Advancements to Visualization Control System (VCS, part of UV-CDAT), a Visualization Package Designed for Climate Scientists

Climate Data Analysis Tools (UV-CDAT, https://uvcdat.llnl.gov) is a data analysis and visualization software package developed at Lawrence Livermore National Laboratory and designed for climate scientists. Core components of UV-CDAT include: 1) Community Data Management System (CDMS) which provides I/O support and a data model for climate data;2) CDAT Utilities (GenUtil) that processes data using spatial and temporal averaging and statistic functions; and 3) Visualization Control System (VCS) for interactive visualization of the data. VCS is a Python visualization package primarily built for climate scientists, however, because of its generality and breadth of functionality, it can be a useful tool to other scientific applications. VCS provides 1D, 2D and 3D visualization functions such as scatter plot and line graphs for 1d data, boxfill, meshfill, isofill, isoline for 2d scalar data, vector glyphs and streamlines for 2d vector data and 3d_scalar and 3d_vector for 3d data. Specifically for climate data our plotting routines include projections, Skew-T plots and Taylor diagrams. While VCS provided a user-friendly API, the previous implementation of VCS relied on slow performing vector graphics (Cairo) backend which is suitable for smaller dataset and non-interactive graphics. LLNL and Kitware team has added a new backend to VCS that uses the Visualization Toolkit (VTK) as its visualization backend. VTK is one of the most popular open source, multi-platform scientific visualization library written in C++. Its use of OpenGL and pipeline processing architecture results in a high performant VCS library. Its multitude of available data formats and visualization algorithms results in easy adoption of new visualization methods and new data formats in VCS. In this presentation, we describe recent contributions to VCS that includes new visualization plots, continuous integration testing using Conda and CircleCI, tutorials and examples using Jupyter notebooks as well as upgrades that we are planning in the near future which will improve its ease of use and reliability and extend its capabilities.