(All code examples in the post have been included in the nrp_base.py module, which can be downloaded from this repository.)

As presented in the previous post, Cooley-Tukey’s FFT algorithm has a clear limitation: it can only be used to speed the calculation of DFTs of a size that is a power of two. It isn’t hard, though, to extend the same idea to a more general factorization of the input signal size. We start again from the formula that defines the DFT,

Say that N = P·Q. Following the previous approach, we could try to split the calculation of the DFT into Q DFTs of size P, by rewriting it as

where n = Q·p + q. We are again faced with the same issue to turn the inner summation into a proper DFT: while p ranges from 0 to P-1, k goes all the way up to N-1. To get around this, we can make k = P·r + s, and replacing in the previous formula we get

where the inner summation has been reduced to a standard DFT of size P. Furthermore, the splitting of the first exponential in two factors unveils that the outer summation is also a standard DFT, this one of size Q, of the product of the DFT of size P by twiddle factors.

The subscripts in the above formula can get confusing to follow, so take note that q and r go from 0 to Q-1, while p and s go from 0 to P-1. Therefore the computation of the size N DFT has effectively been split into the computation of Q DFTs of size P, followed by twiddle factor multiplication, and then by P DFTs of size Q. It may be helpful to picture the linear array of input data as a PxQ matirx stored in row-major order. Then the Q DFTs of size P are performed on the columns of this matrix, and the P DFTs of size Q on the rows. Note though that the output has to be rearranged, in what would be equivalent to a matrix transposition, before getting to the final answer. As an example, the following picture illustrates the process on an input of size 15.

#### Planning a Generalized Cooley-Tukey FFT

Disregarding the multiplication by twiddle factors and the matrix transpositions, the algorithm turns a calculation which requires a time proportional to N^{2} = P^{2}Q^{2}, into one which requires a time proportional to P·Q·(P+Q). This very simplistic complexity analysis suggests that the factoring of the size of the original signal into two multiplicative factors isn’t arbitrary, and will affect the performance of the algorithm. And in principle a split into two similarly sized factors, P ~ Q ~ √N should, although often doesn’t, outperform any other approach.

In many practical applications a small P or Q is used, which is then called the radix. If the smaller DFT is in the outer loop. i.e. if Q is the radix, the scheme is termed decimation in time (DIT), while if it is in the inner loop, i.e. if P is the radix, we have decimation in frequency (DIF), which explains the long name of the simplest Cooley-Tukey algorithm described earlier.

The splitting of the input size, N, into the two multiplicative factors, P and Q, can be done in d(N) – 2 different ways, where d(N) is the divisor function, i.e. the number of divisors of N. If we use the same algorithm recursively, the possible paths to the solution can grow very fast, and determining the optimal one is anything but easy. It is in any case a point where we don’t want to make excessive assumptions, and which should be left sufficiently open for user experimentation. So I’ve decided to copy FFTW‘s approach: the first step to peform a FFT is to plan it. In our case, a plan is a data structure which is similar to a tree, holding the information of the factorizations chosen, the function that should be called to perform the next step, and references to those next steps. The following picture sketches two possible plans for a DFT of size 210 = 2·3·5·7.

In line with the image, I have coded a planning function that gives two choices to the user:

**decimation in time**(`dit=True`

) vs.**decimation in frequency**(`dif=True`

)**minimum possible radix**(`min_rad=True`

) vs.**maximum possible radix**(`max_rad=True`

)

Note that the standard radix 2 with decimation in time plan would result from setting `dit=True`

and `min_rad=True`

when the input size is a power of two, and that it would be the default behavior of this planner.

@nrp_base.performance_timer def plan_fft(x, **kwargs) : """ Returns an FFT plan for x. The plan uses direct DFT for prime sizes, and the generalized CT algorithm with either maximum or minimum radix for others. Keyword Arguments dif -- If True, will use decimation in frequency. Default is False. dit -- If True, will use decimation in time. Overrides dif. Default is True. max_rad -- If True, will use tje largest possible radix. Default is False. min_rad -- If True, will use smallest possible radix. Overrides max_rad. Default is True. """ dif = kwargs.pop('dif', None) dit = kwargs.pop('dit', None) max_rad = kwargs.pop('max_rad', None) min_rad = kwargs.pop('min_rad', None) if dif is None and dit is None : dif = False dit = True elif dit is None : dit = not dif else : dif = not dit if max_rad is None and min_rad is None : max_rad = False min_rad = True elif min_rad is None : min_rad = not max_rad else : max_rad = not min_rad N = len(x) plan = [] if min_rad : fct = sorted(nrp_numth.naive_factorization(N)) NN = N ptr = plan for f in fct : NN //= f P = [f, dft, None] Q = [NN, fft_CT, []] if dit : ptr += [Q, P] else : ptr += [P, Q] if NN == fct[-1] : Q[1:3] = [dft, None] break else : ptr = Q[2] else : items ={} def follow_tree(plan, N, fct, **kwargs) : if fct is None: fct = nrp_numth.naive_factorization(N) divs = sorted(nrp_numth.divisors(fct)) val_P = divs[(len(divs) - 1) // 2] P = [val_P] if val_P in items : P = items[val_P] else : fct_P = nrp_numth.naive_factorization(val_P) if len(fct_P) > 1 : P += [fft_CT, []] follow_tree(P[2], val_P, fct_P, dit = dit) else : P +=[dft, None] items[val_P] = P val_Q = N // val_P Q = [val_Q] if val_Q in items : Q = items[val_Q] else : fct_Q = nrp_numth.naive_factorization(val_Q) if len(fct_Q) > 1 : Q += [fft_CT, []] follow_tree(Q[2], val_Q, fct_Q, dit = dit) else : Q +=[dft, None] items[val_Q] = Q if dit : plan += [Q, P] else : plan += [P, Q] follow_tree(plan, N, None, dit = dit) return plan

While the planner can probably be further optimized, especially for `max_rad=True`

, with all the repeated factorizations, this shouldn’t be the critical part of the DFT calculation. Specially so since the plan can be calculated in advance, and reused for all inputs of the same size.

#### Twiddle Factors Reloaded

As already discussed in previous posts, one of the simplest ways of trimming down the computation time is to store the twiddle factors, which are almost certain to show up again, and reuse them whenever possible, rather than going through complex exponentiations over and over again. There is a slight difference though with regard to the radix 2 case. There, when computing a size N FFT, the twiddle factors that intervened in the calculations where all those with numerators in the exponent (apart from the ±2·π·i) ranging from 0 to N/2 – 1, inclusive. If N is split as P·Q, rather than as 2·N/2, then the largest numerator that will appear will be (P-1)·(Q-1), which is definitely larger than N/2, but not all of the items up to that value will be required, with many of them missing.

Take for instance a DFT of size 512. If we go for the radix 2 FFT, we will in total use 256 different twiddle factors, ranging from 0 to 255. If rather than that we go for a `max_rad=True`

approach, the first split will be 512 = 16·32, and so the largest twiddle factor will have numerator 15·31 = 465. But with all the intermediate twiddle factors not showing up in the calculation, overall only 217 different ones are needed.

It is also interesting to note that the same twiddle factors that are used in the FFTs, may appear in the DFTs of the leaves of the planning tree. So it is worth modifying the current `dft`

function, so that it can accept precalculated twiddles. Because the twiddle factors sequence will have gaps, a list is no longer the most convenient data structure to hold these values, a dictionary being a better fit for the job. Combining all of these ideas, we can rewrite our function as

@nrp_base.performance_timer def dft(x, **kwargs) : """ Computes the (inverse) discrete Fourier transform naively. Arguments x - Te list of values to transform Keyword Arguments inverse - True to compute an inverse transform. Default is False. normalize - True to normalize inverse transforms. Default is same as inverse. twiddles - Dictionary of twiddle factors to be used by function, used for compatibility as last step for FFT functions, don't set manually!!! """ inverse = kwargs.pop('inverse',False) normalize = kwargs.pop('normalize',inverse) N = len(x) inv = -1 if not inverse else 1 twiddles = kwargs.pop('twiddles', {'first_N' : N, 0 : 1}) NN = twiddles['first_N'] X =[0] * N for k in xrange(N) : for n in xrange(0,N) : index = (n * k * NN // N) % NN twid= twiddles.setdefault(index, math.e**(inv*2j*math.pi*index/NN)) X[k] += x[n] * twid if inverse and normalize : X[k] /= NN return X

Note also the changes to the user interface, with the addition of the `normalize`

keyword argument. This has been introduced to correct a notorious bug in the inverse DFT calculations for the radix 2 FFT algorithm. The changes are implemented and can be checked in the module nrp_dsp.py, although they are very similar in nature to what will shortly be presented for the generalized factorization FFT.

#### Putting it all Together

We now have most of the building blocks to put together a FFT function using Cooley-Tukey’s algorithm for general factorizations, a possible realization of which follows:

@nrp_base.performance_timer def fft_CT(x, plan,**kwargs) : """ Computes the (inverse) DFT using Cooley-Tukey's generalized algorithm. Arguments x - The list of values to transform. plan - A plan data structure for FFT execution. Keyword Arguments inverse - if True, computes an inverse FFT. Default is False. twiddles - dictionary of twiddle factors to be used by function, used for ptimization during recursion, don't set manually!!! """ inverse = kwargs.pop('inverse', False) inv = 1 if inverse else -1 N = len(x) twiddles = kwargs.pop('twiddles', {'first_N' : N, 0 : 1}) NN = twiddles['first_N'] X = [0] * N P = plan[0] Q = plan[1] # do inner FFT for q in xrange(Q[0]) : X[q::Q[0]] = P[1](x[q::Q[0]], plan=P[2], twiddles=twiddles, inverse=inverse,normalize=False) # multiply by twiddles for q in xrange(Q[0]) : for s in xrange(P[0]) : index = q * s * NN // N mult = twiddles.setdefault(index, math.e**(inv*2j*math.pi*index/NN)) X[Q[0]*s+q] *= mult # do outer FFT for s in xrange(P[0]) : X[Q[0]*s:Q[0]*(s+1)] = Q[1](X[Q[0]*s:Q[0]*(s+1)],plan=Q[2], twiddles=twiddles,inverse=inverse, normalize=False) # transpose matrix ret = [0] * N for s in xrange(P[0]) : ret[s::P[0]] = X[s*Q[0]:(s+1)*Q[0]] if inverse and N == NN : # normalize inverse only in first call ret = [val / N for val in ret] return ret

For power of two sizes, this algorithm is noticeably slower, by a factor of more than 2 depending on the input size, than the hardcoded radix 2 prior version. This is mostly due to the time spent doing matrix inversion in the last step. Also due to the matrix inversion, minimum radix plans usually outperform maximum radix ones, despite the benefits in FFT and twiddle factor computations the latter present over the former. Of course none of this matters if the input size presents several prime factors other than two, in which case the new algorithm clearly outperforms the radix 2 version, which is then close to the DFT version. Still, it is worth noting for later optimizations that matrix inversion should be done in a more efficient fashion.