import numpy as np


#pythran export _Aij(float[:,:], int, int)
#pythran export _Aij(int[:,:], int, int)
def _Aij(A, i, j):
    """Sum of upper-left and lower right blocks of contingency table."""
    # See `somersd` References [2] bottom of page 309
    return A[:i, :j].sum() + A[i+1:, j+1:].sum()


#pythran export _Dij(float[:,:], int, int)
#pythran export _Dij(int[:,:], int, int)
def _Dij(A, i, j):
    """Sum of lower-left and upper-right blocks of contingency table."""
    # See `somersd` References [2] bottom of page 309
    return A[i+1:, :j].sum() + A[:i, j+1:].sum()


# pythran export _concordant_pairs(float[:,:])
# pythran export _concordant_pairs(int[:,:])
def _concordant_pairs(A):
    """Twice the number of concordant pairs, excluding ties."""
    # See `somersd` References [2] bottom of page 309
    m, n = A.shape
    count = 0
    for i in range(m):
        for j in range(n):
            count += A[i, j]*_Aij(A, i, j)
    return count


# pythran export _discordant_pairs(float[:,:])
# pythran export _discordant_pairs(int[:,:])
def _discordant_pairs(A):
    """Twice the number of discordant pairs, excluding ties."""
    # See `somersd` References [2] bottom of page 309
    m, n = A.shape
    count = 0
    for i in range(m):
        for j in range(n):
            count += A[i, j]*_Dij(A, i, j)
    return count


#pythran export _a_ij_Aij_Dij2(float[:,:])
#pythran export _a_ij_Aij_Dij2(int[:,:])
def _a_ij_Aij_Dij2(A):
    """A term that appears in the ASE of Kendall's tau and Somers' D."""
    # See `somersd` References [2] section 4: Modified ASEs to test the null hypothesis...
    m, n = A.shape
    count = 0
    for i in range(m):
        for j in range(n):
            count += A[i, j]*(_Aij(A, i, j) - _Dij(A, i, j))**2
    return count


#pythran export _compute_outer_prob_inside_method(int64, int64, int64, int64)
def _compute_outer_prob_inside_method(m, n, g, h):
    """
    Count the proportion of paths that do not stay strictly inside two
    diagonal lines.

    Parameters
    ----------
    m : integer
        m > 0
    n : integer
        n > 0
    g : integer
        g is greatest common divisor of m and n
    h : integer
        0 <= h <= lcm(m,n)

    Returns
    -------
    p : float
        The proportion of paths that do not stay inside the two lines.

    The classical algorithm counts the integer lattice paths from (0, 0)
    to (m, n) which satisfy |x/m - y/n| < h / lcm(m, n).
    The paths make steps of size +1 in either positive x or positive y
    directions.
    We are, however, interested in 1 - proportion to computes p-values,
    so we change the recursion to compute 1 - p directly while staying
    within the "inside method" a described by Hodges.

    We generally follow Hodges' treatment of Drion/Gnedenko/Korolyuk.
    Hodges, J.L. Jr.,
    "The Significance Probability of the Smirnov Two-Sample Test,"
    Arkiv fiur Matematik, 3, No. 43 (1958), 469-86.

    For the recursion for 1-p see
    Viehmann, T.: "Numerically more stable computation of the p-values
    for the two-sample Kolmogorov-Smirnov test," arXiv: 2102.08037

    """
    # Probability is symmetrical in m, n.  Computation below uses m >= n.
    if m < n:
        m, n = n, m
    mg = m // g
    ng = n // g

    # Count the integer lattice paths from (0, 0) to (m, n) which satisfy
    # |nx/g - my/g| < h.
    # Compute matrix A such that:
    #  A(x, 0) = A(0, y) = 1
    #  A(x, y) = A(x, y-1) + A(x-1, y), for x,y>=1, except that
    #  A(x, y) = 0 if |x/m - y/n|>= h
    # Probability is A(m, n)/binom(m+n, n)
    # Optimizations exist for m==n, m==n*p.
    # Only need to preserve a single column of A, and only a
    # sliding window of it.
    # minj keeps track of the slide.
    minj, maxj = 0, min(int(np.ceil(h / mg)), n + 1)
    curlen = maxj - minj
    # Make a vector long enough to hold maximum window needed.
    lenA = min(2 * maxj + 2, n + 1)
    # This is an integer calculation, but the entries are essentially
    # binomial coefficients, hence grow quickly.
    # Scaling after each column is computed avoids dividing by a
    # large binomial coefficient at the end, but is not sufficient to avoid
    # the large dyanamic range which appears during the calculation.
    # Instead we rescale based on the magnitude of the right most term in
    # the column and keep track of an exponent separately and apply
    # it at the end of the calculation.  Similarly when multiplying by
    # the binomial coefficient
    dtype = np.float64
    A = np.ones(lenA, dtype=dtype)
    # Initialize the first column
    A[minj:maxj] = 0.0
    for i in range(1, m + 1):
        # Generate the next column.
        # First calculate the sliding window
        lastminj, lastlen = minj, curlen
        minj = max(int(np.floor((ng * i - h) / mg)) + 1, 0)
        minj = min(minj, n)
        maxj = min(int(np.ceil((ng * i + h) / mg)), n + 1)
        if maxj <= minj:
            return 1.0
        # Now fill in the values. We cannot use cumsum, unfortunately.
        val = 0.0 if minj == 0 else 1.0
        for jj in range(maxj - minj):
            j = jj + minj
            val = (A[jj + minj - lastminj] * i + val * j) / (i + j)
            A[jj] = val
        curlen = maxj - minj
        if lastlen > curlen:
            # Set some carried-over elements to 1
            A[maxj - minj:maxj - minj + (lastlen - curlen)] = 1

    return A[maxj - minj - 1]


# pythran export siegelslopes(float32[:], float32[:], str)
# pythran export siegelslopes(float64[:], float64[:], str)
def siegelslopes(y, x, method):
    deltax = np.expand_dims(x, 1) - x
    deltay = np.expand_dims(y, 1) - y
    slopes, intercepts = [], []

    for j in range(len(x)):
        id_nonzero, = np.nonzero(deltax[j, :])
        slopes_j = deltay[j, id_nonzero] / deltax[j, id_nonzero]
        medslope_j = np.median(slopes_j)
        slopes.append(medslope_j)
        if method == 'separate':
            z = y*x[j] - y[j]*x
            medintercept_j = np.median(z[id_nonzero] / deltax[j, id_nonzero])
            intercepts.append(medintercept_j)

    medslope = np.median(np.asarray(slopes))
    if method == "separate":
        medinter = np.median(np.asarray(intercepts))
    else:
        medinter = np.median(y - medslope*x)

    return medslope, medinter
