'''Fast Hankel transforms using the FFTLog algorithm.

The implementation closely follows the Fortran code of Hamilton (2000).

added: 14/11/2020 Nicolas Tessore <n.tessore@ucl.ac.uk>
'''

import numpy as np
from warnings import warn
from ._basic import rfft, irfft
from ..special import loggamma, poch

__all__ = [
    'fht', 'ifht',
    'fhtoffset',
]


# constants
LN_2 = np.log(2)


def fht(a, dln, mu, offset=0.0, bias=0.0):
    r'''Compute the fast Hankel transform.

    Computes the discrete Hankel transform of a logarithmically spaced periodic
    sequence using the FFTLog algorithm [1]_, [2]_.

    Parameters
    ----------
    a : array_like (..., n)
        Real periodic input array, uniformly logarithmically spaced.  For
        multidimensional input, the transform is performed over the last axis.
    dln : float
        Uniform logarithmic spacing of the input array.
    mu : float
        Order of the Hankel transform, any positive or negative real number.
    offset : float, optional
        Offset of the uniform logarithmic spacing of the output array.
    bias : float, optional
        Exponent of power law bias, any positive or negative real number.

    Returns
    -------
    A : array_like (..., n)
        The transformed output array, which is real, periodic, uniformly
        logarithmically spaced, and of the same shape as the input array.

    See Also
    --------
    ifht : The inverse of `fht`.
    fhtoffset : Return an optimal offset for `fht`.

    Notes
    -----
    This function computes a discrete version of the Hankel transform

    .. math::

        A(k) = \int_{0}^{\infty} \! a(r) \, J_\mu(kr) \, k \, dr \;,

    where :math:`J_\mu` is the Bessel function of order :math:`\mu`.  The index
    :math:`\mu` may be any real number, positive or negative.

    The input array `a` is a periodic sequence of length :math:`n`, uniformly
    logarithmically spaced with spacing `dln`,

    .. math::

        a_j = a(r_j) \;, \quad
        r_j = r_c \exp[(j-j_c) \, \mathtt{dln}]

    centred about the point :math:`r_c`.  Note that the central index
    :math:`j_c = (n-1)/2` is half-integral if :math:`n` is even, so that
    :math:`r_c` falls between two input elements.  Similarly, the output
    array `A` is a periodic sequence of length :math:`n`, also uniformly
    logarithmically spaced with spacing `dln`

    .. math::

       A_j = A(k_j) \;, \quad
       k_j = k_c \exp[(j-j_c) \, \mathtt{dln}]

    centred about the point :math:`k_c`.

    The centre points :math:`r_c` and :math:`k_c` of the periodic intervals may
    be chosen arbitrarily, but it would be usual to choose the product
    :math:`k_c r_c = k_j r_{n-1-j} = k_{n-1-j} r_j` to be unity.  This can be
    changed using the `offset` parameter, which controls the logarithmic offset
    :math:`\log(k_c) = \mathtt{offset} - \log(r_c)` of the output array.
    Choosing an optimal value for `offset` may reduce ringing of the discrete
    Hankel transform.

    If the `bias` parameter is nonzero, this function computes a discrete
    version of the biased Hankel transform

    .. math::

        A(k) = \int_{0}^{\infty} \! a_q(r) \, (kr)^q \, J_\mu(kr) \, k \, dr

    where :math:`q` is the value of `bias`, and a power law bias
    :math:`a_q(r) = a(r) \, (kr)^{-q}` is applied to the input sequence.
    Biasing the transform can help approximate the continuous transform of
    :math:`a(r)` if there is a value :math:`q` such that :math:`a_q(r)` is
    close to a periodic sequence, in which case the resulting :math:`A(k)` will
    be close to the continuous transform.

    References
    ----------
    .. [1] Talman J. D., 1978, J. Comp. Phys., 29, 35
    .. [2] Hamilton A. J. S., 2000, MNRAS, 312, 257 (astro-ph/9905191)

    Examples
    --------

    This example is the adapted version of ``fftlogtest.f`` which is provided
    in [2]_. It evaluates the integral

    .. math::

        \int^\infty_0 r^{\mu+1} \exp(-r^2/2) J_\mu(k, r) k dr
        = k^{\mu+1} \exp(-k^2/2) .

    >>> import numpy as np
    >>> from scipy import fft
    >>> import matplotlib.pyplot as plt

    Parameters for the transform.

    >>> mu = 0.0                     # Order mu of Bessel function
    >>> r = np.logspace(-7, 1, 128)  # Input evaluation points
    >>> dln = np.log(r[1]/r[0])      # Step size
    >>> offset = fft.fhtoffset(dln, initial=-6*np.log(10), mu=mu)
    >>> k = np.exp(offset)/r[::-1]   # Output evaluation points

    Define the analytical function.

    >>> def f(x, mu):
    ...     """Analytical function: x^(mu+1) exp(-x^2/2)."""
    ...     return x**(mu + 1)*np.exp(-x**2/2)

    Evaluate the function at ``r`` and compute the corresponding values at
    ``k`` using FFTLog.

    >>> a_r = f(r, mu)
    >>> fht = fft.fht(a_r, dln, mu=mu, offset=offset)

    For this example we can actually compute the analytical response (which in
    this case is the same as the input function) for comparison and compute the
    relative error.

    >>> a_k = f(k, mu)
    >>> rel_err = abs((fht-a_k)/a_k)

    Plot the result.

    >>> figargs = {'sharex': True, 'sharey': True, 'constrained_layout': True}
    >>> fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), **figargs)
    >>> ax1.set_title(r'$r^{\mu+1}\ \exp(-r^2/2)$')
    >>> ax1.loglog(r, a_r, 'k', lw=2)
    >>> ax1.set_xlabel('r')
    >>> ax2.set_title(r'$k^{\mu+1} \exp(-k^2/2)$')
    >>> ax2.loglog(k, a_k, 'k', lw=2, label='Analytical')
    >>> ax2.loglog(k, fht, 'C3--', lw=2, label='FFTLog')
    >>> ax2.set_xlabel('k')
    >>> ax2.legend(loc=3, framealpha=1)
    >>> ax2.set_ylim([1e-10, 1e1])
    >>> ax2b = ax2.twinx()
    >>> ax2b.loglog(k, rel_err, 'C0', label='Rel. Error (-)')
    >>> ax2b.set_ylabel('Rel. Error (-)', color='C0')
    >>> ax2b.tick_params(axis='y', labelcolor='C0')
    >>> ax2b.legend(loc=4, framealpha=1)
    >>> ax2b.set_ylim([1e-9, 1e-3])
    >>> plt.show()

    '''

    # size of transform
    n = np.shape(a)[-1]

    # bias input array
    if bias != 0:
        # a_q(r) = a(r) (r/r_c)^{-q}
        j_c = (n-1)/2
        j = np.arange(n)
        a = a * np.exp(-bias*(j - j_c)*dln)

    # compute FHT coefficients
    u = fhtcoeff(n, dln, mu, offset=offset, bias=bias)

    # transform
    A = _fhtq(a, u)

    # bias output array
    if bias != 0:
        # A(k) = A_q(k) (k/k_c)^{-q} (k_c r_c)^{-q}
        A *= np.exp(-bias*((j - j_c)*dln + offset))

    return A


def ifht(A, dln, mu, offset=0.0, bias=0.0):
    r'''Compute the inverse fast Hankel transform.

    Computes the discrete inverse Hankel transform of a logarithmically spaced
    periodic sequence. This is the inverse operation to `fht`.

    Parameters
    ----------
    A : array_like (..., n)
        Real periodic input array, uniformly logarithmically spaced.  For
        multidimensional input, the transform is performed over the last axis.
    dln : float
        Uniform logarithmic spacing of the input array.
    mu : float
        Order of the Hankel transform, any positive or negative real number.
    offset : float, optional
        Offset of the uniform logarithmic spacing of the output array.
    bias : float, optional
        Exponent of power law bias, any positive or negative real number.

    Returns
    -------
    a : array_like (..., n)
        The transformed output array, which is real, periodic, uniformly
        logarithmically spaced, and of the same shape as the input array.

    See Also
    --------
    fht : Definition of the fast Hankel transform.
    fhtoffset : Return an optimal offset for `ifht`.

    Notes
    -----
    This function computes a discrete version of the Hankel transform

    .. math::

        a(r) = \int_{0}^{\infty} \! A(k) \, J_\mu(kr) \, r \, dk \;,

    where :math:`J_\mu` is the Bessel function of order :math:`\mu`.  The index
    :math:`\mu` may be any real number, positive or negative.

    See `fht` for further details.

    '''

    # size of transform
    n = np.shape(A)[-1]

    # bias input array
    if bias != 0:
        # A_q(k) = A(k) (k/k_c)^{q} (k_c r_c)^{q}
        j_c = (n-1)/2
        j = np.arange(n)
        A = A * np.exp(bias*((j - j_c)*dln + offset))

    # compute FHT coefficients
    u = fhtcoeff(n, dln, mu, offset=offset, bias=bias)

    # transform
    a = _fhtq(A, u, inverse=True)

    # bias output array
    if bias != 0:
        # a(r) = a_q(r) (r/r_c)^{q}
        a /= np.exp(-bias*(j - j_c)*dln)

    return a


def fhtcoeff(n, dln, mu, offset=0.0, bias=0.0):
    '''Compute the coefficient array for a fast Hankel transform.
    '''

    lnkr, q = offset, bias

    # Hankel transform coefficients
    # u_m = (kr)^{-i 2m pi/(n dlnr)} U_mu(q + i 2m pi/(n dlnr))
    # with U_mu(x) = 2^x Gamma((mu+1+x)/2)/Gamma((mu+1-x)/2)
    xp = (mu+1+q)/2
    xm = (mu+1-q)/2
    y = np.linspace(0, np.pi*(n//2)/(n*dln), n//2+1)
    u = np.empty(n//2+1, dtype=complex)
    v = np.empty(n//2+1, dtype=complex)
    u.imag[:] = y
    u.real[:] = xm
    loggamma(u, out=v)
    u.real[:] = xp
    loggamma(u, out=u)
    y *= 2*(LN_2 - lnkr)
    u.real -= v.real
    u.real += LN_2*q
    u.imag += v.imag
    u.imag += y
    np.exp(u, out=u)

    # fix last coefficient to be real
    u.imag[-1] = 0

    # deal with special cases
    if not np.isfinite(u[0]):
        # write u_0 = 2^q Gamma(xp)/Gamma(xm) = 2^q poch(xm, xp-xm)
        # poch() handles special cases for negative integers correctly
        u[0] = 2**q * poch(xm, xp-xm)
        # the coefficient may be inf or 0, meaning the transform or the
        # inverse transform, respectively, is singular

    return u


def fhtoffset(dln, mu, initial=0.0, bias=0.0):
    '''Return optimal offset for a fast Hankel transform.

    Returns an offset close to `initial` that fulfils the low-ringing
    condition of [1]_ for the fast Hankel transform `fht` with logarithmic
    spacing `dln`, order `mu` and bias `bias`.

    Parameters
    ----------
    dln : float
        Uniform logarithmic spacing of the transform.
    mu : float
        Order of the Hankel transform, any positive or negative real number.
    initial : float, optional
        Initial value for the offset. Returns the closest value that fulfils
        the low-ringing condition.
    bias : float, optional
        Exponent of power law bias, any positive or negative real number.

    Returns
    -------
    offset : float
        Optimal offset of the uniform logarithmic spacing of the transform that
        fulfils a low-ringing condition.

    See Also
    --------
    fht : Definition of the fast Hankel transform.

    References
    ----------
    .. [1] Hamilton A. J. S., 2000, MNRAS, 312, 257 (astro-ph/9905191)

    '''

    lnkr, q = initial, bias

    xp = (mu+1+q)/2
    xm = (mu+1-q)/2
    y = np.pi/(2*dln)
    zp = loggamma(xp + 1j*y)
    zm = loggamma(xm + 1j*y)
    arg = (LN_2 - lnkr)/dln + (zp.imag + zm.imag)/np.pi
    return lnkr + (arg - np.round(arg))*dln


def _fhtq(a, u, inverse=False):
    '''Compute the biased fast Hankel transform.

    This is the basic FFTLog routine.
    '''

    # size of transform
    n = np.shape(a)[-1]

    # check for singular transform or singular inverse transform
    if np.isinf(u[0]) and not inverse:
        warn('singular transform; consider changing the bias')
        # fix coefficient to obtain (potentially correct) transform anyway
        u = u.copy()
        u[0] = 0
    elif u[0] == 0 and inverse:
        warn('singular inverse transform; consider changing the bias')
        # fix coefficient to obtain (potentially correct) inverse anyway
        u = u.copy()
        u[0] = np.inf

    # biased fast Hankel transform via real FFT
    A = rfft(a, axis=-1)
    if not inverse:
        # forward transform
        A *= u
    else:
        # backward transform
        A /= u.conj()
    A = irfft(A, n, axis=-1)
    A = A[..., ::-1]

    return A
