python - Numpy indexing with a one dimensional boolean array -

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the post, getting grid of matrix via logical indexing in numpy, similar, not answer question since working 1 dimensional boolean array.

i attempting recreate following boolean indexing feature in octave. 

octave-3.2.4:6> = rand(3,3) =     0.249912   0.934266   0.371962       0.505791   0.813354   0.282006     0.439417   0.085733   0.886841   octave-3.2.4:8> a([true false true]) ans =      0.24991   0.43942

however, unable create same results in python numpy. 

>>> import numpy np >>> = np.random.rand(3,3) array([[ 0.94362993,  0.3553076 ,  0.12761322],        [ 0.19764288,  0.35325583,  0.17034005],        [ 0.56812424,  0.48297648,  0.64101657]]) >>> a[[true, false, true]] array([[ 0.19764288,  0.35325583,  0.17034005],        [ 0.94362993,  0.3553076 ,  0.12761322],        [ 0.19764288,  0.35325583,  0.17034005]]) >>> a[np.ix_([true, false, true])] array([[ 0.94362993,  0.3553076 ,  0.12761322],       [ 0.56812424,  0.48297648,  0.64101657]])

how recreate octave's boolean indexing on python numpy?

two problems:

  1. indexing list [true, false, true] not same indexing boolean array array([true,false,true]). list instead interpreted integer indexes [1,0,1]

  2. you need specify want results first column: 

    >>> = np.arange(9).reshape(3,3) >>> array([[0, 1, 2],        [3, 4, 5],        [6, 7, 8]]) >>> mask = np.array([true,false,true]) >>> mask.dtype ## verify have bool array dtype('bool') >>> a[mask,0] array([0, 6])

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