Hi Chris,
No problem, the Internet is made for sharing ideas and solutions ;)
Matthieu
2008/11/6 Chris LeBlanc [EMAIL PROTECTED]:
Hi Matthieu,
Thanks very much, thats exactly the sort of information I was looking
for. I'm heading to a conference this weekend, but hope to get
started on
Hi,
I might be doing something stupid so I thought i'd check here before
filing a bug report.
Firstly:
In [8]: np.__version__
Out[8]: '1.3.0.dev5883'
Basically, pickling an element from a recarray seems to break silently:
In [1]: import numpy as np
In [2]: dtype =
Hi all,
What can be done if
the new shape is not compatible with the original shape ?
The number of columns is fixed and should be 8.
One could split the original array C
C
array([[ 0.e+00, 1.e-01],
[ 4.1500e+01, 1.e-01],
[ 4.1600e+01,
Can you clarify?
-הודעה מקורית-
מאת: [EMAIL PROTECTED] בשם Nils Wagner
נשלח: ה 06-נובמבר-08 17:13
אל: numpy-discussion@scipy.org
נושא: [Numpy-discussion] reshape
Hi all,
What can be done if
the new shape is not compatible with the original shape ?
The number of columns is fixed and
On Thu, 6 Nov 2008 19:47:19 +0200
Nadav Horesh [EMAIL PROTECTED] wrote:
Can you clarify?
I have an array with a number of rows (nrows) and two
columns.
The first column entries correspond to x_i, the second
column contains the corresponding values y_i = f(x_i)
That array should be written
Dear Robert,
indeed, this is the difference ! Thanks ! Seeding numpy with 4357
gives identical sequences...
Giovanni
On 05 Nov 2008, at 19:01, Robert Kern wrote:
On Wed, Nov 5, 2008 at 08:05, Giovanni Samaey
[EMAIL PROTECTED] wrote:
Hi,
how about other seed values ? I thought
cc = C.ravel()
lines_list = cc[i:i+8] for i in range(1, len(cc), 8)]
Nadav
-הודעה מקורית-
מאת: [EMAIL PROTECTED] בשם Nils Wagner
נשלח: ה 06-נובמבר-08 20:59
אל: Discussion of Numerical Python
נושא: Re: [Numpy-discussion] reshape
On Thu, 6 Nov 2008 19:47:19 +0200
Nadav Horesh [EMAIL
A correction:
lines_list = [cc[i:i+8] for i in range(1, len(cc), 8)]
Nadav
-הודעה מקורית-
מאת: [EMAIL PROTECTED] בשם Nadav Horesh
נשלח: ה 06-נובמבר-08 21:43
אל: Discussion of Numerical Python
נושא: RE: [Numpy-discussion] reshape
cc = C.ravel()
lines_list = cc[i:i+8] for i in range(1,
I'm just about to embark on a long-term research project and was
planning to use numpy.random to generate stimuli for our experiments.
We plan to store only the parameters and RandomState seed for each
stimulus and I'm concerned about stability of the API in the long
term: will the parameters and
On Thu, 6 Nov 2008 21:50:52 +0200
Nadav Horesh [EMAIL PROTECTED] wrote:
A correction:
lines_list = [cc[i:i+8] for i in range(1, len(cc), 8)]
Nadav
Hi Nadav,
Thank you very much. My next question;
How can I save lines_list to a file with the following
so called small field format
On Thu, Nov 6, 2008 at 14:05, Barry Wark [EMAIL PROTECTED] wrote:
I'm just about to embark on a long-term research project and was
planning to use numpy.random to generate stimuli for our experiments.
We plan to store only the parameters and RandomState seed for each
stimulus and I'm concerned
On Thu, Nov 6, 2008 at 12:09 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Thu, Nov 6, 2008 at 14:05, Barry Wark [EMAIL PROTECTED] wrote:
I'm just about to embark on a long-term research project and was
planning to use numpy.random to generate stimuli for our experiments.
We plan to store only
On Thu, Nov 6, 2008 at 1:23 PM, T J [EMAIL PROTECTED] wrote:
On Wed, Nov 5, 2008 at 2:09 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
I'm inclined to go with logaddexp and add logsumexp as an alias for
logaddexp.reduce. But I'll wait until tomorrow to see if there are more
comments.
On Thu, Nov 6, 2008 at 15:12, Barry Wark [EMAIL PROTECTED] wrote:
On Thu, Nov 6, 2008 at 12:09 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Thu, Nov 6, 2008 at 14:05, Barry Wark [EMAIL PROTECTED] wrote:
I'm just about to embark on a long-term research project and was
planning to use
On Thu, Nov 6, 2008 at 1:48 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
What is your particular interest in these other bases and why would
they be better than working in base e and converting at the end?
The interest is in information theory, where quantities are
(standardly) represented in
On Thu, Nov 6, 2008 at 3:17 PM, T J [EMAIL PROTECTED] wrote:
On Thu, Nov 6, 2008 at 1:48 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
What is your particular interest in these other bases and why would
they be better than working in base e and converting at the end?
The interest is in
On Thu, Nov 6, 2008 at 1:55 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Thu, Nov 6, 2008 at 15:12, Barry Wark [EMAIL PROTECTED] wrote:
On Thu, Nov 6, 2008 at 12:09 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Thu, Nov 6, 2008 at 14:05, Barry Wark [EMAIL PROTECTED] wrote:
I'm just about to
On Thu, Nov 6, 2008 at 2:36 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
I could add exp2, log2, and logaddexp2 pretty easily. Almost too easily, I
don't want to clutter up numpy with a lot of functions. However, if there is
a community for these functions I will put them in.
I worry about
On Thu, Nov 6, 2008 at 16:58, Barry Wark [EMAIL PROTECTED] wrote:
On Thu, Nov 6, 2008 at 1:55 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Thu, Nov 6, 2008 at 15:12, Barry Wark [EMAIL PROTECTED] wrote:
On Thu, Nov 6, 2008 at 12:09 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Thu, Nov 6, 2008 at
Barry Wark wrote:
In our usage (neural physiology), we've recorded the physiological
response to a given stimulus. So being able to recover the _exact_
original stimulus that produced the recorded data is critical.
I'd be inclined to say that if you really want the exact same string of
Hi all,
I'm trying to import a 16-bit tiff image into a numpy array. I have
found, using google, suggestions to do the following:
After starting with:
i = Image.open('16bitGreyscaleImage.tif')
Stéfan van der Walt suggested:
a = np.array(i.getdata()).reshape(i.size) # a 1d numpy array
and
2008/11/6 Robert Kern [EMAIL PROTECTED]:
On Thu, Nov 6, 2008 at 21:54, Angus McMorland [EMAIL PROTECTED] wrote:
Hi all,
I'm trying to import a 16-bit tiff image into a numpy array. I have
found, using google, suggestions to do the following:
After starting with:
i =
On Thu, Nov 6, 2008 at 21:54, Angus McMorland [EMAIL PROTECTED] wrote:
Hi all,
I'm trying to import a 16-bit tiff image into a numpy array. I have
found, using google, suggestions to do the following:
After starting with:
i = Image.open('16bitGreyscaleImage.tif')
Stéfan van der Walt
Hi,
I need to perform iir filter operation using numpy and could not googled any
useful info for this. Are there filter operation similar the matlab filter
funciton in Numpy?
Thanks
Frank
_
Color coding for safety: Windows
On Fri, Nov 7, 2008 at 00:25, frank wang [EMAIL PROTECTED] wrote:
Hi,
I need to perform iir filter operation using numpy and could not googled any
useful info for this. Are there filter operation similar the matlab filter
funciton in Numpy?
Not in numpy. scipy.signal.lfilter() does, though.
On Tue, Nov 4, 2008 at 7:59 AM, T J [EMAIL PROTECTED] wrote:
So can someone explain why I *must* define ATLAS. I tried a number of
variations on site.cfg and could not get numpy to find atlas with any
of them.
Ok, I took a brief look at this: I forgot that Ubuntu and Debian added
an
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