On Tue, Dec 6, 2011 at 9:50 AM, Xavier Barthelemy <xabart@gmail.com> wrote:

Hi,
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

Xavier

2011/12/6 questions anon <questions.anon@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@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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