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https://github.com/USEPA/IO-Model-Builder

Create environmentally extended input-output models
https://github.com/USEPA/IO-Model-Builder

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Create environmentally extended input-output models

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README 

        
iomb - Input-Output Model Builder

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



``iomb`` is an open source Python library for creating environmentally

extended input-output models (EEIO models) from CSV files in a simple

`data format `__. It includes functions to calculate

different result types (e.g. life cycle assessment results, direct and

upstream contributions, etc.) from such models and convert them into

`JSON-LD data packages `__

that can be imported into `openLCA `__.



Installation

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



``iomb`` is tested with `Python 3.5 `__ but

should also work with older versions of Python 3. The easiest way to

install the package is to do so using pip, which is generally

packaged with a Python installation. Open up the command line and enter:



::



    pip install IO-Model-Builder



This will also install the dependencies of the IO-Model-Builder

(`NumPy `__,

`pandas `__, and

`matplotlib `__) if required. After this you

should be able to use the ``iomb`` package in your Python code. To

uninstall the package, you can again use pip from the command line:



::



    pip uninstall IO-Model-Builder



Usage

-----



You can find a more detailed `example here `__ in

form of a `Jupyter notebook `__ which is a

convenient way to use ``iomb``. The following script shows the basic

usage of ``iomb``. For detailed information about the data format see

the `data format specification `__



.. code:: python



    import iomb



    # optionally show all logging information of iomb

    iomb.log_all()



    # create a direct requirements coefficients matrix from a supply and use table

    # and save it to a CSV file

    drc = iomb.coefficients_from_sut('supply_table.csv', 'use_table.csv')

    drc.to_csv('drc.csv')



    # create an EEIO model from a coefficients matrix, satellite tables, and a

    # LCIA method

    model = iomb.make_model('drc.csv',

                            ['satellite_table1.csv', 'satellite_table2.csv'],

                            "sector_meta_data.csv",

                            ['LCIA_factors1.csv', 'LCIA_factors1.csv'])



    # validate the model

    import iomb.validation as val

    vr = validation.validate(model)

    print(vr)



    # calculate results for a given demand

    result = iomb.calculate(model, {'1111a0/oilseed farming/us': 1})

    print(result.total_result)



    # export the model to a JSON-LD package

    import iomb.olca as olca

    olca.Export(model).to('model_as_json-ld.zip')



License

-------



This project is in the worldwide public domain, released under the `CC0

1.0 Universal Public Domain

Dedication `__.



.. figure:: https://licensebuttons.net/p/zero/1.0/88x31.png

   :alt: Public Domain Dedication



   Public Domain Dedication



Citation

--------



Please cite as: Srocka, M. and W. Ingwersen (2017). IO Model Builder,

v1.1 (or current version). US Environmental Protection Agency.

https://www.github.com/usepa/io-model-builder



A brief description of the iomb is also included in: Yang, Y.,

Ingwersen, W.W., Hawkins, T.R., Srocka, M., Meyer, D.E., 2017. USEEIO: A

New and Transparent United States Environmentally-Extended Input-Output

Model. Journal of Cleaner Production 158, 308-318. DOI:

`10.1016/j.jclepro.2017.04.150 `__