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"""
Discrete Fourier Transforms - basic.py
"""
# Created by Pearu Peterson, August,September 2002
from __future__ import division, print_function, absolute_import

__all__ = ['fft','ifft','fftn','ifftn','rfft','irfft',
           'fft2','ifft2']

from numpy import zeros, swapaxes
import numpy
from . import _fftpack

import atexit
atexit.register(_fftpack.destroy_zfft_cache)
atexit.register(_fftpack.destroy_zfftnd_cache)
atexit.register(_fftpack.destroy_drfft_cache)
atexit.register(_fftpack.destroy_cfft_cache)
atexit.register(_fftpack.destroy_cfftnd_cache)
atexit.register(_fftpack.destroy_rfft_cache)
del atexit


def istype(arr, typeclass):
    return issubclass(arr.dtype.type, typeclass)


def _datacopied(arr, original):
    """
Strict check for `arr` not sharing any data with `original`,
under the assumption that arr = asarray(original)

"""
    if arr is original:
        return False
    if not isinstance(original, numpy.ndarray) and hasattr(original, '__array__'):
        return False
    return arr.base is None

# XXX: single precision FFTs partially disabled due to accuracy issues
# for large prime-sized inputs.
#
# See http://permalink.gmane.org/gmane.comp.python.scientific.devel/13834
# ("fftpack test failures for 0.8.0b1", Ralf Gommers, 17 Jun 2010,
# @ scipy-dev)
#
# These should be re-enabled once the problems are resolved


def _is_safe_size(n):
    """
Is the size of FFT such that FFTPACK can handle it in single precision
with sufficient accuracy?

Composite numbers of 2, 3, and 5 are accepted, as FFTPACK has those
"""
    n = int(n)
    for c in (2, 3, 5):
        while n % c == 0:
            n /= c
    return (n <= 1)


def _fake_crfft(x, n, *a, **kw):
    if _is_safe_size(n):
        return _fftpack.crfft(x, n, *a, **kw)
    else:
        return _fftpack.zrfft(x, n, *a, **kw).astype(numpy.complex64)


def _fake_cfft(x, n, *a, **kw):
    if _is_safe_size(n):
        return _fftpack.cfft(x, n, *a, **kw)
    else:
        return _fftpack.zfft(x, n, *a, **kw).astype(numpy.complex64)


def _fake_rfft(x, n, *a, **kw):
    if _is_safe_size(n):
        return _fftpack.rfft(x, n, *a, **kw)
    else:
        return _fftpack.drfft(x, n, *a, **kw).astype(numpy.float32)


def _fake_cfftnd(x, shape, *a, **kw):
    if numpy.all(list(map(_is_safe_size, shape))):
        return _fftpack.cfftnd(x, shape, *a, **kw)
    else:
        return _fftpack.zfftnd(x, shape, *a, **kw).astype(numpy.complex64)

_DTYPE_TO_FFT = {
# numpy.dtype(numpy.float32): _fftpack.crfft,
        numpy.dtype(numpy.float32): _fake_crfft,
        numpy.dtype(numpy.float64): _fftpack.zrfft,
# numpy.dtype(numpy.complex64): _fftpack.cfft,
        numpy.dtype(numpy.complex64): _fake_cfft,
        numpy.dtype(numpy.complex128): _fftpack.zfft,
}

_DTYPE_TO_RFFT = {
# numpy.dtype(numpy.float32): _fftpack.rfft,
        numpy.dtype(numpy.float32): _fake_rfft,
        numpy.dtype(numpy.float64): _fftpack.drfft,
}

_DTYPE_TO_FFTN = {
# numpy.dtype(numpy.complex64): _fftpack.cfftnd,
        numpy.dtype(numpy.complex64): _fake_cfftnd,
        numpy.dtype(numpy.complex128): _fftpack.zfftnd,
# numpy.dtype(numpy.float32): _fftpack.cfftnd,
        numpy.dtype(numpy.float32): _fake_cfftnd,
        numpy.dtype(numpy.float64): _fftpack.zfftnd,
}


def _asfarray(x):
    """Like numpy asfarray, except that it does not modify x dtype if x is
already an array with a float dtype, and do not cast complex types to
real."""
    if hasattr(x, "dtype") and x.dtype.char in numpy.typecodes["AllFloat"]:
        return x
    else:
        # We cannot use asfarray directly because it converts sequences of
        # complex to sequence of real
        ret = numpy.asarray(x)
        if ret.dtype.char not in numpy.typecodes["AllFloat"]:
            return numpy.asfarray(x)
        return ret


def _fix_shape(x, n, axis):
    """ Internal auxiliary function for _raw_fft, _raw_fftnd."""
    s = list(x.shape)
    if s[axis] > n:
        index = [slice(None)]*len(s)
        index[axis] = slice(0,n)
        x = x[index]
        return x, False
    else:
        index = [slice(None)]*len(s)
        index[axis] = slice(0,s[axis])
        s[axis] = n
        z = zeros(s,x.dtype.char)
        z[index] = x
        return z, True


def _raw_fft(x, n, axis, direction, overwrite_x, work_function):
    """ Internal auxiliary function for fft, ifft, rfft, irfft."""
    if n is None:
        n = x.shape[axis]
    elif n != x.shape[axis]:
        x, copy_made = _fix_shape(x,n,axis)
        overwrite_x = overwrite_x or copy_made

    if n < 1:
        raise ValueError("Invalid number of FFT data points "
                         "(%d) specified." % n)

    if axis == -1 or axis == len(x.shape)-1:
        r = work_function(x,n,direction,overwrite_x=overwrite_x)
    else:
        x = swapaxes(x, axis, -1)
        r = work_function(x,n,direction,overwrite_x=overwrite_x)
        r = swapaxes(r, axis, -1)
    return r


def fft(x, n=None, axis=-1, overwrite_x=False):
    """
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.shape[axis]``, `x` is
truncated. If ``n > x.shape[axis]``, `x` is zero-padded. The
default results in ``n = x.shape[axis]``.
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) = sum[k=0..n-1] x[k] * exp(-sqrt(-1)*j*k* 2*pi/n), j = 0..n-1

Note that ``y(-j) = y(n-j).conjugate()``.

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
``A[0]`` contains the zero-frequency term, ``A[1:n/2]`` contains the
positive-frequency terms, and ``A[n/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`.

For `n` even, ``A[n/2]`` contains the sum of the positive and
negative-frequency terms. For `n` even and `x` real, ``A[n/2]`` will
always be real.

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 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

"""
    tmp = _asfarray(x)

    try:
        work_function = _DTYPE_TO_FFT[tmp.dtype]
    except KeyError:
        raise ValueError("type %s is not supported" % tmp.dtype)

    if not (istype(tmp, numpy.complex64) or istype(tmp, numpy.complex128)):
        overwrite_x = 1

    overwrite_x = overwrite_x or _datacopied(tmp, x)

    if n is None:
        n = tmp.shape[axis]
    elif n != tmp.shape[axis]:
        tmp, copy_made = _fix_shape(tmp,n,axis)
        overwrite_x = overwrite_x or copy_made

    if n < 1:
        raise ValueError("Invalid number of FFT data points "
                         "(%d) specified." % n)

    if axis == -1 or axis == len(tmp.shape) - 1:
        return work_function(tmp,n,1,0,overwrite_x)

    tmp = swapaxes(tmp, axis, -1)
    tmp = work_function(tmp,n,1,0,overwrite_x)
    return swapaxes(tmp, axis, -1)


def ifft(x, n=None, axis=-1, overwrite_x=False):
    """
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.shape[axis]``,
`x` is truncated. If ``n > x.shape[axis]``, `x` is zero-padded.
The default results in ``n = x.shape[axis]``.
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
-----
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.

"""
    tmp = _asfarray(x)

    try:
        work_function = _DTYPE_TO_FFT[tmp.dtype]
    except KeyError:
        raise ValueError("type %s is not supported" % tmp.dtype)

    if not (istype(tmp, numpy.complex64) or istype(tmp, numpy.complex128)):
        overwrite_x = 1

    overwrite_x = overwrite_x or _datacopied(tmp, x)

    if n is None:
        n = tmp.shape[axis]
    elif n != tmp.shape[axis]:
        tmp, copy_made = _fix_shape(tmp,n,axis)
        overwrite_x = overwrite_x or copy_made

    if n < 1:
        raise ValueError("Invalid number of FFT data points "
                         "(%d) specified." % n)

    if axis == -1 or axis == len(tmp.shape) - 1:
        return work_function(tmp,n,-1,1,overwrite_x)

    tmp = swapaxes(tmp, axis, -1)
    tmp = work_function(tmp,n,-1,1,overwrite_x)
    return swapaxes(tmp, axis, -1)


def rfft(x, n=None, axis=-1, overwrite_x=False):
    """
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.shape[axis]``. If ``n < x.shape[axis]``,
`x` is truncated, if ``n > x.shape[axis]``, `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) = sum[k=0..n-1] x[k] * exp(-sqrt(-1)*j*k*2*pi/n)
j = 0..n-1

Note that ``y(-j) == y(n-j).conjugate()``.

See Also
--------
fft, irfft, scipy.fftpack.basic

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

Examples
--------
>>> 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.])

"""
    tmp = _asfarray(x)

    if not numpy.isrealobj(tmp):
        raise TypeError("1st argument must be real sequence")

    try:
        work_function = _DTYPE_TO_RFFT[tmp.dtype]
    except KeyError:
        raise ValueError("type %s is not supported" % tmp.dtype)

    overwrite_x = overwrite_x or _datacopied(tmp, x)

    return _raw_fft(tmp,n,axis,1,overwrite_x,work_function)


def irfft(x, n=None, axis=-1, overwrite_x=False):
    """
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.shape[axis], x is truncated.
If n > x.shape[axis], x is zero-padded.
The default results in n = x.shape[axis].
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

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

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

where for n is even::

y(j) = 1/n (sum[k=1..n/2-1] (x[2*k-1]+sqrt(-1)*x[2*k])
* exp(sqrt(-1)*j*k* 2*pi/n)
+ c.c. + x[0] + (-1)**(j) x[n-1])

and for n is odd::

y(j) = 1/n (sum[k=1..(n-1)/2] (x[2*k-1]+sqrt(-1)*x[2*k])
* exp(sqrt(-1)*j*k* 2*pi/n)
+ c.c. + x[0])

c.c. denotes complex conjugate of preceding expression.

For details on input parameters, see `rfft`.

"""
    tmp = _asfarray(x)
    if not numpy.isrealobj(tmp):
        raise TypeError("1st argument must be real sequence")

    try:
        work_function = _DTYPE_TO_RFFT[tmp.dtype]
    except KeyError:
        raise ValueError("type %s is not supported" % tmp.dtype)

    overwrite_x = overwrite_x or _datacopied(tmp, x)

    return _raw_fft(tmp,n,axis,-1,overwrite_x,work_function)


def _raw_fftnd(x, s, axes, direction, overwrite_x, work_function):
    """ Internal auxiliary function for fftnd, ifftnd."""
    if s is None:
        if axes is None:
            s = x.shape
        else:
            s = numpy.take(x.shape, axes)

    s = tuple(s)
    if axes is None:
        noaxes = True
        axes = list(range(-x.ndim, 0))
    else:
        noaxes = False
    if len(axes) != len(s):
        raise ValueError("when given, axes and shape arguments "
                         "have to be of the same length")

    for dim in s:
        if dim < 1:
            raise ValueError("Invalid number of FFT data points "
                             "(%s) specified." % (s,))

    # No need to swap axes, array is in C order
    if noaxes:
        for i in axes:
            x, copy_made = _fix_shape(x, s[i], i)
            overwrite_x = overwrite_x or copy_made
        return work_function(x,s,direction,overwrite_x=overwrite_x)

    # We ordered axes, because the code below to push axes at the end of the
    # array assumes axes argument is in ascending order.
    id = numpy.argsort(axes)
    axes = [axes[i] for i in id]
    s = [s[i] for i in id]

    # Swap the request axes, last first (i.e. First swap the axis which ends up
    # at -1, then at -2, etc...), such as the request axes on which the
    # operation is carried become the last ones
    for i in range(1, len(axes)+1):
        x = numpy.swapaxes(x, axes[-i], -i)

    # We can now operate on the axes waxes, the p last axes (p = len(axes)), by
    # fixing the shape of the input array to 1 for any axis the fft is not
    # carried upon.
    waxes = list(range(x.ndim - len(axes), x.ndim))
    shape = numpy.ones(x.ndim)
    shape[waxes] = s

    for i in range(len(waxes)):
        x, copy_made = _fix_shape(x, s[i], waxes[i])
        overwrite_x = overwrite_x or copy_made

    r = work_function(x, shape, direction, overwrite_x=overwrite_x)

    # reswap in the reverse order (first axis first, etc...) to get original
    # order
    for i in range(len(axes), 0, -1):
        r = numpy.swapaxes(r, -i, axes[-i])

    return r


def fftn(x, shape=None, axes=None, overwrite_x=False):
    """
Return multidimensional discrete Fourier transform.

The returned array contains::

y[j_1,..,j_d] = sum[k_1=0..n_1-1, ..., k_d=0..n_d-1]
x[k_1,..,k_d] * prod[i=1..d] exp(-sqrt(-1)*2*pi/n_i * j_i * k_i)

where d = len(x.shape) and n = x.shape.
Note that ``y[..., -j_i, ...] = y[..., n_i-j_i, ...].conjugate()``.

Parameters
----------
x : array_like
The (n-dimensional) array to transform.
shape : tuple of ints, 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 ``shape[i] > x.shape[i]``, the i-th dimension is padded with zeros.
If ``shape[i] < x.shape[i]``, the i-th dimension is truncated to
length ``shape[i]``.
axes : array_like of ints, optional
The axes of `x` (`y` if `shape` is not None) along which the
transform is applied.
overwrite_x : bool, optional
If True, the contents of `x` can be destroyed. Default is False.

Returns
-------
y : complex-valued n-dimensional numpy array
The (n-dimensional) DFT of the input array.

See Also
--------
ifftn

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

"""
    return _raw_fftn_dispatch(x, shape, axes, overwrite_x, 1)


def _raw_fftn_dispatch(x, shape, axes, overwrite_x, direction):
    tmp = _asfarray(x)

    try:
        work_function = _DTYPE_TO_FFTN[tmp.dtype]
    except KeyError:
        raise ValueError("type %s is not supported" % tmp.dtype)

    if not (istype(tmp, numpy.complex64) or istype(tmp, numpy.complex128)):
        overwrite_x = 1

    overwrite_x = overwrite_x or _datacopied(tmp, x)
    return _raw_fftnd(tmp,shape,axes,direction,overwrite_x,work_function)


def ifftn(x, shape=None, axes=None, overwrite_x=False):
    """
Return inverse multi-dimensional discrete Fourier transform of
arbitrary type sequence x.

The returned array contains::

y[j_1,..,j_d] = 1/p * sum[k_1=0..n_1-1, ..., k_d=0..n_d-1]
x[k_1,..,k_d] * prod[i=1..d] exp(sqrt(-1)*2*pi/n_i * j_i * k_i)

where ``d = len(x.shape)``, ``n = x.shape``, and ``p = prod[i=1..d] n_i``.

For description of parameters see `fftn`.

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

"""
    return _raw_fftn_dispatch(x, shape, axes, overwrite_x, -1)


def fft2(x, shape=None, axes=(-2,-1), overwrite_x=False):
    """
2-D discrete Fourier transform.

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

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

"""
    return fftn(x,shape,axes,overwrite_x)


def ifft2(x, shape=None, axes=(-2,-1), overwrite_x=False):
    """
2-D discrete inverse Fourier transform of real or complex sequence.

Return inverse two-dimensional discrete Fourier transform of
arbitrary type sequence x.

See `ifft` for more information.

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

"""
    return ifftn(x,shape,axes,overwrite_x)
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