Release Notes

• Surrogate Models Added initial draft of new feature to generate surrogate models automatically from PrognosticsModel (See examples.generate_surrogate.py). Initial implementation uses Dynamic Mode Decomposition. Additional Surrogate Model Generation approaches will be explored for future releases. [Developed by NASA's DRF Project]
• New Example Models Added new DCMotor, ESC, and Powertrain models to prog_models.models (See examples.powertrain.py`) [Developed by NASA's SWS Project]
• Datasets Added new feature that allows users to access prognostic datasets programmatically (See examples.dataset.py)
• Added new LinearModel class - Linear Prognostics Models can be represented by a Linear Model. Similar to PrognosticsModels, LinearModels are created by subclassing the LinearModel class. Some algorithms will only work with Linear Models. See linear_model.py example for detail
• Added new StateContainer/InputContainer/OutputContainer objects for classes which allow for data access in matrix form and enforce expected keys.
• Added new metric for SimResult: Monotonicity
• SimResult.plot() now automatically shows legends
• Added drag to ThrownObject model, making the model non-linear. Degree of nonlinearity can be effected using the model parameters (e.g., coefficient of drag cd).
• observables from previous releases are now called performance_metrics
• model.simulate_to* now returns named tuple, allowing for access by property name (e.g., result.states)
• Updates to SimResult and LazySimResult for robustness
• Various performance improvements and bug fixes

Note

Now input, states, and output should be represented by model.InputContainer, StateContainer, and OutputContainer, respectively

Note

Python 3.6 is no longer supported.