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|
%global _empty_manifest_terminate_build 0
Name: python-lca-algebraic
Version: 1.0.5
Release: 1
Summary: This library provides a layer above brightway2 for defining parametric models and running super fast LCA for monte carlo analysis.
License: BSD
URL: https://github.com/oie-mines-paristech/lca_algebraic/
Source0: https://mirrors.aliyun.com/pypi/web/packages/5f/22/fcead9aef2392679979d511e384e0eafea141f0fd606f67dbff52e9fda8b/lca_algebraic-1.0.5.tar.gz
BuildArch: noarch
Requires: python3-tabulate
Requires: python3-ipywidgets
Requires: python3-pandas
Requires: python3-seaborn
Requires: python3-sympy
Requires: python3-matplotlib
Requires: python3-deprecation
Requires: python3-brightway2
Requires: python3-SALib
%description
# Introduction
This library is a small layer above [**brightway2**](https://brightway.dev/), designed for the definition of **parametric inventories**
with fast computation of LCA impacts, suitable for **monte-carlo** analyis.
**lca-algebraic** provides a set of **helper functions** for :
* **compact** & **human readable** definition of activites :
* search background (tech and biosphere) activities
* create new foreground activites 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 sensivity analysis (Sobol indices)
# Installation
If you already have Anaconda & Jupyter installed, you can install the library with either **pip** or **conda** :
## Conda
> conda install -c oie-minesparistech lca_algebraic
## PIP
> pip install lca_algebraic
## Pre-packaged installer for Windows
Alternatively, you can download and execute [this installer](https://github.com/oie-mines-paristech/lca_algebraic/releases/download/1.0.0/incer-acv-model-installer.exe). It will setup a full anaconda environment with **Jupyter**,
**Brightway2** and **LCA Algebraic**.
# Usage & documentation
Please refer to the [sample notebook (Markdown)](./example-notebook.md) [(or here as ipynb)](./example-notebook.ipynb).
The full API is [documented here](https://oie-mines-paristech.github.io/lca_algebraic/doc/).
# Licence & Copyright
This library has been developed by [OIE - MinesParistech](http://www.oie.mines-paristech.fr), for the project *INCER-ACV*,
lead by [ADEME](https://www.ademe.fr/).
It is distributed under the **BSD licence**.
# Principles
The main idea of this libray is to move from **procedural definition** of models (slow and prone to errors) to a **declarative / purely functionnal** definition of parametric models (models as **pure functions**).
This enables **fast computation of LCA impacts**.
We leverage the **power of symbolic calculus** provided by the great libary [SymPy](https://www.sympy.org/en/index.html).
We define our model in a **separate DB**, as a nested combination of :
* other foreground activities
* background activities :
* Technical, refering **ecoinvent DB**
* Biopshere, refering **brightway2** biosphere activities
The **amounts** in exchanges are expressed either as **static amounts**, or **symbolic expressions** of pre-defined **parameters**.
Each activity of our **root model** is defined as a **parametrized combination** of the **foreground activities**, which can themselves be expressed by the **background activities**.
When computing LCA for foreground models, the library develops the model as a combination of **only background activities**. It computes **once for all** the impact of **background activities** and compiles a **fast numpy** (vectorial) function for each impact, replacing each background activity by the **static value of the corresponding impact**.
By providing **large vectors** of **parameter values** to those numpy functions, we can compute LCA for **thousands of values** at a time.
# Compatibility with brightway2
Under the hood, the activities we define with **lca-algebraic** are standard **brightway2** activities.
The amounts of exchanges are stored as **float values** or **serialized as string** in the property **formula**.
Parameters are also stored in the **brightay2** projets, making it fully compatible with **brightway**.
Thus, a model defined with **lca-algebraic** is stored as a regular **bw2** projet. We can use **bw2** native support for [parametrized dataset](https://2.docs.brightway.dev/intro.html#parameterized-datasets) for computing LCAs, even if much more slower than the method explain here.
%package -n python3-lca-algebraic
Summary: This library provides a layer above brightway2 for defining parametric models and running super fast LCA for monte carlo analysis.
Provides: python-lca-algebraic
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-lca-algebraic
# Introduction
This library is a small layer above [**brightway2**](https://brightway.dev/), designed for the definition of **parametric inventories**
with fast computation of LCA impacts, suitable for **monte-carlo** analyis.
**lca-algebraic** provides a set of **helper functions** for :
* **compact** & **human readable** definition of activites :
* search background (tech and biosphere) activities
* create new foreground activites 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 sensivity analysis (Sobol indices)
# Installation
If you already have Anaconda & Jupyter installed, you can install the library with either **pip** or **conda** :
## Conda
> conda install -c oie-minesparistech lca_algebraic
## PIP
> pip install lca_algebraic
## Pre-packaged installer for Windows
Alternatively, you can download and execute [this installer](https://github.com/oie-mines-paristech/lca_algebraic/releases/download/1.0.0/incer-acv-model-installer.exe). It will setup a full anaconda environment with **Jupyter**,
**Brightway2** and **LCA Algebraic**.
# Usage & documentation
Please refer to the [sample notebook (Markdown)](./example-notebook.md) [(or here as ipynb)](./example-notebook.ipynb).
The full API is [documented here](https://oie-mines-paristech.github.io/lca_algebraic/doc/).
# Licence & Copyright
This library has been developed by [OIE - MinesParistech](http://www.oie.mines-paristech.fr), for the project *INCER-ACV*,
lead by [ADEME](https://www.ademe.fr/).
It is distributed under the **BSD licence**.
# Principles
The main idea of this libray is to move from **procedural definition** of models (slow and prone to errors) to a **declarative / purely functionnal** definition of parametric models (models as **pure functions**).
This enables **fast computation of LCA impacts**.
We leverage the **power of symbolic calculus** provided by the great libary [SymPy](https://www.sympy.org/en/index.html).
We define our model in a **separate DB**, as a nested combination of :
* other foreground activities
* background activities :
* Technical, refering **ecoinvent DB**
* Biopshere, refering **brightway2** biosphere activities
The **amounts** in exchanges are expressed either as **static amounts**, or **symbolic expressions** of pre-defined **parameters**.
Each activity of our **root model** is defined as a **parametrized combination** of the **foreground activities**, which can themselves be expressed by the **background activities**.
When computing LCA for foreground models, the library develops the model as a combination of **only background activities**. It computes **once for all** the impact of **background activities** and compiles a **fast numpy** (vectorial) function for each impact, replacing each background activity by the **static value of the corresponding impact**.
By providing **large vectors** of **parameter values** to those numpy functions, we can compute LCA for **thousands of values** at a time.
# Compatibility with brightway2
Under the hood, the activities we define with **lca-algebraic** are standard **brightway2** activities.
The amounts of exchanges are stored as **float values** or **serialized as string** in the property **formula**.
Parameters are also stored in the **brightay2** projets, making it fully compatible with **brightway**.
Thus, a model defined with **lca-algebraic** is stored as a regular **bw2** projet. We can use **bw2** native support for [parametrized dataset](https://2.docs.brightway.dev/intro.html#parameterized-datasets) for computing LCAs, even if much more slower than the method explain here.
%package help
Summary: Development documents and examples for lca-algebraic
Provides: python3-lca-algebraic-doc
%description help
# Introduction
This library is a small layer above [**brightway2**](https://brightway.dev/), designed for the definition of **parametric inventories**
with fast computation of LCA impacts, suitable for **monte-carlo** analyis.
**lca-algebraic** provides a set of **helper functions** for :
* **compact** & **human readable** definition of activites :
* search background (tech and biosphere) activities
* create new foreground activites 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 sensivity analysis (Sobol indices)
# Installation
If you already have Anaconda & Jupyter installed, you can install the library with either **pip** or **conda** :
## Conda
> conda install -c oie-minesparistech lca_algebraic
## PIP
> pip install lca_algebraic
## Pre-packaged installer for Windows
Alternatively, you can download and execute [this installer](https://github.com/oie-mines-paristech/lca_algebraic/releases/download/1.0.0/incer-acv-model-installer.exe). It will setup a full anaconda environment with **Jupyter**,
**Brightway2** and **LCA Algebraic**.
# Usage & documentation
Please refer to the [sample notebook (Markdown)](./example-notebook.md) [(or here as ipynb)](./example-notebook.ipynb).
The full API is [documented here](https://oie-mines-paristech.github.io/lca_algebraic/doc/).
# Licence & Copyright
This library has been developed by [OIE - MinesParistech](http://www.oie.mines-paristech.fr), for the project *INCER-ACV*,
lead by [ADEME](https://www.ademe.fr/).
It is distributed under the **BSD licence**.
# Principles
The main idea of this libray is to move from **procedural definition** of models (slow and prone to errors) to a **declarative / purely functionnal** definition of parametric models (models as **pure functions**).
This enables **fast computation of LCA impacts**.
We leverage the **power of symbolic calculus** provided by the great libary [SymPy](https://www.sympy.org/en/index.html).
We define our model in a **separate DB**, as a nested combination of :
* other foreground activities
* background activities :
* Technical, refering **ecoinvent DB**
* Biopshere, refering **brightway2** biosphere activities
The **amounts** in exchanges are expressed either as **static amounts**, or **symbolic expressions** of pre-defined **parameters**.
Each activity of our **root model** is defined as a **parametrized combination** of the **foreground activities**, which can themselves be expressed by the **background activities**.
When computing LCA for foreground models, the library develops the model as a combination of **only background activities**. It computes **once for all** the impact of **background activities** and compiles a **fast numpy** (vectorial) function for each impact, replacing each background activity by the **static value of the corresponding impact**.
By providing **large vectors** of **parameter values** to those numpy functions, we can compute LCA for **thousands of values** at a time.
# Compatibility with brightway2
Under the hood, the activities we define with **lca-algebraic** are standard **brightway2** activities.
The amounts of exchanges are stored as **float values** or **serialized as string** in the property **formula**.
Parameters are also stored in the **brightay2** projets, making it fully compatible with **brightway**.
Thus, a model defined with **lca-algebraic** is stored as a regular **bw2** projet. We can use **bw2** native support for [parametrized dataset](https://2.docs.brightway.dev/intro.html#parameterized-datasets) for computing LCAs, even if much more slower than the method explain here.
%prep
%autosetup -n lca_algebraic-1.0.5
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-lca-algebraic -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.5-1
- Package Spec generated
|
