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Minor code cleanups for statistics (GH-19873) 

* Minor cleanups:  Removed unused code.  Move C import near its Python version.

* Clean-up whitespace
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Raymond Hettinger 2020-05-02 19:30:24 -07:00 committed by GitHub
parent 8aab84312e
commit 0400a7f2f8
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1 changed files with 7 additions and 76 deletions
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Lib/statistics.py
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@ -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 @@ class NormalDist:
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())