[Numpy-discussion] np.ma.mean is not working?

Chao YUE chaoyuejoy@gmail....
Tue Oct 18 09:12:12 CDT 2011


thanks. Olivier. I see.

Chao

2011/10/18 Olivier Delalleau <shish@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 <chaoyuejoy@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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>>
>
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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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