2009/11/7 David Goldsmith :
> Thanks, Anne.
>
> On Sat, Nov 7, 2009 at 1:32 PM, Anne Archibald
> wrote:
>>
>> 2009/11/7 David Goldsmith :
>
>
>
>>
>> > Also, my experimenting suggests that the index array ('a', the first
>> > argument in the func. sig.) *must* have shape (choices.shape[-1],) -
>>
On Sat, Nov 7, 2009 at 7:53 PM, David Goldsmith wrote:
> Thanks, Anne.
>
> On Sat, Nov 7, 2009 at 1:32 PM, Anne Archibald
> wrote:
>>
>> 2009/11/7 David Goldsmith :
>
>
>
>>
>> > Also, my experimenting suggests that the index array ('a', the first
>> > argument in the func. sig.) *must* have sha
On 11/7/2009 10:56 PM, a...@ajackson.org wrote:
> I want to build a 2D array of lists, and so I need to initialize the
> array with empty lists :
>
> myarray = array([[[],[],[]] ,[[],[],[]]])
[[[] for i in range(3)] for j in range(2) ]
fwiw,
Alan Isaac
__
I want to build a 2D array of lists, and so I need to initialize the
array with empty lists :
myarray = array([[[],[],[]] ,[[],[],[]]])
Is there a clever way to do this? I could define the array
myarray = zeros( (xdim,ydim), dtype=object)
and then iterate through the elements initializing then t
On Sat, Nov 7, 2009 at 5:38 PM, Pierre GM wrote:
> Linear interpolation with the delaunay package doesn't work great for
> my data. I played with the radial basis functions, but I'm afraid
> they're leading me down the dark, dark path of parameter fiddling. In
> particular, I'm not sure how to pre
On Sat, Nov 7, 2009 at 5:38 PM, Pierre GM wrote:
> Linear interpolation with the delaunay package doesn't work great for
> my data. I played with the radial basis functions, but I'm afraid
> they're leading me down the dark, dark path of parameter fiddling. In
> particular, I'm not sure how to pre
Thanks, Anne.
On Sat, Nov 7, 2009 at 1:32 PM, Anne Archibald wrote:
> 2009/11/7 David Goldsmith :
> > Also, my experimenting suggests that the index array ('a', the first
> > argument in the func. sig.) *must* have shape (choices.shape[-1],) -
> someone
> > please let me know ASAP if this is
On Nov 6, 2009, at 5:45 PM, Pauli Virtanen wrote:
> pe, 2009-11-06 kello 17:20 -0500, Pierre GM kirjoitti:
>> All,
>> I have a vector of observations (latitude,longitude,value) that I
> need
>> to interpolate over a given area.
> You could try to use linear interpolation from the delaynay packa
On Nov 7, 2009, at 2:26 PM, Thomas Robitaille wrote:
>
> Thanks for the advice! I'm somewhat confused by the difference between
> structured and record arrays. My understanding is that record arrays
> allow
> you to access fields by attribute (e.g. r.field_name), but I imagine
> that
> there a
this looks like what I need...I'm not concerned with leaking memory as it's
a borrowed pointer which will be cleaned up in C code later. Thanks for the
pointer.
On Sat, Nov 7, 2009 at 12:36 PM, Zachary Pincus wrote:
> Check out this thread:
>
> http://www.mail-archive.com/numpy-discuss...@lists.s
la, 2009-11-07 kello 18:27 +, Neil Crighton kirjoitti:
[clip]
> I think it would be better to fix this issue. np.min(3,2) should also give
> "ValueError: axis(=2) out of bounds". Fixing this also removes any possibility
> of generating hard-to-find errors by overwriting the builtin min/max. (U
2009/11/7 David Goldsmith :
> Hi, all! I'm working to clarify the docstring for np.choose (Stefan pointed
> out to me that it is pretty unclear, and I agreed), and, now that (I'm
> pretty sure that) I've figured out what it does in its full generality
> (e.g., when the 'choices' array is greater t
Hi, all! I'm working to clarify the docstring for np.choose (Stefan pointed
out to me that it is pretty unclear, and I agreed), and, now that (I'm
pretty sure that) I've figured out what it does in its full generality
(e.g., when the 'choices' array is greater than 2-D), I'm curious as to
use-case
Jake VanderPlas wrote:
> Does anybody know a
> way to directly access the numpy.linalg routines from a C extension,
> without the overhead of a python callback? Thanks for the help.
>
You find a C function pointer wrapped in a CObject in the ._cpointer
attribute.
_
Hello,
I'm working on wrapping a set of C++ routines for manifold learning
(LLE, Isomap, LTSA, etc) in python. In the LLE routine, it is
necessary to loop through the input points and perform an svd of each
local covariance matrix. Presently I have my own C-LAPACK wrapper
that I call within a C l
David Cournapeau wrote:
> On Fri, Nov 6, 2009 at 6:54 AM, David Goldsmith
> wrote:
>
>> Interesting thread, which leaves me wondering two things: is it documented
>> somewhere (e.g., at the IEEE site) precisely how many *decimal* mantissae
>> are representable using the 64-bit IEEE standard fo
2009/11/7 Stas K :
> Thank you, Josef
> It is exactly what I want:
>
> ar[:,None]**2 + ar**2
>
> Do you know something about performance of this? In my real program ar have
> ~ 10k elements, and expression for v more complicated (it has some
> trigonometric functions)
The construction of ar[:,Non
Pierre GM-2 wrote:
>
> Mmh. With a recent (1.3) version of numpy, you should already be able
> to mask individual fields of a structured array without problems. If
> you need fields to be accessed as attributes the np.recarray way, you
> can give numpy.ma.mrecords.MaskedRecords a try. It's
Hi!
I'm not sure, if it's the right group to ask, but it's a kind of a numeric
question involving Python, so...
My faculty is going to spend some money on commercial compiler. The
two choices are:
- PGI C++ compiler
- Intel C++ compiler
I wonder, if anyone as some experience with Python numeri
Thank you, Josef
It is exactly what I want:
ar[:,None]**2 + ar**2
Do you know something about performance of this? In my real program
ar have ~ 10k elements, and expression for v more complicated (it has
some trigonometric functions)
On 07.11.2009, at 21:57, josef.p...@gmail.com wrote:
On 11/7/2009 1:51 PM, Stas K wrote:
> Can I get rid of the loop in this example? And what is the fastest way
> to get v in the example?
>
> ar = array([1,2,3])
> for a in ar:
> for b in ar:
> v = a**2+b**2
>>> a2 = a*a
>>> np.add.outer(a2,a2)
array([[ 2, 5, 10],
[ 5, 8, 13
On Sat, Nov 7, 2009 at 1:51 PM, Stas K wrote:
> Can I get rid of the loop in this example? And what is the fastest way
> to get v in the example?
>
> ar = array([1,2,3])
> for a in ar:
> for b in ar:
> v = a**2+b**2
>>> ar[:,None]**2 + ar**2
array([[ 2, 5, 10],
[ 5, 8, 13],
Can I get rid of the loop in this example? And what is the fastest way
to get v in the example?
ar = array([1,2,3])
for a in ar:
for b in ar:
v = a**2+b**2
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/m
Charles R Harris gmail.com> writes:
> People import these functions -- yes, they shouldn't do that -- and the python
builtin versions are overloaded, causing hard to locate errors.
While I would love less duplication in the numpy namespace, I don't think the
small gain here is worth the pain of
Check out this thread:
http://www.mail-archive.com/numpy-discuss...@lists.sourceforge.net/msg01154.html
In shot, it can be done, but it can be tricky to make sure you don't
leak memory. A better option if possible is to pre-allocate the array
with numpy and pass that buffer into the C code --
Pierre GM wrote:
> I have a vector of observations (latitude,longitude,value) that I need
> to interpolate over a given area.
> Some colleagues complained that the result looked a bit too choppy,
> meaning that too much weight was given to the actual observations.
> What are my other options
2009/11/7 David Warde-Farley :
> Just to confirm my suspicions, what's the preferred way to check if a
> dtype is...
Not sure what the "official" way is, but this method works well:
> a) an integer type vs. a floating point type vs. a string type? I'm
> assuming dt.kind.
np.issubdtype(dt, float)
On Sat, Nov 7, 2009 at 7:12 AM, Charles R Harris
wrote:
> There seems to be a rat's nest of problems showing up in scipy due to the
> recent changes in numpy complex support. The problems are of two basic
> sorts: 1) reserved name conflicts and 2) file conflicts. The first could be
> dealt with, b
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