thanks. Olivier. I see. Chao
2011/10/18 Olivier Delalleau <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> > >> 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 >> >> ************************************************************************************ >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > -- *********************************************************************************** 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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