.. TwinStat documentation master file, created by
   sphinx-quickstart on Tue May 30 11:38:35 2023.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

Welcome to TwinStat's documentation!
====================================

.. toctree::
   :maxdepth: 2
   :caption: Contents:

	modules

Indices and tables
==================

Browse the API:

* :ref:`modindex`

Or search for something specific:

* :ref:`search`




A data science python library (TwinStat) is needed as a toolbox to be used by a variety of other AWS services ranging from Twinmaker, do-pm, SimSpace Weaver, SageMaker, etc.  TwinStat was designed as a toolbox for the TwinFlow Level 4 Digital Twin Framework. The leveling framework can be read about here: https://aws.amazon.com/blogs/iot/digital-twins-on-aws-unlocking-business-value-and-outcomes/


Features
==================

TwinStat includes the following methods:

- Bayesian Estimation and Posterior Distribution Calculation
	- Standard Kalman Filters
	- Smoothing Kalman Filters
	- Adaptive Kalman Filters
	- Unscented Kalman Filters
	- Particle Filters
- Global Heuristic Optimization
    - Genetic Algorithm 
- Sensitivity Analysis
    - Using shapely sensitivities 
- Uncertainty Propagation 
	- polynomial chaos expansion
	- brute monte carlo
- Time Series Analysis
	- AutoArch
	- Auto-regressive neural networks
		- feedforward architecture generation
		- both mean and quantiles
- Quantile K-Nearest Neighbor
- Outlier removal
- Linear Regressions (CPU / GPU)
- AutoML wrappers around (AutoGluon)
- Probablistic IoT deviation checks
	- Example: determine if incoming IoT data is no longer supported by an existing training set
- Gaussian Process Models
	- includes templates for using physics based mean functions
	- update functionality not requiring retraining for streaming data

Requirements: 
==================

- Supported Operating Systems: Linux, Windows
- Python 3.10+

Installation
==================

.. code-block:: python

	git clone git@ssh.gitlab.aws.dev:autonomouscomputesateam/twinstat.git
	cd twinstat/dist
	pip install ./*.whl



API Documentation
==================

Auto-documentation can be found here:

https://gitlab.aws.dev/autonomouscomputesateam/twinstat/-/tree/main/docs/_build/html

Users can:

.. code-block:: python

	git clone git@ssh.gitlab.aws.dev:autonomouscomputesateam/twinstat.git
	cd twinstat/docs/_build/html

View the index.html to review API documentation.

Example
==================

Full tutorials to be published on AWS Samples in Q4 2023.

Short example:

.. code-block:: python

	from twinstat.ts_forecast.AR_NN_models import AR_quantile_neural_network
	ANN = AR_quantile_neural_network(AR=ar,
				n_exog_variables= n_exo,
				hidden_units=16,
				tau=safety_bias,
				include_endog=False)
	ANN.train(var, X=X, patience=50)
	print("Training complete")
	ANN.save_model(weights_path + '/corrector_weights')
	print("model and figures saved")
	ANN.plot(save_fig=True)


License
==================

This repository is released under the MIT-0 License. See the LICENSE file for details.