'''
General quadrature method for Hankel transformations.

Based on the algorithm provided in 
H. Ogata, A Numerical Integration Formula Based on the Bessel Functions,
Publications of the Research Institute for Mathematical Sciences, 
vol. 41, no. 4, pp. 949-970, 2005.
'''

# TODO: Suppress warnings on overflows
# TODO: Write tests
# TODO: Profile.

import numpy as np
from mpmath import fp as mpm
from scipy.special import j0, j1, jn_zeros, jn, yv, jv

class HankelTransform(object):
    """
    The basis of the Hankel Transformation algorithm by Ogata 2005.
    
    This algorithm is used to solve the equation :math:`\int_0^\infty f(x) J_\nu(x) dx`
    where :math:`J_\nu(x)` is a Bessel function of the first kind of order 
    :math:`nu`, and :math:`f(x)` is an arbitrary (slowly-decaying) function.
    
    The algorithm is presented in 
    H. Ogata, A Numerical Integration Formula Based on the Bessel Functions,
    Publications of the Research Institute for Mathematical Sciences, vol. 41, no. 4, pp. 949-970, 2005.
    
    Parameters
    ----------
    nu : int or 0.5, optional, default = 0
        The order of the bessel function (of the first kind) J_nu(x)
    
    N : int, optional, default = 100
        The number of nodes in the calculation. Generally this must increase
        for a smaller value of the step-size h.     
    
    h : float, optional, default = 0.1
        The step-size of the integration.
    """
    def __init__(self, nu=0, N=200, h=0.05):
        self._nu = nu
        self._h = h
        self._zeros = self._roots(N)
        self.x = self._x(h)
        self.j = self._j(self.x)
        self.w = self._weight()
        self.dpsi = self._d_psi(h * self._zeros)

    def _psi(self, t):
        print t
        y = np.sinh(t)
        print y
        return t * np.tanh(np.pi * y / 2)

    def _d_psi(self, t):
        a = (np.pi * t * np.cosh(t) + np.sinh(np.pi * np.sinh(t))) / (1.0 + np.cosh(np.pi * np.sinh(t)))
        a[np.isnan(a)] = 1.0
        return a

    def _weight(self):
        return yv(self._nu, np.pi * self._zeros) / self._j1(np.pi * self._zeros)

    def _roots(self, N):
        if isinstance(self._nu, int):
            return jn_zeros(self._nu, N) / np.pi
        else:
            return np.array([mpm.besseljzero(self._nu, i + 1) for i in range(N)]) / np.pi

    def _j(self, x):
        if self._nu == 0:
            return j0(x)
        elif self._nu == 1:
            return j1(x)
        elif isinstance(self._nu, int):
            return jn(self._nu, x)
        else:
            return jv(self._nu, x)

    def _j1(self, x):
        if self._nu == -1:
            return j0(x)
        elif self._nu == 0:
            return j1(x)
        elif isinstance(self._nu, int):
            return jn(self._nu + 1, x)
        else:
            return jv(self._nu + 1, x)

    def _x(self, h):
        return np.pi * self._psi(h * self._zeros) / h

    def _f(self, f, x):
        return f(x)

    def transform(self, f, ret_err=True, ret_cumsum=False):
        """
        Perform the transform of the function f
        
        Parameters
        ----------
        f : callable
            A function of one variable, representing :math:`f(x)`
            
        ret_err : boolean, optional, default = True
            Whether to return the estimated error
            
        ret_cumsum : boolean, optional, default = False
            Whether to return the cumulative sum
        """
        fres = self._f(f, self.x)
        summation = np.pi * self.w * fres * self.j * self.dpsi
        ret = [np.sum(summation)]
        if ret_err:
            ret.append(summation[-1])
        if ret_cumsum:
            ret.append(np.cumsum(summation))

        return ret


class SphericalHankelTransform(HankelTransform):
    """
    Perform spherical hankel transforms.
    
    Defined as :math:`\int_0^\infty f(x) j_\nu(x) dx
    
    .. Note :: Only does 0th-order transforms currently.
    """
    def __init__(self, nu=0, *args, **kwargs):
        nu += 0.5
        super(SphericalHankelTransform, self).__init__(nu, *args, **kwargs)

    def _f(self, f, x):
        return np.sqrt(np.pi / (2 * x)) * f(x)

    def _roots(self, N):
        if self._nu == 0.5:
            return (np.arange(N) + 1)
        else:
            return super(SphericalHankelTransform, self)._roots(N)


