Model 1: 2D 4-DOF Analytical Reference
- otaf.example_models.models_2_D.model1.eval_credal_set_constraints(x_std, tol=0.31, capa=1.0, X3=10.0)[source]
Evaluate the normalized credal set boundary conditions.
- Parameters:
x_std (np.ndarray) – A 1D array containing standard deviation vector values.
tol (float, default 0.31) – The baseline design tolerance.
capa (float, default 1.0) – The process capability standard multiplier.
X3 (float, default 10.0) – Geometric dimension value for plane evaluation.
- Returns:
An array containing the two normalized constraint scaling metrics.
- Return type:
np.ndarray
- otaf.example_models.models_2_D.model1.eval_scaled_credal_set_constraints(x_scaled, max_std_vect, tracker=None, experiment_key=None, tol=0.31, capa=1.0, X3=10.0)[source]
Map scaled deviations to real values and evaluate constraints.
- Parameters:
x_scaled (np.ndarray) – The scaled standard deviation vector inputs.
max_std_vect (np.ndarray) – The upper-bound limits for standard deviation mapping.
tracker (Any, optional) – Data logging tracker instance. Default is None.
experiment_key (Any, optional) – Unique identifier key for tracking logs. Default is None.
tol (float, default 0.31) – The baseline design tolerance.
capa (float, default 1.0) – The process capability standard multiplier.
X3 (float, default 10.0) – Geometric dimension value for plane evaluation.
- Returns:
The calculated constraint evaluation bounds array.
- Return type:
np.ndarray
- otaf.example_models.models_2_D.model1.get_distribution_params(tol=0.31, capa=1.0, X3=10.0)[source]
Compute defect distribution parameters and variance vectors.
- Parameters:
tol (float, default 0.31) – Tolerance limit value used to compute standard deviations.
capa (float, default 1.0) – Process capability index factor.
X3 (float, default 10.0) – Geometric dimension component used for rotation limits.
- Returns:
RandDeviationVect (otaf.distribution.ComposedDistribution) – The joint normal defect distribution model.
deviation_symbols (list of sympy.Symbol) – The symbolic tracking parameters for spatial deviations.
max_std_vect (np.ndarray) – A 1D array of calculated maximum standard deviations.
np.ndarray – A 1D array of zero-initialized mean parameter offsets.
- Return type:
tuple[Any, list[Symbol], ndarray, ndarray]
- otaf.example_models.models_2_D.model1.get_scaled_credal_set_constraints_function(max_std_vect, tracker=None, experiment_key=None, tol=0.31, capa=1.0, X3=10.0)[source]
Generate a wrapped lambda function for scaled constraints.
- Parameters:
max_std_vect (np.ndarray) – The upper-bound limits for standard deviation mapping.
tracker (Any, optional) – Data logging tracker instance. Default is None.
experiment_key (Any, optional) – Unique identifier key for tracking logs. Default is None.
tol (float, default 0.31) – The baseline design tolerance.
capa (float, default 1.0) – The process capability index multiplier.
X3 (float, default 10.0) – Geometric dimension value for plane evaluation.
- Returns:
A single-argument function mapping x_scaled to its evaluated constraint array.
- Return type:
Callable[[np.ndarray], np.ndarray]
- otaf.example_models.models_2_D.model1.get_system_of_constraints_assembly_model(X1=99.8, X2=100.0, X3=10.0)[source]
Construct the complete system of constraints assembly model.
- Parameters:
X1 (float, default 99.8) – Dimension parameter X1 passed to the assembly data.
X2 (float, default 100.0) – Dimension parameter X2 passed to the assembly data.
X3 (float, default 10.0) – Dimension parameter X3 passed to the assembly data.
- Returns:
The initialized assembly model with embedded optimization variables.
- Return type: