On 07/06/2011 07:51 PM, Chris Barker wrote:
On 7/6/11 11:57 AM, Mark Wiebe wrote:
On Wed, Jul 6, 2011 at 1:25 PM, Christopher Barker
Is this really true? if you use a bitpattern for IGNORE, haven't you
just lost the ability to get the original value back if you want to stop
Dear all,
I have a couple of data files that were written with fortran at a fixed
with. That means its tabular data which might not have spaces (it is
just specified how many characters each field has and what type it is).
Is there anything to read that with scipy and or numpy?
Cheers
The function numpy.genfromtxt reads text files into arrays. There is an
example on how to deal with fixed-width columns using the delimiter
argument in the docstring and in the I/O chapter of the user guide:
http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html#the-delimiter-argument
On Jul 7, 2011, at 8:46 AM, Eric Firing wrote:
On 07/06/2011 07:51 PM, Chris Barker wrote:
On 7/6/11 11:57 AM, Mark Wiebe wrote:
On Wed, Jul 6, 2011 at 1:25 PM, Christopher Barker
Is this really true? if you use a bitpattern for IGNORE, haven't you
just lost the ability to get the
On 07/07/2011 07:51 AM, Chris Barker wrote:
On 7/6/11 11:57 AM, Mark Wiebe wrote:
On Wed, Jul 6, 2011 at 1:25 PM, Christopher Barker
Is this really true? if you use a bitpattern for IGNORE, haven't you
just lost the ability to get the original value back if you want to stop
Thanks. That is exactley what I need.
On 7/07/11 10:51 AM, Miguel de Val-Borro wrote:
The function numpy.genfromtxt reads text files into arrays. There is an
example on how to deal with fixed-width columns using the delimiter
argument in the docstring and in the I/O chapter of the user guide:
The error is below:
creating build/temp.linux-x86_64-2.6/numpy/core/blasdot
compile options: '-DATLAS_INFO=\None\ -Inumpy/core/blasdot
-Inumpy/core/include
-Ibuild/src.linux-x86_64-2.6/numpy/core/include/numpy
-Inumpy/core/src/private -Inumpy/core/src -Inumpy/core
-Inumpy/core/src/npymath
Hi!
With numpy 1.5, I get this:
a = np.ones((2, 2))
(2 * a.T).strides
(16, 8)
With 1.6, I get this:
(2 * a.T).strides
(8, 16)
So, this means I can't count on new arrays being C-contiguous any more.
I guess there is a good reason for this.
Anyway, I just thought I would mention it here -
2011/7/7 Jens Jørgen Mortensen je...@fysik.dtu.dk
Hi!
With numpy 1.5, I get this:
a = np.ones((2, 2))
(2 * a.T).strides
(16, 8)
With 1.6, I get this:
(2 * a.T).strides
(8, 16)
So, this means I can't count on new arrays being C-contiguous any more.
I guess there is a good reason
thank you for pointing that out!
so how do you change your numpy related c code now, would you like to share?
best regards, yoshi
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Hi all,
Is there an way to get random numbers from an arbitrary distribution
already built-in to numpy. I am interested to do that for a black body
distribution
Thanks
Wolfgang
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2011/7/7 Jens Jørgen Mortensen je...@fysik.dtu.dk
Hi!
With numpy 1.5, I get this:
a = np.ones((2, 2))
(2 * a.T).strides
(16, 8)
With 1.6, I get this:
(2 * a.T).strides
(8, 16)
So, this means I can't count on new arrays being C-contiguous any more.
I guess there is a good reason
On Thu, Jul 7, 2011 at 9:26 AM, Wolfgang Kerzendorf
wkerzend...@googlemail.com wrote:
Hi all,
Is there an way to get random numbers from an arbitrary distribution
already built-in to numpy. I am interested to do that for a black body
distribution
What's a black body distributions? From a
On Thu, Jul 7, 2011 at 7:24 AM, Yoshi Rokuko yo...@rokuko.net wrote:
thank you for pointing that out!
so how do you change your numpy related c code now, would you like to
share?
Either you have to deal with the axes in the c-code -- cython is an option
there -- or you can check and make a
+ Mark Wiebe ---+
One way to deal with this is to use PyArray_FromAny with the
NPY_C_CONTIGUOUS flag to ensure you have a C-aligned array. If you need to
write to the array, you should also use the NPY_UPDATEIFCOPY flag. Here's
how
On 07/01/2011 04:46 PM, Ralf Gommers wrote:
Hi,
I am pleased to announce the availability (only a little later than
planned) of the second release candidate of NumPy 1.6.1. This is a
bugfix release, list of fixed bugs:
#1834 einsum fails for specific shapes
#1837 einsum throws nan or
On 07/07/2011 05:23 AM, Jeffrey Spencer wrote:
The error is below:
creating build/temp.linux-x86_64-2.6/numpy/core/blasdot
compile options: '-DATLAS_INFO=\None\ -Inumpy/core/blasdot
-Inumpy/core/include
-Ibuild/src.linux-x86_64-2.6/numpy/core/include/numpy
-Inumpy/core/src/private
It's been a day less than two weeks since I posted my first feedback request
on a masked array implementation of missing data. I'd like to thank everyone
that contributed to the discussion, and that continues to contribute.
I believe my design is very solid thanks to all the feedback, and I
On Wed, Jul 6, 2011 at 1:56 PM, Christoph Gohlke cgoh...@uci.edu wrote:
On 7/6/2011 10:57 AM, Russell E. Owen wrote:
In article
cabl7cqhnnjkzk9xnrlvdarsdknwrm4ev0mxdurjsaxq73eb...@mail.gmail.com,
Ralf Gommersralf.gomm...@googlemail.com wrote:
On Tue, Jul 5, 2011 at 11:41 PM, Russell E.
On Thu, Jul 7, 2011 at 9:56 AM, Bruce Southey bsout...@gmail.com wrote:
On Wed, Jul 6, 2011 at 1:56 PM, Christoph Gohlke cgoh...@uci.edu wrote:
On 7/6/2011 10:57 AM, Russell E. Owen wrote:
In article
cabl7cqhnnjkzk9xnrlvdarsdknwrm4ev0mxdurjsaxq73eb...@mail.gmail.com,
Ralf
On 07/07/2011 10:06 AM, Mark Wiebe wrote:
On Thu, Jul 7, 2011 at 9:56 AM, Bruce Southey bsout...@gmail.com
mailto:bsout...@gmail.com wrote:
On Wed, Jul 6, 2011 at 1:56 PM, Christoph Gohlke cgoh...@uci.edu
mailto:cgoh...@uci.edu wrote:
On 7/6/2011 10:57 AM, Russell E.
On Thu, Jul 7, 2011 at 11:11 AM, Bruce Southey bsout...@gmail.com wrote:
**
On 07/07/2011 10:06 AM, Mark Wiebe wrote:
On Thu, Jul 7, 2011 at 9:56 AM, Bruce Southey bsout...@gmail.com wrote:
On Wed, Jul 6, 2011 at 1:56 PM, Christoph Gohlke cgoh...@uci.edu
wrote:
On 7/6/2011 10:57 AM,
On 07/07/2011 11:18 AM, Mark Wiebe wrote:
On Thu, Jul 7, 2011 at 11:11 AM, Bruce Southey bsout...@gmail.com
mailto:bsout...@gmail.com wrote:
On 07/07/2011 10:06 AM, Mark Wiebe wrote:
On Thu, Jul 7, 2011 at 9:56 AM, Bruce Southey bsout...@gmail.com
mailto:bsout...@gmail.com wrote:
In numpy 1.5.1, the functions PyArray_MoveInto and PyArray_CopyInto
don't appear to treat strides correctly.
Evidence:
PyNumber_InPlaceAdd(dst, src), and modifies the correct subarray to
which dst points.
In the same context, PyArray_MoveInto(dst, src) modifies the first two
rows of the
On Thu, Jul 7, 2011 at 11:03 AM, James Bergstra
bergs...@iro.umontreal.cawrote:
In numpy 1.5.1, the functions PyArray_MoveInto and PyArray_CopyInto
don't appear to treat strides correctly.
Evidence:
PyNumber_InPlaceAdd(dst, src), and modifies the correct subarray to
which dst points.
In
My system is Mac OSX 10.6.8, python.org 2.7.1. When I run numpy.test(), I see
the following warning:
import numpy as np
np.test()
Running unit tests for numpy
NumPy version 1.6.1rc2
NumPy is installed in
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy
On Thu, Jul 7, 2011 at 1:10 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Thu, Jul 7, 2011 at 11:03 AM, James Bergstra bergs...@iro.umontreal.ca
wrote:
In numpy 1.5.1, the functions PyArray_MoveInto and PyArray_CopyInto
don't appear to treat strides correctly.
Evidence:
Hi,
the current HEAD of NumPy fails to build.
To be more precise: compilation of
`numpy/core/src/multiarray/multiarraymodule_onefile.c' fails. It looks
like that is caused by splitting the `nditer.c.src' stuff in the same
directory into `nditer_api.c', `nditer_constr.c' and
`nditer_templ.c.src':
On Thu, Jul 7, 2011 at 5:48 PM, Dirk Ullrich dirk.ullr...@googlemail.comwrote:
Hi,
the current HEAD of NumPy fails to build.
To be more precise: compilation of
`numpy/core/src/multiarray/multiarraymodule_onefile.c' fails. It looks
like that is caused by splitting the `nditer.c.src' stuff in
Hi all,
I want to first apologize for stepping into this discussion a bit late and for
not being able to participate adequately. However, I want to offer a couple
of perspectives, and my opinion about what we should do as well as clarify what
I have instructed Mark to do as part of his
Hello,
I am pleased to announce that EPD (Enthought Python Distribution)
version 7.1 has been released. The most significant change is the
addition of an EPD Free version, which has its own very liberal
license, and can be downloaded and used free of any charge by
anyone (not only academics).
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