# Discrete Fourier transforms using Sagemath

Here are some Sagemath examples for DFTs, DCTs, and DST’s. You can try copying and pasting them into the Sagemath cloud, for example.

The Sagemath dft command applies to a sequence S indexed by a set J computes the un-normalized DFT: (in Python)

[sum([S[i]*chi(zeta**(i*j)) for i in J]) for j in J]Here are some examples which explain the syntax:

```sage: J = range(6)
sage: A = [ZZ(1) for i in J]
sage: s = IndexedSequence(A,J)
sage: s.dft(lambda x:x^2)
Indexed sequence: [6, 0, 0, 6, 0, 0]
indexed by [0, 1, 2, 3, 4, 5]
sage: s.dft()
Indexed sequence: [6, 0, 0, 0, 0, 0]
indexed by [0, 1, 2, 3, 4, 5]
sage: G = SymmetricGroup(3)
sage: J = G.conjugacy_classes_representatives()
sage: s = IndexedSequence([1,2,3],J) # 1,2,3 are the values of a class fcn on G
sage: s.dft()   # the "scalar-valued Fourier transform" of this class fcn
Indexed sequence: [8, 2, 2]
indexed by [(), (1,2), (1,2,3)]
sage: J = AbelianGroup(2,[2,3],names='ab')
sage: s = IndexedSequence([1,2,3,4,5,6],J)
sage: s.dft()   # the precision of output is somewhat random and arch. dependent.
Indexed sequence: [21.0000000000000, -2.99999999999997 - 1.73205080756885*I, -2.99999999999999 + 1.73205080756888*I, -9.00000000000000 + 0.0000000000000485744257349999*I, -0.00000000000000976996261670137 - 0.0000000000000159872115546022*I, -0.00000000000000621724893790087 - 0.0000000000000106581410364015*I]
indexed by Multiplicative Abelian Group isomorphic to C2 x C3
sage: J = CyclicPermutationGroup(6)
sage: s = IndexedSequence([1,2,3,4,5,6],J)
sage: s.dft()   # the precision of output is somewhat random and arch. dependent.
Indexed sequence: [21.0000000000000, -2.99999999999997 - 1.73205080756885*I, -2.99999999999999 + 1.73205080756888*I, -9.00000000000000 + 0.0000000000000485744257349999*I, -0.00000000000000976996261670137 - 0.0000000000000159872115546022*I, -0.00000000000000621724893790087 - 0.0000000000000106581410364015*I]
indexed by Cyclic group of order 6 as a permutation group
sage: p = 7; J = range(p); A = [kronecker_symbol(j,p) for j in J]
age: s = IndexedSequence(A,J)
sage: Fs = s.dft()
sage: c = Fs.list(); [x/c for x in Fs.list()]; s.list()
[0, 1, 1, -1, 1, -1, -1]
[0, 1, 1, -1, 1, -1, -1]```

The DFT of the values of the quadratic residue symbol is itself, up to a constant factor (denoted c on the last line above).

Here is a 2nd example:

```sage: J = range(5)
sage: A = [ZZ(1) for i in J]
sage: s = IndexedSequence(A,J)
sage: fs = s.dft(); fs
Indexed sequence: [5, 0, 0, 0, 0]
indexed by [0, 1, 2, 3, 4]
sage: it = fs.idft(); it
Indexed sequence: [1, 1, 1, 1, 1]
indexed by [0, 1, 2, 3, 4]
age: it == s
True
sage: t = IndexedSequence(B,J)
sage: s.convolution(t)
[1, 2, 3, 4, 5, 4, 3, 2, 1]```

Here is a 3rd example:

```sage: J = range(5)
sage: A = [exp(-2*pi*i*I/5) for i in J]
sage: s = IndexedSequence(A,J)
sage: s.dct()    # discrete cosine
Indexed sequence: [2.50000000000011 + 0.00000000000000582867087928207*I, 2.50000000000011 + 0.00000000000000582867087928207*I, 2.50000000000011 + 0.00000000000000582867087928207*I, 2.50000000000011 + 0.00000000000000582867087928207*I, 2.50000000000011 + 0.00000000000000582867087928207*I]
indexed by [0, 1, 2, 3, 4]
sage: s.dst()        # discrete sine
Indexed sequence: [0.0000000000000171529457304586 - 2.49999999999915*I, 0.0000000000000171529457304586 - 2.49999999999915*I, 0.0000000000000171529457304586 - 2.49999999999915*I, 0.0000000000000171529457304586 - 2.49999999999915*I, 0.0000000000000171529457304586 - 2.49999999999915*I]
indexed by [0, 1, 2, 3, 4]```

Here is a 4th example:

```sage: I = range(3)
sage: A = [ZZ(i^2)+1 for i in I]
sage: s = IndexedSequence(A,I)
sage: P1 = s.plot()
sage: P2 = s.plot_histogram()```

P1 and P2 are displayed below: