Metadata-Version: 2.1
Name: acgc
Version: 0.2.2
Summary: A collection of data analysis programs used by the Atmospheric Chemistry and Global Change (ACGC) research group
Author-email: Christopher Holmes <cdholmes@fsu.edu>
Maintainer-email: Christopher Holmes <cdholmes@fsu.edu>
License: GNU LESSER GENERAL PUBLIC LICENSE
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Project-URL: Homepage, https://github.com/cdholmes/acgc-python
Project-URL: Documentation, https://cdholmes.github.io/acgc-python
Project-URL: Repository, https://github.com/cdholmes/acgc-python
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Requires-Python: >=3.4
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib
Requires-Dist: matplotlib-inline
Requires-Dist: cartopy
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas
Requires-Dist: netCDF4
Requires-Dist: scikit-learn
Requires-Dist: statsmodels

# ACGC

## Overview
The acgc package is a collection of data analysis functions used by the Atmospheric Chemistry and Global Change Research Group (ACGC). Programs are written in Python 3.

## Installation

For conda users:

 `conda install -c conda-forge acgc`

For pip users:

 `pip install acgc`

#### For developers
If you plan to modify or improve the acgc package, an editable installation may be better:
`pip install -e git+https://github.com/cdholmes/acgc-python`
Your local files can then be managed with git, including keeping up-to-date with the github source repository (e.g. `git pull`).

#### Classic version
The old version of this package (before conversion to an importable python module) is accessible as the "classic" branch of this repository on github.

## Documentation

See https://cdholmes.github.io/acgc-python

## Demos
The [`demo`](./demo/) folder contains examples of how to accomplish common data analysis and visualization tasks, including using many of the functions within the `acgc` library.

