
import scipy.special
from numpy import logical_and, asarray, pi, zeros_like, \
     piecewise, array, arctan2, tan, zeros, arange, floor
from numpy.core.umath import sqrt, exp, greater, less, cos, add, sin, \
     less_equal, greater_equal
from spline import *      # C-modules
from scipy.misc import comb

gamma = scipy.special.gamma
def factorial(n):
    return gamma(n+1)

def spline_filter(Iin, lmbda=5.0):
    """Smoothing spline (cubic) filtering of a rank-2 array.

    Filter an input data set, Iin, using a (cubic) smoothing spline of
    fall-off lmbda.
    """
    intype = Iin.dtype.char
    hcol = array([1.0,4.0,1.0],'f')/6.0
    if intype in ['F','D']:
        Iin = Iin.astype('F')
        ckr = cspline2d(Iin.real,lmbda)
        cki = cspline2d(Iin.imag,lmbda)
        outr = sepfir2d(ckr,hcol,hcol)
        outi = sepfir2d(cki,hcol,hcol)
        out = (outr + 1j*outi).astype(intype)
    elif intype in ['f','d']:
        ckr = cspline2d(Iin,lmbda)
        out = sepfir2d(ckr, hcol, hcol)
        out = out.astype(intype)
    else:
        raise TypeError;
    return out

_splinefunc_cache = {}

def _bspline_piecefunctions(order):
    """Returns the function defined over the left-side
    pieces for a bspline of a given order.  The 0th piece
    is the first one less than 0.  The last piece is
    a function identical to 0 (returned as the constant 0).

    (There are order//2 + 2 total pieces).

    Also returns the condition functions that when evaluated
    return boolean arrays for use with numpy.piecewise
    """
    try:
        return _splinefunc_cache[order]
    except KeyError:
        pass

    def condfuncgen(num, val1, val2):
        if num == 0:
            return lambda x: logical_and(less_equal(x, val1),
                                         greater_equal(x, val2))
        elif num == 2:
            return lambda x: less_equal(x, val2)
        else:
            return lambda x: logical_and(less(x, val1),
                                         greater_equal(x, val2))

    last = order // 2 + 2
    if order % 2:
        startbound = -1.0
    else:
        startbound = -0.5
    condfuncs = [condfuncgen(0, 0, startbound)]
    bound = startbound
    for num in xrange(1,last-1):
        condfuncs.append(condfuncgen(1, bound, bound-1))
        bound = bound-1
    condfuncs.append(condfuncgen(2, 0, -(order+1)/2.0))

    # final value of bound is used in piecefuncgen below

    # the functions to evaluate are taken from the left-hand-side
    #  in the general expression derived from the central difference
    #  operator (because they involve fewer terms).

    fval = factorial(order)
    def piecefuncgen(num):
        Mk = order // 2 - num
        if (Mk < 0): return 0  # final function is 0
        coeffs = [(1-2*(k%2))*float(comb(order+1, k, exact=1))/fval for k in xrange(Mk+1)]
        shifts = [-bound - k for k in xrange(Mk+1)]
        #print "Adding piece number %d with coeffs %s and shifts %s" % (num, str(coeffs), str(shifts))
        def thefunc(x):
            res = 0.0
            for k in range(Mk+1):
                res += coeffs[k]*(x+shifts[k])**order
            return res
        return thefunc

    funclist = [piecefuncgen(k) for k in xrange(last)]

    _splinefunc_cache[order] = (funclist, condfuncs)

    return funclist, condfuncs

def bspline(x,n):
    """bspline(x,n):  B-spline basis function of order n.
    uses numpy.piecewise and automatic function-generator.
    """
    ax = -abs(asarray(x))
    # number of pieces on the left-side is (n+1)/2
    funclist, condfuncs = _bspline_piecefunctions(n)
    condlist = [func(ax) for func in condfuncs]
    return piecewise(ax, condlist, funclist)

def gauss_spline(x,n):
    """Gaussian approximation to B-spline basis function of order n.
    """
    signsq = (n+1) / 12.0
    return 1/sqrt(2*pi*signsq) * exp(-x**2 / 2 / signsq)

def cubic(x):
    """Special case of bspline.  Equivalent to bspline(x,3).
    """
    ax = abs(asarray(x))
    res = zeros_like(ax)
    cond1 = less(ax, 1)
    if cond1.any():
        ax1 = ax[cond1]
        res[cond1] = 2.0/3 - 1.0/2*ax1**2 * (2-ax1)
    cond2 = ~cond1 & less(ax, 2)
    if cond2.any():
        ax2 = ax[cond2]
        res[cond2] = 1.0/6*(2-ax2)**3
    return res

def quadratic(x):
    """Special case of bspline. Equivalent to bspline(x,2).
    """
    ax = abs(asarray(x))
    res = zeros_like(ax)
    cond1 = less(ax, 0.5)
    if cond1.any():
        ax1 = ax[cond1]
        res[cond1] = 0.75-ax1**2
    cond2 = ~cond1 & less(ax, 1.5)
    if cond2.any():
        ax2 = ax[cond2]
        res[cond2] = (ax2-1.5)**2 / 2.0
    return res

def c0_P(order):
    # values taken from Unser, et.al. 1993 IEEE
    if order == 0:
        c0 = 1
        P = array([1])
    elif order == 1:
        c0 = 1
        P = array([0,1])
    elif order == 2:
        c0 = 8
        P = array([1,6,1])
    elif order == 3:
        c0 = 6
        P = array([1,4,1])
    elif order == 4:
        c0 = 384
        P = array([1,76,230,76,1])
    elif order == 5:
        c0 = 120
        P = array([1,26,66,26,1])
    elif order == 6:
        c0 = 46080
        P = array([1,722,10543,23548, 10543, 722, 1])
    elif order == 7:
        c0 = 5040
        P = array([1,120,1191,2416,1191, 120, 1])
    else:
        raise ValueError, "Unknown order."

def _coeff_smooth(lam):
    xi = 1 - 96*lam + 24*lam * sqrt(3 + 144*lam)
    omeg = arctan2(sqrt(144*lam-1),sqrt(xi))
    rho = (24*lam - 1 - sqrt(xi)) / (24*lam)
    rho = rho * sqrt((48*lam + 24*lam * sqrt(3+144*lam))/xi)
    return rho,omeg

def _hc(k,cs,rho,omega):
    return cs / sin(omega) * (rho**k)*sin(omega*(k+1))*(greater(k,-1))

def _hs(k,cs,rho,omega):
    c0 = cs*cs * (1 + rho*rho) / (1 - rho*rho) / (1-2*rho*rho*cos(2*omega) + rho**4)
    gamma = (1-rho*rho) / (1+rho*rho) / tan(omega)
    ak = abs(k)
    return c0 * rho**ak * (cos(omega*ak) + gamma*sin(omega*ak))

def _cubic_smooth_coeff(signal,lamb):
    rho, omega = _coeff_smooth(lamb)
    cs = 1-2*rho*cos(omega) + rho*rho
    K = len(signal)
    yp = zeros((K,),signal.dtype.char)
    k = arange(K)
    yp[0] = _hc(0,cs,rho,omega)*signal[0] + \
            add.reduce(_hc(k+1,cs,rho,omega)*signal)

    yp[1] = _hc(0,cs,rho,omega)*signal[0] + \
            _hc(1,cs,rho,omega)*signal[1] + \
            add.reduce(_hc(k+2,cs,rho,omega)*signal)

    for n in range(2,K):
        yp[n] = cs * signal[n] + 2*rho*cos(omega)*yp[n-1] - rho*rho*yp[n-2]

    y = zeros((K,),signal.dtype.char)

    y[K-1] = add.reduce((_hs(k,cs,rho,omega) + _hs(k+1,cs,rho,omega))*signal[::-1])
    y[K-2] = add.reduce((_hs(k-1,cs,rho,omega) + _hs(k+2,cs,rho,omega))*signal[::-1])

    for n in range(K-3,-1,-1):
        y[n] = cs*yp[n] + 2*rho*cos(omega)*y[n+1] - rho*rho*y[n+2]

    return y

def _cubic_coeff(signal):
    zi = -2 + sqrt(3)
    K = len(signal)
    yplus = zeros((K,),signal.dtype.char)
    powers = zi**arange(K)
    yplus[0] = signal[0] + zi*add.reduce(powers*signal)
    for k in range(1,K):
        yplus[k] = signal[k] + zi*yplus[k-1]
    output = zeros((K,),signal.dtype)
    output[K-1] = zi / (zi-1)*yplus[K-1]
    for k in range(K-2,-1,-1):
        output[k] = zi*(output[k+1]-yplus[k])
    return output*6.0

def _quadratic_coeff(signal):
    zi = -3 + 2*sqrt(2.0)
    K = len(signal)
    yplus = zeros((K,),signal.dtype.char)
    powers = zi**arange(K)
    yplus[0] = signal[0] + zi*add.reduce(powers*signal)
    for k in range(1,K):
        yplus[k] = signal[k] + zi*yplus[k-1]
    output = zeros((K,),signal.dtype.char)
    output[K-1] = zi / (zi-1)*yplus[K-1]
    for k in range(K-2,-1,-1):
        output[k] = zi*(output[k+1]-yplus[k])
    return output*8.0

def cspline1d(signal,lamb=0.0):
    """Compute cubic spline coefficients for rank-1 array.

    Description:

      Find the cubic spline coefficients for a 1-D signal assuming
      mirror-symmetric boundary conditions.   To obtain the signal back from
      the spline representation mirror-symmetric-convolve these coefficients
      with a length 3 FIR window [1.0, 4.0, 1.0]/ 6.0 .

    Inputs:

      signal -- a rank-1 array representing samples of a signal.
      lamb -- smoothing coefficient (default = 0.0)

    Output:

      c -- cubic spline coefficients.
    """
    if lamb != 0.0:
        return _cubic_smooth_coeff(signal,lamb)
    else:
        return _cubic_coeff(signal)


def qspline1d(signal,lamb=0.0):
    """Compute quadratic spline coefficients for rank-1 array.

    Description:

      Find the quadratic spline coefficients for a 1-D signal assuming
      mirror-symmetric boundary conditions.   To obtain the signal back from
      the spline representation mirror-symmetric-convolve these coefficients
      with a length 3 FIR window [1.0, 6.0, 1.0]/ 8.0 .

    Inputs:

      signal -- a rank-1 array representing samples of a signal.
      lamb -- smoothing coefficient (must be zero for now.)

    Output:

      c -- cubic spline coefficients.
    """
    if lamb != 0.0:
        raise ValueError, "Smoothing quadratic splines not supported yet."
    else:
        return _quadratic_coeff(signal)


def cspline1d_eval(cj, newx, dx=1.0, x0=0):
    """Evaluate a spline at the new set of points.
    dx is the old sample-spacing while x0 was the old origin.

    In other-words the old-sample points (knot-points) for which the cj
    represent spline coefficients were at equally-spaced points of

    oldx = x0 + j*dx  j=0...N-1

    N=len(cj)

    edges are handled using mirror-symmetric boundary conditions.
    """
    newx = (asarray(newx)-x0)/float(dx)
    res = zeros_like(newx)
    if (res.size == 0):
        return res
    N = len(cj)
    cond1 = newx < 0
    cond2 = newx > (N-1)
    cond3 = ~(cond1 | cond2)
    # handle general mirror-symmetry
    res[cond1] = cspline1d_eval(cj, -newx[cond1])
    res[cond2] = cspline1d_eval(cj, 2*(N-1)-newx[cond2])
    newx = newx[cond3]
    if newx.size == 0:
        return res
    result = zeros_like(newx)
    jlower = floor(newx-2).astype(int)+1
    for i in range(4):
        thisj = jlower + i
        indj = thisj.clip(0,N-1) # handle edge cases
        result += cj[indj] * cubic(newx - thisj)
    res[cond3] = result
    return res

def qspline1d_eval(cj, newx, dx=1.0, x0=0):
    """Evaluate a quadratic spline at the new set of points.
    dx is the old sample-spacing while x0 was the old origin.

    In other-words the old-sample points (knot-points) for which the cj
    represent spline coefficients were at equally-spaced points of

    oldx = x0 + j*dx  j=0...N-1

    N=len(cj)

    edges are handled using mirror-symmetric boundary conditions.
    """
    newx = (asarray(newx)-x0)/dx
    res = zeros_like(newx)
    if (res.size == 0):
        return res
    N = len(cj)
    cond1 = newx < 0
    cond2 = newx > (N-1)
    cond3 = ~(cond1 | cond2)
    # handle general mirror-symmetry
    res[cond1] = qspline1d_eval(cj, -newx[cond1])
    res[cond2] = qspline1d_eval(cj, 2*(N-1)-newx[cond2])
    newx = newx[cond3]
    if newx.size == 0:
        return res
    result = zeros_like(newx)
    jlower = floor(newx-1.5).astype(int)+1
    for i in range(3):
        thisj = jlower + i
        indj = thisj.clip(0,N-1) # handle edge cases
        result += cj[indj] * quadratic(newx - thisj)
    res[cond3] = result
    return res
