Metadata-Version: 2.1
Name: trackintel
Version: 0.4.1
Summary: Human mobility and movement analysis framework.
Home-page: https://github.com/mie-lab/trackintel
Author: Dominik Bucher, Henry Martin, Ye Hong
Author-email: dobucher@ethz.ch, martinhe@ethz.ch
License: MIT
Description: 
        # The trackintel Framework
        
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/mie-lab/trackintel/master?filepath=%2Fexamples%2Fexample_geolife%2FTrackintel_introduction.ipynb)
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        [![Build Status](https://travis-ci.org/mie-lab/trackintel.svg?branch=master)](https://travis-ci.org/mie-lab/trackintel)
        [![Documentation Status](https://readthedocs.org/projects/trackintel/badge/?version=latest)](https://trackintel.readthedocs.io/en/latest/?badge=latest)
        [![codecov.io](https://codecov.io/gh/mie-lab/trackintel/coverage.svg?branch=master)](https://codecov.io/gh/mie-lab/trackintel)
                  
        Focusing on human mobility data, *trackintel* provides functionalities for data quality enhancement, integrating data from various sources, performing quantitative analysis and mining tasks, and visualizing the data and/or analysis results.
        In addition to these core functionalities, packages are provided for user mobility profiling and trajectory-based learning analytics.
        
        You can find the documentation on the [trackintel documentation page](https://trackintel.readthedocs.io/en/latest).
        
        ## Target Users and Assumptions
        
        *trackintel* is intended for use mainly by researchers with:
        
        * Programming experience in Python
        * Proficiency in movement data mining and analysis
        
        These assumptions concern your data:
        
        * Movement data exists in csv, (geo)json, gpx or PostGIS format
        * Movement data consists of points with x,y-coordinates, a time stamp, a user ID, and an optional accuracy
        * One of the following transportation modes was used at any time: car, walking, bike, bus, tram, train, plane, ship, e-car, e-bike
        * The tracking data can be reasonably segmented into 
          * positionfixes (raw tracking points)
          * triplegs (or stages) (aggregated tracking points based on the transport mode)
          * trips (aggregated activities based on the visited destination / staypoint)
          * tours (aggregated trips starting / ending at the same location / staypoint)
        
        ## Installation and Usage
        
        This is not on [pypi.org](https://pypi.org/) yet, so to install you have to `git clone` the repository and install it with `pip install .` or `pipenv install -e .`.
        If you choose the second approach and you are on Windows, you might have to install individual wheels (e.g., from https://www.lfd.uci.edu/~gohlke/pythonlibs).
        For this, activate the environment using `pipenv shell` and install everything using `pip install ...` (in particular: `GDAL`, `numpy`, `sklean`, `Rtree`, `fiona` and `osmnx`).
        You can quit this shell at any time using `exit`.
        
        You should then be able to run the examples in the `examples` folder or import trackintel using:
        ```{python}
        import trackintel
        ```
        
        ## Development
        
        You can install *trackintel* locally using `pip install .`.
        For quick testing, use `trackintel.print_version()`.
        
        Testing is done using [pytest](https://docs.pytest.org/en/latest).
        Simply run the tests using `pytest` in the top-level trackintel folder.
        In case you use `pipenv`, install *pytest* first (`pip install pytest`), then run *pytest* using this version: `python -m pytest`.
        The use of [fixtures](https://pypi.org/project/fixtures/) for data generation (e.g., trips and trackpoints) is still an open todo.
        As for now, there are some smaller datasets in the `tests` folder.
        
        Versions use [semantic numbering](https://semver.org/).
        Commits follow the standard of [Conventional Commits](https://www.conventionalcommits.org).
        You can generate them easily using [Commitizen](https://github.com/commitizen/cz-cli).
        
        You can find the development roadmap under `ROADMAP.md`.
        
        ### Documentation
        
        The documentation follws the [pandas resp. numpy docstring standard](https://pandas-docs.github.io/pandas-docs-travis/development/contributing.html#contributing-to-the-documentation).
        In particular, it uses [Sphinx](http://www.sphinx-doc.org/en/master/) to create the documentation.
        You can install Sphinx using `pip install -U sphinx` or `conda install sphinx`.
        
        If you use additional dependencies during development, do not forget to add them to `autodoc_mock_imports` in `docs/conf.py` for readthedocs.org to work properly.
        
        You can then generate the documentation using `sphinx-build -b html docs docs.gen`.
        This will put the documentation in `docs.gen`, which is in `.gitignore`.
        
        ### Continuous Integration
        
        There are travis and appveyor CIs set up for Unix/Windows builds.
        You can find the corresponding scripts in `.travis.yml` and `appveyor.yml`.
        Adding [Coveralls](https://coveralls.io) is an open todo.
        
        ## Contributors
        
        trackintel is primarily maintained by the Mobility Information Engineering Lab at ETH Zurich ([mie-lab.ethz.ch](http://mie-lab.ethz.ch)).
        If you want to contribute, send a pull request and put yourself in the `AUTHORS.md` file.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
