This library provides a layer above brightway2 for defining parametric models and running super fast LCA for monte carlo analysis.
Project description
Introduction
This library is a layer above brightway2 designed for the definition of parametric inventories with fast computation of LCA impacts, suitable for monte-carlo / global sensitivity analysis
It integrates the magic of Sympy in order to write parametric formulas as regular Python expressions.
lca-algebraic provides a set of helper functions for :
- compact & human readable definition of activities :
- search background (tech and biosphere) activities
- create new foreground activities with parametrized amounts
- parametrize / update existing background activities (extending the class Activity)
- Definition of parameters
- Fast computation of LCAs
- Computation of monte carlo method and global sensitivity analysis (Sobol indices)
Installation
We don't provide conda package anymore.
This packages is available via pip /pypi
1) Setup separate environement
First create a python environment, with Python [>=3.9] :
With Conda (or mamba)
conda env create -n lca python==3.10
conda activate lca
With virtual env
python3.10 -m venv .venv
source .venv/bin/activate
2) Install lca_algebraic
pip install lca_algebraic
Licence & Copyright
This library has been developed by OIE - MinesParistech, for the project INCER-ACV, lead by ADEME.
It is distributed under the BSD License
Documentation
Full documentation and example notebooks are hosted on readthedocs
Project details
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