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Release/v1.1

- Python3 Support
- Addition of reanalysis schemas to the Sphinx documentation
- Easy import of EIA data using new module: Metadata_Fetch
- Updated contributing.md document
- Quality checks for reanalysis data
- Improved installation instructions
- Integration tests are now performed in CI
- Performed PEP8 linting
- Improved logging support in analysis methods
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readme.md

OpenOA

This library provides a generic framework for working with large timeseries data from wind plants. Its development has been motivated by the WP3 Benchmarking (PRUF) project, which aims to provide a reference implementaiton for plant-level performance assessment.

The implementation makes use of a flexible backend, so that data loading, processing, and analysis can be performed locally (e.g., with Pandas dataframes), in a semi-distributed manner (e.g., with Dask dataframes), or in a fully distributed matter (e.g., with Spark dataframes).

Analysis routines are grouped by purpose into methods, and these methods in turn rely on more abstract toolkits. In addition to the provided analysis methods, anyone can write their own, which is intended to provide natural growth of tools within this framework.

Requirements

  • Python 2.7+, 3.6+ (e.g., from Anaconda) with pip

We recommend creating a new virtual environment or Anaconda environment before attempting to install OpenOA. To create and activate such a new environment with the name "openoa-env" using Anaconda:

conda create --name openoa-env python=2.7
conda activate openoa-env

Microsoft Windows:

For users Microsoft Windows, the Anaconda python distribution is required. The reason is that pip on windows requires Visual Studio libraries to compile some of the dependencies. This can be resolved by manually installing the following packages via conda, which installs pre-built binaries of these dependencies, before attempting a pip install of OpenOA.

conda install shapely
conda install geos
conda install fiona

If errors about Visual Studio persist, you can try downloading the Microsoft Visual Studio compiler for Python: https://www.microsoft.com/en-us/download/details.aspx?id=44266

Installation:

Clone the repository and install the library and its dependencies using pip:

git clone https://github.com/NREL/OpenOA.git
pip install ./OpenOA

You should now be able to import operational_analysis from the Python interpreter:

python
>>> import operational_analysis

Testing

All tests are runnable from setuptools. They are written in the Python unittest framework.

To run unit tests with code coverage reporting:

cd ./OpenOA
python setup.py test

To run integration tests (longer running, requires data) first unzip the example data:

cd OpenOA/examples/operational_AEP_analysis/data
unzip eia_example_data.zip

cd OpenOA/examples/turbine_analysis/data
unzip example_20180829.zip

cd OpenOA

Then, you can run the integration test:

python setup.py integrate

To output junit xml from integration test (used for Jenkins testing):

python setup.py integrate -a "--junitxml=./path_to_outputfile.xml"

Documentation

Documentation is provided by sphinx. To (re)build the documentation:

pip install sphinx_rtd_theme ipython m2r

cd sphinx
make html

Development

We provide a frozen environment in a requirements.txt file which can be used to install the precise versions of each dependency present in our own development environment. We recommend utilizing a fresh virtual environment or Anaconda root before installing these requirements. To use requirements.txt:

pip install -r ./OpenOA/requirements.txt

Next, we recommend installing OpenOA in editable mode:

pip install -e ./OpenOA

Credit

Alphabetically: Anna Craig, Jason Fields, Travis Kemper, Joseph Lee, Monte Lunacek, John Meissner, Mike Optis, Jordan Perr-Sauer, Caleb Phillips, Eliot Quon, Sheungwen Sheng, Eric Simley, and Lindy Williams.

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