Robert Kern wrote:
They're fine. Ignore them. They are silenced from the main setup.py with
config.set_options(quiet=True)
What are the cases where those message are meaningful ? I did not
understand from the distutils code what kind of issues were related to
this message,
cheers,
Hi.
Does the C api have some convenience functions for creating slices?
For example : if I have a PyArrayObject *A, which represents lets say
a 2d ndarray A in Python, is there a C api function to easily do the
equivalent of A[a:b:c,d:e:f] ?
thanks,
rahul
Thanks, Jarrod.
Should I replace the old numpy 1.0.4 information at
http://www.scipy.org/Download with the 1.1.0? It's still listing 1.0.4,
but I wonder if there's some compatibility with scipy 0.6 issue that
should cause it to stay at 1.0.4. In either case, I think the page
should be updated
This looks good:
import numpy as np
x = np.random.rand(2,3)
x.mean(None, out=x)
---
ValueError: wrong shape for output
But this is strange:
x.std(None, out=x)
0.28264369725
x
array([[ 0.54718012, 0.94296181,
2008/5/23 Keith Goodman [EMAIL PROTECTED]:
On Fri, May 23, 2008 at 11:44 AM, Robert Kern [EMAIL PROTECTED] wrote:
On Fri, May 23, 2008 at 12:22 PM, Keith Goodman [EMAIL PROTECTED] wrote:
But the first example
x = mp.matrix([[mp.nan]])
x
matrix([[ NaN]])
x.all()
True
x.any()
True
On Thu, May 29, 2008 at 9:26 AM, Stéfan van der Walt [EMAIL PROTECTED] wrote:
2008/5/23 Keith Goodman [EMAIL PROTECTED]:
On Fri, May 23, 2008 at 11:44 AM, Robert Kern [EMAIL PROTECTED] wrote:
On Fri, May 23, 2008 at 12:22 PM, Keith Goodman [EMAIL PROTECTED] wrote:
But the first example
x =
On Thu, May 29, 2008 at 9:26 AM, Stéfan van der Walt [EMAIL PROTECTED] wrote:
2008/5/23 Keith Goodman [EMAIL PROTECTED]:
On Fri, May 23, 2008 at 11:44 AM, Robert Kern [EMAIL PROTECTED] wrote:
On Fri, May 23, 2008 at 12:22 PM, Keith Goodman [EMAIL PROTECTED] wrote:
But the first example
x =
I have a question about histogram2d. Say I do something like:
import numpy
from numpy import random
import pylab
x=random.rand(1000)-0.5
y=random.rand(1000)*10-5
xbins=numpy.linspace(-10,10,100)
ybins=numpy.linspace(-10,10,100)
h,x,y=numpy.histogram2d(x,y,bins=[xbins,ybins])
to, 2008-05-29 kello 10:53 -0700, Keith Goodman kirjoitti:
On Thu, May 29, 2008 at 9:26 AM, Stéfan van der Walt [EMAIL PROTECTED]
wrote:
2008/5/23 Keith Goodman [EMAIL PROTECTED]:
On Fri, May 23, 2008 at 11:44 AM, Robert Kern [EMAIL PROTECTED] wrote:
[clip]
That makes sense. Hopefully it
Travis,
What are the fundamental types for ndarrays? We have the c types,
'bBhHiIlLqQfdg', together with the boolean and complex types. Then we have
types defined by length, int8, uint8, etc. The long types change length
going from 32 to 64 bit machines, so there can be a couple of c-types
All,
I have a set of arrays that I want to transform to records. Viewing them as a
new dtype is usually sufficient, but fails occasionally. Here's an example:
#---
import numpy as np
testdtype = [('a',float),('b',float),('c',float)]
test =
On Thu, May 29, 2008 at 2:05 PM, Pierre GM [EMAIL PROTECTED] wrote:
All,
I have a set of arrays that I want to transform to records. Viewing them as
a
new dtype is usually sufficient, but fails occasionally. Here's an example:
#---
import numpy as np
On Thursday 29 May 2008 16:25:24 Charles R Harris wrote:
* Could somebody explain me what goes wrong in the second case
(transpose+view) ? Is it because the transpose doesn't own the data ?
* Is there a way to transform my (3,5) array into a (5,) recordarray
without a
copy ?
I don't
On Thu, May 29, 2008 at 3:37 PM, Jarrod Millman [EMAIL PROTECTED]
wrote:
On Thu, May 29, 2008 at 7:57 AM, Andrew Straw [EMAIL PROTECTED] wrote:
Should I replace the old numpy 1.0.4 information at
http://www.scipy.org/Download with the 1.1.0? It's still listing 1.0.4,
but I wonder if
On Thu, May 29, 2008 at 2:59 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
No, I will take care of it. I was away from home and decided to make
a relatively quiet release, since I might not be able to respond in
case their were problems. I only sent the email to the NumPy
discussion list
Hi all,
The NumPy documentation project has taken another leap forward! Pauli
Virtanen has, in a week of superhuman coding, produced a web
application that enhances the work-flow and editing experience of
NumPy docstrings on the web.
Unfortunately, this means that those of you who signed up
On Thu, May 29, 2008 at 3:28 PM, Stéfan van der Walt [EMAIL PROTECTED] wrote:
The NumPy documentation project has taken another leap forward! Pauli
Virtanen has, in a week of superhuman coding, produced a web
application that enhances the work-flow and editing experience of
NumPy docstrings
2008/5/29 Jarrod Millman [EMAIL PROTECTED]:
On Thu, May 29, 2008 at 3:28 PM, Stéfan van der Walt [EMAIL PROTECTED]
wrote:
The NumPy documentation project has taken another leap forward! Pauli
Virtanen has, in a week of superhuman coding, produced a web
application that enhances the
On Thu, May 29, 2008 at 5:28 PM, Stéfan van der Walt [EMAIL PROTECTED] wrote:
Hi all,
The NumPy documentation project has taken another leap forward! Pauli
Virtanen has, in a week of superhuman coding, produced a web
application that enhances the work-flow and editing experience of
NumPy
I'm new to using numpy. Today I experimented a bit with indexing
motivated by the finding that although
a[a0.5] and a[where(a0.5)] give the same expected result (elements of
a greater than 0.5)
a[argwhere(a0.5)] results in something else (rows of a in different order).
I tried to figure out
On Fri, May 30, 2008 at 12:36 AM, Raul Kompass [EMAIL PROTECTED] wrote:
I'm new to using numpy. Today I experimented a bit with indexing
motivated by the finding that although
a[a0.5] and a[where(a0.5)] give the same expected result (elements of
a greater than 0.5)
a[argwhere(a0.5)] results
On Fri, May 30, 2008 at 12:57 AM, Robin [EMAIL PROTECTED] wrote:
You are indexing here with a 1d list [0,1]. Since you don't provide a
column index you get rows 0 and 1.
If you do a[ [0,1] , [0,1] ] then you get element [0,0] and element [0,1].
Whoops - you get [0,0] and [1,1].
Robin
On Thu, May 29, 2008 at 4:36 PM, Raul Kompass [EMAIL PROTECTED] wrote:
I'm new to using numpy. Today I experimented a bit with indexing
motivated by the finding that although
a[a0.5] and a[where(a0.5)] give the same expected result (elements of
a greater than 0.5)
a[argwhere(a0.5)] results
[ This is meant as a heads-up here, please keep the discussion on the
SciPy user list so we can focus the conversation in one list only. ]
Hi all,
Travis Oliphant and myself have signed up to coordinate the tutorials
sessions at this year's SciPy conference. Our tentative plan is
described
On Thu, May 29, 2008 at 6:32 PM, Alan G Isaac [EMAIL PROTECTED] wrote:
On Thu, 29 May 2008, Keith Goodman apparently wrote:
a[[0,1]]
That one looks odd. But it is just shorthand for:
a[[0,1],:]
Do you mean that ``a[[0,1],:]`` is a more primitive
expression than ``a[[0,1]]``? In what
On Thu, 29 May 2008, Keith Goodman apparently wrote:
a[[0,1]]
That one looks odd. But it is just shorthand for:
a[[0,1],:]
On Thu, May 29, 2008 at 6:32 PM, Alan G Isaac
[EMAIL PROTECTED] wrote:
Do you mean that ``a[[0,1],:]`` is a more primitive
expression than ``a[[0,1]]``? In what
On 5/29/08, Fernando Perez [EMAIL PROTECTED] wrote:
[ This is meant as a heads-up here, please keep the discussion on the
SciPy user list so we can focus the conversation in one list only. ]
Hi all,
Travis Oliphant and myself have signed up to coordinate the tutorials
sessions at this
On Thu, May 29, 2008 at 8:50 PM, Alan G Isaac [EMAIL PROTECTED] wrote:
Is ``a[[0,1]]`` completely equivalent to ``a[[0,1],...]``
and ``a[[0,1],:]``?
They look, smell, and taste the same. But I can't read array's
__getitem__ since it is in C instead of python.
np.index_exp[[0,1]]
([0, 1],)
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