From b915cc5dce8e42f0c63506b299ed67b054355e95 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 6 Nov 2024 11:43:24 -0800 Subject: [PATCH] Documentation updates --- .../api_ref/progpy/CompositeModel.doctree | Bin 20674 -> 20578 bytes .../api_ref/progpy/DataModel.doctree | Bin 389596 -> 388535 bytes .../api_ref/progpy/LinearModel.doctree | Bin 253150 -> 252161 bytes docs/.doctrees/api_ref/progpy/Loading.doctree | Bin 58957 -> 58483 bytes .../api_ref/progpy/Predictor.doctree | Bin 45958 -> 56944 bytes .../api_ref/progpy/PrognosticModel.doctree | Bin 285762 -> 284773 bytes docs/.doctrees/environment.pickle | Bin 2313647 -> 2315438 bytes docs/.doctrees/guide.doctree | Bin 22778 -> 29805 bytes docs/.doctrees/index.doctree | Bin 24771 -> 25897 bytes docs/.doctrees/prog_models_guide.doctree | Bin 172386 -> 173110 bytes .../playback.py | 2 +- docs/_images/ProgPyComponents.png | Bin 0 -> 32361 bytes docs/_sources/guide.rst | 18 + docs/_sources/index.rst | 20 +- 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\ No newline at end of file diff --git a/examples/playback.py b/examples/playback.py index e52099c..3140116 100644 --- a/examples/playback.py +++ b/examples/playback.py @@ -84,7 +84,7 @@ def future_loading(t, x=None): z = {'t': float(row[2]), 'v': float(row[3])} # State Estimation Step - filt.estimate(t, i, z) + filt.estimate(t, i, z) eod = batt.event_state(filt.x.mean)['EOD'] print(" - Event State: ", eod) diff --git a/sphinx-config/guide.rst b/sphinx-config/guide.rst index 229dac0..6a6e575 100644 --- a/sphinx-config/guide.rst +++ b/sphinx-config/guide.rst @@ -11,16 +11,20 @@ ProgPy Guide prog_server_guide The ProgPy framework consists of three key components that combine to create a flexible and extendible prognostics architecture. ---- + .. image:: images/ProgPyComponents.png + :align: center -1. The **Prognostics Models** are the backbone of the ProgPy architecture. Models describe the specific system that prognostics will be applied to and how the system will evolve with time. Everything else within ProgPy (e.g. simulation capabilities and prognostics tools) are built on top of a model. +1. + The **Prognostics Models** are the backbone of the ProgPy architecture. Models describe the specific system that prognostics will be applied to and how the system will evolve with time. Everything else within ProgPy (e.g. simulation capabilities and prognostics tools) are built on top of a model. -ProgPy supports models that are physics-based, data-driven, or hybrid. ProgPy includes some built-in models (see examples below) but is also written in an easily adaptable way so users can implement models specific to their use-cases. + ProgPy supports models that are physics-based, data-driven, or hybrid. ProgPy includes some built-in models (see examples below) but is also written in an easily adaptable way so users can implement models specific to their use-cases. -2. The **Prognostics Engine** encapsulates the complex logic of prognostics in a way that is modular and extendable. It includes the necessary tools to perform prognostics on the model, including state estimation, prediction, and uncertainty management. The modularity of the framework allows these capabilities to work with any model (built-in or user-defined) and the extensibility of the architecture allows users to additionally create their own methodologies. +2. + The **Prognostics Engine** encapsulates the complex logic of prognostics in a way that is modular and extendable. It includes the necessary tools to perform prognostics on the model, including state estimation, prediction, and uncertainty management. The modularity of the framework allows these capabilities to work with any model (built-in or user-defined) and the extensibility of the architecture allows users to additionally create their own methodologies. -3. The **Prognostics Support Tools** are a collection of capabilities to help users build new functionalities or understand prognostics results. +3. + The **Prognostics Support Tools** are a collection of capabilities to help users build new functionalities or understand prognostics results. These three key components come together to create the comprehensive framework that is ProgPy. More details will be shared in the coming pages. diff --git a/sphinx-config/index.rst b/sphinx-config/index.rst index 9075a31..0fd3988 100644 --- a/sphinx-config/index.rst +++ b/sphinx-config/index.rst @@ -38,6 +38,12 @@ Installing progpy $ pip install progpy + If you will be using the datadriven tools (e.g., LSTM model), install the datadriven dependencies as well using the following command: + + .. code-block:: console + + $ pip install progpy[datadriven] + .. tab:: Pre-Release Users who would like to contribute to ProgPy or would like to use pre-release features can do so using the `ProgPy GitHub repo `__. This isn't recommended for most users as this version may be unstable. To do this, use the following commands: @@ -49,6 +55,12 @@ Installing progpy $ git checkout dev $ pip install -e . + If you will be using the datadriven tools (e.g., LSTM model), install the datadriven dependencies as well using the following command: + + .. code-block:: console + + $ pip install -e '.[datadriven]' + Citing This Repository ----------------------- Use the following to cite this repository: diff --git a/sphinx-config/prog_models_guide.rst b/sphinx-config/prog_models_guide.rst index b0e8ef2..01f1436 100644 --- a/sphinx-config/prog_models_guide.rst +++ b/sphinx-config/prog_models_guide.rst @@ -323,6 +323,13 @@ State-transition Models The :py:func:`progpy.data_models.DataModel.from_data` and :py:func:`progpy.data_models.DataModel.from_model` methods are used to construct new models from data or an existing model (i.e., :term:`surrogate`), respectively. The use of these is demonstrated in the following examples. + .. note:: + To use a data-driven model distributed with progpy you need to install the data-driven dependencies. + + .. code-block:: console + + $ pip install progpy[datadriven] + * :download:`examples.lstm_model <../../progpy/examples/lstm_model.py>` .. automodule:: lstm_model diff --git a/src/progpy/composite_model.py b/src/progpy/composite_model.py index 1440202..126569c 100644 --- a/src/progpy/composite_model.py +++ b/src/progpy/composite_model.py @@ -16,7 +16,7 @@ class CompositeModel(PrognosticsModel): A CompositeModel is a PrognosticsModel that is composed of multiple PrognosticsModels. This is a tool for modeling system-of-systems. I.e., interconnected systems, where the behavior and state of one system effects the state of another system. The composite prognostics models are connected using defined connections between the output or state of one model, and the input of another model. The resulting CompositeModel behaves as a single model. Args: - models ({list[{PrognosticsModel, function}], list[tuple[str, {PrognosticsModel, function}]]): + models (list[PrognosticsModel or function] or list[tuple[str, PrognosticsModel or function]]): A list of PrognosticsModels to be combined into a single model. Provided in one of two forms: @@ -34,6 +34,8 @@ class CompositeModel(PrognosticsModel): Keyword Args: outputs (list[str]): Model outputs in format "model_name.output_name". Must be subset of all outputs from models. If not provided, all outputs will be included. + + """ def __init__(self, models: list, connections: list = [], **kwargs): diff --git a/src/progpy/predictors/monte_carlo.py b/src/progpy/predictors/monte_carlo.py index 405304b..929d38a 100644 --- a/src/progpy/predictors/monte_carlo.py +++ b/src/progpy/predictors/monte_carlo.py @@ -53,9 +53,9 @@ def predict(self, state: UncertainData, future_loading_eqn: Callable=None, event events : list[str], optional Events to predict (subset of model.events) e.g., ['event1', 'event2'] event_strategy: str, optional - Strategy for stopping evaluation. Default is 'all'. One of:\n - 'first': Will stop when first event in `events` list is reached. - 'all': Will stop when all events in `events` list have been reached + Strategy for stopping evaluation. Default is 'first'. One of:\n + * *first*: Will stop when first event in `events` list is reached.\n + * *all*: Will stop when all events in `events` list have been reached horizon : float, optional Prediction horizon (s) n_samples : int, optional diff --git a/src/progpy/predictors/predictor.py b/src/progpy/predictors/predictor.py index 0a52fd5..669cdb3 100644 --- a/src/progpy/predictors/predictor.py +++ b/src/progpy/predictors/predictor.py @@ -48,10 +48,20 @@ def predict(self, state: UncertainData, future_loading_eqn: Callable, **kwargs) Parameters ---------- - state : UncertainData + state : UncertainData Distribution representing current state of the system future_loading_eqn : function (t, x) -> z Function to generate an estimate of loading at future time t, and state x + options (optional, kwargs): configuration options\n + Any additional configuration values. Additional keyword arguments may be supported that are are specific to the predictor, but include \n + * t0: Starting time (s) + * dt : Step size (s) + * horizon : Prediction horizon (s) + * events : List of events to be predicted (subset of model.events, default is all events) + * event_strategy: str, optional + Strategy for stopping evaluation. Default is 'first'. One of:\n + * *first*: Will stop when first event in `events` list is reached.\n + * *all*: Will stop when all events in `events` list have been reached Return ---------- diff --git a/src/progpy/predictors/unscented_transform.py b/src/progpy/predictors/unscented_transform.py index a16f81a..d9541ab 100644 --- a/src/progpy/predictors/unscented_transform.py +++ b/src/progpy/predictors/unscented_transform.py @@ -141,6 +141,10 @@ def predict(self, state, future_loading_eqn: Callable = None, events=None, **kwa * dt : Step size (s) * horizon : Prediction horizon (s) * events : List of events to be predicted (subset of model.events, default is all events) + * event_strategy: str, optional + Strategy for stopping evaluation. Default is 'first'. One of:\n + * *first*: Will stop when first event in `events` list is reached.\n + * *all*: Will stop when all events in `events` list have been reached Returns (PredictionResults) ------- diff --git a/src/progpy/prognostics_model.py b/src/progpy/prognostics_model.py index 8d20bf5..462d85f 100644 --- a/src/progpy/prognostics_model.py +++ b/src/progpy/prognostics_model.py @@ -788,8 +788,8 @@ def simulate_to_threshold(self, future_loading_eqn: abc.Callable = None, first_o If blank, simulation will occur if any event will be met () event_strategy: str, optional Strategy for stopping evaluation. Default is 'first'. One of:\n - 'first': Will stop when first event in `events` list is reached. - 'all': Will stop when all events in `events` list have been reached + * *first*: Will stop when first event in `events` list is reached.\n + * *all*: Will stop when all events in `events` list have been reached t0 : float, optional Starting time for simulation in seconds (default: 0.0) \n dt : float, tuple, str, or function, optional @@ -1176,7 +1176,7 @@ def calc_error(self, times: List[float], inputs: List[InputContainer], outputs: * DTW (Dynamic Time Warping) x0 (StateContainer, optional): Initial state dt (float, optional): Maximum time step in simulation. Time step used in simulation is lower of dt and time between samples. Defaults to time between samples. - stability_tol (double, optional): Configurable parameter. + stability_tol (double, optional): Configurable cutoff Configurable cutoff value, between 0 and 1, that determines the fraction of the data points for which the model must be stable. In some cases, a prognostics model will become unstable under certain conditions, after which point the model can no longer represent behavior. stability_tol represents the fraction of the provided argument `times` that are required to be met in simulation, @@ -1185,6 +1185,7 @@ def calc_error(self, times: List[float], inputs: List[InputContainer], outputs: If the model goes unstable before stability_tol is met, a ValueError is raised. Else, model goes unstable after stability_tol is met, the mean squared error calculated from data up to the instability is returned. aggr_method (func, optional): When multiple runs are provided, users can state how to aggregate the results of the errors. Defaults to taking the mean. + short_sim_penalty (double, optional): Only for MSE method: penalty added for simulation becoming unstable before stability_tol, added for each % below tol. If set to None, operation will return an error if simulation becomes unstable before stability_tol. Default is 100 Returns: float: error