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.

Contents

The acgc package includes the following sub-modules. Import these via from acgc import <submodule> or import acgc.<submodule> as <alias>. Example: import acgc.gctools as gct. See help(acgc.<submodule>) for complete list of features.

• figstyle
  Changes Matplotlib style to make figures closer to publication ready.
• stats
  Collection of statistical methods. Useful functions include BivariateStatistics, line fitting methods (sma, sen, york), weighted statistics (wmean, wmedian, wcov, wcorr, etc.), partial_corr, among others. See help(acgc.stats) for complete list of methods.
• erroranalysis
  Automatic error propagation through complex models
• gctools
  Tools for handling GEOS-Chem model output
• hytools
  Tools for running HYSPLIT and reading HYSPLIT output
• icartt
  Tools for reading data in ICARTT format
• igra
  Tools for reading IGRA radiosonde data
• map_scalebar
  Add a length scale bar on a map
• mettools
  Miscelaneous functions for PBL properties
• modetools
  Visualization of eigenmode systems
• nctools
  High-level functions for reading and writing netCDF files. Legacy code. write_geo_nc is still useful for concisely creating netCDF files, but xarray is better for reading netCDF.
• solar
  Solar zenith angle, declination, equation of time
• time_tools
  Functions for calculating with dates. e.g. converting dates to fractional years. Legacy code. Use pandas.Timestamps or similar for new projects.

Demos

The demo folder contains examples of how to accomplish common data analysis and visualization tasks, including using many of the functions within the acgc library.