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Composition and Dynamics","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"AC_tools: Atmospheric Chemistry (AC) tools\n======================================\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4789901.svg\n   :target: https://doi.org/10.5281/zenodo.4789901\n\n**Atmospheric Chemistry Tools (AC_Tools)** contains functions and scripts used for \nworking with atmospheric model output and observational data. \nMany functions are included for working with global and regional \nchemical transport model (CTM) ouput from the GEOS-Chem model.\n\nThis package started as a just collection of scripts that were\nfound to be useful for work in atmospheric chemistry and now\nsimply aims to contain functionality outside the remit of the \nmore specialised community packages (e.g PyGChem_, xbpch_, and \ngcpy_) and use the existing Python stack (e.g. dask_, xarray_, \npandas_). \n`Please raise any questions/comments or bugs as issues here on GitHub \u003chttps://github.com/tsherwen/AC_tools/issues\u003e`_ \nand `pull requests are welcome! \u003chttps://github.com/tsherwen/AC_tools/pulls\u003e`_\n\nInstallation\n------------\n\n**AC_Tools** is currently only installable from source. To do this, you\ncan either install directly via pip (recommended and this includes any dependencies)::\n\n\n    $ pip install git+https://github.com/tsherwen/AC_tools.git\n\n\nor (not recommended), clone the source directory and manually install::\n\n\n    $ git clone https://github.com/tsherwen/AC_tools.git\n    $ cd AC_tools\n    $ python setup.py install\n\n\nIf 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. \n\n\nQuick Start\n-----------\n\nFunctions within **AC_Tools** can be used for various tasks for handling model output and observations. \n\nAn 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. \n\nIf using within a python3 environment and GEOS-Chem \n\n.. code:: python\n\n    import AC_tools as AC\n    folder = '\u003cfolder containing GEOS-Chem output\u003e'\n    # Get the GEOS-Chem NetCDF output as a xarray dataset object\n    # NOTE: this is just a wrapper of get_GEOSChem_files_as_ds, which can retrieve GEOS-Chem NetCDFs as a dataset\n    ds = AC.GetSpeciesConcDataset(wd=folder)\n    # Average dataset over time\n    ds = ds.mean(dim='time')   \n    # Select the surface level\n    ds = ds.sel( lev=ds.lev[0] )      \n    # Select ozone and do plot basic plot\n    spec = 'O3' \n    #ds['SpeciesConc_'+spec].plot() # very simple plot\n    AC.quick_map_plot( ds, var2plot='SpeciesConc_'+spec) # basic lat-lon plot\n    plt.show()\n    # Get global average surface CO \n    spec = 'CO'\n    ratio = (ds['SpeciesConc_'+spec] * ds['AREA']).sum() / ds['AREA'].sum()\n    ratio = float(ratio.values) \n    # Make a formatted string and then print using this to screen\n    prt_str = \"The global average surface mixing ratio of {spec} (ppbv) is: {ratio}\" \n    print(prt_str.format(spec=spec, ratio=ratio*1E9))\n\n\nIf 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).\n\n.. code:: python\n\n    import AC_tools as AC\n    folder = '\u003cfolder containing GEOS-Chem output\u003e'\n    # Get the atmospheric ozone burden in Gg O3 as a np.array\n    array = AC.get_O3_burden_bpch(folder)\n    print( \"The ozone burden is: {burden}\".format(burden=array.sum()))\n    # Get surface area for resolution \n    s_area = get_surface_area(res)[..., 0]  # m2 land map\n    # Get global average surface CO \n    spec = 'CO'\n    array = AC.get_GC_output(wd=folder, vars=['IJ_AVG_S__{}'.format(spec)])\n    ratio = AC.get_2D_arr_weighted_by_X(array, res='4x5', s_area=s_area) \n    # Make a formatted string and then print using this to screen\n    prt_str = \"The global average surface mixing ratio of {spec} (ppbv) is: {ratio}\"\n    print( prt_str.format(spec=spec, ratio=ratio*1E9))\n    \n    \nUsage\n------------\n\nExample analysis code for using AC_tools is available in the \nscripts folder. \n\nFor more information, please visit the AC_tools_wiki_.\n\n\nLicense\n-------\n\nCopyright (c) 2015 `Tomas Sherwen`_\n\nThis work is licensed under a permissive MIT License.\n\nContact\n-------\n\n`Tomas Sherwen`_ - tomas.sherwen@york.ac.uk\n\n.. _`Tomas Sherwen`: http://github.com/tsherwen\n.. _conda: http://conda.pydata.org/docs/\n.. _dask: http://dask.pydata.org/\n.. _licensed: LICENSE\n.. _GEOS-Chem: http://www.geos-chem.org\n.. _xarray: http://xarray.pydata.org/\n.. _pandas: https://pandas.pydata.org/\n.. _gcpy: https://github.com/geoschem/gcpy\n.. _PyGChem: https://github.com/benbovy/PyGChem\n.. _xbpch: https://github.com/darothen/xbpch\n.. _iris: https://scitools.org.uk/iris/docs/latest/\n.. _bpch2netCDF: https://github.com/tsherwen/AC_tools/blob/master/Scripts/bpch2netCDF.py\n.. _AC_tools_wiki: https://github.com/tsherwen/AC_tools/wiki\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.4789901"],"works":{"https://doi.org/10.5281/zenodo.4789901":null},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["atmospheric-modelling","atmospheric-composition","cloud-computing","pbs","simulations","slurm","aerosols","atmospheric-chemistry-modeling","carbon-cycle","climate"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/58197","html_url":"https://ost.ecosyste.ms/projects/58197"}