems seems deeper, appearing inside numpy:

na = np.zeros((30000, 10000)) * np.nan np.place(na, np.ones((30000, 10000)), np.ravel(np.ones((30000, 10000))))

resulting in

array([[  1.,   1.,   1., ...,   1.,   1.,   1.],        [  1.,   1.,   1., ...,   1.,   1.,   1.],        [  1.,   1.,   1., ...,   1.,   1.,   1.],        ...,         [ nan,  nan,  nan, ...,  nan,  nan,  nan],        [ nan,  nan,  nan, ...,  nan,  nan,  nan],        [ nan,  nan,  nan, ...,  nan,  nan,  nan]])

added bug report: https://github.com/numpy/numpy/issues/7207

the problem seems integer overflow happening within arr_insert_loop function in numpy/core/src/multiarray/compiled_base.c. i've opened pull request should fix issue.


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