Stephan Hoyer writes:
> On Tue, Sep 13, 2016 at 11:05 AM, Lluís Vilanova wrote:
> Whenever we repr an array using 'S', we can instead show a unicode in py3.
> That
> keeps the binary representation, but will always show the expected result
> to
> us
Chris Barker writes:
> We had a big long discussion about this on this list a while back (maybe 2 yrs
> ago???) please search the archives to find it. Though I'm pretty sure that we
> never did come to a conclusion. I think it stared with wanting better support
> ofr unicode in loadtxt and the lik
Sebastian Berg writes:
> On Di, 2016-09-13 at 15:02 +0200, Lluís Vilanova wrote:
>> Hi! I'm giving a shot to issue #3184 [1], based on the observation
>> that the
>> string dtype ('S') under python 3 uses byte arrays instead of unicode
>> (th
Sebastian Berg writes:
> On Di, 2016-09-13 at 15:02 +0200, Lluís Vilanova wrote:
>> Hi! I'm giving a shot to issue #3184 [1], based on the observation
>> that the
>> string dtype ('S') under python 3 uses byte arrays instead of unicode
>> (th
Hi! I'm giving a shot to issue #3184 [1], based on the observation that the
string dtype ('S') under python 3 uses byte arrays instead of unicode (the only
readable string type in python 3).
This brings two major problems:
* numpy code has to go through loops to open and read files as binary data
Benjamin Root writes:
> Seems like you are talking about xarray: https://github.com/pydata/xarray
Oh, I wasn't aware of xarray, but there's also this:
https://people.gso.ac.upc.edu/vilanova/doc/sciexp2/user_guide/data.html#basic-indexing
https://people.gso.ac.upc.edu/vilanova/doc/sciexp2/u
Hi all! I'm really happy to make the first public announcement of SciExp^2
(actually, it's release 1.1.2).
Home page: https://projects.gso.ac.upc.edu/projects/sciexp2
Description
---
SciExp² (aka SciExp square or simply SciExp2) stands for Scientific Experiment
Exploration, which contain
Hi,
TL;DR: There's a pending pull request deprecating some behaviour I find
unexpected. Does anyone object?
Some time ago I noticed that numpy yields unexpected results in some very
specific cases. An array can be used to index multiple elements of a single
dimension:
>>> a = np.arang
