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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

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Bruce W. Ford
Clear Science, Inc.
br...@cl...