thanks. Olivier. I see.<br><br>Chao<br><br><div class="gmail_quote">2011/10/18 Olivier Delalleau <span dir="ltr"><<a href="mailto:shish@keba.be">shish@keba.be</a>></span><br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
As far as I can tell ma.mean() is working as expected here: it computes the mean only over non-masked values.<br>If you want to get rid of any mean that was computed over a series containing masked value you can do:<br><br>
b = a.mean(0)<br>b.mask[a.mask.any(0)] = True<br><br>Then b will be:<br><br>masked_array(data = [5.0 -- -- 8.0 9.0 -- 11.0 12.0 -- 14.0],<br> mask = [False True True False False True False False True False],<br>
fill_value = 1e+20)<br><br>-=- Olivier<br><br><div class="gmail_quote">2011/10/18 Chao YUE <span dir="ltr"><<a href="mailto:chaoyuejoy@gmail.com" target="_blank">chaoyuejoy@gmail.com</a>></span><br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div><div></div><div class="h5">
Dear all,<br><br>previoulsy I think np.ma.mean() will automatically filter the masked (missing) value but it's not?<br>In [489]: a=np.arange(20.).reshape(2,10)<br><br>In [490]: a=np.ma.masked_array(a,(a==2)|(a==5)|(a==11)|(a==18),fill_value=np.nan)<br>
<br>In [491]: a<br>Out[491]: <br>masked_array(data =<br> [[0.0 1.0 -- 3.0 4.0 -- 6.0 7.0 8.0 9.0]<br> [10.0 -- 12.0 13.0 14.0 15.0 16.0 17.0 -- 19.0]],<br> mask =<br> [[False False True False False True False False False False]<br>
[False True False False False False False False True False]],<br> fill_value = nan)<br><br>In [492]: a.mean(0)<br>Out[492]: <br>masked_array(data = [5.0 1.0 12.0 8.0 9.0 15.0 11.0 12.0 8.0 14.0],<br> mask = [False False False False False False False False False False],<br>
fill_value = 1e+20)<br><br>In [494]: np.ma.mean(a,0)<br>Out[494]: <br>masked_array(data = [5.0 1.0 12.0 8.0 9.0 15.0 11.0 12.0 8.0 14.0],<br> mask = [False False False False False False False False False False],<br>
fill_value = 1e+20)<br><br>In [495]: np.ma.mean(a,0)==a.mean(0)<br>Out[495]: <br>masked_array(data = [ True True True True True True True True True True],<br> mask = False,<br> fill_value = True)<br>
<br>only use a.filled().mean(0) can I get the result I want:<br>In [496]: a.filled().mean(0)<br>Out[496]: array([ 5., NaN, NaN, 8., 9., NaN, 11., 12., NaN, 14.])<br><br>I am doing this because I tried to have a small fuction from the web to do moving average for data:<br>
<br>import numpy as np<br>def rolling_window(a, window):<br> if window < 1:<br> raise ValueError, "`window` must be at least 1."<br> if window > a.shape[-1]:<br> raise ValueError, "`window` is too long."<br>
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)<br> strides = a.strides + (a.strides[-1],)<br> return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) <br><br>def move_ave(a,window):<br>
temp=rolling_window(a,window)<br> pre=int(window)/2<br> post=int(window)-pre-1<br> return np.concatenate((a[...,0:pre],np.mean(temp,-1),a[...,-post:]),axis=-1)<br><br><br>In [489]: a=np.arange(20.).reshape(2,10)<br>
<br>In [499]: move_ave(a,4)<br>Out[499]: <br>masked_array(data =<br> [[ 0. 1. 1.5 2.5 3.5 4.5 5.5 6.5 7.5 9. ]<br> [ 10. 11. 11.5 12.5 13.5 14.5 15.5 16.5 17.5 19. ]],<br> mask =<br>
False,<br> fill_value = 1e+20)<br><br>thanks,<br><br>Chao<br clear="all"><font color="#888888"><br>-- <br><div>***********************************************************************************</div>
<div>Chao YUE<br>Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL)<br>UMR 1572 CEA-CNRS-UVSQ<br>Batiment 712 - Pe 119<br>91191 GIF Sur YVETTE Cedex</div>
<div>Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16<br></div>
<div>************************************************************************************</div><br>
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<div>Chao YUE<br>Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL)<br>UMR 1572 CEA-CNRS-UVSQ<br>Batiment 712 - Pe 119<br>91191 GIF Sur YVETTE Cedex</div>
<div>Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16<br></div>
<div>************************************************************************************</div><br>