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

[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)
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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