On Wed, Dec 10, 2014 at 8:40 PM, Sturla Molden sturla.mol...@gmail.com
wrote:
I haven't managed to trigger a segfault yet but it sure looks like I
could...
You can also trigger random errors. If the array is small, Python's memory
mamager might keep the memory in the heap for reuse by
On Mi, 2014-12-10 at 07:25 +, Sturla Molden wrote:
Nathaniel Smith n...@pobox.com wrote:
This should be pretty trivial to implement. AFAICT you don't need any
complicated cython
I have a bad habit of thinking in terms of too complicated C instead of
just using NumPy.
On Tue, Dec 9, 2014 at 11:03 PM, Sturla Molden sturla.mol...@gmail.com
wrote:
Nathaniel Smith n...@pobox.com wrote:
@contextmanager
def tmp_zeros(*args, **kwargs):
arr = np.zeros(*args, **kwargs)
try:
yield arr
finally:
arr.resize((0,),
The second argument is named `refcheck` rather than check_refs.
Eric
On Wed, Dec 10, 2014 at 2:36 PM, Chris Barker chris.bar...@noaa.gov wrote:
On Tue, Dec 9, 2014 at 11:03 PM, Sturla Molden sturla.mol...@gmail.com
wrote:
Nathaniel Smith n...@pobox.com wrote:
@contextmanager
def
On 10 December 2014 at 20:36, Chris Barker chris.bar...@noaa.gov wrote:
On Tue, Dec 9, 2014 at 11:03 PM, Sturla Molden sturla.mol...@gmail.com
wrote:
Nathaniel Smith n...@pobox.com wrote:
@contextmanager
def tmp_zeros(*args, **kwargs):
arr = np.zeros(*args, **kwargs)
try:
On Wed, Dec 10, 2014 at 11:44 AM, Andrea Gavana andrea.gav...@gmail.com
wrote:
The argument is not check_refs, but refcheck.
thanks -- yup, that works.
Useful -- but dangerous!
I haven't managed to trigger a segfault yet but it sure looks like I
could...
-CHB
--
Christopher Barker,
On Wed, Dec 10, 2014 at 9:03 PM, Chris Barker chris.bar...@noaa.gov wrote:
On Wed, Dec 10, 2014 at 11:44 AM, Andrea Gavana andrea.gav...@gmail.com
wrote:
The argument is not check_refs, but refcheck.
thanks -- yup, that works.
Useful -- but dangerous!
I haven't managed to trigger a
Chris Barker chris.bar...@noaa.gov wrote:
I haven't managed to trigger a segfault yet but it sure looks like I
could...
You can also trigger random errors. If the array is small, Python's memory
mamager might keep the memory in the heap for reuse by PyMem_Malloc. And
then you can actually
I wonder if ndarray should be a context manager so we can write
something like this:
with np.zeros(n) as x:
[...]
The difference should be that __exit__ should free the memory in x (if
owned by x) and make x a zero size array.
Unlike the current ndarray, which does not have an
My impression is that this level of optimization does and should not fall
within the scope of numpy..
-Original Message-
From: Sturla Molden sturla.mol...@gmail.com
Sent: 9-12-2014 16:02
To: numpy-discussion@scipy.org numpy-discussion@scipy.org
Subject: [Numpy-discussion] Should
I don't think that makes much sense, context managers are useful for
managing the lifetime of objects owning resources not already managed by
the garbage collector.
E.g. file descriptors, a gc has no clue that a piece of memory contains
a descriptor and thus never has a reason to release it in
On 09/12/14 18:39, Julian Taylor wrote:
A context manager will also not help you with reference cycles.
If will because __exit__ is always executed. Even if the PyArrayObject
struct lingers, the data buffer will be released.
Sturla
___
On Tue, Dec 9, 2014 at 5:57 PM, Julian Taylor jtaylor.deb...@googlemail.com
wrote:
On 09.12.2014 18:55, Sturla Molden wrote:
On 09/12/14 18:39, Julian Taylor wrote:
A context manager will also not help you with reference cycles.
If will because __exit__ is always executed. Even if the
On Tue, Dec 9, 2014 at 7:01 AM, Sturla Molden sturla.mol...@gmail.com
wrote:
I wonder if ndarray should be a context manager so we can write
something like this:
with np.zeros(n) as x:
[...]
The difference should be that __exit__ should free the memory in x (if
owned by x)
On Tue, Dec 9, 2014 at 8:15 PM, Chris Barker chris.bar...@noaa.gov wrote:
(although I'm still confused as to why it's so important (in cPython) to
have a file context manager..)
Because you want the file to close when the exception is raised and not at
some indeterminate point thereafter when
Chris Barker chris.bar...@noaa.gov wrote:
my first thought iust that you can just do:
x = np.zeros(n)
[... your code here ]
del x
x's ref count will go down, and it will be deleted if there are no other
references to it.
1. This depends on reference counting. PyPy supports numpy too
On 9 Dec 2014 15:03, Sturla Molden sturla.mol...@gmail.com wrote:
I wonder if ndarray should be a context manager so we can write
something like this:
with np.zeros(n) as x:
[...]
The difference should be that __exit__ should free the memory in x (if
owned by x) and make x a
Nathaniel Smith n...@pobox.com wrote:
@contextmanager
def tmp_zeros(*args, **kwargs):
arr = np.zeros(*args, **kwargs)
try:
yield arr
finally:
arr.resize((0,), check_refs=False)
That one is interesting. I have actually never used ndarray.resize(). It
did not
Nathaniel Smith n...@pobox.com wrote:
This should be pretty trivial to implement. AFAICT you don't need any
complicated cython
I have a bad habit of thinking in terms of too complicated C instead of
just using NumPy.
@contextmanager
def tmp_zeros(*args, **kwargs):
arr =
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