https://github.com/python/cpython/commit/0400a7f2f8abec8d441990e951cc25f69a2... commit: 0400a7f2f8abec8d441990e951cc25f69a2a4036 branch: master author: Raymond Hettinger <rhettinger@users.noreply.github.com> committer: GitHub <noreply@github.com> date: 2020-05-02T19:30:24-07:00 summary: Minor code cleanups for statistics (GH-19873) * Minor cleanups: Removed unused code. Move C import near its Python version. * Clean-up whitespace files: M Lib/statistics.py diff --git a/Lib/statistics.py b/Lib/statistics.py index 9beafb341b3ad..c76a6ca519e40 100644 --- a/Lib/statistics.py +++ b/Lib/statistics.py @@ -894,6 +894,13 @@ def _normal_dist_inv_cdf(p, mu, sigma): return mu + (x * sigma) +# If available, use C implementation +try: + from _statistics import _normal_dist_inv_cdf +except ImportError: + pass + + class NormalDist: "Normal distribution of a random variable" # https://en.wikipedia.org/wiki/Normal_distribution @@ -1111,79 +1118,3 @@ def __hash__(self): def __repr__(self): return f'{type(self).__name__}(mu={self._mu!r}, sigma={self._sigma!r})' - -# If available, use C implementation -try: - from _statistics import _normal_dist_inv_cdf -except ImportError: - pass - - -if __name__ == '__main__': - - # Show math operations computed analytically in comparsion - # to a monte carlo simulation of the same operations - - from math import isclose - from operator import add, sub, mul, truediv - from itertools import repeat - import doctest - - g1 = NormalDist(10, 20) - g2 = NormalDist(-5, 25) - - # Test scaling by a constant - assert (g1 * 5 / 5).mean == g1.mean - assert (g1 * 5 / 5).stdev == g1.stdev - - n = 100_000 - G1 = g1.samples(n) - G2 = g2.samples(n) - - for func in (add, sub): - print(f'\nTest {func.__name__} with another NormalDist:') - print(func(g1, g2)) - print(NormalDist.from_samples(map(func, G1, G2))) - - const = 11 - for func in (add, sub, mul, truediv): - print(f'\nTest {func.__name__} with a constant:') - print(func(g1, const)) - print(NormalDist.from_samples(map(func, G1, repeat(const)))) - - const = 19 - for func in (add, sub, mul): - print(f'\nTest constant with {func.__name__}:') - print(func(const, g1)) - print(NormalDist.from_samples(map(func, repeat(const), G1))) - - def assert_close(G1, G2): - assert isclose(G1.mean, G1.mean, rel_tol=0.01), (G1, G2) - assert isclose(G1.stdev, G2.stdev, rel_tol=0.01), (G1, G2) - - X = NormalDist(-105, 73) - Y = NormalDist(31, 47) - s = 32.75 - n = 100_000 - - S = NormalDist.from_samples([x + s for x in X.samples(n)]) - assert_close(X + s, S) - - S = NormalDist.from_samples([x - s for x in X.samples(n)]) - assert_close(X - s, S) - - S = NormalDist.from_samples([x * s for x in X.samples(n)]) - assert_close(X * s, S) - - S = NormalDist.from_samples([x / s for x in X.samples(n)]) - assert_close(X / s, S) - - S = NormalDist.from_samples([x + y for x, y in zip(X.samples(n), - Y.samples(n))]) - assert_close(X + Y, S) - - S = NormalDist.from_samples([x - y for x, y in zip(X.samples(n), - Y.samples(n))]) - assert_close(X - Y, S) - - print(doctest.testmod())