[Numpy-discussion] [Cdat-discussion] Arrays containing NaNs

Charles Doutriaux doutriaux1@llnl....
Fri Jul 25 12:09:56 CDT 2008


I mean not having to it myself.
data is a numpy array with NaN in it
masked_data = numpy.ma.array(data)
returns a masked array with a mask where NaN were in data

C.

Bruce Southey wrote:
> Charles Doutriaux wrote:
>   
>> Hi Bruce,
>>
>> Thx for the reply, we're aware of this, basically the question was why 
>> not mask NaN automatically when creating a nump.ma array?
>>
>> C.
>>
>> Bruce Southey wrote:
>>   
>>     
>>> Charles Doutriaux wrote:
>>>   
>>>     
>>>       
>>>> Hi Stephane,
>>>>
>>>> This is a good suggestion, I'm ccing the numpy list on this. Because I'm 
>>>> wondering if it wouldn't be a better fit to do it directly at the 
>>>> numpy.ma level.
>>>>
>>>> I'm sure they already thought about this (and 'inf' values as well) and 
>>>> if they don't do it , there's probably some good reason we didn't think 
>>>> of yet.
>>>> So before i go ahead and do it in MV2 I'd like to know the reason why 
>>>> it's not in numpy.ma, they are probably valid for MVs too.
>>>>
>>>> C.
>>>>
>>>> Stephane Raynaud wrote:
>>>>   
>>>>     
>>>>       
>>>>         
>>>>> Hi,
>>>>>
>>>>> how about automatically (or at least optionally) masking all NaN 
>>>>> values when creating a MV array?
>>>>>
>>>>> On Thu, Jul 24, 2008 at 11:43 PM, Arthur M. Greene 
>>>>> <amg@iri.columbia.edu <mailto:amg@iri.columbia.edu>> wrote:
>>>>>
>>>>>     Yup, this works. Thanks!
>>>>>
>>>>>     I guess it's time for me to dig deeper into numpy syntax and
>>>>>     functions, now that CDAT is using the numpy core for array
>>>>>     management...
>>>>>
>>>>>     Best,
>>>>>
>>>>>     Arthur
>>>>>
>>>>>
>>>>>     Charles Doutriaux wrote:
>>>>>
>>>>>         Seems right to me,
>>>>>
>>>>>         Except that the syntax might scare a bit the new users :)
>>>>>
>>>>>         C.
>>>>>
>>>>>         Andrew.Dawson@uea.ac.uk <mailto:Andrew.Dawson@uea.ac.uk> wrote:
>>>>>
>>>>>             Hi,
>>>>>
>>>>>             I'm not sure if what I am about to suggest is a good idea
>>>>>             or not, perhaps Charles will correct me if this is a bad
>>>>>             idea for any reason.
>>>>>
>>>>>             Lets say you have a cdms variable called U with NaNs as
>>>>>             the missing
>>>>>              value. First we can replace the NaNs with 1e20:
>>>>>
>>>>>             U.data[numpy.where(numpy.isnan(U.data))] = 1e20
>>>>>
>>>>>             And remember to set the missing value of the variable
>>>>>             appropriately:
>>>>>
>>>>>             U.setMissing(1e20)
>>>>>
>>>>>             I hope that helps, Andrew
>>>>>
>>>>>
>>>>>
>>>>>                 Hi Arthur,
>>>>>
>>>>>                 If i remember correctly the way i used to do it was:
>>>>>                 a= MV2.greater(data,1.) b=MV2.less_equal(data,1)
>>>>>                 c=MV2.logical_and(a,b) # Nan are the only one left
>>>>>                 data=MV2.masked_where(c,data)
>>>>>
>>>>>                 BUT I believe numpy now has way to deal with nan I
>>>>>                 believe it is numpy.nan_to_num But it replaces with 0
>>>>>                 so it may not be what you
>>>>>                  want
>>>>>
>>>>>                 C.
>>>>>
>>>>>
>>>>>                 Arthur M. Greene wrote:
>>>>>
>>>>>                     A typical netcdf file is opened, and the single
>>>>>                     variable extracted:
>>>>>
>>>>>
>>>>>                                 fpr=cdms.open('prTS2p1_SEA_allmos.cdf')
>>>>>                                 pr0=fpr('prcp') type(pr0)
>>>>>
>>>>>                     <class 'cdms2.tvariable.TransientVariable'>
>>>>>
>>>>>                     Masked values (indicating ocean in this case) show
>>>>>                     up here as NaNs.
>>>>>
>>>>>
>>>>>                                 pr0[0,-15:-5,0]
>>>>>
>>>>>                     prcp array([NaN NaN NaN NaN NaN NaN 0.37745094
>>>>>                     0.3460784 0.21960783 0.19117641])
>>>>>
>>>>>                     So far this is all consistent. A map of the first
>>>>>                     time step shows the proper land-ocean boundaries,
>>>>>                     reasonable-looking values, and so on. But there
>>>>>                     doesn't seem to be any way to mask
>>>>>                      this array, so, e.g., an 'xy' average can be
>>>>>                     computed (it
>>>>>                     comes out all nans). NaN is not equal to anything
>>>>>                     -- even
>>>>>                     itself -- so there does not seem to be any
>>>>>                     condition, among the
>>>>>                      MV.masked_xxx options, that can be applied as a
>>>>>                     test. Also, it
>>>>>                      does not seem possible to compute seasonal averages,
>>>>>                     anomalies, etc. -- they also produce just NaNs.
>>>>>
>>>>>                     The workaround I've come up with -- for now -- is
>>>>>                     to first generate a new array of identical shape,
>>>>>                     filled with 1.0E+20. One test I've found that can
>>>>>                     detect NaNs is numpy.isnan:
>>>>>
>>>>>
>>>>>                                 isnan(pr0[0,0,0])
>>>>>
>>>>>                     True
>>>>>
>>>>>                     So it is _possible_ to tediously loop through
>>>>>                     every value in the old array, testing with isnan,
>>>>>                     then copying to the new array if the test fails.
>>>>>                     Then the axes have to be reset...
>>>>>
>>>>>                     isnan does not accept array arguments, so one
>>>>>                     cannot do, e.g.,
>>>>>
>>>>>                     prmasked=MV.masked_where(isnan(pr0),pr0)
>>>>>
>>>>>                     The element-by-element conversion is quite slow.
>>>>>                     (I'm still waiting for it to complete, in fact).
>>>>>                     Any suggestions for dealing with NaN-infested data
>>>>>                     objects?
>>>>>
>>>>>                     Thanks!
>>>>>
>>>>>                     AMG
>>>>>
>>>>>                     P.S. This is 5.0.0.beta, RHEL4.
>>>>>
>>>>>
>>>>>     *^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*
>>>>>     Arthur M. Greene, Ph.D.
>>>>>     The International Research Institute for Climate and Society
>>>>>     The Earth Institute, Columbia University, Lamont Campus
>>>>>     Monell Building, 61 Route 9W, Palisades, NY  10964-8000 USA
>>>>>     amg*at*iri-dot-columbia\dot\edu | http://  iri.columbia.edu
>>>>>     *^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*
>>>>>
>>>>>
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>>>>>
>>>>>
>>>>>
>>>>> -- 
>>>>> Stephane Raynaud
>>>>> ------------------------------------------------------------------------
>>>>>
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>>>>>   
>>>>>     
>>>>>       
>>>>>         
>>>>>           
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>>>>
>>>>   
>>>>     
>>>>       
>>>>         
>>> Please look the various NumPy functions to ignore NaN like nansum(). See 
>>> the NumPy example list 
>>> (http://  www.  scipy.org/Numpy_Example_List_With_Doc) for examples under 
>>> nan or individual functions.
>>>
>>> To get the mean you can do something like:
>>>
>>> import numpy
>>> x = numpy.array([2, numpy.nan, 1])
>>> numpy.nansum(x)/(x.shape[0]-numpy.isnan(x).sum())
>>> x_masked = numpy.ma.masked_where(numpy.isnan(x) , x)
>>> x_masked.mean()
>>>
>>> The real advantage of masked arrays is that you have greater control 
>>> over the filtering so you can also filter extreme values:
>>>
>>> y = numpy.array([2, numpy.nan, 1, 1000])
>>> y_masked =numpy.ma.masked_where(numpy.isnan(y) , y)
>>> y_masked =numpy.ma.masked_where(y_masked > 100 , y_masked)
>>> y_masked.mean()
>>>
>>> Regards
>>> Bruce
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>>>
>>>
>>>   
>>>     
>>>       
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>>   
>>     
> You mean like doing:
>
> import numpy
> y=numpy.ma.MaskedArray([ 2., numpy.nan, 1., 1000.], numpy.isnan(y))
>
> ?
>
> Bruce
>
>
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