python - Bug or meant to be: numpy raises "ValueError: too many boolean indices" for repeated boolean indices -


i doing simulations in experimental cosmology, , encountered problem while working numpy arrays. i'm new numpy, not sure if i'm doing wrong or if it's bug. run:

enthought python distribution -- www.enthought.com version: 7.3-1 (32-bit)  python 2.7.3 |epd 7.3-1 (32-bit)| (default, apr 12 2012, 11:28:34)  [gcc 4.0.1 (apple inc. build 5493)] on darwin type "credits", "demo" or "enthought" more information. >>> import numpy np >>> t = np.arange(10) >>> t[t < 8][t < 5] traceback (most recent call last):   file "<stdin>", line 1, in <module> valueerror: many boolean indices >>>

i expected return:

array([0, 1, 2, 3, 4])

since t[t < 8] should presumably treated ndarray?

the numpy documentation (http://docs.scipy.org/doc/numpy/user/basics.indexing.html) says boolean arrays indices:

as index arrays, returned copy of data, not view 1 gets slices.

running type(t[t < 8]) gives ndarray, guess should have properties of numpy array. should perhaps better list expressions? have not done timed comparison yet, imagine problem large 2d arrays.

t[ t < 8 ] indeed give array, however, doesn't give array of same size started with. t < 8 give boolean array same shape t. when use index t, pull out elements boolean array true leaving shorter array. when again:

result = t[t<8] result[t<5]

then boolean index array same shape t once again, you're using index smaller array causes error.

the documentation correct. new array isn't view original ... copy of data, doesn't imply new array same shape or size original.


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