YETI is a tool for street level bottom-up traffic emission calculation. It helps you create high-resolution traffic emission inventories.
YETI supports common emission calculation methodologies like COPERT or HBEFA. It was originally built to work with data for the City of Berlin, but is flexible enough to be adopted to different datasets and regions.
This README is intended as a first introduction to the project. For more detailed information, see the docs.
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This project requires Python 3.6 or above. You can find our your Python version by running python --version on the command line. If your Python version is below 3.6, please upgrade to a newer version.
Note that YETI is tested for Python 3.6 and 3.7. However it should also work with newer Python versions. When in doubt run the tests on your computer. If they pass you are good to go.
Clone the GitHub repositiory by running git clone https://github.com/twollnik/YETI.git on the command line. You need to have git installed for this step. If you don't have git, get it here.
These directories will be downloaded: code, diagrams, docs, example, and tests.
Install dependencies with pip by running pip install -r requirements.txt on the command line from the repository root directory. If you want to do development work you should also install dev dependencies: pip install -r requirements-dev.txt.
We have included example configuration files and example data for you to try out. You can find the example files in the folder example/. To run the demo, execute the following command on the command line from the repository root directory: python -m run_yeti -c example/example_configs/copert_hot_config.yaml. Instead of the copert_hot_config.yaml you can use any of the config files in example/example_configs/.
All interactions with YETI use the script run_yeti.py. Run the script on the command line: python -m run_yeti. Make sure to run the script from the repository root directory.
run_yeti.py uses a configuration file in YAML format where a Strategy for the emission calculation method is defined together with all the necessary input/output file locations and other parameters.
You may specify the location of the config file: python -m run_yeti -c path/to/config.yaml. If you don't specify a location for the config file explicitly, the path ./config.yaml is used. Look here for more detailed information what should be included in the config file.
You can pass the argument -q to run YETI in quiet mode: python -m run_yeti -q. In quiet mode no DEBUG information will be displayed.
Run python -m run_yeti --help for short usage information.
Output of a model run are one or multiple emissions csv files and a file run_info.txt. All output files will be in the output_folder that you specify in the configuration file.
We include Python unit tests to test most of the YETI code. If you modified the code and want to see if it still works, you may want to execute the tests. Note that the tests are also run on our test server (Travis CI)automatically every time someone pushes to the GitHub repository.
Execute the tests by running make test on the command line from the repository root directory. Note that GNU Make needs to be installed on your computer for this to work. If you don't have GNU Make installed, you can run the tests with python -m unittest tests/*/test*.py tests/test*.py.
YETI is a street level model. This means that the road network you want to calculate emissions for needs to be divided into street links.
Find example datasets in example/example_berlin_format_data and example/example_yeti_format_data.
We differentiate between berlin_format and yeti_format.
berlin_format is data in the format that we were using at the start of this project. It is not ideal for the calculations and needs to be transformed to a different format more suitable for the emissions calculation.
yeti_format is data in a unified format. It defines a layer of abstraction between the berlin_format data and the emission calculation. We provide functions to transform berlin_format data to yeti_format data for all Strategies.
The data that you are working with is likely in a different format than our berlin_format, however chances are that you can tranform your data to fit the yeti_format. If this is the case, you only need to write a function to convert your data to yeti_format. Once this is done you can use YETI with your data and don't need to adapt any other part of the system.
The data requirements depend on how you want to calculate emissions. For example calculating emissions with the COPERT methodology requires different input data than a calculation with the HBEFA methodology.
Take a look at the docs page of the Strategy you want to use to find out about the data requirements for that Strategy.
All data files are csv files. They use comma (' , ') as seperator and the dot (' . ') for decimal points.
We are open for collaboration, however we have limited resources to review contributions.
Anyhow, all contributions should follow these guidelines:
- Code should comply with the PEP8 style guide as much as possible.
- All new features should be tested. YETI uses the built-in
unittestmodule. If you are new to testing in Python, this website is a good starting point: unittest introduction. - We follow a green build policy. This means that all the tests should succeed on the test server before a Pull Request is merged.