Pierre GM wrote:
On Dec 4, 2008, at 7:22 AM, Manuel Metz wrote:
Will loadtxt in that case remain as is? Or will the _faulttolerantconv
class be used?
No idea, we need to discuss it. There's a problem with
_faulttolerantconv: using np.nan as default value will not work in
Python2.6
Pierre GM wrote:
All,
Here's the second round of genloadtxt. That's a tad cleaner version than
the previous one, where I tried to take into account the different
comments and suggestions that were posted. So, tabs should be supported
and explicit whitespaces are not collapsed.
FYI, in
Alan G Isaac wrote:
If I know my data is already clean
and is handled nicely by the
old loadtxt, will I be able to turn
off and the special handling in
order to retain the old load speed?
Alan Isaac
Hi all,
that's going in the same direction I was thinking about.
When I thought about
Manuel Metz wrote:
Alan G Isaac wrote:
If I know my data is already clean
and is handled nicely by the
old loadtxt, will I be able to turn
off and the special handling in
order to retain the old load speed?
Alan Isaac
Hi all,
that's going in the same direction I was thinking about
Pierre GM wrote:
On Nov 27, 2008, at 3:08 AM, Manuel Metz wrote:
Certainly, yes! Dealing with fixed-length fields would be necessary.
The
case I had in mind had both -- a separator (|) __and__ fixed-length
fields -- and is probably very special in that sense. But such
data-files exists out
Pierre GM wrote:
On Nov 26, 2008, at 5:55 PM, Ryan May wrote:
Manuel Metz wrote:
Ryan May wrote:
3) Better support for missing values. The docstring mentions a
way of
handling missing values by passing in a converter. The problem
with this is
that you have to pass in a converter
Ryan May wrote:
Hi,
I have a couple more changes to loadtxt() that I'd like to code up in time
for 1.3, but I thought I should run them by the list before doing too much
work. These are already implemented in some fashion in
matplotlib.mlab.csv2rec(), but the code bases are different
Claude Gouedard wrote:
Hi ,
I'm just surprised by the behaviour of numpy.asarray on lists.
Can someone comment this :
=
a=(1)
aa=asarray(a)
print aa.size , aa.shape
1 ( )
=
The shape doesnot reflect the actual size.
Because a is not a
Hi list,
are there any plans to implement a routine to solve the generalized
eigenvector problem as is done in matlab ?
see http://www.mathworks.com/access/helpdesk/help/techdoc/ref/eig.html
manuel
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Jonathan Wright wrote:
Manuel Metz wrote:
are there any plans to implement a routine to solve the generalized
eigenvector problem as is done in matlab ?
see http://www.mathworks.com/access/helpdesk/help/techdoc/ref/eig.html
import numpy
help(numpy.linalg.eig)
Is it what you
Chris Withers wrote:
Hi All,
Say I have an aribtary number of arrays:
arrays = [array([1,2,3]),array([4,5,6]),array([7,8,9])]
How can I sum these all together?
My only solution so far is this:
sum = arrays[0]
for a in arrays[1:]:
sum += a
...which is ugly :-S
cheers,
Manuel Metz wrote:
Chris Withers wrote:
Hi All,
Say I have an aribtary number of arrays:
arrays = [array([1,2,3]),array([4,5,6]),array([7,8,9])]
How can I sum these all together?
My only solution so far is this:
sum = arrays[0]
for a in arrays[1:]:
sum += a
...which is ugly :-S
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