Hello,
I learned you answered me, but I did not get
your message : can you send it to me again ?
Thanks ,
David Landriu
David Landriu DAPNIA/SAp CEA SACLAY (France)
Phone : (
On Aug 31, 2006, at 6:19 AM, LANDRIU David SAp wrote:
>I learned you answered me, but I did not get
> your message : can you send it to me again ?
Does this help?
http://sourceforge.net/mailarchive/forum.php?
thread_id=30384097&forum_id=4890
-steve
---
Fernando Perez wrote:
> On 8/30/06, Stefan van der Walt <[EMAIL PROTECTED]> wrote:
>
>
>> The current behaviour makes sense, but is maybe not consistent:
>>
>> N.array([],dtype=object).size == 1
>> N.array([[],[]],dtype=object).size == 2
>>
>
> Yes, including one more term in this check:
>
Torgil Svensson wrote:
> I'm using windows datetimes (100nano-seconds since 0001,1,1) as time
> in a numpy array and was hit by this behaviour.
>
>
numpy.__version__
> '1.0b4'
>
a=numpy.array([63292539433000L],numpy.uint64)
t=a[0]
t
> 632925
In version 0.9.6 one used to be able to do this:
In [4]: numpy.__version__
Out[4]: '0.9.6'
In [6]: numpy.array([numpy.array([4,5,6]), numpy.array([1,2,3])],
dtype=object).shape
Out[6]: (2, 3)
In beta 1 numpy.PyObject no longer exists. I was trying to get the
same behavior with dtype=object bu
When trying to install 1.0b4 I had trouble getting it to detect my
installed atlas. This was because the shipped site.cfg had;
[atlas]
library_dirs = /usr/lib/atlas/3dnow/
atlas_libs = lapack, blas
but I had to change 3dnow to sse2 due to my current state of
pentiumness. In any case it should p
On Tuesday 29 August 2006 19:24, Fernando Perez wrote:
> On 8/29/06, Travis Oliphant <[EMAIL PROTECTED]> wrote:
> > Hi all,
> >
> > Classes start for me next Tuesday, and I'm teaching a class for which I
> > will be using NumPy / SciPy extensively. I need to have a release of
> > these two (and ho
Hi everybody
I've succesfully built Numpy-1.0b4 on HP-UX 11.11 with
all tests passed succesfully (gcc 4.1.1) on Python 2.4.2
I try to import 2 basic Fortran modules compiled with numpy/f2py
(fcompiler = gfortran (gcc 4.1.1))
The modules hello1 and hello2 are almost identical:
C File hello1.
> Yes, because you are adding a signed scalar to an unsigned scalar and a
> float64 is the only thing that can handle it
>
> t+numpy.uint64(1)
Thanks, this make sense. This is a good thing to have back in the head.
//Torgil
On 8/31/06, Travis Oliphant <[EMAIL PROTECTED]> wrote:
> Torgil Svensso
On 8/31/06, Travis Oliphant <[EMAIL PROTECTED]> wrote:
> What about
>
> N.array(3).size
>
> N.array([3]).size
>
> N.array([3,3]).size
>
> Essentially, the [] is being treated as an object when you explicitly
> ask for an object array in exactly the same way as 3 is being treated as
> a number in t
On 8/31/06, Fernando Perez <[EMAIL PROTECTED]> wrote:
On 8/31/06, Travis Oliphant <[EMAIL PROTECTED]> wrote:> What about>> N.array(3).size>> N.array([3]).size>> N.array
([3,3]).size>> Essentially, the [] is being treated as an object when you explicitly> ask for an object array in exactly the same
Hi,
sorry to bother you with probably plenty of stupid question but I would like
to clarify my mind with dtype.
I have a problem to view a recarray, I'm not sure but I suspect a bug or at
least a problem
I have an array with some data, the array is very big but I have no prob
On 8/31/06, Tom Denniston <[EMAIL PROTECTED]> wrote:
In version 0.9.6 one used to be able to do this:In [4]: numpy.__version__Out[4]: '0.9.6'In [6]: numpy.array([numpy.array([4,5,6]), numpy.array([1,2,3])],dtype=object).shapeOut[6]: (2, 3)
In beta 1 numpy.PyObject no longer exists. I was trying to
For this simple example yes, but if one of the nice things about the array constructors is that they know that lists, tuple and arrays are just sequences and any combination of them is valid numpy input. So for instance a list of tuples yields a 2d array. A list of tuples of 1d arrays yields a 3d
On 8/31/06, Tom Denniston <[EMAIL PROTECTED]> wrote:
For this simple example yes, but if one of the nice things about the array constructors is that they know that lists, tuple and arrays are just sequences and any combination of them is valid numpy input. So for instance a list of tuples yields a
Jonathan Taylor wrote:
> When trying to install 1.0b4 I had trouble getting it to detect my
> installed atlas. This was because the shipped site.cfg had;
>
> [atlas]
> library_dirs = /usr/lib/atlas/3dnow/
> atlas_libs = lapack, blas
>
> but I had to change 3dnow to sse2 due to my current state o
Fernando Perez wrote:
> In [8]: N.array(3).shape
> Out[8]: ()
> In [11]: N.array([]).shape
> Out[11]: (0,)
> I guess my only remaining question is: what is the difference between
> outputs #8 and #11 above? Is an empty shape tuple == array scalar,
> while a (0,) shape indicates a one-dimensional
On 8/31/06, Christopher Barker <[EMAIL PROTECTED]> wrote:
Jonathan Taylor wrote:> When trying to install 1.0b4 I had trouble getting it to detect my> installed atlas. This was because the shipped site.cfg had;>> [atlas]> library_dirs = /usr/lib/atlas/3dnow/
> atlas_libs = lapack, blas>> but I had
Christopher Barker wrote:
> Fernando Perez wrote:
>
>> In [8]: N.array(3).shape
>> Out[8]: ()
>>
>
>
>> In [11]: N.array([]).shape
>> Out[11]: (0,)
>>
>
>
>> I guess my only remaining question is: what is the difference between
>> outputs #8 and #11 above? Is an empty shape tupl
But i have hetergenious arrays that have numbers and strings and NoneType, etc.
Take for instance:
In [11]: numpy.array([numpy.array([1,'A', None]),
numpy.array([2,2,'Some string'])], dtype=object)
Out[11]:
array([[1, A, None],
[2, 2, Some string]], dtype=object)
In [12]: numpy.array([num
On 8/31/06, Christopher Barker <[EMAIL PROTECTED]> wrote:
Fernando Perez wrote:> In [8]: N.array(3).shape> Out[8]: ()> In [11]: N.array([]).shape> Out[11]: (0,)> I guess my only remaining question is: what is the difference between> outputs #8 and #11 above? Is an empty shape tuple == array scalar
On 8/31/06, Tom Denniston <[EMAIL PROTECTED]> wrote:
But i have hetergenious arrays that have numbers and strings and NoneType, etc.Take for instance:In [11]: numpy.array([numpy.array([1,'A', None]),numpy.array([2,2,'Some string'])], dtype=object)Out[11]:
array([[1, A, None], [2, 2, Some stri
On 8/31/06, Charles R Harris <[EMAIL PROTECTED]> wrote:
On 8/31/06, Tom Denniston <[EMAIL PROTECTED]
> wrote:
But i have hetergenious arrays that have numbers and strings and NoneType, etc.Take for instance:In [11]: numpy.array([numpy.array([1,'A', None]),numpy.array([2,2,'Some string'])], dtype=o
Yes one can take a toy example and hack it to work but I don't necessarily have control over the input as to whether it is a list of object arrays, list of 1d heterogenous arrays, etc. Before I didn't need to worry about the input because numpy understood that a list of 1d arrays is a 2d piece of
wrote the last email before reading your a = array([1,'A', None]) comment. I definately agree with you on that.
On 8/31/06, Tom Denniston <[EMAIL PROTECTED]> wrote:
Yes one can take a toy example and hack it to work but I don't necessarily have control over the input as to whether it is a list o
Hi,
I have the following script
import fileinput
import string
from math import *
from scipy import *
from rpy import *
import Numeric
import shelve
import sys
def dpolya1(n,N,b,a):
a=float(a)
b=float(b)
L=784
probs=((special.gammaln(N+1)+special.gammaln(L*(a/b))+special.gammal
> Yes. fromiter(iterable, dtype, count) works.
Oh. Thanks. I probably had too old documentation to see this (15
June). If it's not updated since I'll give Travis a rest about this,
at least until 1.0 is released :)
> Regardless, L is only iterated over once.
How can this be true? If no size is
def list2index(L):
uL=sorted(set(L))
idx=dict((y,x) for x,y in enumerate(uL))
return uL,asmatrix(fromiter((idx[x] for x in L),dtype=int,count=len(L)))
adding the count will save you a little more time, and temporary
memory [see related thread].
//Torgil
On 8/29/06, Keith Goodman <[EM
I submitted a ticket for this.On 8/31/06, Tom Denniston <[EMAIL PROTECTED]> wrote:
wrote the last email before reading your a = array([1,'A', None]) comment. I definately agree with you on that.
On 8/31/06, Tom Denniston <[EMAIL PROTECTED]
> wrote:
Yes one can take a toy example and hack it to
Angelo Secchi wrote:
> Hi,
> I have the following script
>
> import fileinput
> import string
> from math import *
> from scipy import *
> from rpy import *
> import Numeric
> import shelve
> import sys
>
> def dpolya1(n,N,b,a):
> a=float(a)
> b=float(b)
> L=784
>
> probs=((spec
Torgil Svensson wrote:
>> Yes. fromiter(iterable, dtype, count) works.
>>
>
> Oh. Thanks. I probably had too old documentation to see this (15
> June). If it's not updated since I'll give Travis a rest about this,
> at least until 1.0 is released :)
>
Actually I just knew 'cause I wrote it.
Tim Hochberg wrote:
>> That's what I'd expect, but what if you start with a (0,) array:
>> >>> a = N.array([]).sum(); a.shape; a.size; a
>> ()
>> 1
>> 0
>>
>> where did that zero come from?
>>
> More or less from:
>
> >>> numpy.add.identity
> 0
I'm not totally sure, but I think I'd r
Tom Denniston wrote:
> So my question is what is the _advantage_ of the new semantics?
what if the list don't have the same length, and therefor can not be
made into an array, now you get a weird result:
>>>N.array([N.array([1,'A',None],dtype=object),N.array([2,2,'Somestring',5],dtype=object)]
I would think one would want to throw an error when the data has inconsistent dimensions. This is what numpy does for other dtypes:
In [10]: numpy.array(([1,2,3], [4,5,6]))Out[10]:array([[1, 2, 3], [4, 5, 6]])
In [11]: numpy.array(([1,3], [4,5,6]))
Tom Denniston wrote:
> I would think one would want to throw an error when the data has
> inconsistent dimensions.
But it doesn't have inconsistent dimensions - they are perfectly
consistent with a (2,) array of objects. How is the code to know what
you intended?
With numeric types, it is unamb
On 8/31/06, Christopher Barker <[EMAIL PROTECTED]> wrote:
Tom Denniston wrote:> I would think one would want to throw an error when the data has> inconsistent dimensions.But it doesn't have inconsistent dimensions - they are perfectlyconsistent with a (2,) array of objects. How is the code to know
On Aug 30, 2006, at 11:53 , Keith Goodman wrote:
> I plan to build an amd64 box and run debian etch. Are there any big,
> 64-bit, show-stopping problems in numpy? Any minor annoyances?
Shouldn't be; I regularly build and test it on an amd64 box running
Debian unstable, and I know several others
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