lotsampling 0.2.0

Create, evaluate, and visualize zero-acceptance sampling plans.

# lotsampling

A Python library for creating, evaluating, and visualizing zero-acceptance acceptance sampling plans.

## Features

- Binomial sampling plans

- Hypergeometric sampling plans

- AOQL calculations

- Operating Characteristic (OC) curves

- Continuous sampling simulation

- Simulate escaped defects

- Visualize the Markov chain

- Streamlit demo application

## Installation

pip install lotsampling

## Example

from lotsampling import evaluate\_binomial\_plan



result = evaluate\_binomial\_plan(

    sample\_size=30,

    lot\_size=500,

)



print(result.aoql)

## Continuous sampling simulation

from lotsampling import simulate\_continuous\_sampling



result = simulate\_continuous\_sampling(

    selection\_probability=1 / 3,

    failure\_probability=0.05,

    runs=1000,

    random\_state=0,

)



print(result.state\_summary())



\## Simulate escaped defects



```python

from lotsampling import simulate\_sampling\_escapes



result = simulate\_sampling\_escapes(

    selection\_probability=1 / 3,

    failure\_probability=0.05,

    runs=1000,

    batch\_size=100,

    random\_state=0,

)



print(result.mean\_escapes)

print(result.escape\_rate)



\## Visualize the Markov chain



```python
import matplotlib.pyplot as plt



from lotsampling import plot\_continuous\_sampling\_chain



figure = plot\_continuous\_sampling\_chain(

    selection\_probability=1 / 3,

    failure\_probability=0.05,

)



plt.show()







\## License



MIT