Fourier Transforms (:mod:`scipy.fftpack`)
=========================================

.. sectionauthor:: Scipy Developers

.. currentmodule:: scipy.fftpack

.. contents::


Fourier analysis is a method for expressing a function as a sum of periodic
components, and for recovering the signal from those components.  When both
the function and its Fourier transform are replaced with discretized
counterparts, it is called the discrete Fourier transform (DFT).  The DFT has
become a mainstay of numerical computing in part because of a very fast
algorithm for computing it, called the Fast Fourier Transform (FFT), which was
known to Gauss (1805) and was brought to light in its current form by Cooley
and Tukey [CT65]_.  Press et al. [NR]_ provide an accessible introduction to
Fourier analysis and its applications.


Fast Fourier transforms
-----------------------

One dimensional discrete Fourier transforms
___________________________________________

The FFT `y[k]` of length :math:`N` of the length-:math:`N` sequence `x[n]` is
defined as

.. math::

    y[k] = \sum_{n=0}^{N-1} e^{-2 \pi j \frac{k n}{N} } x[n] \, ,

and the inverse transform is defined as follows

.. math::

    x[n] = \frac{1}{N} \sum_{n=0}^{N-1} e^{2 \pi j \frac{k n}{N} } y[k] \, .

These transforms can be calculated by means of :func:`fft` and :func:`ifft`,
respectively as shown in the following example.

>>> from scipy.fftpack import fft, ifft
>>> x = np.array([1.0, 2.0, 1.0, -1.0, 1.5])
>>> y = fft(x)
>>> y
[ 4.50000000+0.j          2.08155948-1.65109876j -1.83155948+1.60822041j
 -1.83155948-1.60822041j  2.08155948+1.65109876j]
>>> yinv = ifft(y)
>>> yinv
[ 1.0+0.j  2.0+0.j  1.0+0.j -1.0+0.j  1.5+0.j]


From the definition of the FFT it can be seen that

.. math::

    y[0] = \sum_{n=0}^{N-1} x[n] \, .

In the example

>>> np.sum(x)
4.5

which corresponds to :math:`y[0]`. For N even, the elements
:math:`y[1]...y[N/2-1]` contain the positive-frequency terms, and the elements
:math:`y[N/2]...y[N-1]` contain the negative-frequency terms, in order of
decreasingly negative frequency. For N odd, the elements
:math:`y[1]...y[(N-1)/2]` contain the positive- frequency terms, and the
elements :math:`y[(N+1)/2]...y[N-1]` contain the negative- frequency terms, in
order of decreasingly negative frequency.

In case the sequence x is real-valued, the values of :math:`y[n]` for positive
frequencies is the conjugate of the values :math:`y[n]` for negative
frequencies (because the spectrum is symmetric). Typically, only the FFT
corresponding to positive frequencies is plotted.

The example plots the FFT of the sum of two sines.

.. plot::

    >>> from scipy.fftpack import fft
    >>> # Number of samplepoints
    >>> N = 600
    >>> # sample spacing
    >>> T = 1.0 / 800.0
    >>> x = np.linspace(0.0, N*T, N)
    >>> y = np.sin(50.0 * 2.0*np.pi*x) + 0.5*np.sin(80.0 * 2.0*np.pi*x)
    >>> yf = fft(y)
    >>> xf = np.linspace(0.0, 1.0/(2.0*T), N/2)
    >>> import matplotlib.pyplot as plt
    >>> plt.plot(xf, 2.0/N * np.abs(yf[0:N/2]))
    >>> plt.grid()
    >>> plt.show()


The FFT input signal is inherently truncated. This truncation can be modelled
as multiplication of an inifinte signal with a rectangular window function. In
the spectral domain this multiplication becomes convolution of the signal
spectrum with the window function spectrum, being of form :math:`sin(x)/x`.
This convolution is the cause of an effect called spectral leakage (see
[WPW]_). Windowing the signal with a dedicated window function helps mitigate
spectral leakage. The example below uses a Blackman window from scipy.signal
and shows the effect of windowing (the zero component of the FFT has been
truncated illustrative purposes).

.. plot::

    >>> from scipy.fftpack import fft
    >>> # Number of samplepoints
    >>> N = 600
    >>> # sample spacing
    >>> T = 1.0 / 800.0
    >>> x = np.linspace(0.0, N*T, N)
    >>> y = np.sin(50.0 * 2.0*np.pi*x) + 0.5*np.sin(80.0 * 2.0*np.pi*x)
    >>> yf = fft(y)
    >>> from scipy.signal import blackman
    >>> w = blackman(N)
    >>> ywf = fft(y*w)
    >>> xf = np.linspace(0.0, 1.0/(2.0*T), N/2)
    >>> import matplotlib.pyplot as plt
    >>> plt.semilogy(xf[1:N/2], 2.0/N * np.abs(yf[1:N/2]), '-b')
    >>> plt.semilogy(xf[1:N/2], 2.0/N * np.abs(ywf[1:N/2]), '-r')
    >>> plt.legend(['FFT', 'FFT w. window'])
    >>> plt.grid()
    >>> plt.show()


In case the sequence x is complex-valued, the spectrum is no longer symmetric.
To simplify working wit the FFT functions, scipy provides the following two
helper functions.

The function :func:`fftfreq` returns the FFT sample frequency points.

>>> from scipy.fftpack import fftfreq
>>> freq = fftfreq(np.arange(8), 0.125)
[ 0.  1.  2.  3. -4. -3. -2. -1.]

In a similar spirit, the function :func:`fftshift` allows swapping the lower
and upper halves of a vector, so that it becomes suitable for display.

>>> from scipy.fftpack import fftfreq
>>> x = np.arange(8)
>>> sf.fftshift(x)
[4 5 6 7 0 1 2 3]

The example below plots the FFT of two complex exponentials; note the
asymmetric spectrum.

.. plot::

    >>> from scipy.fftpack import fft, fftfreq, fftshift
    >>> # number of signal points
    >>> N = 400
    >>> # sample spacing
    >>> T = 1.0 / 800.0
    >>> x = np.linspace(0.0, N*T, N)
    >>> y = np.exp(50.0 * 1.j * 2.0*np.pi*x) + 0.5*np.exp(-80.0 * 1.j * 2.0*np.pi*x)
    >>> yf = fft(y)
    >>> xf = fftfreq(N, T)
    >>> xf = fftshift(xf)
    >>> yplot = fftshift(yf)
    >>> import matplotlib.pyplot as plt
    >>> plt.plot(xf, 1.0/N * np.abs(yplot))
    >>> plt.grid()
    >>> plt.show()


The function :func:`rfft` calculates the FFT of a real sequence and outputs
the FFT coefficients :math:`y[n]` with separate real and imaginary parts. In
case of N being even: :math:`[y[0], Re(y[1]), Im(y[1]),..., Re(y[N/2])]`; in
case N being odd :math:`[y[0], Re(y[1]),Im(y[1]),..., Re(y[N/2]),
Im(y[N/2])]`.

The corresponding function :func:`irfft` calculates the IFFT of the FFT
coefficients with this special ordering.

>>> from scipy.fftpack import fft, rfft, irfft
>>> x = np.array([1.0, 2.0, 1.0, -1.0, 1.5, 1.0])
>>> fft(x)
[ 5.50+0.j          2.25-0.4330127j  -2.75-1.29903811j  1.50+0.j
 -2.75+1.29903811j  2.25+0.4330127j ]
>>> yr = rfft(x)
[ 5.5         2.25       -0.4330127  -2.75       -1.29903811  1.5       ]
>>> irfft(yr)
[ 1.   2.   1.  -1.   1.5  1. ]
>>> x = np.array([1.0, 2.0, 1.0, -1.0, 1.5])
>>> fft(x)
[ 4.50000000+0.j          2.08155948-1.65109876j -1.83155948+1.60822041j
 -1.83155948-1.60822041j  2.08155948+1.65109876j]
>>> yr = rfft(x)
[ 4.5         2.08155948 -1.65109876 -1.83155948  1.60822041]


Two and n-dimensional discrete Fourier transforms
_________________________________________________

The functions :func:`fft2` and :func:`ifft2` provide 2-dimensional FFT, and
IFFT, respectively. Similar, :func:`fftn` and :func:`ifftn` provide
n-dimensional FFT, and IFFT, respectively.

The example below demonstrates a 2-dimensional IFFT and plots the resulting
(2-dimensional) time-domain signals.

.. plot::

    >>> from scipy.fftpack import ifftn
    >>> import matplotlib.pyplot as plt
    >>> import matplotlib.cm as cm
    >>> N = 30
    >>> f, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, sharex='col', sharey='row')
    >>> xf = np.zeros((N,N))
    >>> xf[0, 5] = 1
    >>> xf[0, N-5] = 1
    >>> Z = ifftn(xf)
    >>> ax1.imshow(xf, cmap=cm.Reds)
    >>> ax4.imshow(np.real(Z), cmap=cm.binary)
    >>> xf = np.zeros((N, N))
    >>> xf[5, 0] = 1
    >>> xf[N-5, 0] = 1
    >>> Z = ifftn(xf)
    >>> ax2.imshow(xf, cmap=cm.Reds)
    >>> ax5.imshow(np.real(Z), cmap=cm.binary)
    >>> xf = np.zeros((N, N))
    >>> xf[5, 10] = 1
    >>> xf[N-5, N-10] = 1
    >>> Z = ifftn(xf)
    >>> ax3.imshow(xf, cmap=cm.Reds)
    >>> ax6.imshow(np.real(Z), cmap=cm.binary)
    >>> plt.show()


FFT convolution
_______________

`scipy.fftpack.convolve` performs a convolution of two one-dimensional
arrays in frequency domain.


Discrete Cosine Transforms
--------------------------

Scipy provides a DCT with the function :func:`dct` and a corresponding IDCT
with the function :func:`idct`. There are 8 types of the DCT [WPC]_, [Mak]_;
however, only the first 3 types are implemented in scipy. "The" DCT generally
refers to DCT type 2, and "the" Inverse DCT generally refers to DCT type 3. In
addition, the DCT coefficients can be normalized differently (for most types,
scipy provides ``None`` and ``ortho``). Two parameters of the dct/idct
function calls allow setting the DCT type and coefficient normalization.

For a single dimension array x, dct(x, norm='ortho') is equal to
MATLAB dct(x).


Type I DCT
__________

Scipy uses the following definition of the unnormalized DCT-I
(``norm='None'``):

.. math::

    y[k] = x_0 + (-1)^k x_{N-1} + 2\sum_{n=1}^{N-2} x[n]
    \cos\left({\pi nk\over N-1}\right),
    \qquad 0 \le k < N.

Only ``None`` is supported as normalization mode for DCT-I. Note also that the
DCT-I is only supported for input size > 1

Type II DCT
___________

Scipy uses the following definition of the unnormalized DCT-II
(``norm='None'``):

.. math::

    y[k] = 2 \sum_{n=0}^{N-1} x[n] \cos \left({\pi(2n+1)k \over 2N} \right)
    \qquad 0 \le k < N.

In case of the normalized DCT (``norm='ortho'``), the DCT coefficients
:math:`y[k]` are multiplied by a scaling factor `f`:

.. math::

    f = \begin{cases} \sqrt{1/(4N)}, & \text{if $k = 0$} \\    \sqrt{1/(2N)},
    & \text{otherwise} \end{cases} \, .

In this case, the DCT "base functions" :math:`\phi_k[n] = 2 f \cos
\left({\pi(2n+1)k \over 2N} \right)` become orthonormal:

.. math::

   \sum_{n=0}^{N-1} \phi_k[n] \phi_l[n] = \delta_{lk}


Type III DCT
____________

Scipy uses the following definition of the unnormalized DCT-III
(``norm='None'``):

.. math::

    y[k] = x_0 + 2 \sum_{n=1}^{N-1} x[n] \cos\left({\pi n(2k+1) \over 2N}\right)
    \qquad 0 \le k < N,

or, for ``norm='ortho'``:

.. math::

    y[k] = {x_0\over\sqrt{N}} + {2\over\sqrt{N}} \sum_{n=1}^{N-1} x[n]
    \cos\left({\pi n(2k+1) \over 2N}\right) \qquad 0 \le k < N.


DCT and IDCT
____________


The (unnormalized) DCT-III is the inverse of the (unnormalized) DCT-II, up to
a factor `2N`. The orthonormalized DCT-III is exactly the inverse of the orthonormalized DCT-
II. The function :func:`idct` performs the mappings between the DCT and IDCT types.

The example below shows the relation between DCT and IDCT for different types
and normalizations.

>>> from scipy.fftpack import dct, idct
>>> x = np.array([1.0, 2.0, 1.0, -1.0, 1.5])
>>> dct(dct(x, type=2, norm='ortho'), type=3, norm='ortho')
[1.0, 2.0, 1.0, -1.0, 1.5]
>>>  # scaling factor 2*N = 10
>>> idct(dct(x, type=2), type=2)
[ 10.  20.  10. -10.  15.]
>>>  # no scaling factor
>>> idct(dct(x, type=2, norm='ortho'), type=2, norm='ortho')
[ 1.   2.   1.  -1.   1.5]
>>>  # scaling factor 2*N = 10
>>> idct(dct(x, type=3), type=3)
[ 10.  20.  10. -10.  15.]
>>>  # no scaling factor
>>> idct(dct(x, type=3, norm='ortho'), type=3, norm='ortho')
[ 1.   2.   1.  -1.   1.5]
>>>  # scaling factor 2*(N-1) = 8
>>> idct(dct(x, type=1), type=1)
[  8.  16.   8.  -8.  12.]


Example
_______

The DCT exhibits the "energy compaction property", meaning that for many
signals only the first few DCT coefficients have significant magnitude.
Zeroing out the other coefficients leads to a small reconstruction error, a
fact which is exploited in lossy signal compression (e.g. JPEG compression).

The example below shows a signal x and two reconstructions (:math:`x_{20}` and
:math:`x_{15}`)from the signal's DCT coefficients. The signal :math:`x_{20}`
is reconstructed from the first 20 DCT coefficients, :math:`x_{15}` is
reconstructed from the first 15 DCT coefficients. It can be seen that the
relative error of using 20 coefficients is still very small (~0.1%), but
provides a five-fold compression rate.


.. plot::

    >>> from scipy.fftpack import dct, idct
    >>> import matplotlib.pyplot as plt
    >>> N = 100
    >>> t = np.linspace(0,20,N)
    >>> x = np.exp(-t/3)*np.cos(2*t)
    >>> y = dct(x, norm='ortho')
    >>> window = np.zeros(N)
    >>> window[:20] = 1
    >>> yr = idct(y*window, norm='ortho')
    >>> sum(abs(x-yr)**2) / sum(abs(x)**2)
    0.0010901402257
    >>> plt.plot(t, x, '-bx')
    >>> plt.plot(t, yr, 'ro')
    >>> window = np.zeros(N)
    >>> window[:15] = 1
    >>> yr = idct(y*window, norm='ortho')
    >>> sum(abs(x-yr)**2) / sum(abs(x)**2)
    0.0718818065008
    >>> plt.plot(t, yr, 'g+')
    >>> plt.legend(['x', '$x_{20}$', '$x_{15}$'])
    >>> plt.grid()
    >>> plt.show()

Discrete Sine Transforms
------------------------

Scipy provides a DST [Mak]_ with the function :func:`dst` and a corresponding IDST
with the function :func:`idst`.

There are theoretically 8 types of the DST for different combinations of
even/odd boundary conditions and boundary off sets [WPS]_, only the first 3
types are implemented in scipy.

Type I DST
__________

DST-I assumes the input is odd around n=-1 and n=N. Scipy uses the following
definition of the unnormalized DST-I (``norm='None'``):

.. math::

    y[k] = 2\sum_{n=0}^{N-1} x[n]  \sin\left( \pi {(n+1) (k+1)}\over{N+1}
    \right), \qquad 0 \le k < N.

Only ``None`` is supported as normalization mode for DST-I. Note also that the
DST-I is only supported for input size > 1. The (unnormalized) DST-I is its
own inverse, up to a factor `2(N+1)`.

Type II DST
___________

DST-II assumes the input is odd around n=-1/2 and even around n=N. Scipy uses
the following definition of the unnormalized DST-II (``norm='None'``):

.. math::

    y[k] = 2 \sum_{n=0}^{N-1} x[n]  \sin\left( {\pi (n+1/2)(k+1)} \over N
    \right), \qquad 0 \le k < N.

Type III DST
____________

DST-III assumes the input is odd around n=-1 and even around n=N-1. Scipy uses
the following definition of the unnormalized DST-III (``norm='None'``):

.. math::

    y[k] = (-1)^k x[N-1] + 2 \sum_{n=0}^{N-2} x[n] \sin \left( {\pi
    (n+1)(k+1/2)} \over N \right), \qquad 0 \le k < N.


DST and IDST
____________


The example below shows the relation between DST and IDST for different types
and normalizations.

>>> from scipy.fftpack import dst, idst
>>> x = np.array([1.0, 2.0, 1.0, -1.0, 1.5])
>>>  # scaling factor 2*N = 10
>>> idst(dst(x, type=2), type=2)
[ 10.  20.  10. -10.  15.]
>>>  # no scaling factor
>>> idst(dst(x, type=2, norm='ortho'), type=2, norm='ortho')
[ 1.   2.   1.  -1.   1.5]
>>>  # scaling factor 2*N = 10
>>> idst(dst(x, type=3), type=3)
[ 10.  20.  10. -10.  15.]
>>>  # no scaling factor
>>> idst(dst(x, type=3, norm='ortho'), type=3, norm='ortho')
[ 1.   2.   1.  -1.   1.5]
>>>  # scaling factor 2*(N+1) = 8
>>> idst(dst(x, type=1), type=1)
[  8.  16.   8.  -8.  12.]


Cache Destruction
-----------------

To accelerate repeat transforms on arrays of the same shape and dtype,
scipy.fftpack keeps a cache of the prime factorization of length of the array
and pre-computed trigonometric functions.  These caches can be destroyed by
calling the appropriate function in `scipy.fftpack._fftpack`.  
dst(type=1) and idst(type=1) share a cache (``*dst1_cache``).  As do dst(type=2),
dst(type=3), idst(type=3), and idst(type=3) (``*dst2_cache``).


References
----------

.. [CT65] Cooley, James W., and John W. Tukey, 1965, "An algorithm for the
        machine calculation of complex Fourier series," *Math. Comput.*
        19: 297-301.

.. [NR] Press, W., Teukolsky, S., Vetterline, W.T., and Flannery, B.P.,
        2007, *Numerical Recipes: The Art of Scientific Computing*, ch.
        12-13.  Cambridge Univ. Press, Cambridge, UK.

.. [Mak] J. Makhoul, 1980, 'A Fast Cosine Transform in One and Two Dimensions',
       `IEEE Transactions on acoustics, speech and signal processing`
       vol. 28(1), pp. 27-34, http://dx.doi.org/10.1109/TASSP.1980.1163351

.. [WPW] http://en.wikipedia.org/wiki/Window_function

.. [WPC] http://en.wikipedia.org/wiki/Discrete_cosine_transform

.. [WPS] http://en.wikipedia.org/wiki/Discrete_sine_transform
