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Ecology","sub_category":"Input Output Model","monthly_downloads":8748,"total_dependent_repos":10,"total_dependent_packages":3,"readme":"\n.. image:: https://raw.githubusercontent.com/IndEcol/pymrio/master/doc/source/_static/textlogo.png\n    :target: http://pymrio.readthedocs.io/en/latest/?badge=latest\n    :alt: pymrio logo\n\n-------\n\n*******************************************************\npymrio: Multi-Regional Input-Output Analysis in Python\n*******************************************************\n\n.. image:: https://img.shields.io/pypi/v/pymrio.svg\n    :target: https://pypi.python.org/pypi/pymrio/\n.. image:: https://anaconda.org/conda-forge/pymrio/badges/version.svg   \n    :target: https://anaconda.org/conda-forge/pymrio\n.. image:: https://github.com/IndEcol/pymrio/workflows/build/badge.svg\n    :target: https://github.com/IndEcol/pymrio/actions\n.. image:: https://coveralls.io/repos/github/IndEcol/pymrio/badge.svg?branch=master\n    :target: https://coveralls.io/github/IndEcol/pymrio\n.. image:: https://readthedocs.org/projects/pymrio/badge/?version=latest\n    :target: http://pymrio.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n.. image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg\n    :target: https://www.gnu.org/licenses/gpl-3.0\n.. image:: https://zenodo.org/badge/21688312.svg\n    :target: https://zenodo.org/badge/latestdoi/21688312\n\nWhat is it\n==========\n\nPymrio is an open source tool for analysing global environmentally extended multi-regional input-output tables (EE MRIOs). \nPymrio aims to provide a high-level abstraction layer for global EE MRIO databases in order to simplify common EE MRIO data tasks. \nPymrio includes automatic download functions and parsers for available EE MRIO databases like EXIOBASE_, WIOD_ and EORA26_. \nIt 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. \nVarious data report and visualization methods help to explore the dataset by comparing the different accounts across countries. \n\nFurther functions include:\n\n- analysis methods to identify where certain impacts occur\n- modifying region/sector classification\n- restructuring extensions\n- export to various formats\n- visualization routines and \n- automated report generation\n  \n\nWhere to get it\n===============\n\nThe full source code is available on Github at: https://github.com/IndEcol/pymrio\n\nPymrio is registered at PyPI and on the Anaconda Cloud. Install it by:\n\n.. code:: bash\n\n    pip install pymrio --upgrade\n    \nor when using conda install it by\n\n.. code:: bash\n\n    conda install -c conda-forge pymrio\n\nor update to the latest version by\n\n.. code:: bash\n\n    conda update -c conda-forge pymrio\n\nThe source-code of Pymrio available at the GitHub repo: https://github.com/IndEcol/pymrio  \n\nThe master branch in that repo is supposed to be ready for use and might be \nahead of the official releases. To install directly from the master branch use:\n\n.. code:: bash\n\n    pip install git+https://github.com/IndEcol/pymrio@master\n\n\nDevelopment installation\n========================\n\nTo set up your development environment, install\n``uv``, then sync the project dependencies, including test and lint requirements:\n\n.. code-block:: bash\n\n   uv sync --all-extras\n\nSee ./CONTRIBUTING.rst for further instructions about contributing to pymrio.\n\n\nQuickstart    \n==========\n\nA small test mrio is included in the package. \n\nTo use it call\n\n.. code:: python\n\n    import pymrio\n    test_mrio = pymrio.load_test()\n\nThe test mrio consists of six regions and eight sectors:  \n\n.. code:: python\n\n\n    print(test_mrio.sectors)\n    print(test_mrio.regions)\n    print(test_mrio.extensions)\n\nThe test mrio includes tables flow tables and some satellite accounts. \nTo show these:\n\n.. code:: python\n\n    test_mrio.Z\n    test_mrio.emissions.F\n    \nHowever, 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: \n\n.. code:: python\n\n    test_mrio.calc_all()\n\nNow, all accounts are calculated, including footprints and emissions embodied in trade:\n\n.. code:: python\n\n    test_mrio.A\n    test_mrio.emissions.D_cba\n    test_mrio.emissions.D_exp\n\nTo visualize the accounts:\n\n\n.. code:: python\n\n    import matplotlib.pyplot as plt\n    test_mrio.emissions.plot_account('emission_type1')\n    plt.show()\n\nEverything can be saved with\n\n.. code:: python\n    \n    test_mrio.save_all('some/folder')\n\nSee the documentation_ , tutorials_ and  `Stadler 2021`_ for further examples.\n\nTutorials\n=========\n\nThe documentation_ includes information about how to use pymrio for automatic downloading_ and parsing_ of the EE MRIOs EXIOBASE_, WIOD_, OECD_ and EORA26_ as well as tutorials_ for the handling, aggregating and analysis of these databases. \n\nCitation\n========\n\nIf you use Pymrio in your research, citing the article describing the package \n(`Stadler 2021`_) is very much appreciated. \n\n.. _`Stadler 2021`: https://openresearchsoftware.metajnl.com/articles/10.5334/jors.251/\n\nFor the full bibtex key see CITATION_ file.\n\n.. _CITATION: CITATION\n\nContributing\n=============\n\nWant to contribute? 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