API Reference¶

soerp3 provides a Python implementation of the SOERP method (Cox 1979) for second-order error propagation. See the Theory and Quickstart for mathematical background and usage.

Main Features¶

• Transparent calculations with automatic derivatives
• Basic NumPy support
• Nearly all standard math module functions supported via soerp3.umath (e.g., sin, exp, sqrt, etc.)
• Analytical derivatives up to second order
• Easy continuous distribution constructors:
  • N(mu, sigma): Normal
  • U(a, b): Uniform
  • Exp(lamda, [mu]): Exponential
  • Gamma(k, theta): Gamma
  • Beta(alpha, beta, [a, b]): Beta
  • LogN(mu, sigma): Log-normal
  • Chi2(k): Chi-squared
  • F(d1, d2): F-distribution
  • Tri(a, b, c): Triangular
  • T(v): T-distribution
  • Weib(lamda, k): Weibull

Core Classes and Functions¶

• uv: Uncertain variable constructor (accepts moments or a scipy.stats distribution)
• N, U, Exp, Gamma, Chi2, ...: Distribution shortcuts for common continuous distributions
• umath: Math functions for uncertain variables
• describe(): Print mean, variance, skewness, kurtosis
• moments(): Return moments of a variable
• d(), d2(), d2c(): First and second derivatives, mixed derivatives
• gradient(), hessian(): Vector/matrix of derivatives
• error_components(pprint=True/False): Variance decomposition and error component breakdown

Example Workflows¶

soerp3 supports both direct moment input and distribution-based construction. You can:

• Create uncertain variables from moments, scipy.stats distributions, or constructors
• Combine variables using arithmetic and math functions
• Compute and print all moments, derivatives, and error components

See the Quickstart for full code examples, including:

• Assembly stack-up
• Orifice flow
• Manufacturing tolerance stackup
• Scheduling facilities
• Two-bar truss

All examples demonstrate both moment-based and distribution-based usage, as well as advanced features like error decomposition.