Multi-dimensional statistical tests with python
- 2D Kolmogorov–Smirnov test (KS test)
- 1D and 2D energy distance statistics test
The code has been cited in ~40 papers according to Google Scholar 🤩
If you used this code in your paper, I would be grateful for a simple footnote mentioning the author and URL of this repo, such as
We perform the 2D Kolmogorov–Smirnov test ...
using the public code \textsc{ndtest}\footenote{Written by Zhaozhou Li, \url{https://github.com/syrte/ndtest}}.
As to references for the algorithms, please check the docstrings.
pip install git+https://github.com/syrte/ndtestimport numpy as np
import ndtest
# generate mock samples for the test
np.random.seed(42)
x1, y1 = np.random.randn(2, 100)
x2, y2 = np.random.randn(2, 100) # same distribution as (x1, y1)
x3, y3 = np.random.randn(2, 100) * 1.5 + 0.5 # different distribution from (x1, y1)
# 2D KS
P, D = ndtest.ks2d2s(x1, y1, x2, y2, extra=True)
print(f"{P=:.3g}, {D=:.3g}")
# P=0.219, D=0.17
P, D = ndtest.ks2d2s(x1, y1, x3, y3, extra=True)
print(f"{P=:.3g}, {D=:.3g}")
# P=2.36e-05, D=0.385 # very small P, as expected.
See the docstring for the detailed usage and explanation.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent