path: root/python-carculator.spec

%global _empty_manifest_terminate_build 0
Name:		python-carculator
Version:	1.8.2
Release:	1
Summary:	Prospective life cycle assessment of vehicles made blazing fast
License:	BSD 3-Clause License  Copyright (c) 2020, Paul Scherrer Institut  Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:  * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.  * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.  * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 
URL:		https://github.com/romainsacchi/carculator
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/4b/5a/07279103357557dd72b36f15aca8371ef058310ab647f2543b5589581590/carculator-1.8.2.tar.gz
BuildArch:	noarch

Requires:	python3-carculator-utils

%description
# ``carculator``

<p align="center">
  <img style="height:130px;" src="https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/mediumsmall.png">
</p>

<p align="center">
  <a href="https://badge.fury.io/py/carculator" target="_blank"><img src="https://badge.fury.io/py/carculator.svg"></a>
  <a href="https://github.com/romainsacchi/carculator" target="_blank"><img src="https://github.com/romainsacchi/carculator/actions/workflows/main.yml/badge.svg?branch=master"></a>
  <a href="https://coveralls.io/github/romainsacchi/carculator" target="_blank"><img src="https://coveralls.io/repos/github/romainsacchi/carculator/badge.svg"></a>
  <a href="https://carculator.readthedocs.io/en/latest/" target="_blank"><img src="https://readthedocs.org/projects/carculator/badge/?version=latest"></a>
  <a href="https://doi.org/10.5281/zenodo.3778259"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.3778259.svg" alt="DOI"></a>
</p>

Prospective environmental and economic life cycle assessment of vehicles made blazing fast.

A fully parameterized Python model developed by the [Technology Assessment group](https://www.psi.ch/en/ta) of the
[Paul Scherrer Institut](https://www.psi.ch/en) to perform life cycle assessments (LCA) of passenger cars and light-duty vehicles.

See [the documentation](https://carculator.readthedocs.io/en/latest/index.html) for more detail, validation, etc.

See our [examples notebook](https://github.com/romainsacchi/carculator/blob/master/examples/Examples.ipynb) as well.

## Table of Contents

- [Background](#background)
  - [What is Life Cycle Assessment](#what-is-life-cycle-assessment)
  - [Why carculator](#why-carculator)
- [Install](#install)
- [Usage](#usage)
  - [As a Python library](#as-a-python-library)
  - [As a web app](#as-a-web-app)
- [Support](#support)
- [Maintainers](#maintainers)
- [Contributing](#contributing)
- [License](#license)

## Background

### What is Life Cycle Assessment?

Life Cycle Assessment (LCA) is a systematic way of accounting for environmental impacts along the relevant phases of the life of a product or service.
Typically, the LCA of a passenger vehicle includes the raw material extraction, the manufacture of the vehicle, its distribution, use and maintenance, as well as its disposal.
The compiled inventories of material and energy required along the life cycle of the vehicle is characterized against some impact categories (e.g., climate change).

In the research field of mobility, LCA is widely used to investigate the superiority of a technology over another one.

### Why ``carculator``?

``carculator`` allows to:
* produce [life cycle assessment (LCA)](https://en.wikipedia.org/wiki/Life-cycle_assessment) results that include conventional midpoint impact assessment indicators as well cost indicators
*  ``carculator`` uses time- and energy scenario-differentiated background inventories for the future, based on outputs of Integrated Asessment Model [REMIND](https://www.pik-potsdam.de/research/transformation-pathways/models/remind/remind). 
* calculate hot pollutant and noise emissions based on a specified driving cycle
* produce error propagation analyzes (i.e., Monte Carlo) while preserving relations between inputs and outputs
* control all the parameters sensitive to the foreground model (i.e., the vehicles) but also to the background model
(i.e., supply of fuel, battery chemistry, etc.)
* and easily export the vehicle models as inventories to be further imported in the [Brightway2](https://brightwaylca.org/) LCA framework
  or the [SimaPro](https://www.simapro.com/) LCA software.

``carculator`` integrates well with the [Brightway](https://brightwaylca.org/) LCA framework.

``carculator`` was built based on work described in [Uncertain environmental footprint of current and future battery electric vehicles by Cox, et al (2018)](https://pubs.acs.org/doi/abs/10.1021/acs.est.8b00261).

## Install

``carculator`` is at an early stage of development and is subject to continuous change and improvement.
Three ways of installing ``carculator`` are suggested.

We recommend the installation on **Python 3.7 or above**.

### Installation of the latest version, using conda

    conda install -c romainsacchi carculator

### Installation of a stable release from Pypi

    pip install carculator

## Usage

### As a Python library

Calculate the fuel efficiency (or ``Tank to wheel`` energy requirement) in km/L of petrol-equivalent of current SUVs for the driving cycle WLTC 3.4
over 800 Monte Carlo iterations:

```python

    from carculator import *
    import matplotlib.pyplot as plt
    
    cip = CarInputParameters()
    cip.stochastic(800)
    dcts, array = fill_xarray_from_input_parameters(cip)
    cm = CarModel(array, cycle='WLTC 3.4')
    cm.set_all()
    TtW_energy = 1 / (cm.array.sel(size='SUV', year=2020, parameter='TtW energy') / 42000)  # assuming 42 MJ/L petrol
    
    l_powertrains = TtW_energy.powertrain
    [plt.hist(e, bins=50, alpha=.8, label=e.powertrain.values) for e in TtW_energy]
    plt.xlabel('km/L petrol-equivalent')
    plt.ylabel('number of iterations')
    plt.legend()
```

![MC results](https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/stochastic_example_ttw.png)

Compare the carbon footprint of electric vehicles with that of rechargeable hybrid vehicles for different size categories today and in the future
over 500 Monte Carlo iterations:

```python

    from carculator import *
    cip = CarInputParameters()
    cip.stochastic(500)
    dcts, array = fill_xarray_from_input_parameters(cip)
    cm = CarModel(array, cycle='WLTC')
    cm.set_all()
    scope = {
      'powertrain': ['BEV', 'PHEV'],
    }
    ic = InventoryCalculation(cm)
    
    results = ic.calculate_impacts()
    data_MC = results.sel(impact_category='climate change').sum(axis=3).to_dataframe('climate change')
    plt.style.use('seaborn')
    data_MC.unstack(level=[0, 1, 2]).boxplot(showfliers=False, figsize=(20, 5))
    plt.xticks(rotation=70)
    plt.ylabel('kg CO2-eq./vkm')
```

![MC results](https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/example_stochastic_BEV_PHEV.png)

For more examples, see [examples](https://github.com/romainsacchi/carculator/blob/master/examples/Examples.ipynb).

## As a Web app

``carculator`` has a [graphical user interface](https://carculator.psi.ch) for fast comparisons of vehicles.

## Support

Do not hesitate to contact the development team at [carculator@psi.ch](mailto:carculator@psi.ch).

## Maintainers

* [Romain Sacchi](https://github.com/romainsacchi)
* [Chris Mutel](https://github.com/cmutel/)

## Contributing

See [contributing](https://github.com/romainsacchi/carculator/blob/master/CONTRIBUTING.md).

## License

[BSD-3-Clause](https://github.com/romainsacchi/carculator/blob/master/LICENSE). Copyright 2023 Paul Scherrer Institut.


%package -n python3-carculator
Summary:	Prospective life cycle assessment of vehicles made blazing fast
Provides:	python-carculator
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-carculator
# ``carculator``

<p align="center">
  <img style="height:130px;" src="https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/mediumsmall.png">
</p>

<p align="center">
  <a href="https://badge.fury.io/py/carculator" target="_blank"><img src="https://badge.fury.io/py/carculator.svg"></a>
  <a href="https://github.com/romainsacchi/carculator" target="_blank"><img src="https://github.com/romainsacchi/carculator/actions/workflows/main.yml/badge.svg?branch=master"></a>
  <a href="https://coveralls.io/github/romainsacchi/carculator" target="_blank"><img src="https://coveralls.io/repos/github/romainsacchi/carculator/badge.svg"></a>
  <a href="https://carculator.readthedocs.io/en/latest/" target="_blank"><img src="https://readthedocs.org/projects/carculator/badge/?version=latest"></a>
  <a href="https://doi.org/10.5281/zenodo.3778259"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.3778259.svg" alt="DOI"></a>
</p>

Prospective environmental and economic life cycle assessment of vehicles made blazing fast.

A fully parameterized Python model developed by the [Technology Assessment group](https://www.psi.ch/en/ta) of the
[Paul Scherrer Institut](https://www.psi.ch/en) to perform life cycle assessments (LCA) of passenger cars and light-duty vehicles.

See [the documentation](https://carculator.readthedocs.io/en/latest/index.html) for more detail, validation, etc.

See our [examples notebook](https://github.com/romainsacchi/carculator/blob/master/examples/Examples.ipynb) as well.

## Table of Contents

- [Background](#background)
  - [What is Life Cycle Assessment](#what-is-life-cycle-assessment)
  - [Why carculator](#why-carculator)
- [Install](#install)
- [Usage](#usage)
  - [As a Python library](#as-a-python-library)
  - [As a web app](#as-a-web-app)
- [Support](#support)
- [Maintainers](#maintainers)
- [Contributing](#contributing)
- [License](#license)

## Background

### What is Life Cycle Assessment?

Life Cycle Assessment (LCA) is a systematic way of accounting for environmental impacts along the relevant phases of the life of a product or service.
Typically, the LCA of a passenger vehicle includes the raw material extraction, the manufacture of the vehicle, its distribution, use and maintenance, as well as its disposal.
The compiled inventories of material and energy required along the life cycle of the vehicle is characterized against some impact categories (e.g., climate change).

In the research field of mobility, LCA is widely used to investigate the superiority of a technology over another one.

### Why ``carculator``?

``carculator`` allows to:
* produce [life cycle assessment (LCA)](https://en.wikipedia.org/wiki/Life-cycle_assessment) results that include conventional midpoint impact assessment indicators as well cost indicators
*  ``carculator`` uses time- and energy scenario-differentiated background inventories for the future, based on outputs of Integrated Asessment Model [REMIND](https://www.pik-potsdam.de/research/transformation-pathways/models/remind/remind). 
* calculate hot pollutant and noise emissions based on a specified driving cycle
* produce error propagation analyzes (i.e., Monte Carlo) while preserving relations between inputs and outputs
* control all the parameters sensitive to the foreground model (i.e., the vehicles) but also to the background model
(i.e., supply of fuel, battery chemistry, etc.)
* and easily export the vehicle models as inventories to be further imported in the [Brightway2](https://brightwaylca.org/) LCA framework
  or the [SimaPro](https://www.simapro.com/) LCA software.

``carculator`` integrates well with the [Brightway](https://brightwaylca.org/) LCA framework.

``carculator`` was built based on work described in [Uncertain environmental footprint of current and future battery electric vehicles by Cox, et al (2018)](https://pubs.acs.org/doi/abs/10.1021/acs.est.8b00261).

## Install

``carculator`` is at an early stage of development and is subject to continuous change and improvement.
Three ways of installing ``carculator`` are suggested.

We recommend the installation on **Python 3.7 or above**.

### Installation of the latest version, using conda

    conda install -c romainsacchi carculator

### Installation of a stable release from Pypi

    pip install carculator

## Usage

### As a Python library

Calculate the fuel efficiency (or ``Tank to wheel`` energy requirement) in km/L of petrol-equivalent of current SUVs for the driving cycle WLTC 3.4
over 800 Monte Carlo iterations:

```python

    from carculator import *
    import matplotlib.pyplot as plt
    
    cip = CarInputParameters()
    cip.stochastic(800)
    dcts, array = fill_xarray_from_input_parameters(cip)
    cm = CarModel(array, cycle='WLTC 3.4')
    cm.set_all()
    TtW_energy = 1 / (cm.array.sel(size='SUV', year=2020, parameter='TtW energy') / 42000)  # assuming 42 MJ/L petrol
    
    l_powertrains = TtW_energy.powertrain
    [plt.hist(e, bins=50, alpha=.8, label=e.powertrain.values) for e in TtW_energy]
    plt.xlabel('km/L petrol-equivalent')
    plt.ylabel('number of iterations')
    plt.legend()
```

![MC results](https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/stochastic_example_ttw.png)

Compare the carbon footprint of electric vehicles with that of rechargeable hybrid vehicles for different size categories today and in the future
over 500 Monte Carlo iterations:

```python

    from carculator import *
    cip = CarInputParameters()
    cip.stochastic(500)
    dcts, array = fill_xarray_from_input_parameters(cip)
    cm = CarModel(array, cycle='WLTC')
    cm.set_all()
    scope = {
      'powertrain': ['BEV', 'PHEV'],
    }
    ic = InventoryCalculation(cm)
    
    results = ic.calculate_impacts()
    data_MC = results.sel(impact_category='climate change').sum(axis=3).to_dataframe('climate change')
    plt.style.use('seaborn')
    data_MC.unstack(level=[0, 1, 2]).boxplot(showfliers=False, figsize=(20, 5))
    plt.xticks(rotation=70)
    plt.ylabel('kg CO2-eq./vkm')
```

![MC results](https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/example_stochastic_BEV_PHEV.png)

For more examples, see [examples](https://github.com/romainsacchi/carculator/blob/master/examples/Examples.ipynb).

## As a Web app

``carculator`` has a [graphical user interface](https://carculator.psi.ch) for fast comparisons of vehicles.

## Support

Do not hesitate to contact the development team at [carculator@psi.ch](mailto:carculator@psi.ch).

## Maintainers

* [Romain Sacchi](https://github.com/romainsacchi)
* [Chris Mutel](https://github.com/cmutel/)

## Contributing

See [contributing](https://github.com/romainsacchi/carculator/blob/master/CONTRIBUTING.md).

## License

[BSD-3-Clause](https://github.com/romainsacchi/carculator/blob/master/LICENSE). Copyright 2023 Paul Scherrer Institut.


%package help
Summary:	Development documents and examples for carculator
Provides:	python3-carculator-doc
%description help
# ``carculator``

<p align="center">
  <img style="height:130px;" src="https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/mediumsmall.png">
</p>

<p align="center">
  <a href="https://badge.fury.io/py/carculator" target="_blank"><img src="https://badge.fury.io/py/carculator.svg"></a>
  <a href="https://github.com/romainsacchi/carculator" target="_blank"><img src="https://github.com/romainsacchi/carculator/actions/workflows/main.yml/badge.svg?branch=master"></a>
  <a href="https://coveralls.io/github/romainsacchi/carculator" target="_blank"><img src="https://coveralls.io/repos/github/romainsacchi/carculator/badge.svg"></a>
  <a href="https://carculator.readthedocs.io/en/latest/" target="_blank"><img src="https://readthedocs.org/projects/carculator/badge/?version=latest"></a>
  <a href="https://doi.org/10.5281/zenodo.3778259"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.3778259.svg" alt="DOI"></a>
</p>

Prospective environmental and economic life cycle assessment of vehicles made blazing fast.

A fully parameterized Python model developed by the [Technology Assessment group](https://www.psi.ch/en/ta) of the
[Paul Scherrer Institut](https://www.psi.ch/en) to perform life cycle assessments (LCA) of passenger cars and light-duty vehicles.

See [the documentation](https://carculator.readthedocs.io/en/latest/index.html) for more detail, validation, etc.

See our [examples notebook](https://github.com/romainsacchi/carculator/blob/master/examples/Examples.ipynb) as well.

## Table of Contents

- [Background](#background)
  - [What is Life Cycle Assessment](#what-is-life-cycle-assessment)
  - [Why carculator](#why-carculator)
- [Install](#install)
- [Usage](#usage)
  - [As a Python library](#as-a-python-library)
  - [As a web app](#as-a-web-app)
- [Support](#support)
- [Maintainers](#maintainers)
- [Contributing](#contributing)
- [License](#license)

## Background

### What is Life Cycle Assessment?

Life Cycle Assessment (LCA) is a systematic way of accounting for environmental impacts along the relevant phases of the life of a product or service.
Typically, the LCA of a passenger vehicle includes the raw material extraction, the manufacture of the vehicle, its distribution, use and maintenance, as well as its disposal.
The compiled inventories of material and energy required along the life cycle of the vehicle is characterized against some impact categories (e.g., climate change).

In the research field of mobility, LCA is widely used to investigate the superiority of a technology over another one.

### Why ``carculator``?

``carculator`` allows to:
* produce [life cycle assessment (LCA)](https://en.wikipedia.org/wiki/Life-cycle_assessment) results that include conventional midpoint impact assessment indicators as well cost indicators
*  ``carculator`` uses time- and energy scenario-differentiated background inventories for the future, based on outputs of Integrated Asessment Model [REMIND](https://www.pik-potsdam.de/research/transformation-pathways/models/remind/remind). 
* calculate hot pollutant and noise emissions based on a specified driving cycle
* produce error propagation analyzes (i.e., Monte Carlo) while preserving relations between inputs and outputs
* control all the parameters sensitive to the foreground model (i.e., the vehicles) but also to the background model
(i.e., supply of fuel, battery chemistry, etc.)
* and easily export the vehicle models as inventories to be further imported in the [Brightway2](https://brightwaylca.org/) LCA framework
  or the [SimaPro](https://www.simapro.com/) LCA software.

``carculator`` integrates well with the [Brightway](https://brightwaylca.org/) LCA framework.

``carculator`` was built based on work described in [Uncertain environmental footprint of current and future battery electric vehicles by Cox, et al (2018)](https://pubs.acs.org/doi/abs/10.1021/acs.est.8b00261).

## Install

``carculator`` is at an early stage of development and is subject to continuous change and improvement.
Three ways of installing ``carculator`` are suggested.

We recommend the installation on **Python 3.7 or above**.

### Installation of the latest version, using conda

    conda install -c romainsacchi carculator

### Installation of a stable release from Pypi

    pip install carculator

## Usage

### As a Python library

Calculate the fuel efficiency (or ``Tank to wheel`` energy requirement) in km/L of petrol-equivalent of current SUVs for the driving cycle WLTC 3.4
over 800 Monte Carlo iterations:

```python

    from carculator import *
    import matplotlib.pyplot as plt
    
    cip = CarInputParameters()
    cip.stochastic(800)
    dcts, array = fill_xarray_from_input_parameters(cip)
    cm = CarModel(array, cycle='WLTC 3.4')
    cm.set_all()
    TtW_energy = 1 / (cm.array.sel(size='SUV', year=2020, parameter='TtW energy') / 42000)  # assuming 42 MJ/L petrol
    
    l_powertrains = TtW_energy.powertrain
    [plt.hist(e, bins=50, alpha=.8, label=e.powertrain.values) for e in TtW_energy]
    plt.xlabel('km/L petrol-equivalent')
    plt.ylabel('number of iterations')
    plt.legend()
```

![MC results](https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/stochastic_example_ttw.png)

Compare the carbon footprint of electric vehicles with that of rechargeable hybrid vehicles for different size categories today and in the future
over 500 Monte Carlo iterations:

```python

    from carculator import *
    cip = CarInputParameters()
    cip.stochastic(500)
    dcts, array = fill_xarray_from_input_parameters(cip)
    cm = CarModel(array, cycle='WLTC')
    cm.set_all()
    scope = {
      'powertrain': ['BEV', 'PHEV'],
    }
    ic = InventoryCalculation(cm)
    
    results = ic.calculate_impacts()
    data_MC = results.sel(impact_category='climate change').sum(axis=3).to_dataframe('climate change')
    plt.style.use('seaborn')
    data_MC.unstack(level=[0, 1, 2]).boxplot(showfliers=False, figsize=(20, 5))
    plt.xticks(rotation=70)
    plt.ylabel('kg CO2-eq./vkm')
```

![MC results](https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/example_stochastic_BEV_PHEV.png)

For more examples, see [examples](https://github.com/romainsacchi/carculator/blob/master/examples/Examples.ipynb).

## As a Web app

``carculator`` has a [graphical user interface](https://carculator.psi.ch) for fast comparisons of vehicles.

## Support

Do not hesitate to contact the development team at [carculator@psi.ch](mailto:carculator@psi.ch).

## Maintainers

* [Romain Sacchi](https://github.com/romainsacchi)
* [Chris Mutel](https://github.com/cmutel/)

## Contributing

See [contributing](https://github.com/romainsacchi/carculator/blob/master/CONTRIBUTING.md).

## License

[BSD-3-Clause](https://github.com/romainsacchi/carculator/blob/master/LICENSE). Copyright 2023 Paul Scherrer Institut.


%prep
%autosetup -n carculator-1.8.2

%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-carculator -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 1.8.2-1
- Package Spec generated