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https://github.com/tsherwen/AC_tools

Module for working with global/regional Chemical Transport Model (CTM) output and observations
https://github.com/tsherwen/AC_tools

atmospheric-chemistry ctm geos-cf geos-chem geos5 hemco kpp scientific-computing

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Module for working with global/regional Chemical Transport Model (CTM) output and observations

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README 

        
AC_tools: Atmospheric Chemistry (AC) tools

======================================

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4789901.svg

   :target: https://doi.org/10.5281/zenodo.4789901



**Atmospheric Chemistry Tools (AC_Tools)** contains functions and scripts used for 

working with atmospheric model output and observational data. 

Many functions are included for working with global and regional 

chemical transport model (CTM) ouput from the GEOS-Chem model.



This package started as a just collection of scripts that were

found to be useful for work in atmospheric chemistry and now

simply aims to contain functionality outside the remit of the 

more specialised community packages (e.g PyGChem_, xbpch_, and 

gcpy_) and use the existing Python stack (e.g. dask_, xarray_, 

pandas_). 

`Please raise any questions/comments or bugs as issues here on GitHub `_ 

and `pull requests are welcome! `_



Installation

------------



**AC_Tools** is currently only installable from source. To do this, you

can either install directly via pip (recommended and this includes any dependencies)::



    $ pip install git+https://github.com/tsherwen/AC_tools.git



or (not recommended), clone the source directory and manually install::



    $ git clone https://github.com/tsherwen/AC_tools.git

    $ cd AC_tools

    $ python setup.py install



If you specifically wish to use the legacy "bpch to NetCDF" (`bpch2netCDF`_) capability, you will need to run AC_tools in a Python2 environment due to use of an `iris`_ backend via `PyGChem`_. In this specific case, please install using the later method and ensure that you have `iris`_ (version 1.13.0) installed. You will also need to install `PyGChem`_ (version 0.3.0) by the same route. 



Quick Start

-----------



Functions within **AC_Tools** can be used for various tasks for handling model output and observations. 



An example would be importing NetCDF files or converting ctm.bpch files from a directory of GEOS-Chem_ output (with ``tracerinfo.dat`` and ``diaginfo.dat`` files). Or using GEOS-Chem_ NetCDF output to make a quick plot of surface ozone. 



If using within a python3 environment and GEOS-Chem 



.. code:: python



    import AC_tools as AC

    folder = ''

    # Get the GEOS-Chem NetCDF output as a xarray dataset object

    # NOTE: this is just a wrapper of get_GEOSChem_files_as_ds, which can retrieve GEOS-Chem NetCDFs as a dataset

    ds = AC.GetSpeciesConcDataset(wd=folder)

    # Average dataset over time

    ds = ds.mean(dim='time')   

    # Select the surface level

    ds = ds.sel( lev=ds.lev[0] )      

    # Select ozone and do plot basic plot

    spec = 'O3' 

    #ds['SpeciesConc_'+spec].plot() # very simple plot

    AC.quick_map_plot( ds, var2plot='SpeciesConc_'+spec) # basic lat-lon plot

    plt.show()

    # Get global average surface CO 

    spec = 'CO'

    ratio = (ds['SpeciesConc_'+spec] * ds['AREA']).sum() / ds['AREA'].sum()

    ratio = float(ratio.values) 

    # Make a formatted string and then print using this to screen

    prt_str = "The global average surface mixing ratio of {spec} (ppbv) is: {ratio}" 

    print(prt_str.format(spec=spec, ratio=ratio*1E9))



If using within a python2 environment, the below example is a way of accessing GEOS-Chem data. The data is converted from bpch to NetCDF by default via an iris backend through PyGChem (using bpch2netCDF.py).



.. code:: python



    import AC_tools as AC

    folder = ''

    # Get the atmospheric ozone burden in Gg O3 as a np.array

    array = AC.get_O3_burden_bpch(folder)

    print( "The ozone burden is: {burden}".format(burden=array.sum()))

    # Get surface area for resolution 

    s_area = get_surface_area(res)[..., 0]  # m2 land map

    # Get global average surface CO 

    spec = 'CO'

    array = AC.get_GC_output(wd=folder, vars=['IJ_AVG_S__{}'.format(spec)])

    ratio = AC.get_2D_arr_weighted_by_X(array, res='4x5', s_area=s_area) 

    # Make a formatted string and then print using this to screen

    prt_str = "The global average surface mixing ratio of {spec} (ppbv) is: {ratio}"

    print( prt_str.format(spec=spec, ratio=ratio*1E9))

    

    

Usage

------------



Example analysis code for using AC_tools is available in the 

scripts folder. 



For more information, please visit the AC_tools_wiki_.



License

-------



Copyright (c) 2015 `Tomas Sherwen`_



This work is licensed under a permissive MIT License.



Contact

-------



`Tomas Sherwen`_ - [email protected]



.. _`Tomas Sherwen`: http://github.com/tsherwen

.. _conda: http://conda.pydata.org/docs/

.. _dask: http://dask.pydata.org/

.. _licensed: LICENSE

.. _GEOS-Chem: http://www.geos-chem.org

.. _xarray: http://xarray.pydata.org/

.. _pandas: https://pandas.pydata.org/

.. _gcpy: https://github.com/geoschem/gcpy

.. _PyGChem: https://github.com/benbovy/PyGChem

.. _xbpch: https://github.com/darothen/xbpch

.. _iris: https://scitools.org.uk/iris/docs/latest/

.. _bpch2netCDF: https://github.com/tsherwen/AC_tools/blob/master/Scripts/bpch2netCDF.py

.. _AC_tools_wiki: https://github.com/tsherwen/AC_tools/wiki