prog_models v1.3

Release v1.3

• 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.
• New Example Models Added new DCMotor, ESC, and Powertrain models to prog_models.models (See examples.powertrain.py`).
• 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.

Acknowledgments

Thank you to our intern Lawrence Hwang (@lawrence-hwang) for his help with this release.

This release includes contributions from NASA's Autonomous Spacecraft Operations (ASO), Data and Reasoning Fabric (DRF), and System Wide Safety (SWS) projects. Thank you for your support!