To all,
I came across an old thread in the archives in which Alexander Schmolck gave
an example of a Matlab like matrix formatter he authored for Python. Is this
formatter still available some where? Or has anyone else done anything
similar?
-Simon
P.S. Here is a short example of the output:
*
Thank you both for your replies - the difference is clear to me now.
Chris
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Last year I wrote a program that uses the affine_transform()
function in numarray to resample and co-add datacubes with WCS
offsets in 3D. This function makes it relatively easy to handle
N-D offsets and rotations with a smooth interpolant, which is
exactly what I wanted. However, I am finding that
On Fri, 9 Mar 2007 16:11:28 +
[EMAIL PROTECTED] wrote:
> Hi,
>
> I've done a patch for allowing compiling the last version of numexpr with
> the MSVC Toolkit 2003 compiler on Windows platforms. You can fetch it
> from:
>
> http://www.pytables.org/trac/changeset/2514/trunk
Checked in. It did
lechtlr wrote:
>I would very much appreciate, if someone can give suggestions to implement
> a loop to generate a string in the following format.
> Variables with assigned values are given in A:
> A = {a1:2.0, a2:4.0,………,an:5.0}
> I want to transform what is in the dictionary in
On Friday 09 March 2007 14:41:23 lechtlr wrote:
>I would very much appreciate, if someone can give suggestions to
> implement a loop to generate a string in the following format. Variables
> with assigned values are given in A:
> A = {a1:2.0, a2:4.0,
,an:5.0}
> I want to transform w
On 3/9/07, Bill Baxter <[EMAIL PROTECTED]> wrote:
> Has enough time passed with no top level random function that we can
> now put one back in?
> If I recall, the former top level rand() was basically removed because
> it didn't adhere to the "shapes are always tuples" convention.
Top-level rand f
| Date: Fri, 9 Mar 2007 06:58:32 -0800
| From: "Sebastian Haase" <[EMAIL PROTECTED]>
| Subject: Re: [Numpy-discussion] Numpy-discussion Digest, Vol 6, Issue 18
| To: "Discussion of Numerical Python"
|
| On 3/9/07, James A. Bednar <[EMAIL PROTECTED]> wrote:
| > | From: Robert Kern <[EMAIL
Has enough time passed with no top level random function that we can
now put one back in?
If I recall, the former top level rand() was basically removed because
it didn't adhere to the "shapes are always tuples" convention.
Has enough time passed now that we can put something like it back in
the t
str(A)[1:-1] ?
--bb
On 3/10/07, lechtlr <[EMAIL PROTECTED]> wrote:
I would very much appreciate, if someone can give suggestions to
implement a loop to generate a string in the following format.
Variables with assigned values are given in A:
A = {a1:2.0, a2:4.0,………,an:5.0}
I want to transf
Robert Kern wrote:
> What version of numpy are you using?
>>> from numpy import *
>>> print __version__
1.0.1
>>> a = array(0, dtype=int)
>>> b = array(0, dtype=int)
>>> divide(a, b)
Warning: divide by zero encountered in divide
0
>>> geterr()
{'over': 'print', 'divide': 'print', 'invalid': '
I would very much appreciate, if someone can give suggestions to implement a
loop to generate a string in the following format.
Variables with assigned values are given in A:
A = {a1:2.0, a2:4.0,
,an:5.0}
I want to transform what is in the dictionary into a string in the
follo
Oh dear, sorry, I should have read your email more carefully.
Matthew
On 3/8/07, Daniel Mahler <[EMAIL PROTECTED]> wrote:
> On 3/8/07, Matthew Brett <[EMAIL PROTECTED]> wrote:
> > > > My problem is not space, but time.
> > > > I am creating a small array over and over,
> > > > and this is turning
Anne Archibald wrote:
> On 09/03/07, Robert Kern <[EMAIL PROTECTED]> wrote:
>> Mark P. Miller wrote:
>>> As an aside, are the random number generators from scipy.random the same
>>> as those for numpy.random? If not, will those of us who need to just
>>> use a few random numbers here and there thr
=
Announcing PyTables 2.0b1
=
The PyTables development team is very happy to announce the public
availability of the first *beta* version of PyTables 2.0. Starting with
this release, both the API and the file format have entered in the stage
of fre
Mark P. Miller wrote:
> Robert Kern wrote:
> > scipy.random is not a package. scipy/__init__.py does a "from numpy
> import *"
> > and thus pulls in numpy.random.
>
> Got it...and one more question:
>
> What about using something like
> from numpy.random import mtrand
>
> And then using mtran
Robert Kern wrote:
> scipy.random is not a package. scipy/__init__.py does a "from numpy
import *"
> and thus pulls in numpy.random.
>
Got it...and one more question:
What about using something like
from numpy.random import mtrand
And then using mtrand.seed and mtrand.normal in code?
Would
On 09/03/07, Robert Kern <[EMAIL PROTECTED]> wrote:
> Mark P. Miller wrote:
> > As an aside, are the random number generators from scipy.random the same
> > as those for numpy.random? If not, will those of us who need to just
> > use a few random numbers here and there throughout our code (we don'
Christopher Barker wrote:
> Robert Kern wrote:
>> However, I believe that divide() on two Python ints goes through a different
>> path than for numpy arrays and numpy scalars. Rather, they are recognized as
>> objects that implement .__div__(), and that is called instead.
>
> Then wouldn't this be
Mark P. Miller wrote:
> As an aside, are the random number generators from scipy.random the same
> as those for numpy.random? If not, will those of us who need to just
> use a few random numbers here and there throughout our code (we don't
> need arrays of random numbers or broadcasting abiliti
A Divendres 09 Març 2007 18:40, Sebastian Haase escrigué:
> Hi !
> This is really only one question:
>
> Which dtypes are supported by numexpr ?
Well, numexpr does support any dtype that is homogeneous, except 'uint64'.
This is because internally all the unsigned types are upcasted to the
smalle
Hi !
This is really only one question:
Which dtypes are supported by numexpr ?
We are very interested in numexpr !
Where is the latest / most-up-to-date documentation ?
Thanks,
Sebastian Haase
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Robert Kern wrote:
> However, I believe that divide() on two Python ints goes through a different
> path than for numpy arrays and numpy scalars. Rather, they are recognized as
> objects that implement .__div__(), and that is called instead.
Then wouldn't this be different:
>>> a = N.array(0, dt
This discussion has much in common with a previous thread that I started
("When and where to use Numpy...").
I fully admit to being a naive numpy user, but it seems to me that it
would be helpful if the documentation provided some explicit statements
to inform potential users about the best typ
Hi,
I've done a patch for allowing compiling the last version of numexpr with
the MSVC Toolkit 2003 compiler on Windows platforms. You can fetch it
from:
http://www.pytables.org/trac/changeset/2514/trunk
BTW, I understand now why Tim Hochberg was so worried about the time
that it takes to compil
On 3/9/07, James A. Bednar <[EMAIL PROTECTED]> wrote:
> | From: Robert Kern <[EMAIL PROTECTED]>
> | Subject: Re: [Numpy-discussion] in place random generation
> |
> | Daniel Mahler wrote:
> | > On 3/8/07, Charles R Harris <[EMAIL PROTECTED]> wrote:
> |
> | >> Robert thought this might relate t
| From: Robert Kern <[EMAIL PROTECTED]>
| Subject: Re: [Numpy-discussion] in place random generation
|
| Daniel Mahler wrote:
| > On 3/8/07, Charles R Harris <[EMAIL PROTECTED]> wrote:
|
| >> Robert thought this might relate to Travis' changes adding
| >> broadcasting to the random number
Robert Kern <[EMAIL PROTECTED]> writes:
> Berthold Höllmann wrote:
>
>> My question is, is "kind='merge'" sorting garanteed to be conservative?
>
> Yes, such a sort is called "stable" (at least in US computer science) and is
> documented in the docstring.
I looked in the numpy book, but this info
Berthold Höllmann wrote:
> My question is, is "kind='merge'" sorting garanteed to be conservative?
Yes, such a sort is called "stable" (at least in US computer science) and is
documented in the docstring.
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enig
Arnd Baecker wrote:
> On Fri, 9 Mar 2007, Gregory Lielens wrote:
>
>
>> Hi,
>> we are looking for Bessel/Hankel ufuncs, and naturally found the cephes
>> module developed by Travis. Had a quick look, and it seems the latest
>> version I found (cepehs-1.3) is quite old, targeted to python 1.5 an
On Fri, 9 Mar 2007, Gregory Lielens wrote:
> Hi,
> we are looking for Bessel/Hankel ufuncs, and naturally found the cephes
> module developed by Travis. Had a quick look, and it seems the latest
> version I found (cepehs-1.3) is quite old, targeted to python 1.5 and
> numerics. Questions and disc
Hi,
we are looking for Bessel/Hankel ufuncs, and naturally found the cephes
module developed by Travis. Had a quick look, and it seems the latest
version I found (cepehs-1.3) is quite old, targeted to python 1.5 and
numerics. Questions and discussions about it on newsgroup seems to have
died o
In our process of switching from Numeric to numpy we had a problem
with a function using argsort. The function expected a conservative
order of the argsort result but we got:
>>> import numpy
>>> numpy.argsort(numpy.ones(16, dtype=numpy.dtype('float64')))
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8,
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