# [Numpy-discussion] [ANN]: Taylorpoly, an implementation of vectorized Taylor polynomial operations and request for opinions

Friedrich Romstedt friedrichromstedt@gmail....
Sat Feb 27 16:11:32 CST 2010

```Ok, it took me about one hour, but here they are: Fourier-accelerated
polynomials.

> python
Python 2.4.1 (#65, Mar 30 2005, 09:13:57) [MSC v.1310 32 bit (Intel)] on win32
>>> import gdft_polynomial
>>> p1 = gdft_polynomial.Polynomial([1])
>>> p2 = gdft_polynomial.Polynomial([2])
>>> p1 * p2
<gdft_polynomial.polynomial.Polynomial instance at 0x00E78A08>
>>> print p1 * p2
[ 2.+0.j]
>>> p1 = gdft_polynomial.Polynomial([1, 1])
>>> p2 = gdft_polynomial.Polynomial([1])
>>> print p1 * p2
[ 1. +6.12303177e-17j  1. -6.12303177e-17j]
>>> p2 = gdft_polynomial.Polynomial([1, 2])
>>> print p1 * p2
[ 1. +8.51170986e-16j  3. +3.70074342e-17j  2. -4.44089210e-16j]
>>> p1 = gdft_polynomial.Polynomial([1, 2, 3, 4, 3, 2, 1])
>>> p2 = gdft_polynomial.Polynomial([4, 3, 2, 1, 2, 3, 4])
>>> print (p1 * p2).coefficients.real
[  4.  11.  20.  30.  34.  35.  36.  35.  34.  30.  20.  11.   4.]
>>>

github.com/friedrichromstedt/gdft_polynomials

It's open for bug hunting :-)

Haven't checked the last result.

I used my own gdft module.  Maybe one could incorporate numpy.fft
easily.  But that's your job, Sebastian, isn't it?  Feel free to push