pseudo_diffs.py

"""
Differential and pseudo-differential operators.
"""
# Created by Pearu Peterson, September 2002
from __future__ import division, print_function, absolute_import


__all__ = ['diff',
           'tilbert','itilbert','hilbert','ihilbert',
           'cs_diff','cc_diff','sc_diff','ss_diff',
           'shift']

from numpy import pi, asarray, sin, cos, sinh, cosh, tanh, iscomplexobj
from . import convolve

from scipy.fftpack.basic import _datacopied

import atexit
atexit.register(convolve.destroy_convolve_cache)
del atexit


_cache = {}


def diff(x,order=1,period=None, _cache=_cache):
    """
    Return k-th 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.

    """
    tmp = asarray(x)
    if order == 0:
        return tmp
    if iscomplexobj(tmp):
        return diff(tmp.real,order,period)+1j*diff(tmp.imag,order,period)
    if period is not None:
        c = 2*pi/period
    else:
        c = 1.0
    n = len(x)
    omega = _cache.get((n,order,c))
    if omega is None:
        if len(_cache) > 20:
            while _cache:
                _cache.popitem()

        def kernel(k,order=order,c=c):
            if k:
                return pow(c*k,order)
            return 0
        omega = convolve.init_convolution_kernel(n,kernel,d=order,
                                                 zero_nyquist=1)
        _cache[(n,order,c)] = omega
    overwrite_x = _datacopied(tmp, x)
    return convolve.convolve(tmp,omega,swap_real_imag=order % 2,
                             overwrite_x=overwrite_x)


del _cache


_cache = {}


def tilbert(x, h, period=None, _cache=_cache):
    """
    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.

    """
    tmp = asarray(x)
    if iscomplexobj(tmp):
        return tilbert(tmp.real, h, period) + \
               1j * tilbert(tmp.imag, h, period)

    if period is not None:
        h = h * 2 * pi / period

    n = len(x)
    omega = _cache.get((n, h))
    if omega is None:
        if len(_cache) > 20:
            while _cache:
                _cache.popitem()

        def kernel(k, h=h):
            if k:
                return 1.0/tanh(h*k)

            return 0

        omega = convolve.init_convolution_kernel(n, kernel, d=1)
        _cache[(n,h)] = omega

    overwrite_x = _datacopied(tmp, x)
    return convolve.convolve(tmp,omega,swap_real_imag=1,overwrite_x=overwrite_x)


del _cache


_cache = {}


def itilbert(x,h,period=None, _cache=_cache):
    """
    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`.

    """
    tmp = asarray(x)
    if iscomplexobj(tmp):
        return itilbert(tmp.real,h,period) + \
               1j*itilbert(tmp.imag,h,period)
    if period is not None:
        h = h*2*pi/period
    n = len(x)
    omega = _cache.get((n,h))
    if omega is None:
        if len(_cache) > 20:
            while _cache:
                _cache.popitem()

        def kernel(k,h=h):
            if k:
                return -tanh(h*k)
            return 0
        omega = convolve.init_convolution_kernel(n,kernel,d=1)
        _cache[(n,h)] = omega
    overwrite_x = _datacopied(tmp, x)
    return convolve.convolve(tmp,omega,swap_real_imag=1,overwrite_x=overwrite_x)


del _cache


_cache = {}


def hilbert(x, _cache=_cache):
    """
    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.

    """
    tmp = asarray(x)
    if iscomplexobj(tmp):
        return hilbert(tmp.real)+1j*hilbert(tmp.imag)
    n = len(x)
    omega = _cache.get(n)
    if omega is None:
        if len(_cache) > 20:
            while _cache:
                _cache.popitem()

        def kernel(k):
            if k > 0:
                return 1.0
            elif k < 0:
                return -1.0
            return 0.0
        omega = convolve.init_convolution_kernel(n,kernel,d=1)
        _cache[n] = omega
    overwrite_x = _datacopied(tmp, x)
    return convolve.convolve(tmp,omega,swap_real_imag=1,overwrite_x=overwrite_x)


del _cache


def ihilbert(x):
    """
    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 -hilbert(x)


_cache = {}


def cs_diff(x, a, b, period=None, _cache=_cache):
    """
    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.

    """
    tmp = asarray(x)
    if iscomplexobj(tmp):
        return cs_diff(tmp.real,a,b,period) + \
               1j*cs_diff(tmp.imag,a,b,period)
    if period is not None:
        a = a*2*pi/period
        b = b*2*pi/period
    n = len(x)
    omega = _cache.get((n,a,b))
    if omega is None:
        if len(_cache) > 20:
            while _cache:
                _cache.popitem()

        def kernel(k,a=a,b=b):
            if k:
                return -cosh(a*k)/sinh(b*k)
            return 0
        omega = convolve.init_convolution_kernel(n,kernel,d=1)
        _cache[(n,a,b)] = omega
    overwrite_x = _datacopied(tmp, x)
    return convolve.convolve(tmp,omega,swap_real_imag=1,overwrite_x=overwrite_x)


del _cache


_cache = {}


def sc_diff(x, a, b, period=None, _cache=_cache):
    """
    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.

    """
    tmp = asarray(x)
    if iscomplexobj(tmp):
        return sc_diff(tmp.real,a,b,period) + \
               1j*sc_diff(tmp.imag,a,b,period)
    if period is not None:
        a = a*2*pi/period
        b = b*2*pi/period
    n = len(x)
    omega = _cache.get((n,a,b))
    if omega is None:
        if len(_cache) > 20:
            while _cache:
                _cache.popitem()

        def kernel(k,a=a,b=b):
            if k:
                return sinh(a*k)/cosh(b*k)
            return 0
        omega = convolve.init_convolution_kernel(n,kernel,d=1)
        _cache[(n,a,b)] = omega
    overwrite_x = _datacopied(tmp, x)
    return convolve.convolve(tmp,omega,swap_real_imag=1,overwrite_x=overwrite_x)


del _cache


_cache = {}


def ss_diff(x, a, b, period=None, _cache=_cache):
    """
    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``

    """
    tmp = asarray(x)
    if iscomplexobj(tmp):
        return ss_diff(tmp.real,a,b,period) + \
               1j*ss_diff(tmp.imag,a,b,period)
    if period is not None:
        a = a*2*pi/period
        b = b*2*pi/period
    n = len(x)
    omega = _cache.get((n,a,b))
    if omega is None:
        if len(_cache) > 20:
            while _cache:
                _cache.popitem()

        def kernel(k,a=a,b=b):
            if k:
                return sinh(a*k)/sinh(b*k)
            return float(a)/b
        omega = convolve.init_convolution_kernel(n,kernel)
        _cache[(n,a,b)] = omega
    overwrite_x = _datacopied(tmp, x)
    return convolve.convolve(tmp,omega,overwrite_x=overwrite_x)


del _cache


_cache = {}


def cc_diff(x, a, b, period=None, _cache=_cache):
    """
    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``

    """
    tmp = asarray(x)
    if iscomplexobj(tmp):
        return cc_diff(tmp.real,a,b,period) + \
               1j*cc_diff(tmp.imag,a,b,period)
    if period is not None:
        a = a*2*pi/period
        b = b*2*pi/period
    n = len(x)
    omega = _cache.get((n,a,b))
    if omega is None:
        if len(_cache) > 20:
            while _cache:
                _cache.popitem()

        def kernel(k,a=a,b=b):
            return cosh(a*k)/cosh(b*k)
        omega = convolve.init_convolution_kernel(n,kernel)
        _cache[(n,a,b)] = omega
    overwrite_x = _datacopied(tmp, x)
    return convolve.convolve(tmp,omega,overwrite_x=overwrite_x)


del _cache


_cache = {}


def shift(x, a, period=None, _cache=_cache):
    """
    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``.
    """
    tmp = asarray(x)
    if iscomplexobj(tmp):
        return shift(tmp.real,a,period)+1j*shift(tmp.imag,a,period)
    if period is not None:
        a = a*2*pi/period
    n = len(x)
    omega = _cache.get((n,a))
    if omega is None:
        if len(_cache) > 20:
            while _cache:
                _cache.popitem()

        def kernel_real(k,a=a):
            return cos(a*k)

        def kernel_imag(k,a=a):
            return sin(a*k)
        omega_real = convolve.init_convolution_kernel(n,kernel_real,d=0,
                                                      zero_nyquist=0)
        omega_imag = convolve.init_convolution_kernel(n,kernel_imag,d=1,
                                                      zero_nyquist=0)
        _cache[(n,a)] = omega_real,omega_imag
    else:
        omega_real,omega_imag = omega
    overwrite_x = _datacopied(tmp, x)
    return convolve.convolve_z(tmp,omega_real,omega_imag,
                               overwrite_x=overwrite_x)


del _cache