Nick Fotopoulos wrote:
On July 17, 2006, at 9:01 PM, Travis Oliphant wrote:
I'd like to make release 1.0beta on Thursday. Please submit
bug-reports
and fixes before then.
-Travis
Is it possible to incorporate v7 mat-file support before the
new-feature freeze?
That is in SciPy. I'm
The recent message by Ferenc.Pintye (how does one pronounce that BTW)
reminded me of something I've been meaning to discuss: I think we can do
a better job dealing with stacked matrices. By stacked matrices I mean 3
(or more) dimensional arrays where the last two dimensions are
considered to
On Jul 20, 2006, at 6:00 PM, Travis Oliphant wrote:
Is it possible to incorporate v7 mat-file support before the new-
feature freeze?
That is in SciPy. I'm talking about the NumPy 1.0beta release.
But, I would like to get the v7 mat-file support into SciPy soon.
Perhaps you are arguing
Stefan van der Walt wrote:
Hi Travis
Albert and I are busy doing some final beta testing. Quick question:
can Fortran-order arrays be contiguous, ever?
In [62]: N.empty((3,3),order='F').flags['CONTIGUOUS']
Out[62]: False
Thank you very much for the testing. You two have been
Fernando Perez wrote:
On 7/18/06, Tim Hochberg [EMAIL PROTECTED] wrote:
Eric Emsellem wrote:
thanks for the tips. (indeed your add.reduce is correct: I just
wrote
this down too quickly, in the script I have a sum included).
And yes you are right for the memory issue, so I may just keep
On 7/20/06, Michael Sorich [EMAIL PROTECTED] wrote:
Can you give an specific example of how this would work? The codes
really is ugly and it is not clear to me what exactly it does.
ok here's a quick example:
import numpy
n=5
i=3
j=4
A=numpy.random.randint(0,2,(n,n)) #make random graph
I looked into the various concatenation methods a bit more to better
understand what's going on under the hood.
Here's essentially what these different methods do:
vstack(tup):
concatenate( map(atleast_2d,tup), axis=0 )
hstack(tup):
concatenate( map(atleast_1d,tup),axis=1 )
Hi,
The function bincount() counts the number of each value found in the
input array:
In [15]: numpy.bincount( array([1,3,3,3,4],dtype=int32) )
Out[15]: array([0, 1, 0, 3, 1])
According to the documentation, the input array must be non-negative
integers.
However an exception occurs when the
Howdy,
Is there any nicer syntax for the following operations on arrays?
Append a row:
a = vstack((a,row))
Append a column:
a = hstack((a,col))
Append a row of zeros:
a = vstack((a,zeros((1,a.shape[1]
Append a col of zeros:
a = hstack((a,zeros((a.shape[0],1
Insert a row before row j
a
While playing a little more with bincount(), one modification would be
handy: Allow negative integers in the bin list, but skip them when
counting bins
My specific use case is calculating subtotals on columns of large
datasets (1m rows x 30 cols), where some rows need to be excluded. The
I'm having a problem converting a C extension module that was
originally written for numarray to use numpy. I using swig to create
a wrapper flle for the C code. I have added the
numpy.get_numarray_include() method to my setup.py file and have
changed the numarray/libnumarray.h to use
NumPy wrote:
#188: dtype should have nice looking str representation
-+--
Reporter: sebhaase |Owner: oliphant
Type: enhancement | Status: closed
Priority: normal |
I've created the 1.0b1 release tag in SVN and will be uploading files
shortly to Sourceforge.
I've also created a 1.0 release branch called ver1.0
The trunk is now version 1.1 of NumPy and should be used for
new-development only. I don't expect 1.1 to come out for at least a year.
Bug-fixes
Bill Baxter schrieb:
Finally, I noticed that the atleast_nd methods return arrays
regardless of input type. At a minimum, atleast_1d and atleast_2d on
matrices should return matrices. I'm not sure about atleast_3d, since
matrices can't be 3d. (But my opinon is that the matrix type should
Hi Tim,
many thanks for the tipps, i used the same way
with vectorized (chunk) method on the indexing operation.
..
..
# out = zeros((size_mcf[0],sizes_smatrix[2]+5),Float32)
# size_mcf[0] ~ 24
eig = zeros((size_mcf[0],3,3),dtype=Float32)
On 7/21/06, Sven Schreiber [EMAIL PROTECTED] wrote:
Bill Baxter schrieb:
Finally, I noticed that the atleast_nd methods return arrays
regardless of input type.
Are you sure? I reported that issue with *stack and I remember it was fixed.
Doh! My bad. You're right. I was looking at the
Hi folks,
Since 1.0 release is eminent, I just wanted to draw the attention to
two failures I get when I run numpy.test(1).
I've never been able to get numpy to pass all test cases, but now it
fails a second one, so .. I'm pasting it below. Please let me know if
these are
Hi,
I have a (medical) image file.
I wrote a nice interface based on memmap using numarray.
The class design I used was essentially to return a numarray array
object with a new custom attribute giving access to special
information about the base file.
Now with numpy I noticed that a numpy
Hi!
I'm trying to convert my numarray records code to numpy.
type(m.hdrArray)
class 'numpy.core.records.recarray'
m.hdrArray.d
[(array([ 1., 1., 1.], dtype=float32),)]
but I get:
m.hdrArray[0].getfield('d')
5.43230922614e-312
Am I missing something or is this a bug ?
Further details:
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