Hi,
I noticed some frustrating inconsistencies in the various ways to evaluate polynomials using numpy. Numpy has three ways of evaluating polynomials (that I know of) and each of them has a different syntax:

• numpy.polynomial.polynomial.Polynomial: You define a polynomial by a list of coefficients in order of increasing degree, and then use the class’s call() function.

• np.polyval: Evaluates a polynomial at a point. First argument is the polynomial, or list of coefficients in order of decreasing degree, and the second argument is the point to evaluate at.

• np.polynomial.polynomial.polyval: Also evaluates a polynomial at a point, but has more support for vectorization. First argument is the point to evaluate at, and second argument the list of coefficients in order of increasing degree.

Not only the order of arguments is changed between different methods, but the order of the coefficients is reversed as well, leading to puzzling bugs (in my experience). What could be the reason for this madness? As polyval is a shameless ripoff of Matlab’s function of the same name anyway, why not just use matlab’s syntax (polyval([c0, c1, c2...], x)) across the board?