pymrio 0.3.1

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pymrio
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Pymrio: Multi-Regional Input-Output Analysis in Python.

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What is it
==========

Pymrio is an open source tool for analysing global environmentally extended multi-regional input-output tables (EE MRIOs).
Pymrio aims to provide a high-level abstraction layer for global EE MRIO databases in order to simplify common EE MRIO data tasks.
Pymrio includes automatic download functions and parsers for available EE MRIO databases like EXIOBASE_, WIOD_ and EORA26_.
It automatically checks parsed EE MRIOs for missing data necessary for calculating standard EE MRIO accounts (such as footprint, territorial, impacts embodied in trade) and calculates all missing tables.
Various data report and visualization methods help to explore the dataset by comparing the different accounts across countries.

Further functions include:

- analysis methods to identify where certain impacts occurr
- modifying region/sector classification
- restructuring extensions
- export to various formats
- visualization routines and
- automated report generation


Where to get it
===============

The full source code is available on Github at: https://github.com/konstantinstadler/pymrio

pymrio is registered at PyPI. Install it by:

.. code:: bash

pip install pymrio --upgrade

Quickstart
==========

A small test mrio is included in the package.

To use it call

.. code:: python

import pymrio
test_mrio = pymrio.load_test()

The test mrio consists of six regions and eight sectors:

.. code:: python


print(test_mrio.get_sectors())
print(test_mrio.get_regions())

The test mrio includes tables flow tables and some satellite accounts.
To show these:

.. code:: python

test_mrio.Z
test_mrio.emissions.F

However, some tables necessary for calculating footprints (like test_mrio.A or test_mrio.emissions.S) are missing. pymrio automatically identifies which tables are missing and calculates them:

.. code:: python

test_mrio.calc_all()

Now, all accounts are calculated, including footprints and emissions embodied in trade:

.. code:: python

test_mrio.A
test_mrio.emissions.D_fp
test_mrio.emissions.D_exp

To visualize the accounts:


.. code:: python

import matplotlib as plt
test_mrio.emissions.plot_account('emission_type1')
plt.show()

Everything can be saved with

.. code:: python

test_mrio.save_all('some/folder')

See the documentation_ and tutorials_ for further examples.

Tutorials
=========

The documentation_ includes information about how to use pymrio for automatic downloading_ and parsing_ of the EE MRIOs EXIOBASE_, WIOD_ and EORA26_ as well as tutorials_ for the handling, aggregating and analysis of these databases.

Contributing
=============

Want to contribute? Great!
Please check `CONTRIBUTING.rst`_ if you want to help to improve pymrio.

.. _CONTRIBUTING.rst: https://github.com/konstantinstadler/pymrio/blob/master/CONTRIBUTING.rst

Communication, issues, bugs and enhancements
============================================

Please use the issue tracker for documenting bugs, proposing enhancements and all other communication related to pymrio.

You can follow me on twitter_ to get the latest news about all my open-source and research projects (and occasionally some random retweets).

.. _twitter: https://twitter.com/kst_stadler

.. _downloading: http://pymrio.readthedocs.io/en/latest/notebooks/autodownload.html
.. _documentation: http://pymrio.readthedocs.io/en/latest/
.. _tutorials: http://pymrio.readthedocs.io/en/latest/handling.html
.. _EXIOBASE: http://www.exiobase.eu/
.. _WIOD: http://www.wiod.org/home
.. _EORA26: http://www.worldmrio.com/simplified/