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lca_algebraic

Layer over brightway2 for algebraic definition of parametric models and super fast computation of LCA

https://github.com/oie-mines-paristech/lca_algebraic

Science Score: 36.0%

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    Low similarity (12.1%) to scientific vocabulary

Keywords

brightway2 foreground-activities lca lca-algebraic monte-carlo numpy symbolic-expressions

Keywords from Contributors

projection interactive serializer cycles packaging charts network-simulation archival shellcodes hacking
Last synced: 6 months ago · JSON representation

Repository

Layer over brightway2 for algebraic definition of parametric models and super fast computation of LCA

Basic Info
  • Host: GitHub
  • Owner: oie-mines-paristech
  • License: bsd-2-clause
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 16.6 MB
Statistics
  • Stars: 50
  • Watchers: 6
  • Forks: 22
  • Open Issues: 27
  • Releases: 5
Topics
brightway2 foreground-activities lca lca-algebraic monte-carlo numpy symbolic-expressions
Created almost 6 years ago · Last pushed 6 months ago
Metadata Files
Readme License

README.md

logo

Python 3.10 Python 3.11 Python 3.12

Brightway 2.4 Brightway 2.5

main status dev status     bw25 status dev25 status

lca_algebraic is a layer above Brightway 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) * Support for automatic check of homogeneity of physical units

⚙ Installation

We support both Brightway 2.4 (legacy) and Brightway 2.5 via two separate branches / libraries :

1) Setup separate environment

First create a python environment, with Python [>=3.10, <3.13] :

With Conda (or mamba)

bash conda create -n lca python==3.10 conda activate lca

With virtual env

bash python3.10 -m venv .venv source .venv/bin/activate

2) Install lca_algebraic

pip install lca_algebraic

Or, for brightway 25 :

pip install lcaalgebraicbw25

3) [Optional] Install Jupyter & Activity Browser

You may also install Jupyter and Activity Browser on the same environment.

Jupyter :

pip install jupyter

Activity Browser can only be installed via conda/mamba. Note that it can also be installed on a separate Python env and will still be able to access and browse the projects created programmatically with lca_algebraic / Brightway.

conda install activity-browser

NOTE While the inventories created in lca_algebraic are stored in the Brightway project, the formulas and parameters are not compatible with Activity Browser Before computing impacts with vanilla Brightway2 or Activity Browser, you may use the function freezeParams() to update the amounts in your database for a given scenario / set of parameter values.

📚 Documentation & resources

Full documentation is hosted on readthedocs

We provide some notebooks : * Example notebook : Basic functionalities
* Handbook : More examples, also showing the usage of the Brightway functions. * Workshop : A "real life" exercise used as a short training on lca_algebraic

📧 Mailing list

Please register to this dedicated mailing list to discuss the evolutions of this library and be informed of future releases :

lca_algebraic@groupes.mines-paristech.fr

© Licence & Copyright

This library has been developed by MinesParis - PSL - O.I.E team, for the project INCER-ACV, lead by ADEME.

It is distributed under the BSD License

Logo

Please use the following logo to advertise about this librairy.

Owner

  • Name: OIE - Mines ParisTech
  • Login: oie-mines-paristech
  • Kind: organization
  • Location: Sophia Antipolis - France

Centre de recherche Observation, Impacts, Energie

GitHub Events

Total
  • Issues event: 11
  • Watch event: 12
  • Issue comment event: 6
  • Push event: 24
  • Pull request event: 5
  • Fork event: 2
  • Create event: 7
Last Year
  • Issues event: 11
  • Watch event: 12
  • Issue comment event: 6
  • Push event: 25
  • Pull request event: 5
  • Fork event: 2
  • Create event: 7

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 231
  • Total Committers: 6
  • Avg Commits per committer: 38.5
  • Development Distribution Score (DDS): 0.043
Past Year
  • Commits: 18
  • Committers: 1
  • Avg Commits per committer: 18.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Raphael Jolivet r****t@m****r 221
Benoît Gschwind g****d@g****t 4
Elias Sebastian Azzi e****i@U****E 3
tristan_debonnet 7****t 1
f.pollet f****t@h****r 1
dependabot[bot] 4****] 1
Committer Domains (Top 20 + Academic)
ug.kth.se: 1 gnu-log.net: 1 mines-paristech.fr: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 46
  • Total pull requests: 35
  • Average time to close issues: 9 months
  • Average time to close pull requests: about 2 months
  • Total issue authors: 24
  • Total pull request authors: 11
  • Average comments per issue: 1.11
  • Average comments per pull request: 0.17
  • Merged pull requests: 10
  • Bot issues: 0
  • Bot pull requests: 5
Past Year
  • Issues: 10
  • Pull requests: 5
  • Average time to close issues: 15 days
  • Average time to close pull requests: 4 days
  • Issue authors: 8
  • Pull request authors: 2
  • Average comments per issue: 0.7
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • raphaeljolivet (11)
  • juliana-steinbach (3)
  • n1c0l492 (3)
  • felixpollet (3)
  • RomainBes (3)
  • gschwind (2)
  • simaosr (2)
  • simb-sdu (2)
  • zann2011 (2)
  • thomasgibon (1)
  • AmeliePzk (1)
  • joanna-schles (1)
  • inrDL (1)
  • siegarmi (1)
  • ntropy-esa (1)
Pull Request Authors
  • gschwind (15)
  • dependabot[bot] (5)
  • n1c0l492 (3)
  • RomainBes (3)
  • felixpollet (2)
  • A-JMinor (2)
  • mgo-cea (1)
  • raphaeljolivet (1)
  • mijafro (1)
  • tdebonnet (1)
  • ntropy-esa (1)
Top Labels
Issue Labels
enhancement (1)
Pull Request Labels
dependencies (5)

Packages

  • Total packages: 4
  • Total downloads:
    • pypi 911 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 2
    (may contain duplicates)
  • Total versions: 68
  • Total maintainers: 1
pypi.org: lca-algebraic-dev

This library provides a layer above brightway2 for defining parametric models and running super fast LCA for monte carlo analysis.

  • Versions: 40
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 83 Last month
Rankings
Dependent packages count: 7.3%
Forks count: 10.2%
Stargazers count: 11.9%
Average: 15.8%
Dependent repos count: 22.1%
Downloads: 27.6%
Maintainers (1)
oie-minesparistech
Last synced: 6 months ago
pypi.org: lca-algebraic

This library provides a layer above brightway2 for defining parametric models and running super fast LCA for monte carlo analysis.

  • Versions: 24
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 485 Last month
Rankings
Dependent packages count: 7.3%
Forks count: 10.2%
Stargazers count: 11.9%
Average: 16.2%
Dependent repos count: 22.1%
Downloads: 29.3%
Maintainers (1)
oie-minesparistech
Last synced: 6 months ago
pypi.org: lca-algebraic-bw25

This library provides a layer above brightway2 for defining parametric models and running super fast LCA for monte carlo analysis.

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 317 Last month
Rankings
Dependent packages count: 8.6%
Average: 28.7%
Dependent repos count: 48.7%
Maintainers (1)
oie-minesparistech
Last synced: 6 months ago
pypi.org: lca-algebraic-inventory-loops

This library provides a layer above brightway2 for defining parametric models and running super fast LCA for monte carlo analysis.

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 26 Last month
Rankings
Dependent packages count: 8.9%
Average: 29.5%
Dependent repos count: 50.1%
Maintainers (1)
oie-minesparistech
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • SALib ==1.3.8
  • brightway2 ==2.3
  • bw2data ==3.6.2
  • ipython ==7.16.3
  • ipywidgets ==7.5.1
  • matplotlib ==3.1.1
  • nbconvert ==5.6.1
  • nbformat ==4.4.0
  • numpy ==1.16.6
  • pandas ==1.0.1
  • scipy ==1.3.2
  • seaborn ==0.9.0
  • sympy ==1.5.1
  • tabulate ==0.8.6
setup.py pypi
  • SALib *
  • brightway2 ==2.3
  • deprecation *
  • ipywidgets *
  • matplotlib *
  • pandas *
  • seaborn *
  • sympy *
  • tabulate *