Quickstart¶
git clone https://github.com/sanskarpan/probviz.git
cd probviz
pip install -r requirements.txt
streamlit run web/app.py
Open http://localhost:8501, pick Continuous/Discrete, choose a distribution, move the
sliders, and inspect the PDF/PMF, CDF, samples, statistics, and quantiles.
Python API¶
import numpy as np
from probviz.distributions import NormalDistribution, BinomialDistribution
normal = NormalDistribution(mu=0, sigma=1)
x = np.linspace(-4, 4, 200)
pdf, cdf = normal.pdf(x), normal.cdf(x)
samples = normal.rvs(size=1000, random_state=42)
print(normal.get_statistics())
binomial = BinomialDistribution(n=10, p=0.3)
print(binomial.pdf(np.arange(0, 11)))
CLI¶
pip install -e ".[test]"
probviz app # launch the Streamlit UI
probviz test # run the test suite
probviz version # print the version
Next steps¶
- Installation — Docker, Compose, and dev setup
- Distributions — parameters, supports, and recipes
- Examples — CLT, Binomial→Poisson, comparison plots