Hi All, I can't find any docs on this behavior.
So, I have a python function. To keep it simple, lets just do addition: def add(x,y): print x,y retun x+y So, I can turn this into a ufunc as follows: uadd = np.frompyfunc(add,2,1) Now, I can apply it to an array: >>> uadd.accumulate(np.arange(3,10)) 3 4 7 5 12 6 18 7 25 8 33 9 array([3, 7, 12, 18, 25, 33, 42], dtype=object) Okay, but where did the initial 3 come from? http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.accumulate.html#numpy.ufunc.accumulate suggests that: r = np.empty(len(A)) t = op.identity # op = the ufunc being applied to A's elements for i in xrange(len(A)): t = op(t, A[i]) r[i] = t return r ...but: >>> print uadd.identity None ...and: >>> add(None,3) None 3 Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<stdin>", line 3, in add TypeError: unsupported operand type(s) for +: 'NoneType' and 'int' So, where is the reason that the 3 ends up in the output array documented? Also, what if I want to specify the identity of my newly created ufunc? I have a case where I want to specify it as zero: >>> uadd.identity = 0 Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: attribute 'identity' of 'numpy.ufunc' objects is not writable Any help gratefully received! Chris _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
