automatic import of python-lca-algebraicopeneuler20.03

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+/lca_algebraic-1.0.5.tar.gz
diff --git a/python-lca-algebraic.spec b/python-lca-algebraic.spec
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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.
+
+![](https://oie-mines-paristech.github.io/lca_algebraic/doc/lca-algebraic.png)
+
+# 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.
+
+![](https://oie-mines-paristech.github.io/lca_algebraic/doc/lca-algebraic.png)
+
+# 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.
+
+![](https://oie-mines-paristech.github.io/lca_algebraic/doc/lca-algebraic.png)
+
+# 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
diff --git a/sources b/sources
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--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+15cc46206d9e00c7f7d2fa97d65d408c  lca_algebraic-1.0.5.tar.gz