#!/usr/bin/env python
# This file is part of Xpra.
# Copyright (C) 2011-2017 Antoine Martin <antoine@xpra.org>
# Xpra is released under the terms of the GNU GPL v2, or, at your option, any
# later version. See the file COPYING for details.

import unittest
import random

from xpra.os_util import monotonic_time
from xpra.util import iround
try:
    from xpra.server import cystats
except ImportError:
    cystats = None


class TestCystats(unittest.TestCase):

    def test_calculate_timesize_weighted_average(self):
        #event_time, size, elapsed_time
        now = monotonic_time()
        sample_size = 1000
        data = []
        ts = now - sample_size
        for _ in range(sample_size):
            s = random.randint(1000, 10000)
            v = random.random()
            data.append((ts, s, v))
            ts += 1
        a, ra = cystats.calculate_timesize_weighted_average(data)
        assert 0<a and 0<ra
        #the calculations use the ratio of the size divided by the elapsed time,
        #so check that a predictable ratio gives the expected value:
        for x in (5, 1000):
            v = [(now, i*x, x) for i in range(1, 1000)]
            a, ra = cystats.calculate_size_weighted_average(v)
            #but we need to round to an int to compare
            self.assertEqual(x, iround(a), "average should be %i, got %i" % (x, a))
            self.assertEqual(x, iround(ra), "recent average should be %i, got %i" % (x, ra))
        def t(v, ea, era):
            a, ra = cystats.calculate_size_weighted_average(v)
            self.assertEqual(iround(a), iround(ea), "average should be %s, got %s" % (iround(ea), iround(a)))
            self.assertEqual(iround(ra), iround(era), "recent average should be %s, got %s" % (iround(era), iround(ra)))
        #an old record won't make any difference
        #compared with one that was taken just now:
        for v in (1, 10, 1000):
            #1 day ago:
            if now>60*60*24:
                t([(now-60*60*24, 1000, 1000), (now, 1000, v)], v, v)
                t([(now-60*60*24, 2*1000, 1000), (now, 1000, v)], v, v)
            #1 hour ago:
            if now>60*60:
                t([(now-60*60, 1000, 10), (now, 1000, v)], v, v)
        #but 100s ago starts to make a difference:
        t([(now-100, 1000, 1), (now, 1000, 100)], 99, 100)
        #with only 10s:
        t([(now-10, 1000, 1), (now, 1000, 100)], 92, 100)
        #1 second:
        t([(now-1, 1000, 1), (now, 1000, 100)], 67, 92)
        #if using the same time, then size matters more:
        v = [(now, 100*1000, 1000), (now, 50*1000, 1000)]
        a, ra = cystats.calculate_size_weighted_average(v)
        #recent is the same as "normal" average:
        self.assertEqual(iround(a), iround(ra))
        self.assertGreater(a, 75)
        #real data:
        T = monotonic_time()
        v =[(T-21.557095, 157684, 9110), (T-22.23345, 3744, 1279), (T-22.376621, 3744, 706),
            (T-22.515456, 3744, 1302), (T-23.013887, 78, 1342), (T-43.707768, 78, 920),
            (T-44.043399, 78, 1558), (T-44.046686, 78, 1119), (T-44.048169, 78, 1007),
            (T-44.049807, 1716, 626), (T-44.053967, 78, 2841), (T-44.23714, 78, 1393),
            (T-44.238555, 78, 2903), (T-44.242623, 78, 1167), (T-44.244426, 1716, 1032),
            (T-44.245675, 78, 720), (T-44.392009, 78, 784), (T-44.392771, 78, 737),
            (T-44.396293, 78, 911), (T-44.397466, 1716, 772), (T-44.398027, 78, 1234),
            (T-44.538323, 78, 1200), (T-44.539683, 78, 586), (T-44.542575, 78, 1203),
            (T-44.544646, 1716, 1129), (T-44.546205, 78, 979), (T-44.701881, 78, 901),
            (T-44.703987, 78, 448), (T-44.708965, 78, 474), (T-44.711481, 1716, 1444),
            (T-44.713157, 78, 1033), (T-44.848487, 78, 860), (T-44.850604, 78, 1172),
            (T-44.857039, 78, 1367), (T-44.858723, 1716, 1078), (T-44.859743, 78, 1876),
            (T-44.993883, 78, 824), (T-44.99714, 78, 796), (T-45.001942, 78, 714),
            (T-45.002884, 1716, 744), (T-45.004841, 78, 652), (T-45.772856, 78, 652)]
        raw_v = [x[2] for x in v]
        min_v = min(raw_v)
        max_v = max(raw_v)
        a, ra = cystats.calculate_size_weighted_average(v)
        self.assertLess(a, max_v)
        self.assertLess(ra, max_v)
        self.assertGreater(a, min_v)
        self.assertGreater(ra, min_v)

    def test_calculate_time_weighted_average(self):
        now = monotonic_time()
        sample_size = 100
        data = []
        t = now - sample_size
        for _ in range(sample_size):
            v = random.random()
            data.append((t, v))
            t += 1
        a, ra = cystats.calculate_time_weighted_average(data)
        assert 0<a<1 and 0<ra<1

    def test_logp(self):
        for _ in range(1000):
            x = random.random()
            v = cystats.logp(x)
            assert v>=0 and v<=1
        for x in (0, 1):
            v = cystats.logp(x)
            assert v>=0 and v<=1


def main():
    if cystats:
        unittest.main()

if __name__ == '__main__':
    main()