On 10/18/2011 09:12 AM, Chao YUE wrote:
thanks. Olivier. I see.
Chao
2011/10/18 Olivier Delalleau <sh...@keba.be <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 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 -- -- 8.0 9.0 -- 11.0 12.0 -- 14.0],
mask = [False True True False False True False
False True False],
fill_value = 1e+20)
-=- Olivier
2011/10/18 Chao YUE <chaoyue...@gmail.com
<mailto:chaoyue...@gmail.com>>
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 -- 6.0 7.0 8.0 9.0]
[10.0 -- 12.0 13.0 14.0 15.0 16.0 17.0 -- 19.0]],
mask =
[[False False True False False True False False False False]
[False True False False False False False False True False]],
fill_value = nan)
In [492]: a.mean(0)
Out[492]:
masked_array(data = [5.0 1.0 12.0 8.0 9.0 15.0 11.0 12.0 8.0
14.0],
mask = [False False False False False False False
False False False],
fill_value = 1e+20)
In [494]: np.ma.mean(a,0)
Out[494]:
masked_array(data = [5.0 1.0 12.0 8.0 9.0 15.0 11.0 12.0 8.0
14.0],
mask = [False False False False False False False
False False False],
fill_value = 1e+20)
In [495]: np.ma.mean(a,0)==a.mean(0)
Out[495]:
masked_array(data = [ True True True True True True
True True True True],
mask = False,
fill_value = True)
only use a.filled().mean(0) can I get the result I want:
In [496]: a.filled().mean(0)
Out[496]: array([ 5., NaN, NaN, 8., 9., NaN, 11.,
12., NaN, 14.])
I am doing this because I tried to have a small fuction from
the web to do moving average for data:
import numpy as np
def rolling_window(a, window):
if window < 1:
raise ValueError, "`window` must be at least 1."
if window > a.shape[-1]:
raise ValueError, "`window` is too long."
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = a.strides + (a.strides[-1],)
return np.lib.stride_tricks.as_strided(a, shape=shape,
strides=strides)
def move_ave(a,window):
temp=rolling_window(a,window)
pre=int(window)/2
post=int(window)-pre-1
return
np.concatenate((a[...,0:pre],np.mean(temp,-1),a[...,-post:]),axis=-1)
In [489]: a=np.arange(20.).reshape(2,10)
In [499]: move_ave(a,4)
Out[499]:
masked_array(data =
[[ 0. 1. 1.5 2.5 3.5 4.5 5.5 6.5 7.5 9. ]
[ 10. 11. 11.5 12.5 13.5 14.5 15.5 16.5 17.5 19. ]],
mask =
False,
fill_value = 1e+20)
thanks,
Chao
--
***********************************************************************************
Chao YUE
Laboratoire des Sciences du Climat et de l'Environnement
(LSCE-IPSL)
UMR 1572 CEA-CNRS-UVSQ
Batiment 712 - Pe 119
91191 GIF Sur YVETTE Cedex
Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16
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--
***********************************************************************************
Chao YUE
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL)
UMR 1572 CEA-CNRS-UVSQ
Batiment 712 - Pe 119
91191 GIF Sur YVETTE Cedex
Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16
************************************************************************************
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NumPy-Discussion mailing list
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Looked at pandas for your rolling window functionality:
http://pandas.sourceforge.net
*"Time series*-specific functionality: date range generation and frequency
conversion, moving window statistics, moving window linear regressions, date shifting and
lagging, etc."
Bruce
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