Common python tools to deal with waze data
There is a ready workspace at a server with all libraries needed to work with geodata in python. To connect just
go to 200.20.164.155:8080 in you browser
the password has to be asked to @joaocarabetta.
Otherwise, you can run it locally with docker with two steps:
sudo docker build -t wazetools .
sudo docker run -d
-p 8888:8080
-v $projdir:/home/jovyan/work
--user root
-e GRANT_SUDO=yes
wazetools:latest
and connect to it at
localhost:8080
├── 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 that has been transformed.
│ ├── 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. Naming convention is a
│ tag describing its use, accepted tags: [dev], [example], [analysis];
│ number (for ordering) if needed;
│ the creator's name, not needed for examples;
│ short `-` delimited description.
│ `[analysis] c1.0-joaoc-initial-data-exploration`.
│
├── 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 download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
├── tox.ini <- tox file with settings for running tox; see tox.testrun.org
|
└── Dockerfile <- Builds geoprocessing enviorinment
Project based on the cookiecutter data science project template. #cookiecutterdatascience
Repo based on previou work of @JoaoCarabetta