Get minimum x and y from 2D numpy array of points

Question

Given a numpy 2D array of points, aka 3D array with size of the 3rd dimension equals to 2, how do I get the minimum x and y coordinate over all points?

Examples:

First:

I edited my original example, since it was wrong.

data = np.array(
      [[[ 0,  1],
        [ 2,  3],
        [ 4,  5]],

       [[11, 12],
        [13, 14],
        [15, 16]]])

minx = 0 # data[0][0][0]
miny = 1 # data[0][0][1]

4 x 4 x 2:

Second:

array([[[ 0, 77],
        [29, 12],
        [28, 71],
        [46, 17]],
       [[45, 76],
        [33, 82],
        [14, 17],
        [ 3, 18]],
       [[99, 40],
        [96,  3],
        [74, 60],
        [ 4, 57]],
       [[67, 57],
        [23, 81],
        [12, 12],
        [45, 98]]])

minx = 0 # data[0][0][0]
miny = 3 # data[2][1][1]

Is there an easy way to get now the minimum x and y coordinates of all points of the data? I played around with amin and different axis values, but nothing worked.

Clarification:

My array stores positions from different robots over time. First dimension is time, second is the index of an robot. The third dimension is then either x or y of a robots for a given time.

Since I want to draw their paths to pixels, I need to normalize my data, so that the points are as close as possible to the origin without getting negative. I thought that subtracting [minx,miny] from every point will do that for me.

The comment behind the ???. Given a 2D matrix of points, return the x-and y-value which is lowest. — jcklie
– jcklie, Commented Jan 5, 2014 at 20:18
So... basically data consists of two 2D arrays, the first representing all the x coordinates and the second representing all the y coordinates, and you want to get the minimum from each? In that case I think your description is a little inconsistent: it would be the last two coordinates, not the first two, that don't have any important meaning. — David Z
– David Z, Commented Jan 5, 2014 at 20:28
@reindeer don't look that you did :) what output you expect if you replace 11, 12 with 13, 12? and please provide sample data with more points — alko
– alko, Commented Jan 5, 2014 at 20:36

ByteHamster · Accepted Answer · 2015-04-28 16:14:54Z

8

alko's answer didn't work for me, so here's what I did:

import numpy as np

array = np.arange(15).reshape(5,3)
x,y = np.unravel_index(np.argmin(array),array.shape)

edited Apr 28, 2015 at 16:14

ByteHamster

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answered Apr 28, 2015 at 16:02

Kshitiz Sethia

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1 Comment

Raketenolli Over a year ago

Not sure that's what OP asked for, but it answered my question "How do I find the position of the minimum element in a 2D array?"!

Matrus · Accepted Answer · 2024-04-15 08:55:20Z

7

Seems like you need consecutive min along axis. For your first example:

>>> np.min(np.min(data, axis=1), axis=0)
array([ 0, 1])

For the second:

>>> np.min(np.min(data, axis=1), axis=0)
array([0, 3])

The same expression can be stated like this (with numpy >= 1.7, as pointed out by @Jaime)

>>> np.min(data, axis=(1, 0))
array([0, 3])

edited Apr 15, 2024 at 8:55

Matrus

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answered Jan 5, 2014 at 20:21

alko

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6 Comments

David Z Over a year ago

I'm pretty sure this is not what the question is asking for. It wants the minimum value from each pair, and the fact that the result is the same as taking a slice is just coincidental.

jcklie Over a year ago

Why do you use first time min and second time amin?

alko Over a year ago

@reindeer just a legacy from your code; this is the same function (np.amin is np.min returs True for sure)

Jaime Over a year ago

In numpy > 1.7 you can do np.min(data, axis=(0, 1)) and spare yourself an intermediate array.

alko Over a year ago

@Jaime just have thought about giving it a try (didn't yet ever), my initial guess was that this functionality would be rather implemented in pandas :) Can't test with exactly 1.7 (have 1.6 and 1.8 at hand), will it work in 1.7?

|

Paulus · Accepted Answer · 2016-07-15 23:45:37Z

1

You should take the min of a slice of the array. Assuming first coordinate is x and second is y

minx = min(a[:,0])
miny = min(a[:,1])

>>>a=np.array([[1,2],[3,4],[5,6]])

>>> a
array([[1, 2],
   [3, 4],
   [5, 6]])

>>> min(a[:,0])
1
>>> max(a[:,0])
5
>>> max(a[:,1])
6

answered Jul 15, 2016 at 23:45

Paulus

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Comments

John1024 · Accepted Answer · 2014-01-05 20:20:51Z

0

minx = np.min(data[0])
miny = np.min(data[1])

answered Jan 5, 2014 at 20:20

John1024

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Comments

optimist · Accepted Answer · 2015-02-19 11:56:19Z

0

You can use function numpy.amin to find the minima along the desired axis.

answered Feb 19, 2015 at 11:56

optimist

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