On Sun, Aug 28, 2011 at 3:03 PM, Bruce Ford <bruce@clearscienceinc.com> wrote:

Just to be sure, what might appear to be all NaNs is not really. To find out for sure, do this:

print np.any(np.isfinite(var1/var2))

If it is True, then the array is not full of NaNs and Infs, if False, then that would be odd. When you print an array to the screen, it will often only print part of the array.

Furthermore, the author of the post that you link to in your other email misses some important facts about representation of numbers in computers. The reason 0 / 0 (integers) did not return a NaN or 2 / 0 return an Inf is that integers can not represent those values. Only floating point numbers can. Furthermore, there is an important difference between Inf and NaN. It is merely out of convenience that nan_to_num() would convert the Infs into the largest (or smallest for -Inf) floating point number. Other functions designed for NaN (such as nansum(), nanmax(), etc) do not treat Infs in this manner.

Ben Root

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

Just to be sure, what might appear to be all NaNs is not really. To find out for sure, do this:

print np.any(np.isfinite(var1/var2))

If it is True, then the array is not full of NaNs and Infs, if False, then that would be odd. When you print an array to the screen, it will often only print part of the array.

Furthermore, the author of the post that you link to in your other email misses some important facts about representation of numbers in computers. The reason 0 / 0 (integers) did not return a NaN or 2 / 0 return an Inf is that integers can not represent those values. Only floating point numbers can. Furthermore, there is an important difference between Inf and NaN. It is merely out of convenience that nan_to_num() would convert the Infs into the largest (or smallest for -Inf) floating point number. Other functions designed for NaN (such as nansum(), nanmax(), etc) do not treat Infs in this manner.

Ben Root