Reliability Engineering Toolkit for Python

Warnings

• breaking The API and functionality can change significantly between minor versions. For instance, version 0.8.1 is noted to be 'significantly different' from more recent versions. Users should consult the latest documentation when upgrading.
  Fix: Always refer to the official documentation for the specific version being used. Test code thoroughly after upgrading.
• gotcha Plotting functions in `reliability` are built upon `matplotlib`. To display plots, you must explicitly import `matplotlib.pyplot` and often call `plt.show()` after generating plots. Expecting plots to appear automatically without this setup is a common mistake.
  Fix: Ensure `import matplotlib.pyplot as plt` is present and `plt.show()` is called to render plots, especially in scripts.
• gotcha The library primarily uses an object-oriented paradigm where distributions are first instantiated as objects (e.g., `Weibull_Distribution`), and then methods are called on these objects for operations like sampling, fitting, or plotting. Users accustomed to a purely functional style (like parts of `scipy.stats`) might initially miss this pattern.
  Fix: Embrace the object-oriented design: create a distribution object, then use its methods (e.g., `dist.random_samples()`, `fit.distribution.SF()`).

Install

• pip install reliability Install latest version

Imports

• Weibull_Distribution

  from reliability.Distributions import Weibull_Distribution

• Fit_Weibull_2P

  from reliability.Fitters import Fit_Weibull_2P

• plot_points

  from reliability.Probability_plotting import plot_points

Quickstart

This quickstart demonstrates how to create a Weibull distribution, draw random samples, fit a 2-parameter Weibull distribution to the data, and then plot both the probability plot and the survival function using the library's object-oriented approach.

import matplotlib.pyplot as plt
from reliability.Distributions import Weibull_Distribution
from reliability.Fitters import Fit_Weibull_2P

# Create a Weibull Distribution object
dist = Weibull_Distribution(alpha=30, beta=2)

# Draw 20 random samples
data = dist.random_samples(20, seed=42)

plt.figure(figsize=(10, 5))
plt.subplot(121)
# Fit a 2-parameter Weibull Distribution to the samples
fit = Fit_Weibull_2P(failures=data, show_probability_plot=True)
plt.title('Weibull Probability Plot')

plt.subplot(122)
# Plot the survival function from the fitted distribution
fit.distribution.SF()
plt.title('Survival Function')

plt.tight_layout()
plt.show()