PyReliabilityPro: A Python Toolkit for Reliability Analysis

PyReliabilityPro is a lightweight, open-source Python toolkit designed for engineers, data scientists, and students to perform common reliability engineering calculations and analyses. The project focuses on providing a clean, intuitive API for statistical analysis of failure data, backed by a robust, modern development workflow.

This project was developed as a comprehensive portfolio piece to showcase skills in Python software development, Test-Driven Development (TDD), Quality Assurance (QA) best practices, and CI/CD automation with GitHub Actions.

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Key Features

• Weibull Distribution Analysis:
  • Parameter Estimation: Estimate 2-parameter (beta, eta) or 3-parameter (beta, eta, gamma) Weibull parameters from failure data using Maximum Likelihood Estimation (MLE) via scipy.stats.
  • Descriptive Functions: Calculate the Probability Density Function (PDF), Cumulative Distribution Function (CDF), Survival Function (SF), and Hazard Function (HF).
• Reliability Metrics:
  • Calculate the theoretical Mean Time To Failure (MTTF) for a given Weibull distribution.
  • Calculate the sample MTTF for data assumed to follow an exponential distribution.
• Exceptional Code Quality & QA Focus:
  • High Test Coverage: Achieved over 95% code coverage with a comprehensive suite of unit tests using pytest. Tests cover normal functionality, edge cases, and input validations.
  • Static Analysis: The codebase is automatically checked for code style (flake8), formatting consistency (black), and type safety (mypy) on every commit.
• Modern CI/CD Pipeline:
  • A full-featured Continuous Integration pipeline built with GitHub Actions.
  • Automated Workflow: On every push and pull request to the main branch, the pipeline automatically:
    1. Installs dependencies.
    2. Runs linters and formatters to check code quality.
    3. Executes the entire test suite across multiple Python versions (3.8, 3.9, 3.10, 3.11).
    4. Generates a code coverage report and uploads it to Codecov for analysis and visualization.

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Installation

(Note: Once published to PyPI, this will be the primary installation method.)

To install PyReliabilityPro, you can use pip:

pip install pyreliabilitypro 

Alternatively, to install the latest development version directly from GitHub:

pip install git+https://github.com/Santtoh19/PyReliabilityPro.git

Quick Start / Usage Example

Here's a simple example of how to use the toolkit to fit a 2-parameter Weibull distribution to some failure data and then analyze it.

import pyreliabilitypro as rel
import numpy as np

# 1. Sample failure data (e.g., in hours)
failure_times = [105, 120, 135, 160, 175, 190, 210, 230, 255, 280]

# 2. Estimate the 2-parameter Weibull parameters from the data
# The weibull_fit function returns (beta, eta, gamma)
# For a 2P fit, gamma will be 0.0.
try:
    beta_est, eta_est, _ = rel.weibull_fit(failure_times)
    print(f"Estimated Beta (Shape): {beta_est:.2f}")
    print(f"Estimated Eta (Scale / Characteristic Life): {eta_est:.2f} hours")

    # 3. Use the estimated parameters to analyze reliability
    
    # Calculate the probability of failure by 150 hours (CDF)
    prob_fail_by_150 = rel.weibull_cdf(x=150, beta=beta_est, eta=eta_est)
    print(f"Probability of failure by 150 hours: {prob_fail_by_150:.2%}")

    # Calculate the reliability (probability of survival) at 150 hours (SF)
    reliability_at_150 = rel.weibull_sf(x=150, beta=beta_est, eta=eta_est)
    print(f"Reliability (survival probability) at 150 hours: {reliability_at_150:.2%}")

    # Calculate the instantaneous failure rate (hazard rate) at 150 hours
    hazard_at_150 = rel.weibull_hf(x=150, beta=beta_est, eta=eta_est)
    print(f"Hazard Rate at 150 hours: {hazard_at_150:.4f} (failures/hour)")

    # Calculate the Mean Time To Failure (MTTF) for this distribution
    mttf = rel.weibull_mttf(beta=beta_est, eta=eta_est)
    print(f"Calculated MTTF for this distribution: {mttf:.2f} hours")

except ValueError as e:
    print(f"An error occurred: {e}")

Development & Contribution

This project is built with modern Python development practices. To set up a local development environment: Clone the repository:

git clone https://github.com/Santtosh19/PyReliabilityPro.git
cd PyReliabilityPro

Create and activate a virtual environment:

python -m venv .venv
# On Windows (PowerShell):
# .\.venv\Scripts\Activate.ps1
# On macOS/Linux:
# source .venv/bin/activate

Install all dependencies:

pip install -r requirements.txt
pip install -r requirements-dev.txt

Run checks and tests locally:

# Run code style and quality checks
flake8 .
black --check .
mypy pyreliabilitypro --ignore-missing-imports

# Run the full test suite
pytest