Thanks all, that helped me work out that I needed to try something else. If anyone else needs to know what method worked:
big_array=N.ma.concatenate(all_FFDI) for x in N.nditer(big_array, op_flags=['readwrite']): if x<100: x[...]=x=0 elif x>=100: x[...]=x=1 sum=big_array.sum(axis=0) On Sat, Sep 3, 2011 at 8:55 AM, Andre' Walker-Loud <walksl...@gmail.com>wrote: > > thank you for all of the resonses, I have attempted all of the > suggestions. It is a numpy array so I can try another list if you would > prefer but I thought I would show the error anyway. > > the error I am receiving is > > ValueError: The truth value of an array with more than one element is > ambiguous. Use a.any() or a.all() > > this is telling you that "value" is not a scalar element, but it has > multiple dimensions. > > do the following: > > >>> big_array=N.ma.concatenate(all_FFDI) > >>> print big_array.shape > > >>> print big_array[0].shape > > > the first print will report the dimensionality of your big_array. The > second print statement will tell you the dimensionality of the 0th element > of big_array. This is the first value of "value" in your for loop. > > Only if the shape is given as "( )", a single (scalar) element, can you > compare it to an integer or float. example > > >>> a = N.zeros([3]) #this builds a one-dimensional array with 3 elements > and puts zeros for each value > >>> a > array([ 0., 0., 0.]) > >>> a.shape > (3,) > >>> a[0].shape > ( ) > >>> a[0] > 0. > > imagine you have a 2-dimensional array > > >>> b = N.zeros([3,3]) # a 3 by 3 array of zeros > >>> b.shape > (3, 3) > >>> b[0] > array([ 0., 0., 0.]) > >>> for i,value in enumerate(b): > ... print value > [ 0., 0., 0.] > [ 0., 0., 0.] > [ 0., 0., 0.] > > you are trying to compare the "value" [ 0., 0., 0.], to an integer. This > is why your code fails - your big_array is a multi-dimensional array. > > The above example is what I mean by "you should play around with the python > interpreter". By doing these things (above) you will begin to learn the > structure of these objects (defined in this case with numpy). > > > Regards, > > Andre > > >
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