API Reference¶

soerp 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 soerp.umath (e.g., sin, exp, sqrt, etc.)
• Analytical derivatives up to second order
• Easy continuous distribution constructors:
  • normal(mu, sigma) or N(mu, sigma): Normal
  • uniform(a, b) or U(a, b): Uniform
  • exponential(lamda, [mu]) or Exp(lamda, [mu]): Exponential
  • gamma(k, theta) or Gamma(k, theta): Gamma
  • beta(alpha, beta, [a, b]) or Beta(alpha, beta, [a, b]): Beta
  • log_normal(mu, sigma) or LogN(mu, sigma): Log-normal
  • chi_squared(k) or Chi2(k): Chi-squared
  • f_distribution(d1, d2) or F(d1, d2): F-distribution
  • triangular(a, b, c) or Tri(a, b, c): Triangular
  • student_t(v) or T(v): T-distribution
  • weibull(lamda, k) or Weib(lamda, k): Weibull

Core Classes and Functions¶

• uv: Uncertain variable constructor (accepts moments or a scipy.stats distribution)
• normal, uniform, exponential, gamma, chi_squared, ...: Distribution functions
• N, U, Exp, Gamma, Chi2, ...: Same functions with uppercase names
• 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¶

soerp 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.