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Label and track unique geospatial features from gridded datasets

Project description

ocetrac

Build Status codecov conda-forge pypi downloads Documentation Status License:MIT

Ocetrac is a Python 3.6+ packaged designed to label and track unique geospatial features from gridded datasets. The package is designed to accept data that have already been preprocessed, meaning that the data only contain values the user is interested in tracking. Ocetrac operates lazily with Dask so that it is memory uninhibited and fast through parallelized execution. We provide examples and demonstrate best practices as developed by the Climate Data Science Lab at Columbia University / Lamont-Doherty Earth Observatory.

When using this software, please cite the package as: ...

Installation

Conda

To install the core package from conda-forge run: conda install -c conda-forge ocetrac

PyPI

To install the core package run: pip install ocetrac.

GitHub

  1. Clone ocetrac to your local machine: git clone https://github.com/ocetrac/ocetrac.git
  2. Change to the parent directory of ocetrac
  3. Install ocetrac with pip install -e ./ocetrac. This will allow changes you make locally, to be reflected when you import the package in Python.

How you can contribute

  • You can get involved by trying ocetrac, filing issues if you find problems, and making pull requests if you make improvements.

Acknowledgements

  • We rely heavily on scikit-image and its community of contributors.
  • This work grew from a collabortion with NCAR during the ASP Graduate Visitor Program attended by Hillary Scannell. This project recieved continued support from the Leonardo DiCaprio Foundation, Microsoft, and the Gordon and Betty Moore Foundation.

Project details


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Files for ocetrac, version 0.1.3
Filename, size File type Python version Upload date Hashes
Filename, size ocetrac-0.1.3-py3-none-any.whl (6.5 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size ocetrac-0.1.3.tar.gz (748.8 kB) File type Source Python version None Upload date Hashes View

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