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MARIO
Multifunctional Analysis of Regions through Input-Output. (Documents <https://mario-suite.readthedocs.io/en/latest/intro.html>_)
MARIO is a python package for handling input-output tables and models inspired by Pymrio <https://github.com/IndEcol/pymrio>_ .
MARIO aims to provide a simple & intuitive API for common IO tasks without
needing in-depth programming knowledge. MARIO supporst automatic parsing of different
structured tables such EXIOBASE, EORA, EUROSTAT, and FIGARO in different formats namely:
When databases are not structured, MARIO supports parsing data from xlsx, csv, txt files or pandas.DataFrames.
More than parsing data, MARIO includes some basic functionalities:
MARIO has been tested on macOS and Windows.
To run MARIO, a couple of things are needed:
Installation
The easiest way to make MARIO software working is to use the free conda package manager which can install the current and future MARIO depencies in an easy and user friendly way.
To get conda, download and install "Anaconda Distribution" <https://www.anaconda.com/products/individual>
. Between differnet options for running python codes, we strongly suggest, Spyder <https://www.spyder-ide.org/>,
which is a free and open source scientific environment written in Python, for Python, and designed by and for scientists,
engineers and data analysts.
You can install mario using pip or from source code. It is suggested to create a new environment by running the following command in the anaconda prompt
.. code-block:: python
conda create -n mario python=3.10
If you create a new environment for mario, to use it, you need to activate the mario environment each time by writing the following line in Anaconda Prompt
.. code-block:: python
conda activate mario
Now you can use pip to install mario on your environment as follow:
.. code-block:: python
pip install mariopy
You can also install from the source code!
Quickstart
A simple test for Input-Output Table (IOT) and Supply-Use Table (SUT) is included in mario.
To use the IOT test, call
.. code-block:: python
import mario test_iot = mario.load_test('IOT')
and to use the SUT test, call
.. code-block:: python
test_sut = mario.load_test('SUT')
To see the configurations of the data, you can print them:
.. code-block:: python
print(test_iot) print(test_sut)
To see specific sets of the tables like regions or value added, get_index function can be used:
.. code-block:: python
print(test_iot.get_index('Region')) print(test_sut.get_index('Factor of production'))
To visualize some data, various plot functions can be used:
.. code-block:: python
test_iot.plot_matrix(....)
Specific modifications on the database can be done, such as SUT to IOT transformation:
.. code-block:: python
reformed_iot = test.to_iot(method='B')
The changes can be tracked by metadata. The history can be checked by calling:
.. code-block:: python
reformed_iot.meta_history
The new database can be saved into excel,txt or csv file:
.. code-block:: python
reformed_iot.to_excel(path='a folder//database.xlsx')
Citation
In case you use mario, you should use our peer reviewed publication (Tahavori, Golinucci, Rinaldi, et al. <https://openresearchsoftware.metajnl.com/articles/10.5334/jors.473>_) for citiation!
.. _RST pckgs:
Read more
The current version of Mario has achieved a test coverage of 49%. This coverage includes a comprehensive 100% assessment of the fundamental mathematical engine.
Additional tests are currently in active development to enhance the package's reliability.
Mario utilizes pytest <https://docs.pytest.org/en/7.4.x/>_ as its primary tool for conducting unit tests. For a more detailed analysis of the test coverage pertaining to mario's unit tests,
you can execute the following command:
.. code-block:: python
pytest --cov=mario tests/
.. note::
Rinaldi et al, Environmental Research Letters, 2024 <https://doi.org/10.1088/1748-9326/ad5bf1>_ )Rinaldi et al, Resources Conservation adn Recycling, 2023 <https://www.sciencedirect.com/science/article/pii/S092134492300037X?via%3Dihub>_ )Nikas et al, Energy, 2024 <https://www.sciencedirect.com/science/article/pii/S0360544224000252?via%3Dihub>_ )Marco Conte et al, IOP Conf. Ser.: Earth Environ. Sci., 2022 <https://iopscience.iop.org/article/10.1088/1755-1315/1106/1/012008>_)Input-Output analysis and modelling with MARIO Open University Course <https://www.open.edu/openlearncreate/course/view.php?id=11723>_ .. image:: https://www.gnu.org/graphics/gplv3-or-later.png :target: https://www.gnu.org/licenses/gpl-3.0.en.html
This work is licensed under a GNU GENERAL PUBLIC LICENSE <https://www.gnu.org/licenses/gpl-3.0.en.html>_
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