<img src="./doc/source/_static/img/logo_lca_algebraic.png" alt="logo" width="200" style="margin:auto;display:block"/>
**lca_algebraic** is a layer above [**brightway2**](https://brightway.dev/) 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](https://www.sympy.org/en/index.html) 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](https://pypi.org/project/lca-algebraic/)
## 1) Setup separate environement
First create a python environment, with **Python** [>=3.9] :
**With Conda (or [mamba](https://mamba.readthedocs.io/en/latest/index.html))**
```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
## 3) [Optional] Install Jupyter & Activity Browser
You may also install Jupyter and [Activity Browser](https://github.com/LCA-ActivityBrowser/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
# 📚 Documentation & resources
Full documentation is [hosted on **readthedocs**](https://lca-algebraic.readthedocs.io/)
We provide some notebooks :
* [Example notebook](./notebooks/example-notebook.ipynb) : Basic functionalities
* [Handbook](./notebooks/handbook.ipynb) : More examples, also showing the usage of the Brightway functions.
* [Workshop](https://git.sophia.mines-paristech.fr/oie/lca-algebraic-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](https://groupes.minesparis.psl.eu/wws/subscribe/lca_algebraic)
# © Licence & Copyright
This library has been developed by [MinesParis - PSL - O.I.E team](https://www.oie.minesparis.psl.eu/), for the project [*INCER-ACV*](https://librairie.ademe.fr/energies-renouvelables-reseaux-et-stockage/4448-incer-acv.html),
lead by [ADEME](https://www.ademe.fr/).
It is distributed under the [BSD License](./LICENSE)
# Logo
Please use the following logo to advertise about this librairy.
![](./doc/source/_static/img/logo_lca_algebraic.png)
Raw data
{
"_id": null,
"home_page": "https://lca-algebraic.readthedocs.io/en/stable/",
"name": "lca-algebraic",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "LCA brightway2 monte-carlo parametric",
"author": "OIE - Mines ParisTech",
"author_email": "raphael.jolivet@mines-paristech.fr",
"download_url": "https://files.pythonhosted.org/packages/d0/f0/a33c0f3ff12902de0881b52e5610f5d46a0d019c9e665fc94230da7a4a55/lca_algebraic-1.2.tar.gz",
"platform": null,
"description": "<img src=\"./doc/source/_static/img/logo_lca_algebraic.png\" alt=\"logo\" width=\"200\" style=\"margin:auto;display:block\"/>\n\n\n**lca_algebraic** is a layer above [**brightway2**](https://brightway.dev/) designed for the definition of **parametric inventories** \nwith fast computation of LCA impacts, suitable for **monte-carlo** / global sensitivity analysis \n\nIt integrates the magic of [Sympy](https://www.sympy.org/en/index.html) in order to write parametric formulas as regular Python expressions.\n\n**lca-algebraic** provides a set of **helper functions** for : \n* **compact** & **human readable** definition of activities : \n * search background (tech and biosphere) activities \n * create new foreground activities with parametrized amounts\n * parametrize / update existing background activities (extending the class **Activity**)\n* Definition of parameters\n* Fast computation of LCAs\n* Computation of monte carlo method and global sensitivity analysis (Sobol indices) \n\n# \u2699 Installation\n\nWe don't provide conda package anymore.\n\nThis packages is available via [pip /pypi](https://pypi.org/project/lca-algebraic/)\n\n## 1) Setup separate environement\n\nFirst create a python environment, with **Python** [>=3.9] :\n\n**With Conda (or [mamba](https://mamba.readthedocs.io/en/latest/index.html))**\n\n```bash\nconda create -n lca python==3.10\nconda activate lca\n```\n\n**With virtual env**\n\n```bash\npython3.10 -m venv .venv\nsource .venv/bin/activate\n```\n\n## 2) Install lca_algebraic\n\n> pip install lca_algebraic \n\n## 3) [Optional] Install Jupyter & Activity Browser \n\nYou may also install Jupyter and [Activity Browser](https://github.com/LCA-ActivityBrowser/activity-browser) on the same \nenvironment.\n\n**Jupyter** :\n> pip install jupyter\n\n**Activity Browser** can only be installed via conda/mamba. Note that it can also be installed on a separate Python env and will \nstill be able to access and browse the projects created programmatically with *lca_algebraic* / *Brightway*. \n> conda install activity-browser\n\n\n# \ud83d\udcda Documentation & resources\n\nFull documentation is [hosted on **readthedocs**](https://lca-algebraic.readthedocs.io/)\n\nWe provide some notebooks :\n* [Example notebook](./notebooks/example-notebook.ipynb) : Basic functionalities \n* [Handbook](./notebooks/handbook.ipynb) : More examples, also showing the usage of the Brightway functions.\n* [Workshop](https://git.sophia.mines-paristech.fr/oie/lca-algebraic-workshop) :\n A \"real life\" exercise used as a short training on *lca_algebraic*\n\n# \ud83d\udce7 Mailing list\n\nPlease register to this dedicated mailing list to discuss the evolutions of this library and be informed of future releases :\n\n[lca_algebraic@groupes.mines-paristech.fr](https://groupes.minesparis.psl.eu/wws/subscribe/lca_algebraic)\n\n\n# \u00a9 Licence & Copyright\n\nThis library has been developed by [MinesParis - PSL - O.I.E team](https://www.oie.minesparis.psl.eu/), for the project [*INCER-ACV*](https://librairie.ademe.fr/energies-renouvelables-reseaux-et-stockage/4448-incer-acv.html), \nlead by [ADEME](https://www.ademe.fr/). \n\nIt is distributed under the [BSD License](./LICENSE)\n\n# Logo\n\nPlease use the following logo to advertise about this librairy.\n![](./doc/source/_static/img/logo_lca_algebraic.png)\n",
"bugtrack_url": null,
"license": "BSD",
"summary": "This library provides a layer above brightway2 for defining parametric models and running super fast LCA for monte carlo analysis.",
"version": "1.2",
"project_urls": {
"Changelog": "https://github.com/oie-mines-paristech/lca_algebraic/blob/main/RELEASE_NOTES.md",
"Documentation": "https://lca-algebraic.readthedocs.io/",
"Homepage": "https://github.com/oie-mines-paristech/lca_algebraic",
"Repository": "https://github.com/oie-mines-paristech/lca_algebraic.git"
},
"split_keywords": [
"lca",
"brightway2",
"monte-carlo",
"parametric"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "e8d0aa6d0ea6ed414149eb26be5b5d8a6cf46b4902bf25716a790296ec480ed1",
"md5": "08bc0ee0d55b5b43132782c5df995095",
"sha256": "3688d907a2c585cf9f954b93ac6342aa6788b029ece5b4739156c2a4a6d1055b"
},
"downloads": -1,
"filename": "lca_algebraic-1.2-py2.py3-none-any.whl",
"has_sig": false,
"md5_digest": "08bc0ee0d55b5b43132782c5df995095",
"packagetype": "bdist_wheel",
"python_version": "py2.py3",
"requires_python": null,
"size": 74898,
"upload_time": "2024-12-10T12:02:24",
"upload_time_iso_8601": "2024-12-10T12:02:24.326081Z",
"url": "https://files.pythonhosted.org/packages/e8/d0/aa6d0ea6ed414149eb26be5b5d8a6cf46b4902bf25716a790296ec480ed1/lca_algebraic-1.2-py2.py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "d0f0a33c0f3ff12902de0881b52e5610f5d46a0d019c9e665fc94230da7a4a55",
"md5": "b88fa65924be41c2cdde8c08ce4e1894",
"sha256": "d3d9b1bd80c5e62d5f453b00f15d7fc72aa24c5fe167cfa7a1683405b7ac8af0"
},
"downloads": -1,
"filename": "lca_algebraic-1.2.tar.gz",
"has_sig": false,
"md5_digest": "b88fa65924be41c2cdde8c08ce4e1894",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 59592,
"upload_time": "2024-12-10T12:02:25",
"upload_time_iso_8601": "2024-12-10T12:02:25.655759Z",
"url": "https://files.pythonhosted.org/packages/d0/f0/a33c0f3ff12902de0881b52e5610f5d46a0d019c9e665fc94230da7a4a55/lca_algebraic-1.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-10 12:02:25",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "oie-mines-paristech",
"github_project": "lca_algebraic",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [
{
"name": "tabulate",
"specs": []
},
{
"name": "ipywidgets",
"specs": []
},
{
"name": "pandas",
"specs": []
},
{
"name": "seaborn",
"specs": []
},
{
"name": "sympy",
"specs": []
},
{
"name": "nbformat",
"specs": []
},
{
"name": "nbconvert",
"specs": []
},
{
"name": "numpy",
"specs": []
},
{
"name": "matplotlib",
"specs": []
},
{
"name": "scipy",
"specs": []
},
{
"name": "brightway2",
"specs": []
},
{
"name": "ipython",
"specs": []
},
{
"name": "SALib",
"specs": []
},
{
"name": "tqdm",
"specs": []
},
{
"name": "python-dotenv",
"specs": []
},
{
"name": "pypardiso",
"specs": []
},
{
"name": "pyarrow",
"specs": []
},
{
"name": "pint",
"specs": [
[
"==",
"0.23"
]
]
},
{
"name": "typing-extensions",
"specs": []
}
],
"lcname": "lca-algebraic"
}