All Functions

Standard Workflow

The core class for managing reliability studies.

A workflow manager for simulation reliability assessment.

Launch the standalone DigiQual dashboard locally.

User Interface for DigiQual Shiny Application

Standalone tools for Sampling, Diagnostics, PoD, and Plotting.

sampling.generate_lhs	Generates a Latin Hypercube Sample and scales it to the provided variable bounds.
diagnostics.validate_simulation	Validates simulation data, coercing to numeric and removing invalid rows.
diagnostics.sample_sufficiency	Performs statistical tests on sampling sufficiency.
diagnostics.ValidationError	Raised when simulation data fails validation checks.
adaptive.run_adaptive_search	Orchestrates the Active Learning loop on raw DataFrames.
adaptive.generate_targeted_samples	Active Learning Engine: Generates new samples based on diagnostic failures.
pod.fit_robust_mean_model	Fits regression models (Polynomials and Kriging) and selects the optimal one.
pod.fit_variance_model	Calculates residuals and prepares the grid for variance estimation.
pod.infer_best_distribution	Selects the best statistical distribution for the standardized residuals using AIC.
pod.plot_model_selection	Generates a normalized bar chart of the Bias-Variance Tradeoff from CV scores,
pod.predict_local_std	Estimates the local standard deviation using Gaussian Kernel Smoothing.
pod.compute_pod_curve	Calculates the Probability of Detection (PoD) curve.
pod.bootstrap_pod_ci	Estimates 95% Confidence Bounds for the PoD curve via Bootstrapping.
plotting.plot_signal_model	Diagnostic Plot 1: Signal vs Parameter of Interest (The Physics).
plotting.plot_pod_curve	Result Plot 2: Probability of Detection (The Reliability).