Hello numpy users:
I have 2d numpy array of 480 rows and 1440 columns as named by 'data' below:
The first element belongs to (49.875S,179.875W),
the second element belongs to (49.625S,179.625W),and the last element
belongs to (49.875N,179.875E).
import os, glob, gdal, numpy as np
fname = '3B42RT
On Sat, Aug 23, 2014 at 12:40 AM, James Crist wrote:
> I suspected as much. This is actually part of my work on numerical
> evaluation in SymPy. In its current state compilation to C and autowrapping
> *works*, but I think it could definitely be more versatile/efficient. Since
> numpy seemed to ha
You can always write your own gufunc with signature '(),(),()->(a, a)', and
write a Python wrapper that always call it with an `out=` parameter of
shape (..., 3, 3), something along the lines of:
def my_wrapper(a, b, c, out=None):
if out is None:
out = np.empty(np.broadcast(a,b,c).shap
I suspected as much. This is actually part of my work on numerical
evaluation in SymPy. In its current state compilation to C and autowrapping
*works*, but I think it could definitely be more versatile/efficient. Since
numpy seemed to have solved the broadcasting/datatype issues internally I
hoped
structured arrays are of VOID dtype, but with a non-None names attribute:
>>> V_.dtype.num
20
>>> V_.dtype.names
('v',)
>>> V_.view(np.void).dtype.num
20
>>> V_.view(np.void).dtype.names
>>>
The comparison function uses the STRING comparison function if names is
None, or a proper field by field c
Oh yeah this could be. Floating point equality and bitwise equality are not the
same thing.
-Original Message-
From: "Jaime Fernández del Río"
Sent: 22-8-2014 16:22
To: "Discussion of Numerical Python"
Subject: Re: [Numpy-discussion] np.unique with structured arrays
I can confirm,
It does not sound like an issue with unique, but rather like a matter of
floating point equality and representation. Do the ' identical' elements pass
an equality test?
-Original Message-
From: "Nicolas P. Rougier"
Sent: 22-8-2014 15:21
To: "Discussion of Numerical Python"
Subject:
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', ' wrote:
>
> Hello,
>
> I've found a strange behavior or I'm missing something obvious (or
> np.unique is not supp
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:
impor
