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.
 31See [example](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_figstyle.ipynb)
 32 
 33## Standard major axis (SMA) line fitting
 34
 35SMA line fitting (also called reduced major axis or RMA) quantifies the linear relationship 
 36between variables in which neither one depends on the other.
 37It is available via `from acgc.stats import sma`.
 38See [example](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_sma.ipynb) 
 39
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 41
 42# Demos
 43The [demo](https://github.com/cdholmes/acgc-python/blob/main/demo) 
 44folder contains examples of how to accomplish common data analysis and visualization tasks. 
 45The examples include uses of the `acgc` library as well as other libraries for 
 46geospatial data analysis.
 47
 48<!-- ----------------------- SECTION BREAK ----------------------- -->
 49
 50# Quick summary of submodules
 51
 52## Key submodules
 53
 54- `acgc.figstyle`    
 55Style settings for matplotlib, for publication-ready figures.
 56[demo](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_figstyle.ipynb)
 57
 58- `acgc.stats`    
 59Collection of statistical methods. 
 60[demo](https://github.com/cdholmes/acgc-python/blob/main/demo/demo_stats.ipynb) 
 61
 62## Other submodules
 63
 64- `acgc.erroranalysis`   
 65Propagation of error through complex numerical models
 66
 67- `acgc.geoschem` or `acgc.gc`       
 68Tools for GEOS-Chem grids (e.g. indexing, remapping, interpolating)
 69
 70- `acgc.hysplit`	        
 71Read HYSPLIT output and write HYSPLIT CONTROL files
 72
 73- `acgc.icartt`	        
 74Read and write ICARTT format files
 75
 76- `acgc.igra`		        
 77Read IGRA radiosonde data files
 78
 79- `acgc.map_scalebar`	        
 80Scale bar for display on maps
 81
 82- `acgc.met`        
 83Miscelaneous functions for PBL properties
 84
 85- `acgc.modetools`	    
 86Visualization of eigenmode systems
 87
 88- `acgc.netcdf`          
 89High-level functions for reading and writing netCDF files. Legacy code. 
 90`acgc.netcdf.write_geo_nc` is still useful for concisely creating netCDF files, 
 91but xarray is better for reading netCDF.
 92
 93- `acgc.solar`        
 94Solar zenith angle, azimuth, declination, equation of time
 95
 96- `acgc.time`       
 97Functions for manimulating times and dates. Legacy code.
 98'''
 99
100def _package_version(package_name):
101    '''Find version string for package name'''
102    from importlib.metadata import version, PackageNotFoundError
103    try:
104        result = version(package_name)
105    except PackageNotFoundError:
106        result = "unknown version"
107    return result
108
109__version__ = _package_version('acgc')
110
111__all__ = [
112    'erroranalysis',
113    'figstyle',
114    'gc',
115    'geoschem',
116    'hysplit',
117    'icartt',
118    'igra',
119    'map_scalebar',
120    'met',
121    'modetools',
122    'netcdf',
123    'stats',
124    'solar',
125    'time'
126]