at this:
# 20070802
# len(dir(pylab))
# 441
# len(dir(P))
# 346
# P.nx.numpy.__version__
# '1.0.1'
# N.__version__
# '1.0.1'
# N.alltrue
# function alltrue at 0x01471B70
# P.alltrue
# function alltrue at 0x019142F0
# N.alltrue.__doc__
Hello,
David Cournapeau wrote:
As far as I can read from the fft code in numpy, only double is
supported at the moment, unfortunately. Note that you can get some speed
by using scipy.fftpack methods instead, if scipy is an option for you.
What I understood is that numpy uses FFTPACK's
Hi,
I ran into this while debugging a script today:
In [1]: import numpy as N
In [2]: N.__version__
Out[2]: '1.0.3'
In [3]: d = N.array([32767], dtype=N.int16)
In [4]: d + 32767
Out[4]: array([-2], dtype=int16)
In [5]: d[0] + 32767
Out[5]: 65534
In [6]: type(d[0] + 32767)
Out[6]: type
using numpy-1.0.3, I believe there are a reference leak somewhere.
Using a debug build of Python 2.5.1 (--with-pydebug), I get the
following
import sys, gc
import numpy
def testleaks(func, args=(), kargs={}, repeats=5):
for i in xrange(repeats):
r1 = sys.gettotalrefcount()
Ryan May wrote:
Hi,
I ran into this while debugging a script today:
In [1]: import numpy as N
In [2]: N.__version__
Out[2]: '1.0.3'
In [3]: d = N.array([32767], dtype=N.int16)
In [4]: d + 32767
Out[4]: array([-2], dtype=int16)
In [5]: d[0] + 32767
Out[5]: 65534
In [6]:
On Thu, 2 Aug 2007, Lars Friedrich wrote:
What I understood is that numpy uses FFTPACK's algorithms.
Sort of. It appears to be a hand translation from F77 to C.
From www.netlib.org/fftpack (is this the right address?) I took that
there is a single-precision and double-precision-version
On 8/2/07, Lisandro Dalcin [EMAIL PROTECTED] wrote:
using numpy-1.0.3, I believe there are a reference leak somewhere.
Using a debug build of Python 2.5.1 (--with-pydebug), I get the
following
import sys, gc
import numpy
def testleaks(func, args=(), kargs={}, repeats=5):
for i in
Ups, I forgot to mention I was using gc.collect(), I accidentally
cleaned it my mail
Anyway, the following
import sys, gc
import numpy
def test():
a = numpy.zeros(5, dtype=float)
while 1:
gc.collect()
b = numpy.asarray(a, dtype=float); del b
gc.collect()
I think the problem is in _array_fromobject (seen as numpy.array in Python)
This function parses its arguments by using the convertor
PyArray_DescrConverter2. which RETURNS A NEW REFERENCE!!! This
reference is never DECREF'ed.
BTW, A lesson I've learned of the pattern
if
On 8/2/07, Warren Focke [EMAIL PROTECTED] wrote:
On Thu, 2 Aug 2007, Lars Friedrich wrote:
What I understood is that numpy uses FFTPACK's algorithms.
Sort of. It appears to be a hand translation from F77 to C.
From www.netlib.org/fftpack (is this the right address?) I took that
This patch corrected the problem for me, numpy test pass...
On 8/2/07, Lisandro Dalcin [EMAIL PROTECTED] wrote:
I think the problem is in _array_fromobject (seen as numpy.array in Python)
--
Lisandro Dalcín
---
Centro Internacional de Métodos Computacionales en Ingeniería
On Thu, 2 Aug 2007, Charles R Harris wrote:
On X86 machines the main virtue would be smaller and more cache friendly
arrays because double precision arithmetic is about the same speed as single
precision, sometimes even a bit faster. The PPC architecture does have
faster single than double
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