How can I create a slice object for Numpy array?

Question

I've tried to find a neat solution to this, but I'm slicing several 2D arrays of the same shape in the same manner. I've tidied it up as much as I can by defining a list containing the 'x,y' center e.g. cpix = [161, 134] What I'd like to do is instead of having to write out the slice three times like so:

a1 = array1[cpix[1]-50:cpix[1]+50, cpix[0]-50:cpix[0]+50] 
a2 = array2[cpix[1]-50:cpix[1]+50, cpix[0]-50:cpix[0]+50] 
a3 = array3[cpix[1]-50:cpix[1]+50, cpix[0]-50:cpix[0]+50]

is just have something predefined (like maybe a mask?) so I can just do a

a1 = array1[predefined_2dslice] 
a2 = array2[predefined_2dslice] 
a3 = array3[predefined_2dslice]

Is this something that numpy supports?

np.s_ produces a tuple of slice objects: (slice(cpix[1]-50:cpix[1]+50), slice(cpix[0]-50:cpix[0]+50)) — hpaulj
– hpaulj, Commented Aug 12, 2016 at 17:47

Kasravnd · Accepted Answer · 2016-08-12 11:55:17Z

50

Yes you can use numpy.s_:

Example:

>>> a = np.arange(10).reshape(2, 5)
>>> 
>>> m = np.s_[0:2, 3:4]
>>> 
>>> a[m]
array([[3],
       [8]])

And in this case:

my_slice = np.s_[cpix[1]-50:cpix[1]+50, cpix[0]-50:cpix[0]+50]

a1 = array1[my_slice] 
a2 = array2[my_slice] 
a3 = array3[my_slice]

You can also use numpy.r_ in order to translates slice objects to concatenation along the first axis.

edited Aug 12, 2016 at 11:55

answered Aug 12, 2016 at 11:48

Kasravnd

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4 Comments

FriskyGrub Over a year ago

This is exactly what I was looking for. I love when learning neat new tricks like this :) Thanks

Kasravnd Over a year ago

@FriskyGrub If you want to learn more about such tricks, I suggest you to delve into the documentation ;-)

hpaulj Over a year ago

And look at the value produced by np.s_. It's just a tuple.

andybuckley Over a year ago

Why give these useful utility functions such cryptic names, esp the underscore that suggests something internal? History?

benjimin · Accepted Answer · 2019-09-11 06:59:16Z

21

You can index a multidimensional array by using a tuple of slice objects.

window = slice(col_start, col_stop), slice(row_start, row_stop)
a1 = array1[window]
a2 = array2[window]

This is not specific to numpy and is simply how subscription/slicing syntax works in python.

class mock_array:
    def __getitem__(self, key):
        print(key)
m = mock_array()
m[1:3, 7:9] # prints tuple(slice(1, 3, None), slice(7, 9, None))

edited Sep 11, 2019 at 6:59

answered Sep 11, 2019 at 3:25

benjimin

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4 Comments

András Aszódi Over a year ago

This answer is more elegant than the accepted one IMO.

Quant Metropolis Over a year ago

numpy.s_ returns slice objects anyway. For 1-D slices the latter command might be more readable, but for higher dimensions I definitely prefer the former.

Jean-Didier Over a year ago

The definition of mock_array is really really close to what's behind np.s_ or np.index_exp, see the source code of np.IndexExpression

alercelik Over a year ago

Order should be (row, column) instead of (column, row)

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