#!/usr/bin/env python

#######################################################
# 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
########################################################

import arrayfire as af

from . import _util


def simple_statistics(verbose=False):
    display_func = _util.display_func(verbose)
    print_func = _util.print_func(verbose)

    a = af.randu(5, 5)
    b = af.randu(5, 5)
    w = af.randu(5, 1)

    display_func(af.mean(a, dim=0))
    display_func(af.mean(a, weights=w, dim=0))
    print_func(af.mean(a))
    print_func(af.mean(a, weights=w))

    display_func(af.var(a, dim=0))
    display_func(af.var(a, bias=af.VARIANCE.SAMPLE, dim=0))
    display_func(af.var(a, weights=w, dim=0))
    print_func(af.var(a))
    print_func(af.var(a, bias=af.VARIANCE.SAMPLE))
    print_func(af.var(a, weights=w))

    mean, var = af.meanvar(a, dim=0)
    display_func(mean)
    display_func(var)
    mean, var = af.meanvar(a, weights=w, bias=af.VARIANCE.SAMPLE, dim=0)
    display_func(mean)
    display_func(var)

    display_func(af.stdev(a, dim=0))
    print_func(af.stdev(a))

    display_func(af.var(a, dim=0))
    display_func(af.var(a, bias=af.VARIANCE.SAMPLE, dim=0))
    print_func(af.var(a))
    print_func(af.var(a, bias=af.VARIANCE.SAMPLE))

    display_func(af.median(a, dim=0))
    print_func(af.median(w))

    print_func(af.corrcoef(a, b))

    data = af.iota(5, 3)
    k = 3
    dim = 0
    order = af.TOPK.DEFAULT  # defaults to af.TOPK.MAX
    assert(dim == 0)  # topk currently supports first dim only
    values, indices = af.topk(data, k, dim, order)
    display_func(values)
    display_func(indices)


_util.tests["statistics"] = simple_statistics