gt; On Aug 24, 2015, at 2:30 PM, Nathaniel Smith wrote:
>
> On Aug 24, 2015 9:29 AM, "Pauli Virtanen" mailto:p...@iki.fi>>
> wrote:
> >
> > 24.08.2015, 01:02, Chris Laumann kirjoitti:
> > [clip]
> > > Is there documentation about the li
Hi all-
Is there documentation about the limits and workarounds for py2/py3
pickle/np.save/load compatibility? I haven't found anything except developer
bug tracking discussions (eg. #4879 in github numpy).
The kinds of errors you get can be really obscure when save/loading complicated
objects
more complicated than can be expressed
as left-right or right-left (because some matrices might be diagonal, CSR or
CSC), which is why the preference is only weak. I don’t see a down-side in the
use-case that it is actually associative (as in matrix-matrix-vector).
Best, Chris
--
Chris Laumann
indistinguishable types for 2d arrays
with slightly different semantics for a small subset of operations is terrible.
Best, C
--
Chris Laumann
Sent with Airmail
On March 14, 2014 at 7:16:24 PM, Christophe Bal (projet...@gmail.com) wrote:
This id good for Numpyists but this will be another operator
builds only two
sentences into the paragraph.
Best, Chris
--
Chris Laumann
Sent with Airmail
On January 31, 2014 at 9:31:40 AM, Julian Taylor
(jtaylor.deb...@googlemail.com) wrote:
On 31.01.2014 18:12, Nathaniel Smith wrote:
> On Fri, Jan 31, 2014 at 4:29 PM, Benjamin Root wr
the scalar return due to the PyObject_Malloc usage in git master, but it doesn't affect 1.8.0
On Fri, Jan 31, 2014 at 7:20 AM, Chris Laumann <chris.laum...@gmail.com> wrote:
Hi all-
The following snippet appears to leak memory badly (about 10 MB per execution):
P = randint(0,2,(30,1
nt(0,2,(30,13))
for i in range(50):
print "\r", i, "/", 50
for ai in ndindex((2,)*13):
j = P.dot(ai)
There is no leak.
Any thoughts? I’m stumped.
Best, Chris
--
Chris Laumann
Sent with Airmail___
NumPy-Discussion
Superpack (numpy 1.9)
right now but I somehow doubt this behavior will change.
Any thoughts?
Best, Chris
--
Chris Laumann
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Good morning all-- didn't realize this would generate quite such a buzz.
To answer a direct question, I'm using the github master. A few thoughts (from
a fairly heavy numpy user for numerical simulations and analysis):
The current behavior is confusing and (as far as i can tell) undocumented.
S
asting rule ''safe''
This seems odd as unsigned ints are the most natural bitfields I can think of
-- the sign bit is just confusing when doing bit manipulation. Python itself of
course doesn't make much a distinction between ints, longs, unsigned etc.
Is this a bug?
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