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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?
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One-Dimensional SlicingThe first item of the array can be sliced by specifying a slice that starts at index 0 and ends at index 1 (one item before the 'to' index). Running the example returns a subarray with the first element. We can also use negative indexes in slices.
To slice elements from two-dimensional arrays, you need to specify both a row index and a column index as [row_index, column_index] . For example, you can use the index [1,2] to query the element at the second row, third column in precip_2002_2013 .
Slicing in python means taking elements from one given index to another given index. We pass slice instead of index like this: [start:end] . We can also define the step, like this: [start:end:step] .
Slicing an array You can slice a numpy array is a similar way to slicing a list - except you can do it in more than one dimension. As with indexing, the array you get back when you index or slice a numpy array is a view of the original array. It is the same data, just accessed in a different order.
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.
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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)) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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