Metadata-Version: 2.1
Name: py-elvis
Version: 0.2.0
Summary: A planning and management tool for electric vehicles charging infrastructure
Home-page: UNKNOWN
Author: Moritz Markschläger, Jonas Zell, Marcus Voss, Izgh Hadachi
Author-email: moritz.markschlaeger@dai-labor.de
License: UNKNOWN
Description: 
        
        <img src="https://i.imgur.com/CVM5RUD.png" alt="Elvis Logo" height="120px" 
        />
        
        # Electric Vehicle Charging Infrastructure Simulator (ELVIS)
        This repository contains the source code for Elvis, a planning and management tool for electric vehicles charging infrastructure.
        ## Installation
        ### Install using pip
        
        To install the package simply run
        ```bash
        pip install py-elvis
        ```
        This installs the package locally using pip and installs required packages, if not available. 
        
        ### Manually download and locally install the elvis package
        
        This may be useful if you want to add changes to the package. Then download or checkout this repository and in the top level that contains the `setup.py` file, run
        ```bash
        pip install -r requirements.txt
        python setup.py install
        ```
        This installs the package locally using pip and installs required packages, if not available. 
        
        ## Usage
        
        Following, a simple example using one of the pre-defined scenario configurations
        ```python
        from elvis import ScenarioConfig, simulate, num_time_steps
        
        import yaml
        with open("elvis/data/config_builder/office.yaml", 'r') as f:
            yaml_str = yaml.safe_load(f)
        config_from_yaml = ScenarioConfig.from_yaml(yaml_str)
        
        results = simulate(config_from_yaml, start_date='2020-01-01 00:00:00', end_date='2020-12-31 23:00:00', resolution='01:00:00')
        load_profile = results.aggregate_load_profile(8760)
        
        import matplotlib.pyplot as plt
        plt.plot(load_profile)
        ```
        
        ## Acknowledgement
        
        This work was supported in part by Stromnetz Berlin, as well as the Federal Minister for Environment, Nature Conservation and Nuclear Safety (BMU) through the research project [FlexNet4E-Mobility](https://www.erneuerbar-mobil.de/projekte/flexnet4e-mobility) (funding reference 16EM3147-2) and the Federal Ministry for Economic Affairs and Energy (BMWi) throught the project [Neue Berliner Luft](https://www.neueberlinerluft.de/) (funding reference 01MZ18013E).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
