On Tue, Oct 11, 2011 at 4:59 PM, Robert Cimrman wrote:
> Hi,
>
> I have now spent several hours hunting down a major slowdown of my code
> caused
> (apparently) by using config.add_library() for a reusable part of C source
> files instead of just config.add_extension().
>
> The reason of the slow
I would say pandas is really cool. More people need to know it. and we
should have better documentation.
cheers,
Chao
2011/10/18 Bruce Southey
> **
> On 10/18/2011 09:12 AM, Chao YUE wrote:
>
> thanks. Olivier. I see.
>
> Chao
>
> 2011/10/18 Olivier Delalleau
>
>> As far as I can tell ma.mean
Hi,
2011/10/18 Frédéric Bastien :
> What about a parameter that allow to select the option the user want?
> it would select between uint, upcasted_int, -MAX and +MAX. This way,
> at least it will be documented and user who care will have the choose.
>
> Personally, when the option is available, I
What about a parameter that allow to select the option the user want?
it would select between uint, upcasted_int, -MAX and +MAX. This way,
at least it will be documented and user who care will have the choose.
Personally, when the option is available, I would prefer the safe
version, uint, but I u
Thanks Bruce.
2011/10/18 Bruce Southey
> **
> On 10/18/2011 09:12 AM, Chao YUE wrote:
>
> thanks. Olivier. I see.
>
> Chao
>
> 2011/10/18 Olivier Delalleau
>
>> As far as I can tell ma.mean() is working as expected here: it computes
>> the mean only over non-masked values.
>> If you want to get
On 10/18/2011 03:13 AM, Olivier Delalleau wrote:
> if hasattr(a, 'mask'): # or if isinstance(a, numpy.ma.core.MaskedArray.)
or
if numpy.ma.isMA(a):
or
if numpy.ma.isMaskedArray(a):
Eric
> code 1
> else
> code 2
>
> -=- Olivier
>
> 2011/10/18 Chao YUE mailto:chaoyue...@gmail.com>>
On 10/18/2011 09:12 AM, Chao YUE wrote:
thanks. Olivier. I see.
Chao
2011/10/18 Olivier Delalleau mailto:sh...@keba.be>>
As far as I can tell ma.mean() is working as expected here: it
computes the mean only over non-masked values.
If you want to get rid of any mean that was compute
thanks Scott. very good explanation.
cheers,
Chao
2011/10/18 Scott Sinclair
> On 18 October 2011 13:56, Chao YUE wrote:
> > but it's strange that if you use b[...,-1],
> > you get:
> > In [402]: b[...,-1]
> > Out[402]: array([ 9, 19])
> >
> > if use b[...,-4:-1],
> > you get:
> > Out[403]:
>
thanks. Olivier. I see.
Chao
2011/10/18 Olivier Delalleau
> As far as I can tell ma.mean() is working as expected here: it computes the
> mean only over non-masked values.
> If you want to get rid of any mean that was computed over a series
> containing masked value you can do:
>
> b = a.mean(0
On 18 October 2011 13:56, Chao YUE wrote:
> but it's strange that if you use b[...,-1],
> you get:
> In [402]: b[...,-1]
> Out[402]: array([ 9, 19])
>
> if use b[...,-4:-1],
> you get:
> Out[403]:
> array([[ 6, 7, 8],
> [16, 17, 18]])
That's because you're mixing two different indexing c
really cool, thanks.
Chao
2011/10/18 Olivier Delalleau
> if hasattr(a, 'mask'): # or if isinstance(a, numpy.ma.core.MaskedArray.)
>
> code 1
> else
> code 2
>
> -=- Olivier
>
>
> 2011/10/18 Chao YUE
>
>> Thanks Olivier. but I don't know how can I check it in the code (not in an
>> i
if hasattr(a, 'mask'): # or if isinstance(a, numpy.ma.core.MaskedArray.)
code 1
else
code 2
-=- Olivier
2011/10/18 Chao YUE
> Thanks Olivier. but I don't know how can I check it in the code (not in an
> interactive mode)?
> I would like:
>
> if ndarray a is a mased array:
> cod
As far as I can tell ma.mean() is working as expected here: it computes the
mean only over non-masked values.
If you want to get rid of any mean that was computed over a series
containing masked value you can do:
b = a.mean(0)
b.mask[a.mask.any(0)] = True
Then b will be:
masked_array(data = [5.0
Thanks Olivier. but I don't know how can I check it in the code (not in an
interactive mode)?
I would like:
if ndarray a is a mased array:
code 1
else
code 2
thanks again,
Chao
2011/10/18 Olivier Delalleau
> You could simply check if it has a 'mask' attribute. You can also check if
You could simply check if it has a 'mask' attribute. You can also check if
it is an instance of numpy.ma.core.MaskedArray.
-=- Olivier
Le 18 octobre 2011 08:49, Chao YUE a écrit :
> Just one more question, how can I check a ndarray is a masked array or not?
>
> Chao
> --
>
> ***
Just one more question, how can I check a ndarray is a masked array or not?
Chao
--
***
Chao YUE
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL)
UMR 1572 CEA-CNRS-UVSQ
Batiment 712 - Pe 119
91191
Dear all,
previoulsy I think np.ma.mean() will automatically filter the masked
(missing) value but it's not?
In [489]: a=np.arange(20.).reshape(2,10)
In [490]:
a=np.ma.masked_array(a,(a==2)|(a==5)|(a==11)|(a==18),fill_value=np.nan)
In [491]: a
Out[491]:
masked_array(data =
[[0.0 1.0 -- 3.0 4.0
you are right Eric,
In [405]: b[...,-4:]
Out[405]:
array([[ 6, 7, 8, 9],
[16, 17, 18, 19]])
cheers,
Chao
2011/10/18 Chao YUE
> Thanks Jean
> I just want the last several numbers by indexing from the end.
>
> In [400]: b=np.arange(20).reshape(2,10)
>
> In [401]: b
> Out[401]:
> array
Thanks Jean
I just want the last several numbers by indexing from the end.
In [400]: b=np.arange(20).reshape(2,10)
In [401]: b
Out[401]:
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]])
I want something like b[...,...(index from the end by using
Le mardi 18 octobre 2011 13:44:00, Chao YUE a écrit :
> Dear all,
>
> if I have
>
> In [395]: a
> Out[395]:
> array([[0, 1, 2, 3, 4],
>[5, 6, 7, 8, 9]])
>
> In [396]: a[...,-1]
> Out[396]: array([4, 9])
>
> In [397]: a[...,-4:-1]
> Out[397]:
> array([[1, 2, 3],
>[6, 7, 8]])
>
>
> how can I pick up something like:
> array([[1, 2, 3, 4],
> [6, 7, 8, 9]])
I'm not sure to understand, should not a[:,1:] be sufficient ?
Did I miss something in your message ?
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http:
Dear all,
if I have
In [395]: a
Out[395]:
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
In [396]: a[...,-1]
Out[396]: array([4, 9])
In [397]: a[...,-4:-1]
Out[397]:
array([[1, 2, 3],
[6, 7, 8]])
In [398]: a[...,-4:0]
Out[398]: array([], shape=(2, 0), dtype=int64)
how can I pick up s
Just in time! I was just working on a cythonic replacement to
ndimage.generic_filter (well, I took a a short two years break in the middle).
thank you very much,
Nadav.
From: numpy-discussion-boun...@scipy.org [numpy-discussion-boun...@scipy.org]
On
23 matches
Mail list logo
