CODEWITHSUNDEEP
pythonarraysnumpyindices
bl3nd3d
bl3nd3d

Reputation: 7

Extract values array using indices

I need to extract certain values from a multidimensional array that are not subsequent.

import numpy as np    
A = np.array([[[ 0.,          4.,          0.        ],
               [ 0.19230769,  4.03846154,  0.        ],
               [-0.4,         4.8,         0.        ],
               [ 2.,          1.,          0.        ]],

              [[ 1.2,         3.4,         0.        ],
               [ 2.11538462,  4.42307692,  0.        ],
               [ 0.,          4.,          0.        ],
               [ 3.6,         1.8,         0.        ]],

              [[ 1.8,         3.1,         0.        ],
               [ 3.17307692,  4.63461538,  0.        ],
               [ 0.,          4.,          0.        ],
               [ 4.,          2.,          0.        ]]])

For every 4x3 block I want to extract an arbitrary row

For instance the following elements:

A[0,2,:]
A[1,1,:]
A[2,1,:]

So basicly the rowsB = [2,1,1], which would give me:

[-0.4         4.8         0.        ]
[ 2.11538462  4.42307692  0.        ]
[ 3.17307692  4.63461538  0.        ]

How to do this efficiently?

Upvotes: 0

Views: 5583

Answers (1)

unutbu
unutbu

Reputation: 879133

You could use "advanced indexing":

In [99]: A[[0,1,2], [2,1,1], :]
Out[99]: 
array([[-0.4       ,  4.8       ,  0.        ],
       [ 2.11538462,  4.42307692,  0.        ],
       [ 3.17307692,  4.63461538,  0.        ]])

Here the indexing arrays are 

ind1 = [0, 1, 2]
ind2 = [2, 1, 1]

and since ind1 is indexing the first axis of A and ind2 is indexing the second axis, and the third axis is getting fully sliced (with :), the resulting array, result, has the same shape as ind1 and ind2 -- i.e. (3,) -- plus the shape of the fully sliced axis, which is also (3,). Thus, result.shape is (3, 3) and 

result[i, j] = A[ind1[i], ind2[i], j]

for i = 0,1,2 and j = 0,1,2.

Upvotes: 2

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