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a6e5201ff3
This is 'the big one', which touches every file so that it all conforms to the given standard.
90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
# This file is part of beets.
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# Copyright 2019, Carl Suster
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#
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# Permission is hereby granted, free of charge, to any person obtaining
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# a copy of this software and associated documentation files (the
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# "Software"), to deal in the Software without restriction, including
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# without limitation the rights to use, copy, modify, merge, publish,
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# distribute, sublicense, and/or sell copies of the Software, and to
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# permit persons to whom the Software is furnished to do so, subject to
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# the following conditions:
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#
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# The above copyright notice and this permission notice shall be
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# included in all copies or substantial portions of the Software.
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"""Test the beets.random utilities associated with the random plugin.
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"""
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import math
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import unittest
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from random import Random
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from test.helper import TestHelper
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from beets import random
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class RandomTest(unittest.TestCase, TestHelper):
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def setUp(self):
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self.lib = None
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self.artist1 = "Artist 1"
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self.artist2 = "Artist 2"
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self.item1 = self.create_item(artist=self.artist1)
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self.item2 = self.create_item(artist=self.artist2)
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self.items = [self.item1, self.item2]
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for _ in range(8):
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self.items.append(self.create_item(artist=self.artist2))
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self.random_gen = Random()
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self.random_gen.seed(12345)
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def tearDown(self):
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pass
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def _stats(self, data):
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mean = sum(data) / len(data)
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stdev = math.sqrt(sum((p - mean) ** 2 for p in data) / (len(data) - 1))
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quot, rem = divmod(len(data), 2)
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if rem:
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median = sorted(data)[quot]
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else:
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median = sum(sorted(data)[quot - 1 : quot + 1]) / 2
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return mean, stdev, median
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def test_equal_permutation(self):
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"""We have a list of items where only one item is from artist1 and the
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rest are from artist2. If we permute weighted by the artist field then
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the solo track will almost always end up near the start. If we use a
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different field then it'll be in the middle on average.
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"""
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def experiment(field, histogram=False):
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"""Permutes the list of items 500 times and calculates the position
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of self.item1 each time. Returns stats about that position.
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"""
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positions = []
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for _ in range(500):
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shuffled = list(
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random._equal_chance_permutation(
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self.items, field=field, random_gen=self.random_gen
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)
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)
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positions.append(shuffled.index(self.item1))
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# Print a histogram (useful for debugging).
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if histogram:
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for i in range(len(self.items)):
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print("{:2d} {}".format(i, "*" * positions.count(i)))
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return self._stats(positions)
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mean1, stdev1, median1 = experiment("artist")
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mean2, stdev2, median2 = experiment("track")
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self.assertAlmostEqual(0, median1, delta=1)
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self.assertAlmostEqual(len(self.items) // 2, median2, delta=1)
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self.assertGreater(stdev2, stdev1)
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def suite():
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return unittest.TestLoader().loadTestsFromName(__name__)
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if __name__ == "__main__":
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unittest.main(defaultTest="suite")
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