np.ma.mean is not working?
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 ************************************************************************************
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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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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-- *********************************************************************************** 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 ************************************************************************************
On 10/18/2011 09:12 AM, Chao YUE wrote:
thanks. Olivier. I see.
Chao
2011/10/18 Olivier Delalleau <shish@keba.be <mailto: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 <mailto: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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-- *********************************************************************************** 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 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
Thanks Bruce. 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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--
*********************************************************************************** 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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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 ************************************************************************************
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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--
*********************************************************************************** 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 ************************************************************************************
participants (3)
-
Bruce Southey -
Chao YUE -
Olivier Delalleau