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.. Getting started
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.. .. _installing-docdir:

Getting Started with DSWE
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`DSWE <https://github.com/TAMU-AML/DSWE-Python>`_ is a python package, written by `Professor Yu Ding's <https://aml.engr.tamu.edu/>`_ research team, to supplement the Data Science for Wind Energy (`DSWE <https://aml.engr.tamu.edu/book-dswe/>`_) book and other state-of-the-art methods used in wind energy applications.

DSWE is on `PyPI <https://pypi.org/project/dswe/>`_.

DSWE Python HELP Document is available via this `link <https://zenodo.org/record/6940865>`_.

Dependencies
*************

DSWE requires:

* Python (>=3.6)
* NumPy (>=1.21.2)
* Pandas (>=1.3.3)
* Scikit-learn (>=1.0)
* Scipy (>=1.7.0)
* Statsmodels (>=0.13.0)
* PyTorch (>=1.0.0)
* Matplotlib (>=3.4.3)

All the required packages don't need to be pre-installed. Installing DSWE would automatically install everything.

Install DSWE
*************

.. code:: console

    $ pip install dswe


To get the latest code changes as they are merged, you can clone this repo and build from source manually.

.. code:: console

    $ git clone https://github.com/TAMU-AML/DSWE-Python/
    $ pip install DSWE-Python/

.. attention:: 

    - **AMK**: The optimal bandwidth selection algorithm i.e., the direct plug-in (DPI) approach, is not implemented yet. You need to provide bandwidth corresponding to each column.
    - **BayesTreePowerCurve**: This module is built on top BartPy which is a python implementation of the Bayesian additive regressions trees (BART). To use the BayesTreePowerCurve model, you need to install the BartPy manually. The BartPy package has not been updated for a long time and simple ``pip install bartypy`` sometimes does not work. You have to explicitly clone the repo and build from source manually. 

You can follow the following steps to install BartPy package.

.. code:: console

    $ git clone https://github.com/JakeColtman/bartpy
    $ pip install bartpy/

References
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* `Data Science for Wind Energy book. <https://aml.engr.tamu.edu/book-dswe/>`_
* **AMK**: Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” Journal of the American Statistical Association, Vol. 110, pp. 56-67. `[pdf] <https://aml.engr.tamu.edu/wp-content/uploads/sites/164/2017/11/J53.pdf>`_
* **TempGP**: Prakash, Tuo, and Ding, 2022, “The temporal overfitting problem with applications in wind power curve modeling,” Technometrics, accepted. `[preprint] <https://arxiv.org/abs/2012.01349>`_
* **CovMatch**: Shin, Ding, and Huang, 2018, “Covariate matching methods for testing and quantifying wind turbine upgrades,” Annals of Applied Statistics, Vol. 12(2), pp. 1271-1292. `[accepted version] <http://aml.engr.tamu.edu/wp-content/uploads/sites/164/2017/11/J64_accepted.pdf>`_
* **FunGP**: Prakash, Tuo, and Ding, 2022, “Gaussian process aided function comparison using noisy scattered data,” Technometrics, Vol. 64, pp. 92-102. `[preprint] <http://aml.engr.tamu.edu/wp-content/uploads/sites/164/2001/09/J78_Main.pdf>`_
* **ComparePCurve**: Ding, Kumar, Prakash, Kio, Liu, Liu, and Li, 2021, “A case study of space-time performance comparison of wind turbines on a wind farm,” Renewable Energy, Vol. 171, pp. 735-746. `[preprint] <https://arxiv.org/abs/2005.08652>`_
* **DNNPowerCurve**: The DNNPowerCurve function is partially based on Karami, Kehtarnavaz, and Rotea, 2021, "Probabilistic neural network to quantify uncertainty of wind power estimation," arXiv:2106.04656. `[preprint] <https://arxiv.org/abs/2106.04656>`_.  Our team refined the network architecture and tuned the training parameters.