This is not yet released (but will be in the near future):
http://readthedocs.org/docs/astropy/en/latest/table/index.html
https://github.com/astropy/astropy/blob/master/astropy/table/table.py
You can at least use this as an example of how to add rows and columns
to a structured array. Or be an
On Sat, Mar 31, 2012 at 2:25 AM, Prashant Saxena animator...@yahoo.com wrote:
Hi,
I am sub-classing numpy.ndarry for vector array representation. The append
function is like this:
def append(self, other):
self = numpy.append(self, [other], axis=0)
Example:
vary =
On Fri, May 4, 2012 at 11:44 PM, Ilan Schnell ischn...@enthought.com wrote:
Hi Chuck,
thanks for the prompt reply. I as curious because because
someone was interested in adding http://pypi.python.org/pypi/Quaternion
to EPD, but Martin and Mark's implementation of quaternions
looks much
On Sat, May 5, 2012 at 12:55 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Sat, May 5, 2012 at 5:27 AM, Tom Aldcroft aldcr...@head.cfa.harvard.edu
wrote:
On Fri, May 4, 2012 at 11:44 PM, Ilan Schnell ischn...@enthought.com
wrote:
Hi Chuck,
thanks for the prompt reply. I
wrote:
On Sat, May 5, 2012 at 11:55 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Sat, May 5, 2012 at 5:27 AM, Tom Aldcroft
aldcr...@head.cfa.harvard.edu wrote:
On Fri, May 4, 2012 at 11:44 PM, Ilan Schnell ischn...@enthought.com
wrote:
Hi Chuck,
thanks for the prompt reply
I ran into a problem trying to build and import the numpy_quaternion
extension on CentOS-5 x86_64:
$ python setup.py build
SNIP
C compiler: gcc -pthread -fno-strict-aliasing -fPIC -g -O2 -DNDEBUG -g
-fwrapv -O3 -Wall -Wstrict-prototypes -fPIC
compile options:
Sorry to bother again, but I am running into an issue with the numpy
quaternion dtype on numpy 1.6.1 :
$ python
ActivePython 2.7.1.4 (ActiveState Software Inc.) based on
Python 2.7.1 (r271:86832, Feb 7 2011, 11:30:54)
[GCC 4.0.2 20051125 (Red Hat 4.0.2-8)] on linux2
Type help, copyright, credits
On Mon, May 7, 2012 at 3:30 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Mon, May 7, 2012 at 7:28 AM, Tom Aldcroft aldcr...@head.cfa.harvard.edu
wrote:
Sorry to bother again, but I am running into an issue with the numpy
quaternion dtype on numpy 1.6.1 :
$ python
ActivePython
Over on the scipy-user mailing list there was a question about
subclassing ndarray and I was interested to see two responses that
seemed to imply that subclassing should be avoided.
From Dag and Nathaniel, respectively:
Subclassing ndarray is a very tricky business -- I did it once and
regretted
I came across this problem which appears to be new in numpy 1.6.2 (vs. 1.6.1):
In [17]: a = np.array([(1, )], dtype=[('a', 'i4')])
In [18]: ra = a.view(np.recarray)
In [19]: '{}'.format(ra[0])
---
RuntimeError
On Tue, May 22, 2012 at 4:07 PM, Dan Goodman dg.gm...@thesamovar.net wrote:
On 22/05/2012 18:20, Nathaniel Smith wrote:
I don't know of anything that the docs are lacking in particular. It's
just that subclassing in general is basically a special form of
monkey-patching: you have this
On Fri, Jul 13, 2012 at 11:15 AM, Paul Natsuo Kishimoto
m...@paul.kishimoto.name wrote:
Hello everyone,
I am a longtime NumPy user, and I just filed my first contribution to
the code as pull request to fix what I felt was a bug in the behaviour
of genfromtxt()
On Mon, Jul 16, 2012 at 3:06 PM, Paul Natsuo Kishimoto
m...@paul.kishimoto.name wrote:
I've implemented this feature with skip_header=-1 as suggested by
Pierre, and in doing so removed the regression. TravisBot seems to like
it: https://github.com/numpy/numpy/pull/351
On Mon, 2012-07-16 at
On Sun, Jul 22, 2012 at 8:54 AM, Dr.Leo fhaxbo...@googlemail.com wrote:
Hi,
I am a seasoned numpy/pandas user mainly interested in financial
applications. These and other applications would greatly benefit from a
decimal data type with flexible rounding rules, precision etc.
Yes, there is
There was a thread in January discussing the non-obvious behavior of
numpy.mean() for large arrays of float32 values [1]. This issue is
nicely discussed at the end of the numpy.mean() documentation [2] with
an example:
a = np.zeros((2, 512*512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
When comparing rows of a structured masked array I'm getting an
exception. A similar operation on an structured ndarray gives the
expected True/False result. Note that this exception only occurs if
one or more of the mask values are True, since otherwise both row
objects are np.void and the
For library compatibility testing I'm trying to use numpy 1.4.1 with Python
2.7.3 on a 64-bit CentOS-5 platform. I installed a clean Python from
source (basically ./configure --prefix=$prefix ; make install) and then
installed numpy 1.4.1 with python setup.py install.
The crash message begins
I'm seeing about a factor of 50 difference in performance between
sorting a random integer array versus sorting that same array viewed
as a structured array. Am I doing anything wrong here?
In [2]: x = np.random.randint(1, size=1)
In [3]: xarr = x.view(dtype=[('a', np.int)])
In [4]:
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