[Numpy-discussion] ignore NAN in numpy.true_divide()

Xavier Barthelemy xabart at gmail.com
Mon Dec 5 17:50:40 EST 2011

I don't know if it is the best choice, but this is what I do in my code:

for each slice:
  indexnonNaN=np.isfinite(SliceOf Toto)
  SliceOf TotoWithoutNan= SliceOf Toto [indexnonNaN]

and then perform all operation I want o on the last array.

i hope it does answer your question


2011/12/6 questions anon <questions.anon at gmail.com>

> Maybe I am asking the wrong question or could go about this another way.
> I have thousands of numpy arrays to flick through, could I just identify
> which arrays have NAN's and for now ignore the entire array. is there a
> simple way to do this?
> any feedback will be greatly appreciated.
> On Thu, Dec 1, 2011 at 12:16 PM, questions anon <questions.anon at gmail.com>wrote:
>> I am trying to calculate the mean across many netcdf files. I cannot use
>> numpy.mean because there are too many files to concatenate and I end up
>> with a memory error. I have enabled the below code to do what I need but I
>> have a few nan values in some of my arrays. Is there a way to ignore these
>> somewhere in my code. I seem to face this problem often so I would love a
>> command that ignores blanks in my array before I continue on to the next
>> processing step.
>> Any feedback is greatly appreciated.
>> netCDF_list=[]
>> for dir in glob.glob(MainFolder + '*/01/')+ glob.glob(MainFolder +
>> '*/02/')+ glob.glob(MainFolder + '*/12/'):
>>         for ncfile in glob.glob(dir + '*.nc'):
>>             netCDF_list.append(ncfile)
>> slice_counter=0
>> print netCDF_list
>> for filename in netCDF_list:
>>         ncfile=netCDF4.Dataset(filename)
>>         TSFC=ncfile.variables['T_SFC'][:]
>>         fillvalue=ncfile.variables['T_SFC']._FillValue
>>         TSFC=MA.masked_values(TSFC, fillvalue)
>>         for i in xrange(0,len(TSFC)-1,1):
>>                 slice_counter +=1
>>                 #print slice_counter
>>                 try:
>>                         running_sum=N.add(running_sum, TSFC[i])
>>                 except NameError:
>>                         print "Initiating the running total of my
>> variable..."
>>                         running_sum=N.array(TSFC[i])
>> TSFC_avg=N.true_divide(running_sum, slice_counter)
>> N.set_printoptions(threshold='nan')
>> print "the TSFC_avg is:", TSFC_avg
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