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 ([example](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_figstyle.ipynb))
 27
 28
 29The default appearance of figures from Matplotlib doesn't meet the standards of most 
 30scientific journals (high-resolution, Helvetica-like font). With the `acgc.figstyle` module,
 31figures meet these criteria. Simply use `from acgc import figstyle` before creating your figures.
 32
 33## Bivariate statistics ([example](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_stats.ipynb))
 34
 35The `BivariateStatistics` class makes it easy to compute and display a large number of
 36bivariate statistics, including line fitting and weighted statistics. 
 37Results can be easily formatted into tables or inset in figures. Use `from acgc.stats import BivariateStatistics`
 38See `acgc.stats.bivariate.BivariateStatistics` for documentation.
 39
 40## Standard major axis (SMA) line fitting ([example](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_sma.ipynb))
 41
 42SMA line fitting (also called reduced major axis or RMA) quantifies the linear relationship 
 43between variables in which neither one depends on the other.
 44It is available via `from acgc.stats import sma`. 
 45
 46<!-- ----------------------- SECTION BREAK ----------------------- -->
 47
 48# Demos
 49The [demo](https://github.com/cdholmes/acgc-python/blob/main/demo) 
 50folder contains examples of how to accomplish common data analysis and visualization tasks. 
 51The examples include uses of the `acgc` library as well as other libraries for 
 52geospatial data analysis.
 53
 54<!-- ----------------------- SECTION BREAK ----------------------- -->
 55
 56# Quick summary of submodules
 57
 58## Key submodules
 59
 60- `acgc.figstyle`    
 61Style settings for matplotlib, for publication-ready figures.
 62[demo](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_figstyle.ipynb)
 63
 64- `acgc.stats`    
 65Collection of statistical methods. 
 66[demo](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_stats.ipynb) 
 67
 68## Other submodules
 69
 70- `acgc.erroranalysis`   
 71Propagation of error through complex numerical models
 72
 73- `acgc.geoschem` or `acgc.gc`       
 74Tools for GEOS-Chem grids (e.g. indexing, remapping, interpolating)
 75
 76- `acgc.hysplit`	        
 77Read HYSPLIT output and write HYSPLIT CONTROL files
 78
 79- `acgc.icartt`	        
 80Read and write ICARTT format files
 81
 82- `acgc.igra`		        
 83Read IGRA radiosonde data files
 84
 85- `acgc.mapping`        
 86Distance calculation, scale bar for display on maps
 87
 88- `acgc.met`        
 89Miscelaneous functions for PBL properties
 90
 91- `acgc.modetools`	    
 92Visualization of eigenmode systems
 93
 94- `acgc.netcdf`          
 95High-level functions for reading and writing netCDF files. Legacy code. 
 96`acgc.netcdf.write_geo_nc` is still useful for concisely creating netCDF files, 
 97but xarray is better for reading netCDF.
 98
 99- `acgc.solar`        
100Solar zenith angle, azimuth, declination, equation of time
101
102- `acgc.time`       
103Functions for manimulating times and dates. Legacy code.
104'''
105
106def _package_version(package_name):
107    '''Find version string for package name'''
108    from importlib.metadata import version, PackageNotFoundError
109    try:
110        result = version(package_name)
111    except PackageNotFoundError:
112        result = "unknown version"
113    return result
114
115__version__ = _package_version('acgc')
116
117__all__ = [
118    'erroranalysis',
119    'figstyle',
120    'gc',
121    'geoschem',
122    'hysplit',
123    'icartt',
124    'igra',
125    'mapping',
126    'met',
127    'modetools',
128    'netcdf',
129    'stats',
130    'solar',
131    'time'
132]