Scikit Data Access Package for accessing scientific data sets.
Project description
- Import scientific data from various sources through one easy Python API.
- Use iterator patterns for each data source (configurable data generators + functions to get next data chunk).
- Skip parser programming and file format handling.
- Enjoy a common namespace for all data and unleash the power of data fusion.
- Handle data distribution in different modes: (1) local download, (2) caching of accessed data, or (3) online stream access
- Easily pull data on cloud servers through Python scripts and facilitate large-scale parallel processing.
- Build on an extensible plattform: Adding access to a new data source only requires addition of its "DataFetcher.py".
- Open source (MIT License)
Supported data sets:
Install
pip install scikit-dataaccess
Documentation
- User Manual: /docs/skdaccess_manual.pdf
- Code documentation (Doxygen): /docs/skdaccess_doxygen.pdf
- Code visualization (treemap): /docs/skdaccess_treemap.png
- Code class diagrams: /docs/class_diagrams
Contributors
Project lead: Victor Pankratius (MIT)
Contributors: Cody M. Rude, Justin D. Li, David M. Blair, Michael G. Gowanlock, Guillaume Rongier, Victor Pankratius
New contributors welcome! Contact to contribute and add interface code for your own datasets :smile:
Acknowledgements
We acknowledge support from NASA AIST14-NNX15AG84G, NASA AIST16-80NSSC17K0125, NSF ACI-1442997, and NSF AGS-1343967.
Examples
Code examples (Jupyter notebooks) for all datasets listed above are available at: /skdaccess/examples
Project details
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scikit-dataaccess-1.9.17.tar.gz
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