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Description:
pymrio 0.5.4
Pymrio: Multi-Regional Input-Output Analysis in Python.
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 occur
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/IndEcol/pymrio
Pymrio is registered at PyPI and on the Anaconda Cloud. Install it by:
pip install pymrio --upgrade
or when using conda install it by
conda install -c conda-forge pymrio
or update to the latest version by
conda update -c conda-forge pymrio
The source-code of Pymrio available at the GitHub repo: https://github.com/IndEcol/pymrio
The master branch in that repo is supposed to be ready for use and might be
ahead of the official releases. To install directly from the master branch use:
pip install git+https://github.com/IndEcol/pymrio@master
Quickstart
A small test mrio is included in the package.
To use it call
import pymrio
test_mrio = pymrio.load_test()
The test mrio consists of six regions and eight sectors:
print(test_mrio.get_sectors())
print(test_mrio.get_regions())
The test mrio includes tables flow tables and some satellite accounts.
To show these:
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:
test_mrio.calc_all()
Now, all accounts are calculated, including footprints and emissions embodied in trade:
test_mrio.A
test_mrio.emissions.D_cba
test_mrio.emissions.D_exp
To visualize the accounts:
import matplotlib as plt
test_mrio.emissions.plot_account('emission_type1')
plt.show()
Everything can be saved with
test_mrio.save_all('some/folder')
See the documentation , tutorials and Stadler 2021 for further examples.
Tutorials
The 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.
Citation
If you use Pymrio in your research, citing the article describing the package
(Stadler 2021) is very much appreciated.
For the full bibtex key see CITATION file.
Contributing
Want to contribute? Great!
Please check CONTRIBUTING.rst if you want to help to improve Pymrio.
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).
Research notice
Please note that this repository is participating in a study into
sustainability of open source projects. Data will be gathered about this
repository for approximately the next 12 months, starting from June
2021.
Data collected will include number of contributors, number of PRs, time
taken to close/merge these PRs, and issues closed.
For more information, please visit the informational
page or
download the participant information
sheet.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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