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 <doc/data_format.md>. 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 <https://github.com/GreenDelta/olca-schema> that can be imported into openLCA <http://openlca.org>__.

Installation

iomb is tested with Python 3.5 <https://docs.python.org/3/>__ 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 <http://www.numpy.org/>, pandas <http://pandas.pydata.org/>, and matplotlib <http://matplotlib.org/>__) 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 <example/example.ipynb> in form of a Jupyter notebook <http://jupyter.org/> 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 <doc/data_format.md>__

.. 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 <https://creativecommons.org/publicdomain/zero/1.0/>__.

.. 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 <https://doi.org/10.1016/j.jclepro.2017.04.150>__