[SciPy-User] memory error - numpy mean - netcdf4

Tim Supinie tsupinie@gmail....
Tue Aug 23 21:54:22 CDT 2011

At what point in the program are you getting the error?  Is there a stack

Pending the answers to those to questions, my first thought is to ask how
much data you're loading into memory?  How many files are there?  It's
possible that you're loading a whole bunch of data that you don't need, and
it's not getting cleared out by the garbage collector, which can generate
memory errors when you run out of memory.  Try removing as much data loading
as you can.  (Are you using TIME?  How big is each array you load in?)
Also, if the lats and lons in all the different files are the same, only
load the lats and lons from one file.  All these will not only help your
program use less memory, but help it run faster.

Finally, if that doesn't work, use the gc module and run gc.collect() after
every loop iteration to make sure Python's cleaning up after itself like it
should.  I think the garbage collector might not always run during loops,
which can create problems when you're loading a whole bunch of unused data.


On Tue, Aug 23, 2011 at 6:00 PM, questions anon <questions.anon@gmail.com>wrote:

> Hi All,
> I am receiving a memory error when I try to calculate the Numpy mean across
> many NetCDF files.
> Is there a way to fix this? The code I am using is below.
> Any feedback will be greatly appreciated.
> from netCDF4 import Dataset
> import matplotlib.pyplot as plt
> import numpy as N
> from mpl_toolkits.basemap import Basemap
> from netcdftime import utime
> from datetime import datetime
> import os
> MainFolder=r"E:/GriddedData/T_SFC/"
> all_TSFC=[]
> for (path, dirs, files) in os.walk(MainFolder):
>     for dir in dirs:
>         print dir
>     path=path+'/'
>     for ncfile in files:
>         if ncfile[-3:]=='.nc':
>             #print "dealing with ncfiles:", ncfile
>             ncfile=os.path.join(path,ncfile)
>             ncfile=Dataset(ncfile, 'r+', 'NETCDF4')
>             TSFC=ncfile.variables['T_SFC'][4::24,:,:]
>             LAT=ncfile.variables['latitude'][:]
>             LON=ncfile.variables['longitude'][:]
>             TIME=ncfile.variables['time'][:]
>             fillvalue=ncfile.variables['T_SFC']._FillValue
>             ncfile.close()
>             #combine all TSFC to make one array for analyses
>             all_TSFC.append(TSFC)
> big_array=N.ma.concatenate(all_TSFC)
> #calculate the mean of the combined array
> Mean=big_array.mean(axis=0)
> print "the mean is", Mean
> #plot output summary stats
> map = Basemap(projection='merc',llcrnrlat=-40,urcrnrlat=-33,
>               llcrnrlon=139.0,urcrnrlon=151.0,lat_ts=0,resolution='i')
> map.drawcoastlines()
> map.drawstates()
> x,y=map(*N.meshgrid(LON,LAT))
> plt.title('TSFC Mean at 3pm')
> ticks=[-5,0,5,10,15,20,25,30,35,40,45,50]
> CS = map.contourf(x,y,Mean, cmap=plt.cm.jet)
> l,b,w,h =0.1,0.1,0.8,0.8
> cax = plt.axes([l+w+0.025, b, 0.025, h])
> plt.colorbar(CS,cax=cax, drawedges=True)
> plt.savefig((os.path.join(MainFolder, 'Mean.png')))
> plt.show()
> plt.close()
> print "end processing"
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