GeezerVison

This PR adds new functionality for computing the optimal estimate for a (nonlinear) I/O system using an explicit cost function of a fixed window of applied inputs and measured outputs. This optimal estimation problem is then used to create a system for solving moving horizon estimation (MHE) problems. Documentation and unit tests are included.

Summary of changes:

• Added the obc.OptimalEstimationProblem class and obc.create_mhe_iosystem() function, which implement that main new functionality.
• Added gaussian_likelyhood_cost() to create cost function corresponding to Gaussian likelihoods for use in optimal estimation.
• Added disturbance_range_constraint() to create a range constraint on disturbances.
• Moved processing of control and disturbance indices for estimation problems into iosys.py and use that functionality consistently for obc.create_estimator_iosystem and obc.OptimalEstimationProblem.
• Created a function for processing legacy keywords in config.py and updated legacy keyword processing class to use that functionality.
• Added a pytest mark for slow tests (@pytest.mark.slow), which can be used during development to skip slow tests by running pytest -m "not slow".
• Added unit tests, docstrings, and user documentation for new functionality, including a Jupyter notebook demonstrating optimal estimation and moving horizon estimation the PVTOL model system (from FBS2e and OBC notes).
• Additional small updates to docstrings to fix typos and formatting issues.