I'll file a ticket.
Incidentally, if tanh(z) is simply programmed as
(1.0 - exp(-2.0*z)) / (1.0 + exp(-2.0*z))
the problem is fixed.
Thanks, Mark
[clip]
Not for large complex values:
In [85]: tanh(1000+0j)
Out[85]: (nan+nan*j)
Yep, it's a bug. Care to file a ticket?
The
Thu, 27 Jan 2011 20:46:22 -0700, Charles R Harris wrote:
Mark Wiebe has proposed making the master branch backward compatible
with 1.5. The argument for doing this is that 1) removing the new bits
for new releases is a chore as the refactor schedule slips and 2) the
new ABI isn't settled and
Fri, 28 Jan 2011 11:25:19 +0100, Mark Bakker wrote:
I'll file a ticket.
Incidentally, if tanh(z) is simply programmed as
(1.0 - exp(-2.0*z)) / (1.0 + exp(-2.0*z))
This will overflow as z - -\infty. The solution is probably to use a
different expression for Re(z) 0, and to check how other
Good point, so we need a better solution that fixes all cases
I'll file a ticket.
Incidentally, if tanh(z) is simply programmed as
(1.0 - exp(-2.0*z)) / (1.0 + exp(-2.0*z))
This will overflow as z - -\infty. The solution is probably to use a
different expression for Re(z) 0, and to check
When I multiply a complex number with inf, I get inf + inf*j:
In [17]: inf * (1+1j)
Out[17]: (inf+inf*j)
Even when the imaginary part is really small:
In [18]: inf * (1+1e-100j)
Out[18]: (inf+inf*j)
Yet when the imaginary part is zero (and it really is a real number), the
imaginary part is
On 01/28/2011 01:01 AM, Travis Oliphant wrote:
Just to start the conversation, and to find out who is interested, I would
like to informally propose generator arrays for NumPy 2.0. This concept
has as one use-case, the deferred arrays that Mark Wiebe has proposed. But,
it also allows
On 01/28/2011 12:37 PM, Dag Sverre Seljebotn wrote:
On 01/28/2011 01:01 AM, Travis Oliphant wrote:
Just to start the conversation, and to find out who is interested, I would
like to informally propose generator arrays for NumPy 2.0. This concept
has as one use-case, the deferred
Follow up:
The behavior is correct for real argument:
In [20]: sinh(1000)
Out[20]: inf
In [21]: cosh(1000)
Out[21]: inf
In [22]: tanh(1000)
Out[22]: 1.0
So maybe we should look there for good logic,
Mark
On Fri, Jan 28, 2011 at 11:45 AM, Mark Bakker mark...@gmail.com wrote:
Good point, so
Fri, 28 Jan 2011 12:57:18 +0100, Mark Bakker wrote:
Follow up:
The behavior is correct for real argument:
[clip]
So maybe we should look there for good logic,
In the real case you can do if (abs(z) cutoff) return sgn(z),
which is not the right thing to do for complex numbers.
Anyway,
On Fri, Jan 28, 2011 at 1:36 AM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Thu, Jan 27, 2011 at 9:17 AM, Mark Wiebe mwwi...@gmail.com wrote:
On Thu, Jan 27, 2011 at 7:09 AM, Ralf Gommers
ralf.gomm...@googlemail.com wrote:
snip
The PIL test can still be fixed before the final
| Date: Thu, 27 Jan 2011 16:32:22 -0700
| From: Charles R Harris charlesr.har...@gmail.com
|
| On Thu, Jan 27, 2011 at 4:23 PM, Robert Kern robert.k...@gmail.com wrote:
|
| On Thu, Jan 27, 2011 at 17:17, Travis Oliphant teoliph...@gmail.com
wrote:
|
|Hey all,
|
|What is the
On Fri, Jan 28, 2011 at 8:29 AM, James A. Bednar jbed...@inf.ed.ac.uk wrote:
| Date: Thu, 27 Jan 2011 16:32:22 -0700
| From: Charles R Harris charlesr.har...@gmail.com
|
| On Thu, Jan 27, 2011 at 4:23 PM, Robert Kern robert.k...@gmail.com wrote:
|
| On Thu, Jan 27, 2011 at 17:17, Travis
Hi guys,
I am using python for a while now and I have a requirement of creating a
numpy array of microscopic tiff images ( this data is 3d, meaning there are
100 z slices of 512 X 512 pixels.) How can I create an array of images? i
then would like to use visvis for visualizing this in 3D.
any
On 01/27/2011 10:58 PM, David Cournapeau wrote:
On Fri, Jan 28, 2011 at 12:46 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
Hi All,
Mark Wiebe has proposed making the master branch backward compatible with
1.5. The argument for doing this is that 1) removing the new bits for new
Fri, 28 Jan 2011 11:49:34 +0100, Mark Bakker wrote:
[clip]
Yet when the imaginary part is zero (and it really is a real number),
the imaginary part is nan:
In [19]: inf * (1+0j)
Out[19]: (inf+nan*j)
That is not correct. It should really given (inf+0*j). (I know where it
comes from, inf*0
Travis Oliphant writes:
This concept has as one use-case, the deferred arrays that Mark Wiebe
has proposed.
Interesting, I didn't read about that.
In fact, I was playing around with a proxy wrapper for ndarrays not long
ago, in order to build a tree of deferred operations that can be later
Python 2.6.6 (r266:84374, Aug 31 2010, 11:00:51)
[GCC 4.0.1 (Apple Inc. build 5493)] on darwin
Type help, copyright, credits or license for more information.
import numpy
numpy.__version__
'1.5.1'
class X:
... pass
...
numpy.asarray([X(), numpy.asarray([1, 1])]).shape
(2,)
A brief history:
I wrote the asinh and acosh functions for the math (or was it cmath?) for
python 2.0. It fixed some problems of GVR implementation, but still it was far
from perfect, and replaced shortly after. My 1/4 cent tip: Do not rush ---
find a good code.
Nadav
On 1/28/11 7:01 AM, Asmi Shah wrote:
I am using python for a while now and I have a requirement of creating a
numpy array of microscopic tiff images ( this data is 3d, meaning there are
100 z slices of 512 X 512 pixels.) How can I create an array of images?
It's quite straightforward to create
Hi,
I'd like to ask for your help regarding the use of SWIG with numpy.
** problem description **
While I can compile successfully the examples provided in ./numpy/doc/swig
I can't compile the first example provided in the Cookbook.
http://www.scipy.org/Cookbook/SWIG_NumPy_examples
A simple
Thanks for the long email. I think there are a lot of thoughts around some of
these ideas and it is good to get as many of them articulated as possible.
I learn much from these kinds of discussions.I think others value them as
well.
I like your ideas about what kind of overloading
On Fri, Jan 28, 2011 at 1:18 PM, Travis Oliphant oliph...@enthought.comwrote:
Just to start the conversation, and to find out who is interested, I would
like to informally propose generator arrays for NumPy 2.0. This concept
has as one use-case, the deferred arrays that Mark Wiebe has
Does anyone have any objections to me merging the branch into the numpy
trunk right now?
Chuck suggested I try to split out the ABI changes, but they're kind of
tangled with the other changes. In particular, they involve fixing the type
promotion code to be enum order-independent, which depended
On Fri, Jan 28, 2011 at 4:37 PM, Mark Wiebe mwwi...@gmail.com wrote:
Does anyone have any objections to me merging the branch into the numpy
trunk right now?
Chuck suggested I try to split out the ABI changes, but they're kind of
tangled with the other changes. In particular, they involve
On Fri, Jan 28, 2011 at 4:14 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
snip
Go ahead and merge it in and we'll see how it goes.
I did the merge, and tried to trigger the buildbot, but it looks like a
github svn issue has reared its head:
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