On Wed, 02 May 2007, Rob De Almeida apparently wrote:
> http://cheeseshop.python.org/pypi/pupynere/
I do not think you were "shameless" enough.
Pupynere is a PUre PYthon NEtcdf REader. It allows
read-access to netCDF files using the same syntax as
the Scientific.IO.Net
James Boyle wrote:
> I am trying to build pynetcdf-0.7, so as to be able to read netCDF
> files from python 2.5/numpy .
Sorry for the shameless plug, but if you only want to read netCDF files
from numpy you can use a pure python netcdf reader that I wrote, called
pupynere:
http://cheeses
OS X 10.4.9, python 2.5, numpy 1.0.3.dev3726, netcdf 3.6.2
I am trying to build pynetcdf-0.7, so as to be able to read netCDF
files from python 2.5/numpy .
So far I have not had success.
Enclosed is the dump of my most recent failure - the complaint comes
from the linker since the linker
Pierre GM wrote:
>> It didn't sound like the OP wanted that. I suspect that what is wanted
>> if for to always be a 1-d array (i.e. vector). To do that, I'd do:
>
> I beg to differ: your option is equivalent to (and I suspect a bit slower
> than) atleast_1d, which is what the OP complained about.
On Wednesday 02 May 2007 14:45:40 Christopher Barker wrote:
> Pierre GM wrote:
> > If you need your inputs to be array or scalar and stay that way
>
> It didn't sound like the OP wanted that. I suspect that what is wanted
> if for to always be a 1-d array (i.e. vector). To do that, I'd do:
I beg t
Pierre GM wrote:
> If you need your inputs to be array or scalar and stay that way
It didn't sound like the OP wanted that. I suspect that what is wanted
if for to always be a 1-d array (i.e. vector). To do that, I'd do:
import numpy as N
>>> def test(a):
...b = N.asarray(a, dtype=N.float)
On Wednesday 02 May 2007 14:15:06 Fernando Perez wrote:
> On 5/2/07, Darren Dale <[EMAIL PROTECTED]> wrote:
> > I know Numeric is no longer supported, but I just upgraded to python-2.5
> > and now I'm having problems indexing Numeric arrays:
>
> Fine on 32-bit ubuntu, using Python 2.5:
> But I thi
On 5/2/07, Darren Dale <[EMAIL PROTECTED]> wrote:
> I know Numeric is no longer supported, but I just upgraded to python-2.5 and
> now I'm having problems indexing Numeric arrays:
Fine on 32-bit ubuntu, using Python 2.5:
In [4]: Numeric.arange(0,10)[:]
Out[4]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
I know Numeric is no longer supported, but I just upgraded to python-2.5 and
now I'm having problems indexing Numeric arrays:
In [1]: import Numeric
In [2]: Numeric.arange(0,10)
Out[2]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [3]: Numeric.arange(0,10)[:10]
Out[3]: array([0, 1, 2, 3, 4, 5, 6, 7
On Wednesday 02 May 2007 12:27:10 mark wrote:
> Any reason NOT to have asarray(3,'d') return an array of length 1?
Because then, it would be "an array, not necessarily a float" ;) You just
noticed yourself that an array of dimension 1 is pretty much like a list,
while an array of dimension 0 is
mark wrote:
> OK, so in my example, I get a zero dimension array. Apparently a
> feature, not a bug.
> What I don't understand is why it isn't an array of lenght one? (or:
> why it isn't a bug?)
Because we need a way to get rank-0 arrays.
> Is there any use for a zero dimension array?
http://pro
OK, so in my example, I get a zero dimension array. Apparently a
feature, not a bug.
What I don't understand is why it isn't an array of lenght one? (or:
why it isn't a bug?)
Is there any use for a zero dimension array?
I would very much like it to be a one dimension array.
In my application I don'
On Wednesday 02 May 2007 11:39:29 Francesc Altet wrote:
> El dc 02 de 05 del 2007 a les 09:52 -0400, en/na Pierre GM va escriure:
> > In your example:
> > > >>> b = asarray(3,'d')
> >
> > b is really a numpy scalar, so it doesn't have a length. But it does have
> > a size (1) and a ndim (0).
>
> Ju
El dc 02 de 05 del 2007 a les 09:52 -0400, en/na Pierre GM va escriure:
> Mark,
> In your example:
> > >>> b = asarray(3,'d')
>
> b is really a numpy scalar, so it doesn't have a length. But it does have a
> size (1) and a ndim (0).
Just one correction in terms of the current naming convention:
On Wednesday 02 May 2007 10:00:58 Charles R Harris wrote:
> On 5/2/07, Pierre GM <[EMAIL PROTECTED]> wrote:
> > Mark,
> Or just array([1],'d')
Except that in that case you need to know in advance the input is a scalar to
put it in a list. The atleast_1d should work better on any input.
_
On 5/2/07, Pierre GM <[EMAIL PROTECTED]> wrote:
Mark,
In your example:
> >>> b = asarray(3,'d')
b is really a numpy scalar, so it doesn't have a length. But it does have
a
size (1) and a ndim (0).
If you need to have arrays with a length, you can force the array to have
a
dimension 1 with atlea
Mark,
In your example:
> >>> b = asarray(3,'d')
b is really a numpy scalar, so it doesn't have a length. But it does have a
size (1) and a ndim (0).
If you need to have arrays with a length, you can force the array to have a
dimension 1 with atleast_1d(b) or array(b,copy=False,ndmin=1)
_
mark wrote:
> Sorry for joining this discussion late.
> If you are only interested in the four largest eigenvalues, there are
> more efficient algorithms out there than just eig().
> There are algorithms that just give you the N largest.
> Then again, I don't know of any Python implementations, but
koara wrote:
> Hello, when saving a sparse matrix via scipy 0.5.2:
> scipy.io.mmio.mmwrite(), an exception is thrown:
>
> scipy.io.mmio.py: line 269: AttributeError: gettypecode not found
>
> changing the line to read
>
> 269: typecode = a.dtype.char
>
> fixes the problem.
>
> _
Hello, when saving a sparse matrix via scipy 0.5.2:
scipy.io.mmio.mmwrite(), an exception is thrown:
scipy.io.mmio.py: line 269: AttributeError: gettypecode not found
changing the line to read
269: typecode = a.dtype.char
fixes the problem.
___
Numpy
Sorry for joining this discussion late.
If you are only interested in the four largest eigenvalues, there are
more efficient algorithms out there than just eig().
There are algorithms that just give you the N largest.
Then again, I don't know of any Python implementations, but I haven't
looked,
Mar
Hello -
I try to convert an input argument to an array of floats.
Input can be an array of integers or floats, or scalar integers or
floats.
The latter seem to give a problem, as they return an array that
doesn't have a length.
I don't quite understand what b really is in the example below.
Doesn't
Anyone know where to find usable rpms from scipy on centos4.4?
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On Tue, May 01, 2007 at 05:36:19PM -0700, koara wrote:
> scipy 0.5.2, in scipy.sparse.lil_matrix.__mul__: the optimization for
> when multiplying by zero scalar is flawed. A copy of the original
> matrix is returned, rather than the correct zero matrix. Nasty bug
> because it only manifests itself
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