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

Chao YUE chaoyuejoy@gmail....
Tue Oct 18 16:25:06 CDT 2011


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 <bsouthey@gmail.com>

> **
> On 10/18/2011 09:12 AM, Chao YUE wrote:
>
> 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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>>> NumPy-Discussion mailing list
>>> NumPy-Discussion@scipy.org
>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
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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
>
> ************************************************************************************
>
>
> _______________________________________________
> NumPy-Discussion mailing listNumPy-Discussion@scipy.orghttp://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>  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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>


-- 
***********************************************************************************
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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