# Source code for arrayfire.statistics

    #######################################################
    # Copyright (c) 2015, ArrayFire
    # All rights reserved.
    #
    # This file is distributed under 3-clause BSD license.
    # The complete license agreement can be obtained at:
    # http://arrayfire.com/licenses/BSD-3-Clause
    ########################################################

    """
    Statistical algorithms (mean, var, stdev, etc).
    """

    from .library import *
    from .array import *

    def mean(a, weights=None, dim=None):
        if dim is not None:
            out = Array()

            if weights is None:
                safe_call(backend.get().af_mean(ct.pointer(out.arr), a.arr, ct.c_int(dim)))
            else:
                safe_call(backend.get().af_mean_weighted(ct.pointer(out.arr), a.arr, weights.arr, ct.c_int(dim)))

            return out
        else:
            real = ct.c_double(0)
            imag = ct.c_double(0)

            if weights is None:
                safe_call(backend.get().af_mean_all(ct.pointer(real), ct.pointer(imag), a.arr))
            else:
                safe_call(backend.get().af_mean_all_weighted(ct.pointer(real), ct.pointer(imag), a.arr, weights.arr))

            real = real.value
            imag = imag.value

            return real if imag == 0 else real + imag * 1j

    def var(a, isbiased=False, weights=None, dim=None):
        if dim is not None:
            out = Array()

            if weights is None:
                safe_call(backend.get().af_var(ct.pointer(out.arr), a.arr, isbiased, ct.c_int(dim)))
            else:
                safe_call(backend.get().af_var_weighted(ct.pointer(out.arr), a.arr, weights.arr, ct.c_int(dim)))

            return out
        else:
            real = ct.c_double(0)
            imag = ct.c_double(0)

            if weights is None:
                safe_call(backend.get().af_var_all(ct.pointer(real), ct.pointer(imag), a.arr, isbiased))
            else:
                safe_call(backend.get().af_var_all_weighted(ct.pointer(real), ct.pointer(imag), a.arr, weights.arr))

            real = real.value
            imag = imag.value

            return real if imag == 0 else real + imag * 1j

    def stdev(a, dim=None):
        if dim is not None:
            out = Array()
            safe_call(backend.get().af_stdev(ct.pointer(out.arr), a.arr, ct.c_int(dim)))
            return out
        else:
            real = ct.c_double(0)
            imag = ct.c_double(0)
            safe_call(backend.get().af_stdev_all(ct.pointer(real), ct.pointer(imag), a.arr))
            real = real.value
            imag = imag.value
            return real if imag == 0 else real + imag * 1j

    def cov(a, isbiased=False, dim=None):
        if dim is not None:
            out = Array()
            safe_call(backend.get().af_cov(ct.pointer(out.arr), a.arr, isbiased, ct.c_int(dim)))
            return out
        else:
            real = ct.c_double(0)
            imag = ct.c_double(0)
            safe_call(backend.get().af_cov_all(ct.pointer(real), ct.pointer(imag), a.arr, isbiased))
            real = real.value
            imag = imag.value
            return real if imag == 0 else real + imag * 1j

    def median(a, dim=None):
        if dim is not None:
            out = Array()
            safe_call(backend.get().af_median(ct.pointer(out.arr), a.arr, ct.c_int(dim)))
            return out
        else:
            real = ct.c_double(0)
            imag = ct.c_double(0)
            safe_call(backend.get().af_median_all(ct.pointer(real), ct.pointer(imag), a.arr))
            real = real.value
            imag = imag.value
            return real if imag == 0 else real + imag * 1j

    def corrcoef(x, y):
        real = ct.c_double(0)
        imag = ct.c_double(0)
        safe_call(backend.get().af_corrcoef(ct.pointer(real), ct.pointer(imag), x.arr, y.arr))
        real = real.value
        imag = imag.value
        return real if imag == 0 else real + imag * 1j