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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