Paraview extract selection download#
You may want to download a development snapshot or build your own binary from the CVS source base.
Please note that many features discussed in this article are recent additions and are not available in 3.2. The subsequent sections address different use cases. In the next section, we introduce the main GUI components that are used in the article. This article uses a use-case driven approach to demonstrate how this selection can be described and used. This selection is associated with a data source (here data source refers to any reader, source or filter) and is shown in every view that displays the data source’s output. ParaView supports a single active selection.
Furthermore, this selection can be converted to a set of global element ids in order to plot the attribute values of those elements over time. For example, the elements of a finite-element mesh that have pressure above a certain threshold can be identified very easily using the threshold selection. This functionality allows users to focus on a smaller subset that is important. This subset can be a set of point or cells or a block of composite dataset. Selection is the mechanism for identifying a subset of a dataset by using user specified criteria. This can be achieved using the selection mechanism described in this article. In addition to better charting, Python-based filtering and statistical analysis tools, we have been working on the capability of focusing the analysis on a specific subset of a dataset. One of the major design goals of ParaView 3 is to add support for quantitative analysis.
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