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CDAT builds on the following key technologies:

  1. Python and its ecosystem (e.g. NumPy, Matplotlib);
  2. Jupyter Notebooks and iPython;
  3. A toolset developed at LLNL for the analysis, visualization, and management of large-scale distributed climate data;
  4. VTK, the Visualization Toolkit, which is open source software for manipulating and displaying scientific data.

These combined tools, along with others such as the R open-source statistical analysis and plotting software and custom packages (e.g. DV3D), form CDAT and provide a synergistic approach to climate modeling, allowing researchers to advance scientific visualization of large-scale climate data sets. The CDAT framework couples powerful software infrastructures through two primary means:

  1. Tightly coupled integration of the CDAT Core with the VTK infrastructure to provide high-performance, parallel-streaming data analysis and visualization of massive climate-data sets (other tighly coupled tools include VCS, DV3D, and ESMF/ESMP);
  2. Loosely coupled integration to provide the flexibility of using tools quickly in the infrastructure such as ViSUS or R for data analysis and visualization as well as to apply customized data analysis applications within an integrated environment.

Within both paradigms, CDAT will provide data-provenance capture and mechanisms to support data analysis.

CDAT is licensed under the [BSD-3][bds3] license.

We'd love to get contributions from you! Please take a look at the Contribution Documents to see how to get your changes merged in.

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