1'''Data analysis programs from the ACGC research group
  2
  3# Installation
  4
  5For conda users:
  6
  7 `conda install -c conda-forge acgc`
  8
  9For pip users:
 10
 11 `pip install acgc`
 12
 13### For developers
 14If you plan to modify or improve the acgc package, an editable installation may be better:
 15`pip install -e git+https://github.com/cdholmes/acgc-python`
 16Your local files can then be managed with git, including keeping up-to-date with the 
 17github source repository (e.g. `git pull`).
 18
 19<!-- ----------------------- SECTION BREAK ----------------------- -->
 20
 21# Get started
 22
 23Submodules within `acgc` contain all the capabilities of the package.
 24Submodules are imported via `import acgc.<submodule>` or `from acgc import <submodule>`. 
 25
 26## Better looking figures 
 27
 28The default appearance of figures from Matplotlib doesn't meet the standards of most 
 29scientific journals (high-resolution, Helvetica-like font). With the `acgc.figstyle` module,
 30figures meet these criteria. Simply use `from acgc import figstyle` before creating your figures.
 31
 32[Example using acgc.figstyle](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_figstyle.ipynb)
 33
 34## Bivariate statistics 
 35
 36The `BivariateStatistics` class makes it easy to compute and display a large number of
 37bivariate statistics, including line fitting and weighted statistics. 
 38Results can be easily formatted into tables or inset in figures. Use `from acgc.stats import BivariateStatistics`
 39See `acgc.stats.bivariate.BivariateStatistics` for documentation.
 40
 41[Example using BivariateStatistics](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_stats.ipynb)
 42
 43## Standard major axis (SMA) line fitting
 44
 45SMA line fitting (also called reduced major axis or RMA) quantifies the linear relationship 
 46between variables in which neither one depends on the other.
 47It is available via `from acgc.stats import sma`. 
 48
 49[Example using SMA](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_sma.ipynb)
 50
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 52
 53# Demos
 54The [demo](https://github.com/cdholmes/acgc-python/blob/main/demo) 
 55folder contains examples of how to accomplish common data analysis and visualization tasks. 
 56The examples include uses of the `acgc` library as well as other libraries for 
 57geospatial data analysis.
 58
 59<!-- ----------------------- SECTION BREAK ----------------------- -->
 60
 61# Quick summary of submodules
 62
 63## Key submodules
 64
 65- `acgc.figstyle`    
 66Style settings for matplotlib, for publication-ready figures.
 67[demo](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_figstyle.ipynb)
 68
 69- `acgc.stats`    
 70Collection of statistical methods. 
 71[demo](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_stats.ipynb) 
 72
 73## Other submodules
 74
 75- `acgc.erroranalysis`   
 76Propagation of error through complex numerical models
 77
 78- `acgc.geoschem` or `acgc.gc`       
 79Tools for GEOS-Chem grids (e.g. indexing, remapping, interpolating)
 80
 81- `acgc.hysplit`	        
 82Read HYSPLIT output and write HYSPLIT CONTROL files
 83
 84- `acgc.icartt`	        
 85Read and write ICARTT format files
 86
 87- `acgc.igra`		        
 88Read IGRA radiosonde data files
 89
 90- `acgc.mapping`        
 91Distance calculation, scale bar for display on maps
 92
 93- `acgc.met`        
 94Miscelaneous functions for PBL properties
 95
 96- `acgc.modetools`	    
 97Visualization of eigenmode systems
 98
 99- `acgc.netcdf`          
100High-level functions for reading and writing netCDF files. Legacy code. 
101`acgc.netcdf.write_geo_nc` is still useful for concisely creating netCDF files, 
102but xarray is better for reading netCDF.
103
104- `acgc.solar`        
105Solar zenith angle, azimuth, declination, equation of time
106
107- `acgc.time`       
108Functions for manimulating times and dates. Legacy code.
109'''
110
111def _package_version(package_name):
112    '''Find version string for package name'''
113    from importlib.metadata import version, PackageNotFoundError
114    try:
115        result = version(package_name)
116    except PackageNotFoundError:
117        result = "unknown version"
118    return result
119
120__version__ = _package_version('acgc')
121
122__all__ = [
123    'erroranalysis',
124    'figstyle',
125    'gc',
126    'geoschem',
127    'hysplit',
128    'icartt',
129    'igra',
130    'mapping',
131    'met',
132    'modetools',
133    'netcdf',
134    'stats',
135    'solar',
136    'time'
137]