CODEWITHSUNDEEP
pythonnumpyscipy
Eric Bal
Eric Bal

Reputation: 1185

Finding Given Coordinate Positions in NumPy Array

import numpy as np

ref_cols = [11, 5, 12, 13, 15]
ref_rows = [1, 11, 2, 3, 5]
rows, cols = np.mgrid[1:6, 11:16]
print cols

[[11 12 13 14 15]
 [11 12 13 14 15]
 [11 12 13 14 15]
 [11 12 13 14 15]
 [11 12 13 14 15]]
print rows

[[1 1 1 1 1]
 [2 2 2 2 2]
 [3 3 3 3 3]
 [4 4 4 4 4]
 [5 5 5 5 5]]

I want to get where the given cols and rows (11,1), (5,11), (12,2), (13,3), (15,5) exists. So the expected answer is follows:

[[True, False, False, False, False],
[False, True, False, False, False],
[False, False, True, False, False],
[False, False, False, False, False],
[False, False, False, False, True]]

I tried as:

rows_indices = np.in1d(rows, ref_rows).reshape(rows.shape)
cols_indices = np.in1d(cols, ref_cols).reshape(cols.shape)
answers = (rows_indices & cols_indices)
print answers

But answer is wrong.

How to do it guys?

Upvotes: 0

Views: 80

Answers (2)

plonser
plonser

Reputation: 3363

Probably there exists a more elegent solution but this works for me and is written in an vectorized way ... 

import numpy as np

ref_cols = np.array([11, 5, 12, 13, 15])
ref_rows = np.array([1, 11, 2, 3, 5])
rows, cols = np.mgrid[1:6, 11:16]

m = (cols[:,:,None]==ref_cols[None,None,:]) & (rows[:,:,None]==ref_rows[None,None,:])

answer = np.any(m,axis=2)
#array([[ True, False, False, False, False],
#       [False,  True, False, False, False],
#       [False, False,  True, False, False],
#       [False, False, False, False, False],
#       [False, False, False, False,  True]], dtype=bool)

Upvotes: 2

oschoudhury
oschoudhury

Reputation: 1146

The reason that your try goes wrong is that you first need to evaluate each pair separately and you cannot evaluate first all rows and columns separately and then combine them in a logical operation.

Here is one way to fix it:

out = np.zeros(rows.shape, dtype=bool)
for r, c in zip(ref_rows, ref_cols):
    out |= (r == rows) & (c == cols)
print out

Upvotes: 2

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