aiphoria 0.9.2

Dynamic MFA tool

Python package for assessing and visualizing dynamic wood material flows

ℹ️ This package is under continuous development

aiphoria is Python package that facilitates the assessment of wood materials flows, associated carbon stocks, and stock changes, as well as and their visualization over time. aiphoria builds on top of ODYM - Open Dynamic Material Systems Model.

Features:

aiphoria allows you to:

• Solve flows provided both in absolute and relative (%) values, for example semi-finished wood product statistics (absolute values) to end-uses (relative values)
• Conduct dynamic MFA as well as temporary carbon storage assessment
• Visualize material flows through a Sankey diagram and provided timestep.

Use cases:

aiphoria is ideal for:

• Any temporal and spatial situation where material systems want to be assessed
• Product sink/stock effects

Installation

aiphoria is available at Python Package Index (PyPi) and as source distribution in Github

Install from PyPi

pip install aiphoria

Install from GitHub

pip install git+https://github.com/EuropeanForestInstitute/aiphoria.git

How to use

Showcase

aiphoria includes helper function to showcase example scenario with visualizations.
Showcase / example scenario can be run by the following code:

from aiphoria.example import run_example

run_example(remove_existing_output_dir=True)

Network and Sankey visualizations are opened automatically in browser and output is generated
inside user home directory to directory called "aiphoria_example_scenario".

Advanced usage

For the users who are already familiar using aiphoria the package exposes function for running scenarios by using the one-liner:

from aiphoria.runner import run_scenarios

run_scenarios(path_to_settings_file="path/to/scenario/file.xlsx",
              path_to_output_dir="~/scenario_result",
              remove_existing_output_dir=False)

Using parameter path_to_output_dir overrides the output path defined in scenario file.
This makes easier to change target from Python script itself or when running multiple scenarios in batch.
Parameters:

• path_to_settings_file (string): Path to scenario settings file
• path_to_output_dir (string): Path to directory where results are saved
• remove_existing_output_dir: If True then existing output directory is deleted (defaults to False). If directory already exists then error is raised and execution is stopped

Documentation

Online documentation can be found in GitHub wiki.

Support:

If you have any questions or need help, do not hesitate to contact us:

• Cleo Orfanidou [e-mail]
• Janne Järvikylä [e-mail]

Special thanks

A huge thank you to the following people who made aiphoria better:

• Gustavo Ezequiel Martinez (virtual flows, system testing)

  • GitHub: GustavoEzMartinez
  • Email: [e-mail]

• Arthur Jakobs (packaging, CI/CD)

  • GitHub: jakobsarthur
  • Email: [e-mail]

Funding:

aiphoria developers / European Forest Institute receive funding from the European Union’s Horizon Europe Research and Innovation Programme ForestPaths (ID No 101056755), Monifun (ID No 101134991) and eco2adapt (ID No 101059498).