Metadata-Version: 2.1
Name: nrelpy
Version: 0.2.1
Summary: nrelpy: A tool for accessing NREL's rich library of data.
Home-page: http://github.com/samgdotson/nrelpy
Download-URL: 
Author: Samuel Dotson
Author-email: samgdotson@gmail.com
Maintainer: Samuel Dotson
Maintainer-email: samgdotson@gmail.com
License: BSD-3
Platform: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Requires: numpy
Requires: pandas
Requires: matplotlib
Requires: pytest
Requires: dill
Requires: openpyxl
Requires: pathlib
Requires-Python: >= 3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: pytest
Requires-Dist: dill
Requires-Dist: openpyxl
Requires-Dist: pathlib

[![Build Status](https://github.com/samgdotson/nrelpy/actions/workflows/python-app.yml/badge.svg)](https://github.com/samgdotson/nrelpy/actions/workflows/python-app.yml)
# nrelpy
Simple API to interact with the National Renewable Energy Laboratory's Annual Technology Baseline

## Features and Datasets

`nrelpy` currently enables access to the following datasets:
* Annual Technology Baseline
    - Transportation (2020)
    - Electricity (2019 - 2022)
* GIS Renewable Energy Potential (state-level resolution)

### Installing

This package may be installed from [PyPI](https://pypi.org/project/nrelpy/) with 

`pip install nrelpy`

### Using

The motivation for this API is to relieve researchers of the need to carry datasets
in their repositories. Therefore, the most basic function of `nrelpy` returns a 
dataset as a pandas dataframe. This basic usage is shown below.


#### ATB

```py
import nrelpy.atb as ATB

year = 2022
database = 'electricity'

df = ATB.as_dataframe(year=year, database=database)
```

#### Renewable Potential

```py
import nrelpy.re_potential as REP

df = REP.as_dataframe()
```

### Testing

From the top-level `nrelpy` directory, run `pytest`.  

You can also check the testing coverage with

```bash
pytest --cov-config=.coveragerc --cov=nrelpy
coverage html
```
`coverage html` creates a nicely formatted html page with 
the entire coverage report. Simply open the `htmlcov/index.html` file in your browser.

### Contributing

Contributors should clone the repository and install an editable installation.

```bash
git clone https://github.com/samgdotson/nrelpy.git

cd nrelpy

pip install -e .
```

*All pull requests must include appropriate, passing, tests.*

Issues and feature requests are welcome.
