# [Numpy-discussion] np.unique with structured arrays

Eelco Hoogendoorn hoogendoorn.eelco at gmail.com
Fri Aug 22 10:54:50 EDT 2014

```Oh yeah this could be. Floating point equality and bitwise equality are not the same thing.

-----Original Message-----
From: "Jaime Fernández del Río" <jaime.frio at gmail.com>
Sent: ‎22-‎8-‎2014 16:22
To: "Discussion of Numerical Python" <numpy-discussion at scipy.org>
Subject: Re: [Numpy-discussion] np.unique with structured arrays

I can confirm, the issue seems to be in sorting:

>>> np.sort(V_)
array([([0.5, 0.0, 1.0],), ([0.5, 0.0, -1.0],), ([0.5, -0.0, 1.0],),
([0.5, -0.0, -1.0],)],
dtype=[('v', '<f4', (3,))])

These I think are handled by the generic sort functions, and it looks like the comparison function being used is the one for a VOID dtype with no fields, so it is being done byte-wise, hence the problems with 0.0 and -0.0. Not sure where exactly the bug is, though...

Jaime

On Fri, Aug 22, 2014 at 6:20 AM, Nicolas P. Rougier <Nicolas.Rougier at inria.fr> wrote:

Hello,

I've found a strange behavior or I'm missing something obvious (or np.unique is not supposed to work with structured arrays).

I'm trying to extract unique values from a simple structured array but it does not seem to work as expected.
Here is a minimal script showing the problem:

import numpy as np

V = np.zeros(4, dtype=[("v", np.float32, 3)])
V["v"] = [ [0.5,    0.0,   1.0],
[0.5, -1.e-16,  1.0], # [0.5, +1.e-16,  1.0] works
[0.5,    0.0,  -1.0],
[0.5, -1.e-16, -1.0]] # [0.5, +1.e-16, -1.0]] works
V_ = np.zeros_like(V)
V_["v"][:,0] = V["v"][:,0].round(decimals=3)
V_["v"][:,1] = V["v"][:,1].round(decimals=3)
V_["v"][:,2] = V["v"][:,2].round(decimals=3)

print np.unique(V_)
[([0.5, 0.0, 1.0],) ([0.5, 0.0, -1.0],) ([0.5, -0.0, 1.0],) ([0.5, -0.0, -1.0],)]

While I would have expected:

[([0.5, 0.0, 1.0],) ([0.5, 0.0, -1.0],)]

Can anyone confirm ?

Nicolas

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