Module Scipy.FftpackSource

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

Return (a,b)-cosh/cosh pseudo-derivative of a periodic sequence.

If x_j and y_j are Fourier coefficients of periodic functions x and y, respectively, then::

y_j = cosh(j*a*2*pi/period)/cosh(j*b*2*pi/period) * x_j

Parameters ---------- x : array_like The array to take the pseudo-derivative from. a,b : float Defines the parameters of the sinh/sinh pseudo-differential operator. period : float, optional The period of the sequence x. Default is ``2*pi``.

Returns ------- cc_diff : ndarray Pseudo-derivative of periodic sequence `x`.

Notes ----- ``cc_diff(cc_diff(x,a,b),b,a) == x``

Return (a,b)-cosh/sinh pseudo-derivative of a periodic sequence.

If ``x_j`` and ``y_j`` are Fourier coefficients of periodic functions x and y, respectively, then::

y_j = -sqrt(-1)*cosh(j*a*2*pi/period)/sinh(j*b*2*pi/period) * x_j y_0 = 0

Parameters ---------- x : array_like The array to take the pseudo-derivative from. a, b : float Defines the parameters of the cosh/sinh pseudo-differential operator. period : float, optional The period of the sequence. Default period is ``2*pi``.

Returns ------- cs_diff : ndarray Pseudo-derivative of periodic sequence `x`.

Notes ----- For even len(`x`), the Nyquist mode of `x` is taken as zero.

Return the Discrete Cosine Transform of arbitrary type sequence x.

Parameters ---------- x : array_like The input array. type :

, 2, 3, 4

, optional Type of the DCT (see Notes). Default type is 2. n : int, optional Length of the transform. If ``n < x.shapeaxis``, `x` is truncated. If ``n > x.shapeaxis``, `x` is zero-padded. The default results in ``n = x.shapeaxis``. axis : int, optional Axis along which the dct is computed; the default is over the last axis (i.e., ``axis=-1``). norm : None, 'ortho', optional Normalization mode (see Notes). Default is None. overwrite_x : bool, optional If True, the contents of `x` can be destroyed; the default is False.

Returns ------- y : ndarray of real The transformed input array.

See Also -------- idct : Inverse DCT

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

There are, theoretically, 8 types of the DCT, only the first 4 types are implemented in scipy. 'The' DCT generally refers to DCT type 2, and 'the' Inverse DCT generally refers to DCT type 3.

**Type I**

There are several definitions of the DCT-I; we use the following (for ``norm=None``)

.. math::

y_k = x_0 + (-1)^k x_N-1 + 2 \sum_n=1^N-2 x_n \cos\left( \frac\pi k nN-1 \right)

If ``norm='ortho'``, ``x0`` and ``xN-1`` are multiplied by a scaling factor of :math:`\sqrt

`, and ``yk`` is multiplied by a scaling factor ``f``

.. math::

f = \begincases \frac

\sqrt\frac{1N-1

}

& \textfk=0\text or N-1, \\ \frac

\sqrt\frac{2N-1

}

& \textotherwise \endcases

.. versionadded:: 1.2.0 Orthonormalization in DCT-I.

.. note:: The DCT-I is only supported for input size > 1.

**Type II**

There are several definitions of the DCT-II; we use the following (for ``norm=None``)

.. math::

y_k = 2 \sum_n=0^N-1 x_n \cos\left(\frac\pi k(2n+1)

N

\right)

If ``norm='ortho'``, ``yk`` is multiplied by a scaling factor ``f``

.. math:: f = \begincases \sqrt\frac{1

N

}

& \textfk=0, \\ \sqrt\frac{1

N

}

& \textotherwise \endcases

which makes the corresponding matrix of coefficients orthonormal (``O @ O.T = np.eye(N)``).

**Type III**

There are several definitions, we use the following (for ``norm=None``)

.. math::

y_k = x_0 + 2 \sum_n=1^N-1 x_n \cos\left(\frac\pi(2k+1)n

N

\right)

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

.. math::

y_k = \fracx_0\sqrt{N

}

1. \sqrt\frac{2N

}

\sum_n=1^N-1 x_n \cos\left(\frac\pi(2k+1)n

N

\right)

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.

**Type IV**

There are several definitions of the DCT-IV; we use the following (for ``norm=None``)

.. math::

y_k = 2 \sum_n=0^N-1 x_n \cos\left(\frac\pi(2k+1)(2n+1)

N

\right)

If ``norm='ortho'``, ``yk`` is multiplied by a scaling factor ``f``

.. math::

f = \frac

\sqrt{2N

}

.. versionadded:: 1.2.0 Support for DCT-IV.

References ---------- .. 1 'A Fast Cosine Transform in One and Two Dimensions', by J. Makhoul, `IEEE Transactions on acoustics, speech and signal processing` vol. 28(1), pp. 27-34, :doi:`10.1109/TASSP.1980.1163351` (1980). .. 2 Wikipedia, 'Discrete cosine transform', https://en.wikipedia.org/wiki/Discrete_cosine_transform

Examples -------- The Type 1 DCT is equivalent to the FFT (though faster) for real, even-symmetrical inputs. The output is also real and even-symmetrical. Half of the FFT input is used to generate half of the FFT output:

>>> from scipy.fftpack import fft, dct >>> fft(np.array(4., 3., 5., 10., 5., 3.)).real array( 30., -8., 6., -2., 6., -8.) >>> dct(np.array(4., 3., 5., 10.), 1) array( 30., -8., 6., -2.)

Return multidimensional Discrete Cosine Transform along the specified axes.

Parameters ---------- x : array_like The input array. type :

, 2, 3, 4

, optional Type of the DCT (see Notes). Default type is 2. shape : int or array_like of ints or None, optional The shape of the result. If both `shape` and `axes` (see below) are None, `shape` is ``x.shape``; if `shape` is None but `axes` is not None, then `shape` is ``scipy.take(x.shape, axes, axis=0)``. If ``shapei > x.shapei``, the ith dimension is padded with zeros. If ``shapei < x.shapei``, the ith dimension is truncated to length ``shapei``. If any element of `shape` is -1, the size of the corresponding dimension of `x` is used. axes : int or array_like of ints or None, optional Axes along which the DCT is computed. The default is over all axes. norm : None, 'ortho', optional Normalization mode (see Notes). Default is None. overwrite_x : bool, optional If True, the contents of `x` can be destroyed; the default is False.

Returns ------- y : ndarray of real The transformed input array.

See Also -------- idctn : Inverse multidimensional DCT

Notes ----- For full details of the DCT types and normalization modes, as well as references, see `dct`.

Examples -------- >>> from scipy.fftpack import dctn, idctn >>> y = np.random.randn(16, 16) >>> np.allclose(y, idctn(dctn(y, norm='ortho'), norm='ortho')) True

Return kth derivative (or integral) of a periodic sequence x.

If x_j and y_j are Fourier coefficients of periodic functions x and y, respectively, then::

y_j = pow(sqrt(-1)*j*2*pi/period, order) * x_j y_0 = 0 if order is not 0.

Parameters ---------- x : array_like Input array. order : int, optional The order of differentiation. Default order is 1. If order is negative, then integration is carried out under the assumption that ``x_0 == 0``. period : float, optional The assumed period of the sequence. Default is ``2*pi``.

Notes ----- If ``sum(x, axis=0) = 0`` then ``diff(diff(x, k), -k) == x`` (within numerical accuracy).

For odd order and even ``len(x)``, the Nyquist mode is taken zero.

Return the Discrete Sine Transform of arbitrary type sequence x.

Parameters ---------- x : array_like The input array. type :

, 2, 3, 4

, optional Type of the DST (see Notes). Default type is 2. n : int, optional Length of the transform. If ``n < x.shapeaxis``, `x` is truncated. If ``n > x.shapeaxis``, `x` is zero-padded. The default results in ``n = x.shapeaxis``. axis : int, optional Axis along which the dst is computed; the default is over the last axis (i.e., ``axis=-1``). norm : None, 'ortho', optional Normalization mode (see Notes). Default is None. overwrite_x : bool, optional If True, the contents of `x` can be destroyed; the default is False.

Returns ------- dst : ndarray of reals The transformed input array.

See Also -------- idst : Inverse DST

Notes ----- For a single dimension array ``x``.

There are, theoretically, 8 types of the DST for different combinations of even/odd boundary conditions and boundary off sets 1_, only the first 4 types are implemented in scipy.

**Type I**

There are several definitions of the DST-I; we use the following for ``norm=None``. DST-I assumes the input is odd around `n=-1` and `n=N`.

.. math::

y_k = 2 \sum_n=0^N-1 x_n \sin\left(\frac\pi(k+1)(n+1)N+1\right)

Note 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)`. The orthonormalized DST-I is exactly its own inverse.

**Type II**

There are several definitions of the DST-II; we use the following for ``norm=None``. DST-II assumes the input is odd around `n=-1/2` and `n=N-1/2`; the output is odd around :math:`k=-1` and even around `k=N-1`

.. math::

y_k = 2 \sum_n=0^N-1 x_n \sin\left(\frac\pi(k+1)(2n+1)

N

\right)

if ``norm='ortho'``, ``yk`` is multiplied by a scaling factor ``f``

.. math::

f = \begincases \sqrt\frac{1

N

}

& \textfk = 0, \\ \sqrt\frac{1

N

}

& \textotherwise \endcases

**Type III**

There are several definitions of the DST-III, we use the following (for ``norm=None``). DST-III assumes the input is odd around `n=-1` and even around `n=N-1`

.. math::

y_k = (-1)^k x_N-1 + 2 \sum_n=0^N-2 x_n \sin\left( \frac\pi(2k+1)(n+1)

N

\right)

The (unnormalized) DST-III is the inverse of the (unnormalized) DST-II, up to a factor `2N`. The orthonormalized DST-III is exactly the inverse of the orthonormalized DST-II.

.. versionadded:: 0.11.0

**Type IV**

There are several definitions of the DST-IV, we use the following (for ``norm=None``). DST-IV assumes the input is odd around `n=-0.5` and even around `n=N-0.5`

.. math::

y_k = 2 \sum_n=0^N-1 x_n \sin\left(\frac\pi(2k+1)(2n+1)

N

\right)

The (unnormalized) DST-IV is its own inverse, up to a factor `2N`. The orthonormalized DST-IV is exactly its own inverse.

.. versionadded:: 1.2.0 Support for DST-IV.

References ---------- .. 1 Wikipedia, 'Discrete sine transform', https://en.wikipedia.org/wiki/Discrete_sine_transform

Return multidimensional Discrete Sine Transform along the specified axes.

Parameters ---------- x : array_like The input array. type :

, 2, 3, 4

, optional Type of the DST (see Notes). Default type is 2. shape : int or array_like of ints or None, optional The shape of the result. If both `shape` and `axes` (see below) are None, `shape` is ``x.shape``; if `shape` is None but `axes` is not None, then `shape` is ``scipy.take(x.shape, axes, axis=0)``. If ``shapei > x.shapei``, the ith dimension is padded with zeros. If ``shapei < x.shapei``, the ith dimension is truncated to length ``shapei``. If any element of `shape` is -1, the size of the corresponding dimension of `x` is used. axes : int or array_like of ints or None, optional Axes along which the DCT is computed. The default is over all axes. norm : None, 'ortho', optional Normalization mode (see Notes). Default is None. overwrite_x : bool, optional If True, the contents of `x` can be destroyed; the default is False.

Returns ------- y : ndarray of real The transformed input array.

See Also -------- idstn : Inverse multidimensional DST

Notes ----- For full details of the DST types and normalization modes, as well as references, see `dst`.

Examples -------- >>> from scipy.fftpack import dstn, idstn >>> y = np.random.randn(16, 16) >>> np.allclose(y, idstn(dstn(y, norm='ortho'), norm='ortho')) True

Return discrete Fourier transform of real or complex sequence.

The returned complex array contains ``y(0), y(1),..., y(n-1)``, where

``y(j) = (x * exp(-2*pi*sqrt(-1)*j*np.arange(n)/n)).sum()``.

Parameters ---------- x : array_like Array to Fourier transform. n : int, optional Length of the Fourier transform. If ``n < x.shapeaxis``, `x` is truncated. If ``n > x.shapeaxis``, `x` is zero-padded. The default results in ``n = x.shapeaxis``. axis : int, optional Axis along which the fft's are computed; the default is over the last axis (i.e., ``axis=-1``). overwrite_x : bool, optional If True, the contents of `x` can be destroyed; the default is False.

Returns ------- z : complex ndarray with the elements::

y(0),y(1),..,y(n/2),y(1-n/2),...,y(-1) if n is even y(0),y(1),..,y((n-1)/2),y(-(n-1)/2),...,y(-1) if n is odd

where::

y(j) = sumk=0..n-1 xk * exp(-sqrt(-1)*j*k* 2*pi/n), j = 0..n-1

See Also -------- ifft : Inverse FFT rfft : FFT of a real sequence

Notes ----- The packing of the result is 'standard': If ``A = fft(a, n)``, then ``A0`` contains the zero-frequency term, ``A1:n/2`` contains the positive-frequency terms, and ``An/2:`` contains the negative-frequency terms, in order of decreasingly negative frequency. So ,for an 8-point transform, the frequencies of the result are 0, 1, 2, 3, -4, -3, -2, -1. To rearrange the fft output so that the zero-frequency component is centered, like -4, -3, -2, -1, 0, 1, 2, 3, use `fftshift`.

Both single and double precision routines are implemented. Half precision inputs will be converted to single precision. Non-floating-point inputs will be converted to double precision. Long-double precision inputs are not supported.

This function is most efficient when `n` is a power of two, and least efficient when `n` is prime.

Note that if ``x`` is real-valued, then ``Aj == An-j.conjugate()``. If ``x`` is real-valued and ``n`` is even, then ``An/2`` is real.

If the data type of `x` is real, a 'real FFT' algorithm is automatically used, which roughly halves the computation time. To increase efficiency a little further, use `rfft`, which does the same calculation, but only outputs half of the symmetrical spectrum. If the data is both real and symmetrical, the `dct` can again double the efficiency by generating half of the spectrum from half of the signal.

Examples -------- >>> from scipy.fftpack import fft, ifft >>> x = np.arange(5) >>> np.allclose(fft(ifft(x)), x, atol=1e-15) # within numerical accuracy. True

2-D discrete Fourier transform.

Return the 2-D discrete Fourier transform of the 2-D argument `x`.

See Also -------- fftn : for detailed information.

Examples -------- >>> from scipy.fftpack import fft2, ifft2 >>> y = np.mgrid:5, :50 >>> y array([0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [4, 4, 4, 4, 4]) >>> np.allclose(y, ifft2(fft2(y))) True

Return the Discrete Fourier Transform sample frequencies.

The returned float array `f` contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second.

Given a window length `n` and a sample spacing `d`::

f = 0, 1, ..., n/2-1, -n/2, ..., -1 / (d*n) if n is even f = 0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1 / (d*n) if n is odd

Parameters ---------- n : int Window length. d : scalar, optional Sample spacing (inverse of the sampling rate). Defaults to 1.

Returns ------- f : ndarray Array of length `n` containing the sample frequencies.

Examples -------- >>> signal = np.array(-2, 8, 6, 4, 1, 0, 3, 5, dtype=float) >>> fourier = np.fft.fft(signal) >>> n = signal.size >>> timestep = 0.1 >>> freq = np.fft.fftfreq(n, d=timestep) >>> freq array( 0. , 1.25, 2.5 , ..., -3.75, -2.5 , -1.25)

Return multidimensional discrete Fourier transform.

The returned array contains::

yj_1,..,j_d = sumk_1=0..n_1-1, ..., k_d=0..n_d-1 xk_1,..,k_d * prodi=1..d exp(-sqrt(-1)*2*pi/n_i * j_i * k_i)

where d = len(x.shape) and n = x.shape.

Parameters ---------- x : array_like The (N-D) array to transform. shape : int or array_like of ints or None, optional The shape of the result. If both `shape` and `axes` (see below) are None, `shape` is ``x.shape``; if `shape` is None but `axes` is not None, then `shape` is ``scipy.take(x.shape, axes, axis=0)``. If ``shapei > x.shapei``, the ith dimension is padded with zeros. If ``shapei < x.shapei``, the ith dimension is truncated to length ``shapei``. If any element of `shape` is -1, the size of the corresponding dimension of `x` is used. axes : int or array_like of ints or None, optional The axes of `x` (`y` if `shape` is not None) along which the transform is applied. The default is over all axes. overwrite_x : bool, optional If True, the contents of `x` can be destroyed. Default is False.

Returns ------- y : complex-valued N-D NumPy array The (N-D) DFT of the input array.

See Also -------- ifftn

Notes ----- If ``x`` is real-valued, then ``y..., j_i, ... == y..., n_i-j_i, ....conjugate()``.

Both single and double precision routines are implemented. Half precision inputs will be converted to single precision. Non-floating-point inputs will be converted to double precision. Long-double precision inputs are not supported.

Examples -------- >>> from scipy.fftpack import fftn, ifftn >>> y = (-np.arange(16), 8 - np.arange(16), np.arange(16)) >>> np.allclose(y, fftn(ifftn(y))) True

Shift the zero-frequency component to the center of the spectrum.

This function swaps half-spaces for all axes listed (defaults to all). Note that ``y0`` is the Nyquist component only if ``len(x)`` is even.

Parameters ---------- x : array_like Input array. axes : int or shape tuple, optional Axes over which to shift. Default is None, which shifts all axes.

Returns ------- y : ndarray The shifted array.

See Also -------- ifftshift : The inverse of `fftshift`.

Examples -------- >>> freqs = np.fft.fftfreq(10, 0.1) >>> freqs array( 0., 1., 2., ..., -3., -2., -1.) >>> np.fft.fftshift(freqs) array(-5., -4., -3., -2., -1., 0., 1., 2., 3., 4.)

Shift the zero-frequency component only along the second axis:

>>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3) >>> freqs array([ 0., 1., 2.], [ 3., 4., -4.], [-3., -2., -1.]) >>> np.fft.fftshift(freqs, axes=(1,)) array([ 2., 0., 1.], [-4., 3., 4.], [-1., -3., -2.])

Return Hilbert transform of a periodic sequence x.

If x_j and y_j are Fourier coefficients of periodic functions x and y, respectively, then::

y_j = sqrt(-1)*sign(j) * x_j y_0 = 0

Parameters ---------- x : array_like The input array, should be periodic. _cache : dict, optional Dictionary that contains the kernel used to do a convolution with.

Returns ------- y : ndarray The transformed input.

See Also -------- scipy.signal.hilbert : Compute the analytic signal, using the Hilbert transform.

Notes ----- If ``sum(x, axis=0) == 0`` then ``hilbert(ihilbert(x)) == x``.

For even len(x), the Nyquist mode of x is taken zero.

The sign of the returned transform does not have a factor -1 that is more often than not found in the definition of the Hilbert transform. Note also that `scipy.signal.hilbert` does have an extra -1 factor compared to this function.

Return the Inverse Discrete Cosine Transform of an arbitrary type sequence.

Parameters ---------- x : array_like The input array. type :

, 2, 3, 4

, optional Type of the DCT (see Notes). Default type is 2. n : int, optional Length of the transform. If ``n < x.shapeaxis``, `x` is truncated. If ``n > x.shapeaxis``, `x` is zero-padded. The default results in ``n = x.shapeaxis``. axis : int, optional Axis along which the idct is computed; the default is over the last axis (i.e., ``axis=-1``). norm : None, 'ortho', optional Normalization mode (see Notes). Default is None. overwrite_x : bool, optional If True, the contents of `x` can be destroyed; the default is False.

Returns ------- idct : ndarray of real The transformed input array.

See Also -------- dct : Forward DCT

Notes ----- For a single dimension array `x`, ``idct(x, norm='ortho')`` is equal to MATLAB ``idct(x)``.

'The' IDCT is the IDCT of type 2, which is the same as DCT of type 3.

IDCT of type 1 is the DCT of type 1, IDCT of type 2 is the DCT of type 3, and IDCT of type 3 is the DCT of type 2. IDCT of type 4 is the DCT of type 4. For the definition of these types, see `dct`.

Examples -------- The Type 1 DCT is equivalent to the DFT for real, even-symmetrical inputs. The output is also real and even-symmetrical. Half of the IFFT input is used to generate half of the IFFT output:

>>> from scipy.fftpack import ifft, idct >>> ifft(np.array( 30., -8., 6., -2., 6., -8.)).real array( 4., 3., 5., 10., 5., 3.) >>> idct(np.array( 30., -8., 6., -2.), 1) / 6 array( 4., 3., 5., 10.)

Return multidimensional Discrete Cosine Transform along the specified axes.

Parameters ---------- x : array_like The input array. type :

, 2, 3, 4

, optional Type of the DCT (see Notes). Default type is 2. shape : int or array_like of ints or None, optional The shape of the result. If both `shape` and `axes` (see below) are None, `shape` is ``x.shape``; if `shape` is None but `axes` is not None, then `shape` is ``scipy.take(x.shape, axes, axis=0)``. If ``shapei > x.shapei``, the ith dimension is padded with zeros. If ``shapei < x.shapei``, the ith dimension is truncated to length ``shapei``. If any element of `shape` is -1, the size of the corresponding dimension of `x` is used. axes : int or array_like of ints or None, optional Axes along which the IDCT is computed. The default is over all axes. norm : None, 'ortho', optional Normalization mode (see Notes). Default is None. overwrite_x : bool, optional If True, the contents of `x` can be destroyed; the default is False.

Returns ------- y : ndarray of real The transformed input array.

See Also -------- dctn : multidimensional DCT

Notes ----- For full details of the IDCT types and normalization modes, as well as references, see `idct`.

Examples -------- >>> from scipy.fftpack import dctn, idctn >>> y = np.random.randn(16, 16) >>> np.allclose(y, idctn(dctn(y, norm='ortho'), norm='ortho')) True

Return the Inverse Discrete Sine Transform of an arbitrary type sequence.

Parameters ---------- x : array_like The input array. type :

, 2, 3, 4

, optional Type of the DST (see Notes). Default type is 2. n : int, optional Length of the transform. If ``n < x.shapeaxis``, `x` is truncated. If ``n > x.shapeaxis``, `x` is zero-padded. The default results in ``n = x.shapeaxis``. axis : int, optional Axis along which the idst is computed; the default is over the last axis (i.e., ``axis=-1``). norm : None, 'ortho', optional Normalization mode (see Notes). Default is None. overwrite_x : bool, optional If True, the contents of `x` can be destroyed; the default is False.

Returns ------- idst : ndarray of real The transformed input array.

See Also -------- dst : Forward DST

Notes ----- 'The' IDST is the IDST of type 2, which is the same as DST of type 3.

IDST of type 1 is the DST of type 1, IDST of type 2 is the DST of type 3, and IDST of type 3 is the DST of type 2. For the definition of these types, see `dst`.

.. versionadded:: 0.11.0

Return multidimensional Discrete Sine Transform along the specified axes.

Parameters ---------- x : array_like The input array. type :

, 2, 3, 4

, optional Type of the DST (see Notes). Default type is 2. shape : int or array_like of ints or None, optional The shape of the result. If both `shape` and `axes` (see below) are None, `shape` is ``x.shape``; if `shape` is None but `axes` is not None, then `shape` is ``scipy.take(x.shape, axes, axis=0)``. If ``shapei > x.shapei``, the ith dimension is padded with zeros. If ``shapei < x.shapei``, the ith dimension is truncated to length ``shapei``. If any element of `shape` is -1, the size of the corresponding dimension of `x` is used. axes : int or array_like of ints or None, optional Axes along which the IDST is computed. The default is over all axes. norm : None, 'ortho', optional Normalization mode (see Notes). Default is None. overwrite_x : bool, optional If True, the contents of `x` can be destroyed; the default is False.

Returns ------- y : ndarray of real The transformed input array.

See Also -------- dstn : multidimensional DST

Notes ----- For full details of the IDST types and normalization modes, as well as references, see `idst`.

Examples -------- >>> from scipy.fftpack import dstn, idstn >>> y = np.random.randn(16, 16) >>> np.allclose(y, idstn(dstn(y, norm='ortho'), norm='ortho')) True

Return discrete inverse Fourier transform of real or complex sequence.

The returned complex array contains ``y(0), y(1),..., y(n-1)``, where

``y(j) = (x * exp(2*pi*sqrt(-1)*j*np.arange(n)/n)).mean()``.

Parameters ---------- x : array_like Transformed data to invert. n : int, optional Length of the inverse Fourier transform. If ``n < x.shapeaxis``, `x` is truncated. If ``n > x.shapeaxis``, `x` is zero-padded. The default results in ``n = x.shapeaxis``. axis : int, optional Axis along which the ifft's are computed; the default is over the last axis (i.e., ``axis=-1``). overwrite_x : bool, optional If True, the contents of `x` can be destroyed; the default is False.

Returns ------- ifft : ndarray of floats The inverse discrete Fourier transform.

See Also -------- fft : Forward FFT

Notes ----- Both single and double precision routines are implemented. Half precision inputs will be converted to single precision. Non-floating-point inputs will be converted to double precision. Long-double precision inputs are not supported.

This function is most efficient when `n` is a power of two, and least efficient when `n` is prime.

If the data type of `x` is real, a 'real IFFT' algorithm is automatically used, which roughly halves the computation time.

Examples -------- >>> from scipy.fftpack import fft, ifft >>> import numpy as np >>> x = np.arange(5) >>> np.allclose(ifft(fft(x)), x, atol=1e-15) # within numerical accuracy. True

2-D discrete inverse Fourier transform of real or complex sequence.

Return inverse 2-D discrete Fourier transform of arbitrary type sequence x.

See `ifft` for more information.

See also -------- fft2, ifft

Examples -------- >>> from scipy.fftpack import fft2, ifft2 >>> y = np.mgrid:5, :50 >>> y array([0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [4, 4, 4, 4, 4]) >>> np.allclose(y, fft2(ifft2(y))) True

Return inverse multidimensional discrete Fourier transform.

The sequence can be of an arbitrary type.

The returned array contains::

yj_1,..,j_d = 1/p * sumk_1=0..n_1-1, ..., k_d=0..n_d-1 xk_1,..,k_d * prodi=1..d exp(sqrt(-1)*2*pi/n_i * j_i * k_i)

where ``d = len(x.shape)``, ``n = x.shape``, and ``p = prodi=1..d n_i``.

For description of parameters see `fftn`.

See Also -------- fftn : for detailed information.

Examples -------- >>> from scipy.fftpack import fftn, ifftn >>> import numpy as np >>> y = (-np.arange(16), 8 - np.arange(16), np.arange(16)) >>> np.allclose(y, ifftn(fftn(y))) True

The inverse of `fftshift`. Although identical for even-length `x`, the functions differ by one sample for odd-length `x`.

Parameters ---------- x : array_like Input array. axes : int or shape tuple, optional Axes over which to calculate. Defaults to None, which shifts all axes.

Returns ------- y : ndarray The shifted array.

See Also -------- fftshift : Shift zero-frequency component to the center of the spectrum.

Examples -------- >>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3) >>> freqs array([ 0., 1., 2.], [ 3., 4., -4.], [-3., -2., -1.]) >>> np.fft.ifftshift(np.fft.fftshift(freqs)) array([ 0., 1., 2.], [ 3., 4., -4.], [-3., -2., -1.])

Return inverse Hilbert transform of a periodic sequence x.

If ``x_j`` and ``y_j`` are Fourier coefficients of periodic functions x and y, respectively, then::

y_j = -sqrt(-1)*sign(j) * x_j y_0 = 0

Return inverse discrete Fourier transform of real sequence x.

The contents of `x` are interpreted as the output of the `rfft` function.

Parameters ---------- x : array_like Transformed data to invert. n : int, optional Length of the inverse Fourier transform. If n < x.shapeaxis, x is truncated. If n > x.shapeaxis, x is zero-padded. The default results in n = x.shapeaxis. axis : int, optional Axis along which the ifft's are computed; the default is over the last axis (i.e., axis=-1). overwrite_x : bool, optional If True, the contents of `x` can be destroyed; the default is False.

Returns ------- irfft : ndarray of floats The inverse discrete Fourier transform.

See Also -------- rfft, ifft, scipy.fft.irfft

Notes ----- The returned real array contains::

y(0),y(1),...,y(n-1)

where for n is even::

y(j) = 1/n (sumk=1..n/2-1 (x2*k-1+sqrt(-1)*x2*k) * exp(sqrt(-1)*j*k* 2*pi/n)

1. c.c. + x0 + (-1)**(j) xn-1)

and for n is odd::

y(j) = 1/n (sumk=1..(n-1)/2 (x2*k-1+sqrt(-1)*x2*k) * exp(sqrt(-1)*j*k* 2*pi/n)

1. c.c. + x0)

c.c. denotes complex conjugate of preceding expression.

For details on input parameters, see `rfft`.

To process (conjugate-symmetric) frequency-domain data with a complex datatype, consider using the newer function `scipy.fft.irfft`.

Examples -------- >>> from scipy.fftpack import rfft, irfft >>> a = 1.0, 2.0, 3.0, 4.0, 5.0 >>> irfft(a) array( 2.6 , -3.16405192, 1.24398433, -1.14955713, 1.46962473) >>> irfft(rfft(a)) array(1., 2., 3., 4., 5.)

Return inverse h-Tilbert transform of a periodic sequence x.

If ``x_j`` and ``y_j`` are Fourier coefficients of periodic functions x and y, respectively, then::

y_j = -sqrt(-1)*tanh(j*h*2*pi/period) * x_j y_0 = 0

For more details, see `tilbert`.

Find the next fast size of input data to `fft`, for zero-padding, etc.

SciPy's FFTPACK has efficient functions for radix

, 3, 4, 5

, so this returns the next composite of the prime factors 2, 3, and 5 which is greater than or equal to `target`. (These are also known as 5-smooth numbers, regular numbers, or Hamming numbers.)

Parameters ---------- target : int Length to start searching from. Must be a positive integer.

Returns ------- out : int The first 5-smooth number greater than or equal to `target`.

Notes ----- .. versionadded:: 0.18.0

Examples -------- On a particular machine, an FFT of prime length takes 133 ms:

>>> from scipy import fftpack >>> min_len = 10007 # prime length is worst case for speed >>> a = np.random.randn(min_len) >>> b = fftpack.fft(a)

Zero-padding to the next 5-smooth length reduces computation time to 211 us, a speedup of 630 times:

>>> fftpack.helper.next_fast_len(min_len) 10125 >>> b = fftpack.fft(a, 10125)

Rounding up to the next power of 2 is not optimal, taking 367 us to compute, 1.7 times as long as the 5-smooth size:

>>> b = fftpack.fft(a, 16384)

Discrete Fourier transform of a real sequence.

Parameters ---------- x : array_like, real-valued The data to transform. n : int, optional Defines the length of the Fourier transform. If `n` is not specified (the default) then ``n = x.shapeaxis``. If ``n < x.shapeaxis``, `x` is truncated, if ``n > x.shapeaxis``, `x` is zero-padded. axis : int, optional The axis along which the transform is applied. The default is the last axis. overwrite_x : bool, optional If set to true, the contents of `x` can be overwritten. Default is False.

Returns ------- z : real ndarray The returned real array contains::

y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2)) if n is even y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2)),Im(y(n/2)) if n is odd

where::

y(j) = sumk=0..n-1 xk * exp(-sqrt(-1)*j*k*2*pi/n) j = 0..n-1

See Also -------- fft, irfft, scipy.fft.rfft

Notes ----- Within numerical accuracy, ``y == rfft(irfft(y))``.

Both single and double precision routines are implemented. Half precision inputs will be converted to single precision. Non-floating-point inputs will be converted to double precision. Long-double precision inputs are not supported.

To get an output with a complex datatype, consider using the newer function `scipy.fft.rfft`.

Examples -------- >>> from scipy.fftpack import fft, rfft >>> a = 9, -9, 1, 3 >>> fft(a) array( 4. +0.j, 8.+12.j, 16. +0.j, 8.-12.j) >>> rfft(a) array( 4., 8., 12., 16.)

DFT sample frequencies (for usage with rfft, irfft).

The returned float array contains the frequency bins in cycles/unit (with zero at the start) given a window length `n` and a sample spacing `d`::

f = 0,1,1,2,2,...,n/2-1,n/2-1,n/2/(d*n) if n is even f = 0,1,1,2,2,...,n/2-1,n/2-1,n/2,n/2/(d*n) if n is odd

Parameters ---------- n : int Window length. d : scalar, optional Sample spacing. Default is 1.

Returns ------- out : ndarray The array of length `n`, containing the sample frequencies.

Examples -------- >>> from scipy import fftpack >>> sig = np.array(-2, 8, 6, 4, 1, 0, 3, 5, dtype=float) >>> sig_fft = fftpack.rfft(sig) >>> n = sig_fft.size >>> timestep = 0.1 >>> freq = fftpack.rfftfreq(n, d=timestep) >>> freq array( 0. , 1.25, 1.25, 2.5 , 2.5 , 3.75, 3.75, 5. )

Return (a,b)-sinh/cosh pseudo-derivative of a periodic sequence x.

If x_j and y_j are Fourier coefficients of periodic functions x and y, respectively, then::

y_j = sqrt(-1)*sinh(j*a*2*pi/period)/cosh(j*b*2*pi/period) * x_j y_0 = 0

Parameters ---------- x : array_like Input array. a,b : float Defines the parameters of the sinh/cosh pseudo-differential operator. period : float, optional The period of the sequence x. Default is 2*pi.

Notes ----- ``sc_diff(cs_diff(x,a,b),b,a) == x`` For even ``len(x)``, the Nyquist mode of x is taken as zero.

Shift periodic sequence x by a: y(u) = x(u+a).

If x_j and y_j are Fourier coefficients of periodic functions x and y, respectively, then::

y_j = exp(j*a*2*pi/period*sqrt(-1)) * x_f

Parameters ---------- x : array_like The array to take the pseudo-derivative from. a : float Defines the parameters of the sinh/sinh pseudo-differential period : float, optional The period of the sequences x and y. Default period is ``2*pi``.

Return (a,b)-sinh/sinh pseudo-derivative of a periodic sequence x.

If x_j and y_j are Fourier coefficients of periodic functions x and y, respectively, then::

y_j = sinh(j*a*2*pi/period)/sinh(j*b*2*pi/period) * x_j y_0 = a/b * x_0

Parameters ---------- x : array_like The array to take the pseudo-derivative from. a,b Defines the parameters of the sinh/sinh pseudo-differential operator. period : float, optional The period of the sequence x. Default is ``2*pi``.

Notes ----- ``ss_diff(ss_diff(x,a,b),b,a) == x``

Return h-Tilbert transform of a periodic sequence x.

If x_j and y_j are Fourier coefficients of periodic functions x and y, respectively, then::

y_j = sqrt(-1)*coth(j*h*2*pi/period) * x_j y_0 = 0

Parameters ---------- x : array_like The input array to transform. h : float Defines the parameter of the Tilbert transform. period : float, optional The assumed period of the sequence. Default period is ``2*pi``.

Returns ------- tilbert : ndarray The result of the transform.

Notes ----- If ``sum(x, axis=0) == 0`` and ``n = len(x)`` is odd, then ``tilbert(itilbert(x)) == x``.

If ``2 * pi * h / period`` is approximately 10 or larger, then numerically ``tilbert == hilbert`` (theoretically oo-Tilbert == Hilbert).

For even ``len(x)``, the Nyquist mode of ``x`` is taken zero.