Update documentation

sphinx-config/api_ref/prog_algs.rst → sphinx-config/api_ref/progpy.rst

@@ -1,4 +1,4 @@
-prog_algs API Reference
+ProgPy API Reference
 ==================================
 
 .. raw:: html
@@ -8,6 +8,8 @@ prog_algs API Reference
 .. toctree::
  :glob:
 
- prog_algs/* 
+ progpy/* 
 
 .. image:: ../images/prog_algs_UML.png
+
+.. image:: ../images/prog_models_UML.png

sphinx-config/api_ref/prog_models/CompositeModel.rst → sphinx-config/api_ref/progpy/CompositeModel.rst

@@ -1,5 +1,5 @@
 CompositeModel
 ================
 
-.. autoclass:: prog_models.CompositeModel
+.. autoclass:: progpy.CompositeModel
  :show-inheritance:

sphinx-config/api_ref/prog_models/DataModel.rst → sphinx-config/api_ref/progpy/DataModel.rst

@@ -8,9 +8,9 @@ The :py:class:`DataModel` class is the base class for all data-based models. It
 
 Examples:
 
-* :download:`examples.lstm_model <../../../../prog_models/examples/lstm_model.py>`
-* :download:`examples.full_lstm_model <../../../../prog_models/examples/full_lstm_model.py>`
-* :download:`examples.custom_model <../../../../prog_models/examples/custom_model.py>`
+* :download:`examples.lstm_model <../../../../progpy/examples/lstm_model.py>`
+* :download:`examples.full_lstm_model <../../../../progpy/examples/full_lstm_model.py>`
+* :download:`examples.custom_model <../../../../progpy/examples/custom_model.py>`
 
 Training DataModels
 -----------------------
@@ -24,7 +24,7 @@ This is the most common way to construct a :py:class:`DataModel` object, using t
 
  .. code-block:: python
 
- >>> from prog_models.models import LSTMStateTransitionModel
+ >>> from progpy.models import LSTMStateTransitionModel
  >>> input_data = [run1.inputs, run2.inputs, run3.inputs]
  >>> output_data = [run1.outputs, run2.outputs, run3.outputs]
  >>> m = LSTMStateTransitionModel.from_data(input_data, output_data)
@@ -37,8 +37,8 @@ Surrogate models are constructed using the :py:func:`DataModel.from_model` Class
 
  .. code-block:: python
 
- >>> from prog_models.models import ThrownObject
- >>> from prog_models.models import LSTMStateTransitionModel
+ >>> from progpy.models import ThrownObject
+ >>> from progpy.models import LSTMStateTransitionModel
  >>> m = ThrownObject()
  >>> def future_loading(t, x=None):
  >>> return m.InputContainer({}) # No input for thrown object 
@@ -56,30 +56,30 @@ Using Constructor
 **********************
 This method is the least frequently used, and it is very specific to the :py:class:`DataModel` class being constructed. For example: :py:class:`DMDModel` classes are constructed using the DMD Matrix, and :py:class:`LSTMStateTransitionModel` classes are constructed using a trained Keras Model.
 
-See example :download:`examples.custom_model <../../../../prog_models/examples/custom_model.py>`
+See example :download:`examples.custom_model <../../../../progpy/examples/custom_model.py>`
 
 Included DataModels
 -------------------------
 The following DataModels are included in the package. A new DataModel can be created by subclassing :py:class:`DataModel`, implementing the abstract methods of both :py:class:`DataModel` and :py:class:`PrognosticsModel`.
 
 DMDModel
 **************************
-.. autoclass:: prog_models.data_models.DMDModel
+.. autoclass:: progpy.data_models.DMDModel
  :members: from_data, from_model
 
 LSTMStateTransitionModel
 **************************
-.. autoclass:: prog_models.data_models.LSTMStateTransitionModel
+.. autoclass:: progpy.data_models.LSTMStateTransitionModel
  :members: from_data, from_model
 
 PolynomialChaosExpansion
 **************************
-.. autoclass:: prog_models.data_models.PolynomialChaosExpansion
+.. autoclass:: progpy.data_models.PolynomialChaosExpansion
  :members: from_data, from_model
 
 DataModel Interface
 ---------------------------
-.. autoclass:: prog_models.data_models.DataModel
+.. autoclass:: progpy.data_models.DataModel
  :members:
  :show-inheritance:
  :inherited-members:

sphinx-config/api_ref/progpy/DataSets.rst

@@ -0,0 +1,15 @@
+Datasets
+================================================================
+
+The `progpy` dataset subpackage is used to download labeled prognostics data for use in model building, analysis, or validation. Every dataset comes equipped with a `load_data` function which loads the specified data. Some datasets require a dataset number or id. This indicates the specific data to load from the larger dataset. The format of the data is specific to the dataset downloaded. Details of the specific datasets are summarized below:
+
+.. note:: To use the dataset feature, you must install the requests package.
+
+Variable Load Battery Data (nasa_battery)
+----------------------------------------------------
+.. autofunction:: progpy.datasets.nasa_battery.load_data
+
+CMAPSS Jet Engine Data (nasa_cmapss)
+----------------------------------------------------
+.. autofunction:: progpy.datasets.nasa_cmapss.load_data
+

sphinx-config/api_ref/prog_models/EnsembleModel.rst → sphinx-config/api_ref/progpy/EnsembleModel.rst

@@ -1,5 +1,5 @@
 EnsembleModel
 ================
 
-.. autoclass:: prog_models.EnsembleModel
+.. autoclass:: progpy.EnsembleModel
  :show-inheritance:

sphinx-config/api_ref/prog_models/IncludedModels.rst → sphinx-config/api_ref/progpy/IncludedModels.rst

@@ -1,6 +1,6 @@
 Included Models
 =================== 
-The :ref:`prog_models<prog_models Guide>` package is distributed with a few pre-constructed models that can be used in simulation or prognostics (with the :ref:`prog_algs <prog_algs Guide>` package). These models are summarized in the following sections.
+The progpy package is distributed with a few pre-constructed models that can be used in simulation or prognostics. These models are summarized in the following sections.
 
 .. .. contents:: 
 .. :backlinks: top
@@ -12,21 +12,21 @@ Battery Model
 
  .. tab:: ElectroChem (EOD)
 
- .. autoclass:: prog_models.models.BatteryElectroChemEOD
+ .. autoclass:: progpy.models.BatteryElectroChemEOD
 
  .. tab:: ElectroChem (EOL)
 
- .. autoclass:: prog_models.models.BatteryElectroChemEOL
+ .. autoclass:: progpy.models.BatteryElectroChemEOL
 
  .. tab:: ElectroChem (Combo)
 
- .. autoclass:: prog_models.models.BatteryElectroChem
+ .. autoclass:: progpy.models.BatteryElectroChem
 
- .. autoclass:: prog_models.models.BatteryElectroChemEODEOL
+ .. autoclass:: progpy.models.BatteryElectroChemEODEOL
 
  .. tab:: Circuit
 
- .. autoclass:: prog_models.models.BatteryCircuit
+ .. autoclass:: progpy.models.BatteryCircuit
 
 
 Pump Model
@@ -38,13 +38,13 @@ There are two variants of the pump model based on if the wear parameters are est
 
  .. tab:: Base Model
 
- .. autoclass:: prog_models.models.CentrifugalPumpBase
+ .. autoclass:: progpy.models.CentrifugalPumpBase
 
  .. tab:: With Wear As State
 
- .. autoclass:: prog_models.models.CentrifugalPump
+ .. autoclass:: progpy.models.CentrifugalPump
 
- .. autoclass:: prog_models.models.CentrifugalPumpWithWear
+ .. autoclass:: progpy.models.CentrifugalPumpWithWear
 
 Pneumatic Valve
 -------------------------------------------------------------
@@ -55,13 +55,13 @@ There are two variants of the valve model based on if the wear parameters are es
 
  .. tab:: Base Model
 
- .. autoclass:: prog_models.models.PneumaticValveBase
+ .. autoclass:: progpy.models.PneumaticValveBase
 
  .. tab:: With Wear As State
 
- .. autoclass:: prog_models.models.PneumaticValve
+ .. autoclass:: progpy.models.PneumaticValve
 
- .. autoclass:: prog_models.models.PneumaticValveWithWear
+ .. autoclass:: progpy.models.PneumaticValveWithWear
 
 DC Motor
 -------------------------------------------------------------
@@ -70,30 +70,30 @@ DC Motor
 
  .. tab:: Single Phase
 
- .. autoclass:: prog_models.models.DCMotorSP
+ .. autoclass:: progpy.models.DCMotorSP
 
  .. tab:: Triple Phase
 
- .. autoclass:: prog_models.models.DCMotor
+ .. autoclass:: progpy.models.DCMotor
 
 ESC
 -------------------------------------------------------------
-.. autoclass:: prog_models.models.ESC
+.. autoclass:: progpy.models.ESC
 
 Powertrain
 -------------------------------------------------------------
-.. autoclass:: prog_models.models.Powertrain
+.. autoclass:: progpy.models.Powertrain
 
 PropellerLoad
 -------------------------------------------------------------
-.. autoclass:: prog_models.models.PropellerLoad
+.. autoclass:: progpy.models.PropellerLoad
 
 Aircraft Models
 -------------------------------------------------------------
-Aircraft model simulate the flight of an aircraft. All aircraft models inherit from :py:class:`prog_models.models.aircraft_model.AricraftModel`. Included models are listed below:
+Aircraft model simulate the flight of an aircraft. All aircraft models inherit from :py:class:`progpy.models.aircraft_model.AricraftModel`. Included models are listed below:
 
-.. autoclass:: prog_models.models.aircraft_model.SmallRotorcraft
+.. autoclass:: progpy.models.aircraft_model.SmallRotorcraft
 
 ThrownObject
 -------------------------------------------------------------
-.. autoclass:: prog_models.models.ThrownObject
+.. autoclass:: progpy.models.ThrownObject

sphinx-config/api_ref/prog_models/LinearModel.rst → sphinx-config/api_ref/progpy/LinearModel.rst

@@ -1,7 +1,7 @@
 LinearModel
 =================
 
-.. autoclass:: prog_models.LinearModel
+.. autoclass:: progpy.LinearModel
  :members:
  :inherited-members:
  :exclude-members: SimulationResults, generate_model, observables

sphinx-config/api_ref/prog_models/Loading.rst → sphinx-config/api_ref/progpy/Loading.rst

@@ -1,7 +1,7 @@
 Loading
 =========
 
-The loading subpackage includes some classes for complex load estimation algorithms. See :download:`examples.future_loading <../../../../prog_models/examples/future_loading.py>` for more details.
+The loading subpackage includes some classes for complex load estimation algorithms. See :download:`examples.future_loading <../../../../progpy/examples/future_loading.py>` for more details.
 
 Load Estimator Class interface
 ------------------------------
@@ -32,15 +32,15 @@ The second approach for load estimators is a load estimation class. This is used
 Load Estimator Classes
 ----------------------
 
-.. autoclass:: prog_models.loading.Piecewise
+.. autoclass:: progpy.loading.Piecewise
 
-.. autoclass:: prog_models.loading.MovingAverage
+.. autoclass:: progpy.loading.MovingAverage
 
-.. autoclass:: prog_models.loading.GaussianNoiseLoadWrapper
+.. autoclass:: progpy.loading.GaussianNoiseLoadWrapper
 
 Controllers
 ------------------
 
-.. autoclass:: prog_models.loading.controllers.LQR
+.. autoclass:: progpy.loading.controllers.LQR
 
-.. autoclass:: prog_models.loading.controllers.LQR_I
+.. autoclass:: progpy.loading.controllers.LQR_I

sphinx-config/api_ref/prog_algs/Prediction.rst → sphinx-config/api_ref/progpy/Prediction.rst

@@ -8,13 +8,13 @@ Two types of predictions are distributed with this package: `Prediction` and `Un
 .. tabs::
  .. tab:: Prediction
 
- .. autoclass:: prog_algs.predictors.Prediction
+ .. autoclass:: progpy.predictors.Prediction
  :members:
  :inherited-members: 
 
  .. tab:: UnweightedSamplesPrediction
 
- .. autoclass:: prog_algs.predictors.UnweightedSamplesPrediction
+ .. autoclass:: progpy.predictors.UnweightedSamplesPrediction
  :members:
  :inherited-members:
  :exclude-members: append, extend, clear, pop, remove, reverse, insert

sphinx-config/api_ref/prog_algs/Predictor.rst → sphinx-config/api_ref/progpy/Predictor.rst

@@ -1,15 +1,15 @@
 Predictors
 ===========================
 
-The :py:class:`Predictor` uses a state estimate (type :py:class:`UncertainData` subclass, output of a :py:class:`StateEstimator`), information about expected future loading, and a :py:class:`PrognosticsModel` (see: `prog_models package <https://github.com/nasa/prog_models>`__) to predict both future states (also outputs, performance metrics, event_states) at predefined points and the time that an event will occur (Time of Event, ToE) with uncertainty.
+The :py:class:`Predictor` uses a state estimate (type :py:class:`UncertainData` subclass, output of a :py:class:`StateEstimator`), information about expected future loading, and a :py:class:`PrognosticsModel` to predict both future states (also outputs, performance metrics, event_states) at predefined points and the time that an event will occur (Time of Event, ToE) with uncertainty.
 
 Here's an example of its use. In this example we use the :py:class:`ThrownObject` model and the :py:class:`MonteCarlo` predictor, and we save the state every 1s. We also use a scalar first state (i.e., no uncertainty).
 
 .. code-block:: python
 
  >>> from prog_models.models import ThrownObject
- >>> from prog_algs.predictors import MonteCarlo
- >>> from prog_algs.uncertain_data import ScalarData
+ >>> from progpy.predictors import MonteCarlo
+ >>> from progpy.uncertain_data import ScalarData
  >>>
  >>> m = ThrownObject()
  >>> pred = MonteCarlo(m)
@@ -30,14 +30,14 @@ The following predictors are included with this package. A new predictor can be
 
  .. tab:: Monte Carlo Predictor
 
- .. autoclass:: prog_algs.predictors.MonteCarlo
+ .. autoclass:: progpy.predictors.MonteCarlo
 
  .. tab:: Unscented Transform Predictor
 
- .. autoclass:: prog_algs.predictors.UnscentedTransformPredictor
+ .. autoclass:: progpy.predictors.UnscentedTransformPredictor
 
 Predictor Interface
 -----------------------
-.. autoclass:: prog_algs.predictors.Predictor
+.. autoclass:: progpy.predictors.Predictor
  :members:
  :inherited-members:

sphinx-config/api_ref/prog_models/PrognosticModel.rst → sphinx-config/api_ref/progpy/PrognosticModel.rst

@@ -1,7 +1,7 @@
 PrognosticsModel
 ====================
 
-.. autoclass:: prog_models.PrognosticsModel
+.. autoclass:: progpy.PrognosticsModel
  :members:
  :inherited-members:
  :exclude-members: SimulationResults, generate_model, observables

sphinx-config/api_ref/prog_models/SimResult.rst → sphinx-config/api_ref/progpy/SimResult.rst

@@ -1,7 +1,7 @@
 SimResult
 ================================
 
-.. autoclass:: prog_models.sim_result.SimResult
+.. autoclass:: progpy.sim_result.SimResult
  :members:
  :inherited-members:
  :exclude-members: append, reverse, count, insert