berkelem - 2 months ago 7x

Python Question

I have a 2d array of coordinates and I want to find the index of the entry that matches a given coordinate.

For example, my array could be

`A`

`A = [[[1.5, 2.0], [1.0, 2.3], [5.4, 2.3]],`

[[3.2, 4.4], [2.0, 3.1], [0.0, 2.3]],

[[1.0, 2.0], [2.3, 3.4], [4.0, 1.1]]]

and the coordinate I want to match is

`x = [1.0, 2.0]`

`[1.0, 2.0]`

`(2, 0)`

Currently I am doing it as follows:

`matching_inds = [(i, j) for i in xrange(len(A)) for j in xrange(len(A[0])) if A[i,j][0] == x[0] and A[i,j][1] == x[1]]`

This works, but I feel like there should be something more efficient (the arrays I'm working with are much larger).

I tried

`np.where()`

Any tips would be appreciated.

Answer

You could use a slightly simpler version of your code:

```
In [115]: A = [[[1.5, 2.0], [1.0, 2.3], [5.4, 2.3]],
...: [[3.2, 4.4], [2.0, 3.1], [0.0, 2.3]],
...: [[1.0, 2.0], [2.3, 3.4], [4.0, 1.1]]]
In [116]: x = [1., 2.]
In [117]: [(i, j) for i, row in enumerate(A) for j, coor in enumerate(row) if coor == x]
Out[117]: [(2, 0)]
```

But if the arrays are large, you'd better use a vectorized approach:

```
In [118]: import numpy as np
In [119]: arr = np.array(A)
In [120]: np.argwhere(np.logical_and(arr[:,:,0] == x[0], arr[:,:,1] == x[1]))
Out[120]: array([[2, 0]], dtype=int64)
```

**Edit**: An efficient and elegant way of getting the job done would be:

```
In [158]: np.argwhere(np.all(arr == x, axis=2))
Out[158]: array([[2, 0]], dtype=int64)
```

Source (Stackoverflow)

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