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.

bruce@...2905...