Python
Install from PyPI:
pip install pragmastat==14.0.1
Source code: https://github.com/AndreyAkinshin/pragmastat/tree/v14.0.1/py
Pragmastat on PyPI: https://pypi.org/project/pragmastat/
Demo
from pragmastat import (
Metric,
Rng,
Sample,
Threshold,
center,
center_bounds,
compare1,
compare2,
disparity,
disparity_bounds,
ratio,
ratio_bounds,
shift,
shift_bounds,
spread,
spread_bounds,
)
from pragmastat.distributions import Additive, Exp, Multiplic, Power, Uniform
def main():
# --- One-Sample ---
x = Sample(list(range(1, 201)))
result = center(x)
print(result.value) # 100.5
bounds = center_bounds(x, 1e-3)
print(f"Bounds(lower={bounds.lower}, upper={bounds.upper})") # Bounds(lower=86.0, upper=115.0)
print(spread(x).value) # 59.0
bounds = spread_bounds(x, 1e-3, seed="demo")
print(f"Bounds(lower={bounds.lower}, upper={bounds.upper})") # Bounds(lower=44.0, upper=87.0)
# --- Two-Sample ---
x = Sample(list(range(1, 201)))
y = Sample(list(range(101, 301)))
print(shift(x, y).value) # -100.0
bounds = shift_bounds(x, y, 1e-3)
print(f"Bounds(lower={bounds.lower}, upper={bounds.upper})") # Bounds(lower=-120.0, upper=-80.0)
print(ratio(x, y).value) # 0.5008354224706334
bounds = ratio_bounds(x, y, 1e-3)
print(
f"Bounds(lower={bounds.lower}, upper={bounds.upper})"
) # Bounds(lower=0.4066666666666668, upper=0.5958333333333332)
print(disparity(x, y).value) # -1.694915254237288
bounds = disparity_bounds(x, y, 1e-3, seed="demo")
print(
f"Bounds(lower={bounds.lower}, upper={bounds.upper})"
) # Bounds(lower=-3.1025641025641026, upper=-0.8494623655913979)
# --- Confirmatory Comparison ---
x = Sample(list(range(1, 201)))
projection = compare1(x, [Threshold(Metric.CENTER, 50.0, 1e-3)])[0]
print(projection.verdict.value) # greater
x = Sample(list(range(1, 201)))
y = Sample(list(range(101, 301)))
projection = compare2(x, y, [Threshold(Metric.SHIFT, 0.0, 1e-3)])[0]
print(projection.verdict.value) # less
# --- Randomization ---
rng = Rng("demo-uniform")
print(rng.uniform_float()) # 0.2640554428629759
print(rng.uniform_float()) # 0.9348534835582796
rng = Rng("demo-uniform-int")
print(rng.uniform_int(0, 100)) # 41
rng = Rng("demo-sample")
print(rng.sample([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 3)) # [3, 8, 9]
rng = Rng("demo-resample")
print(rng.resample([1, 2, 3, 4, 5], 7)) # [3, 1, 3, 2, 4, 1, 2]
rng = Rng("demo-shuffle")
print(rng.shuffle([1, 2, 3, 4, 5])) # [4, 2, 3, 5, 1]
# --- Distributions ---
rng = Rng("demo-dist-additive")
print(Additive(0, 1).sample(rng)) # 0.17410448679568188
rng = Rng("demo-dist-multiplic")
print(Multiplic(0, 1).sample(rng)) # 1.1273244602673853
rng = Rng("demo-dist-exp")
print(Exp(1).sample(rng)) # 0.6589065267276553
rng = Rng("demo-dist-power")
print(Power(1, 2).sample(rng)) # 1.023677535537084
rng = Rng("demo-dist-uniform")
print(Uniform(0, 10).sample(rng)) # 6.54043657816832
if __name__ == "__main__":
main()
1 maintainer
Metadata
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