On Aug 2, 2011, at 1:20 AM, Craig Yoshioka wrote:
> Is there a limit to the number of fields a numpy recarray can have? I was
> getting a strange error about a duplicate column name, but it wasn't a
> duplicate.
And the error was… ?
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Hello,
I have a function that I fitting to a curve via scipy.optimize.leastsq. The
function has 4 parameters and this is all working fine.
For a site, I have a number of curves (n=10 in the example below). I would
like to some of the parameters to be the best fit across all curves (best fit
Is there a limit to the number of fields a numpy recarray can have? I was
getting a strange error about a duplicate column name, but it wasn't a
duplicate.
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2011/8/1 Timo Kluck
> 2011/7/30 Eric Firing
>> Maybe the thing to do is to pre-calculate if len(xp) <= len(x), or some
>> such guess as to which method would be more efficient.
>>
> What you're suggesting is reasonable. The cutoff at len(xp) <= len(x) can
> distinguish between the 'refinement' c
In astronomy we tend to use FITS, which is well-supported by pyfits,
but a little limited. Some new instruments are beginning to use HDF5.
All these generic formats allow very general data storage, so you will
need to come up with a standrdized way to represent your own data.
Used well, these form
On 7/31/11 5:48 AM, Brian Blais wrote:
> I was wondering if there are any recommendations for formats for saving
> scientific data.
every field has it's own standards -- I'd try to find one that is likely
to be used by folks that may care about your results.
For Oceanographic and Atmospheric mo
I personally use pickle, which does exactly what you are asking for (and can
be customized with __getstate__ and __setstate__ if needed). What are your
issues with pickle?
-=- Olivier
2011/7/31 Brian Blais
> Hello,
>
> I was wondering if there are any recommendations for formats for saving
> sc
Hi
On Mon, Aug 1, 2011 at 3:14 PM, Jeffrey Spencer wrote:
> Depends where it is contained but another option is and I find it to
> typically be faster:
>
> B = zeros(A.shape)
> maximum(A,B,A)
>
Since maximum(.) can handle broadcasting
maximum(A, 0, A)
will be even faster.
-eat
>
>
> On 08/01/2
This method is probably simpler:
In [1]: import numpy as N
In [2]: A = N.random.random_integers(-10, 10, 25).reshape((5, 5))
In [3]: A
Out[3]:
array([[ -5, 9, 1, 9, -2],
[ -8, 0, 9, 7, -10],
[ 2, -3, -1, 5, -7],
[ 0, -2, -2, 9, 1],
[ -7, -9,
Depends where it is contained but another option is and I find it to
typically be faster:
B = zeros(A.shape)
maximum(A,B,A)
On 08/01/2011 07:31 PM, dileep kunjaai wrote:
Dear sir,
How can we fill a particular value in the place of number
satisfying certain condition by another number in an
Le lundi 01 août 2011 à 15:01 +0530, dileep kunjaai a écrit :
> Dear sir,
> How can we fill a particular value in the place of number satisfying
> certain condition by another number in an array.
> A contain some negative value i want to change the negative numbers to
> '0'. I used 'masked_where',
Dear Dileep,
the numpy.where function returns the elements from A or 0 depending if
the condition in the first argument is satisfied:
B = np.where(A >= 0, A, 0)
Miguel
On Mon, Aug 01, 2011 at 03:01:13PM +0530, dileep kunjaai wrote:
> Dear sir,
>How can we fill a particular value in the place
Hey Everybody,
I noticed that the c-api docs (2.0.dev-72ab385) lack a clear statement
what the preferred entry point into the c-api is (from a users point of
view). Normally I would expect a sentence or two stating that the api
entry point is arrayobject.h (or whatever).
Instead the docs ponder ab
Dear sir,
How can we fill a particular value in the place of number satisfying
certain condition by another number in an array.
Example:
A=[[[ 9.42233087e-42 - 4.71116544e-42 0.e+00 ...,
1.48303127e+01
1.31524124e+01 1.14745111e+01]
[ 3.91788793e+00 1.95894396e+00 0.
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