Closing the loop...I found the difficulty and applied this:

var = np.nan_to_num(np.divide(var1,var2))

See this page:  http://psaffrey.wordpress.com/2010/07/30/numpy-and-nan-again/

---------------------------------------
Bruce W. Ford
Clear Science, Inc.
[email protected]






On Sun, Aug 28, 2011 at 4:03 PM, Bruce Ford <[email protected]> wrote:
> Getting a strange result trying to divide two 3d arrays.  I am getting
> a matrix of NaNs regardless of how I divide and I can't determine why.
>
> #opened a NetCDF file using python-netcdf4
>
> var1 = nc_file.variables['var1']  ###shape = [31,181,360] with a
> values ranging from 0 - 243 and NO NaNs in the array, dtype float32
> var2 = nc_file.variables['var2']  ###shape = [31,181,360] with a
> values ranging from 0 - 4 (mostly zeros) and NO NaNs in the array,
> dtype float32
>
> np.seterr(all='ignore')  #in case problem has something do to with
> dividing by zero
>
> var1/var2  ###gives array of NaNs with shape of 31,181,360
>
> #doing the division one slice at a time doesn't help...
> for x in range(1,var1.shape[0]):
>            var[x,:,:] = var1[x,:,:]/var2[x,:,:]  ###gives array of
> NaNs with shape of 31,181,360
>
> var = np.divide(var1,var2)  ###gives array of NaNs with shape of 31,181,360
>
>
> print "<p>where max: " + np.where(var1 == np.max(var1))  #prints
> (array([28]), array([79]), array([182]))
> print var1[28,79,182]  #print 545
> print var2[28,79,182]   ##prints 6
>
> #so there are values in this location that should not result in an
> NaN.  Instead I get an entire array of NaNs
>
> What am I missing?
>
> Bruce
>
> ---------------------------------------
> Bruce W. Ford
> Clear Science, Inc.
> [email protected]
>

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