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>
> 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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