Le 26/01/2012 19:19, josef.p...@gmail.com a écrit :
The discussion had this reversed, numpy matches the behavior of
MATLAB, while R (statistics) only returns the cross covariance part as
proposed.
I would also say that there was an attempt to match MATLAB behavior.
However, there is big
It seems weird that it wouldn't work, as this is a pretty standard setup.
Here's a few ideas of things to check:
- Double-check it's really 32 bit Python (checking sys.maxint)
- Is there another Python installation that may cause some conflicts?
- Did you download the numpy superpack from the
Yup, it's 32-bit python:
Python 2.7.2 (default, Jun 12 2011, 15:08:59) [MSC v.1500 32 bit (Intel)]
on win32
Type copyright, credits or license() for more information.
I've only got one python instance installed here :D
Here's where I got the numpy installer,
Dear all,
suppose I have a ndarray a:
In [66]: a
Out[66]: array([0, 1, 2, 3, 4])
how can use it as 5X1 array without doing a=a.reshape(5,1)?
thanks
Chao
--
***
Chao YUE
Laboratoire des Sciences du Climat et de
On 27. jan. 2012, at 14:52, Chao YUE wrote:
Dear all,
suppose I have a ndarray a:
In [66]: a
Out[66]: array([0, 1, 2, 3, 4])
how can use it as 5X1 array without doing a=a.reshape(5,1)?
Several ways, this is one, although not much simpler.
In [6]: a
Out[6]: array([0, 1, 2, 3, 4])
In
On Fri, Jan 27, 2012 at 9:28 AM, Paul Anton Letnes
paul.anton.let...@gmail.com wrote:
On 27. jan. 2012, at 14:52, Chao YUE wrote:
Dear all,
suppose I have a ndarray a:
In [66]: a
Out[66]: array([0, 1, 2, 3, 4])
how can use it as 5X1 array without doing a=a.reshape(5,1)?
On Friday, January 27, 2012, Pierre Haessig pierre.haes...@crans.org
wrote:
Le 26/01/2012 19:19, josef.p...@gmail.com a écrit :
The discussion had this reversed, numpy matches the behavior of
MATLAB, while R (statistics) only returns the cross covariance part as
proposed.
I would also say
Thanks all.
chao
2012/1/27 Tony Yu tsy...@gmail.com
On Fri, Jan 27, 2012 at 9:28 AM, Paul Anton Letnes
paul.anton.let...@gmail.com wrote:
On 27. jan. 2012, at 14:52, Chao YUE wrote:
Dear all,
suppose I have a ndarray a:
In [66]: a
Out[66]: array([0, 1, 2, 3, 4])
how can
On 01/27/2012 09:00 AM, Benjamin Root wrote:
On Friday, January 27, 2012, Pierre Haessig pierre.haes...@crans.org
mailto:pierre.haes...@crans.org wrote:
Le 26/01/2012 19:19, josef.p...@gmail.com
mailto:josef.p...@gmail.com a écrit :
The discussion had this reversed, numpy matches the
Sorry then, I'm afraid I'm out of (simple ideas). Out of curiosity, I tried
to install Python 2.7.2 and numpy 1.6.1 on a Windows 7 computer and it
worked just fine, so it must be something with your specific setup...
-=- Olivier
2012/1/27 William McLendon wcmc...@gmail.com
Yup, it's 32-bit
I have been using numpy for several years and I am very impressed with its
flexibility. However, there is one problem that has always bothered me.
Quite often I need to test consistently whether a variable is any of the
following: an empty list, an empty array or None. Since both arrays and
On Fri, Jan 27, 2012 at 2:46 PM, Bartosz Telenczuk
b.telenc...@biologie.hu-berlin.de wrote:
I have been using numpy for several years and I am very impressed with its
flexibility. However, there is one problem that has always bothered me.
Quite often I need to test consistently whether a
On Fri, Jan 27, 2012 at 20:46, Bartosz Telenczuk
b.telenc...@biologie.hu-berlin.de wrote:
I have been using numpy for several years and I am very impressed with its
flexibility. However, there is one problem that has always bothered me.
Quite often I need to test consistently whether a
In [20]: dt_knobs =
[('pvName',(str,40)),('start','float'),('stop','float'),('mode',(str,10))]
In [21]: r_knobs = np.recarray([],dtype=dt_knobs)
In [22]: r_knobs
Out[22]:
Hi all
I am a fairly recent convert to python and I have got a question that's
got me stumped. I hope this is the right mailing list: here goes :)
I am reading some time series data out of a netcdf file a single
timestep at a time. If the data is NaN, I want to reset it to the
minimum of
On Fri, Jan 27, 2012 at 21:17, Emmanuel Mayssat emays...@gmail.com wrote:
In [20]: dt_knobs =
[('pvName',(str,40)),('start','float'),('stop','float'),('mode',(str,10))]
In [21]: r_knobs = np.recarray([],dtype=dt_knobs)
In [22]: r_knobs
Out[22]:
Thank you for your tips. I was not aware of the possible problems with len.
There is no way to test all of the cases (empty sequence, empty array,
None) in the same way. Usually, it's a bad idea to conflate the three.
I agree that this should be avoided. However, there are cases in which it is
On 1/27/12 5:21 PM, Eric Firing wrote:
On 01/27/2012 11:18 AM, Howard wrote:
Hi all
I am a fairly recent convert to python and I have got a question that's
got me stumped. I hope this is the right mailing list: here goes :)
I am reading some time series data out of a netcdf file a single
On Fri, Jan 27, 2012 at 4:37 PM, Howard how...@renci.org wrote:
I have found, in using tricontourf, that in the mapping from data values
to color values, the range of the data seems to include even the data from
the masked triangles. This causes the data to be either monochromatic or
Hey, Ondřej
2012/1/21 Ondřej Čertík ondrej.cer...@gmail.com:
I read the Mandelbrot code using NumPy at this page:
http://mentat.za.net/numpy/intro/intro.html
I wrote this as a tutorial for beginners, so the emphasis is on
simplicity. Do you have any suggestions on how to improve the code
Eric's probably right and it's indexing with a masked array that's causing
you trouble.
Since you seem to say your NaN values correspond to your mask, you should
be able to simply do:
modelData[modeData.mask] = dataMin
Note that in further processing it may then make more sense to remove the
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