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trajectory-search

transport trajectory search

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data containing candidate trajectory ID and rtree files.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks for visualizing trajectories and compute performance statistics. 
│   ├── pruning_power.py
│   └── trajectory_viz.py
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to process raw data
│   │   └── make_trajectory.py
│   │
│   ├── features       <- Scripts to build rtree
│   │   ├── build_rtree.py
│   │   └── build_bbox.py
│   │
│   ├── models         <- Scripts to search rtrees and then use EDR to compute top-k
│   │   │                 trajectories
│   │   │              <- Script to sequential scan all trajectories and find top-k
│   │   ├── predict_model.py
│   │   ├── search_rtree.py
│   │   └── build_truth.py
│   │
│   └── statistics     <- Scripts to compute top k accuracy of result
│       └── topkAccuracy.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

Project based on the cookiecutter data science project template. #cookiecutterdatascience

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