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How do i "carve" or mask a 2D numpy array according to an index formula? I don't care what the element value is, only its position in the array.
For example, given an mxm array, how do I extract all elements whose address conforms to
for i in range(0,m):
for j in range(0,m):
if j-i-k>=0:
A[i,j] = 1
elif j-p-k>=0:
A[i,j] = 1
elif i-k>=0:
A[i,j] = 1
else:
A[i,j] = 0
j=j+1
i=i+1
where
k and p are an arbitrary fences
Assume
k<m
p<m
This ends up looking like a diagonal slice + a horizontal slice + a vertical slice. Can it be done without the for loops above?
457
Indexing a Two-dimensional Array To access elements in this array, use two indices. One for the row and the other for the column. Note that both the column and the row indices start with 0. So if I need to access the value '10,' use the index '3' for the row and index '1' for the column.
Slice Two-dimensional Numpy Arrays 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 .
Array indexing is the same as accessing an array element. You can access an array element by referring to its index number. The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc.
To create a NumPy array, you can use the function np. array() . All you need to do to create a simple array is pass a list to it. If you choose to, you can also specify the type of data in your list.
In [1]: import numpy as np
In [2]: k = 2
In [3]: i, j = np.ogrid[0:5,0:5]
In [4]: mask = (j-i-k < 0)
In [5]: mask
Out[5]:
array([[ True, True, False, False, False],
[ True, True, True, False, False],
[ True, True, True, True, False],
[ True, True, True, True, True],
[ True, True, True, True, True]], dtype=bool)
In [6]: mask.shape
Out[6]: (5, 5)
In [7]: mask.dtype
Out[7]: dtype('bool')
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