All Functions

Standard Workflow

The core class for managing reliability studies.

core.SimulationStudy

A workflow manager for simulation reliability assessment.

Graphical User Interface

Launch the standalone DigiQual dashboard locally.

dq_ui

User Interface for DigiQual Shiny Application

Functional API

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 a suite of statistical diagnostics to evaluate if the current sample size is sufficient.
diagnostics.ValidationError	Raised when simulation data fails validation checks.
adaptive.run_adaptive_search	Orchestrates the Active Learning loop on raw DataFrames using the Executor architecture.
adaptive.generate_targeted_samples	Active Learning Engine: Generates new samples based on diagnostic failures.
pod.fit_all_robust_mean_models	Fits all polynomial models (and optionally Kriging) and returns them for caching.
pod.generate_latex_equation	Extracts a LaTeX formatted equation from a fitted Polynomial Pipeline.
pod.optimise_bandwidth	Finds the optimal kernel smoothing bandwidth using Leave-One-Out Cross-Validation (LOO-CV).
pod.fit_variance_model	Calculates residuals and defines the smoothing bandwidth 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.
pod.calculate_reliability_point	Calculates the defect size (a90/95) where the Lower Confidence Bound
integration.compute_multi_dim_pod	Calculates the marginal Probability of Detection (PoD) across a grid of Parameters of Interest (PoI).
ahat.fit_linear_a_hat_model	Fits a simple linear regression model according to the standard a-hat vs a method.
ahat.compute_linear_pod_curve	Calculates the PoD curve using the standard a-hat vs a analytical assumptions.
ahat.bootstrap_linear_pod_ci	Estimates 95% Confidence Bounds for the classical linear PoD curve via Bootstrapping.
ahat.plot_linear_signal_model	Diagnostic Plot: Visualizes the standard linear a-hat vs a model.
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).
plotting.plot_pod_surface	Plots a 2D heatmap / contour for multi-dimensional PoD (2 Parameters of Interest).
plotting.plot_signal_surface	Result Plot 1 (Multi-Dimensional): Signal vs Parameters of Interest.
executors.Executor	THE BLUEPRINT:
executors.PythonExecutor	THE IN-MEMORY TRANSLATOR:
executors.CLIExecutor	THE HEAVY-DUTY TRANSLATOR (Process Isolation):
executors.MatlabExecutor	THE MATLAB TRANSLATOR: