Examples

Every script in the examples folder is listed here. They are short, and the test suite keeps them working, so they can be copied and adapted as they stand.

The last column links the page of the manual that walks through the script; read that page with the script open beside it.

Script	What it shows	Explained in
evaluate.py	Evaluating variable vectors without optimizing	Running Optimizations
ensemble.py	Optimizing the mean objective over uncertain realizations	Ensemble-Based Optimization
constrained.py	Linear and nonlinear constraints	Constraints
discrete.py	Integer variables, solved with differential evolution	Discrete and Mixed-Integer Variables
mixed.py	Continuous and integer variables in one problem	Discrete and Mixed-Integer Variables
realization_filter.py	A custom filter that reweights realizations	Realization Filters
function_estimator.py	A custom estimator that aggregates realizations	Function Estimators
sampler.py	A custom sampler that perturbs one variable at a time	Samplers
metadata.py	Tagging an optimization, and recording per-realization data	Working with Results
callback_metadata.py	Drawing noise per batch in an event handler and passing it through event metadata	Running Optimizations
export.py	Exporting results to a pandas or polars frame	Working with Results
scaling.py	Reading results in the configured and the optimizer's units	Working with Results
handlers.py	Collecting results from optimizations that overlap in time	Result Handlers
tables.py	Collecting results into tables of both types with DataFrameHandler	Result Handlers
stopping.py	Stopping an optimization from the result callback	Running Optimizations
failures.py	What a failing realization does, and how to allow some	Troubleshooting
restart.py	Restarting from the best point, collecting every result	Restarting from the Best Point
initial_values.py	Restarting with a larger ensemble, reusing what is known there	Reusing Results at the Starting Point
parallel.py	Evaluating on a thread or process pool	Evaluating in Parallel
multiple_optimizations.py	Running several optimizations concurrently	Running Multiple Optimizations
nested_optimization.py	An inner optimization per outer evaluation, with its own pool	Nested Optimization
programs.py	Running a simulator from the objective, and stopping it on an abort	Running External Programs
simulator.py	A stand-in simulator, run by programs.py	Running External Programs
hpc.py	Submitting evaluations to a cluster queue	Evaluating in Parallel