Coat Java 2D graph visualization library for realtime data
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Coat Tags

Library visualization graph 2D data 2D Graph Plotter java 2d graph create 2d graph make 2d graph 2d graph producer graph visualization Graph Library Visualization Toolkit Visualization Library network graph library java visualization tool graphical 2D data exploration graph vector data 2D graph visualization 2D graph extrapolate graph data 2D graph designer plot 2d graph Java Graph graph data node graph visualization Realtime Data 2D-Graph Component

Coat Description

Coat is a compact realtime 2D graph visualization package for Java. While there are many graph visualization toolkits out there (e.g. JFreeChart, SGT), few are able to update their data in an efficient near-realtime manner. This is the niche Coat wants to fill for you. If you want to draw static (i.e. non-moving) graphs, other (probably more feature-rich) packages may be more fitting for you. The major strengths of Coat are its simplicistic compactness (5 interfaces, 10 core classes, 8 UI classes), its graphics abstraction model (all UI classes are either Swing or JOGL specific and interchangable), speed (render only as much as you need), and good code structure and comments (including my thoughts during programming). Its greatest flaw is most likely the fact that is does not have advanced visualization methods. All the library basically does is drawing points, lines and bars, but this in an elaborate and highly dynamic way. Main features: Lines: width, color, style (solid, dot, dashed, dot-dashed, dot-dot-dashed), marks appropriate to line width Points: shape (rectangle, circle, triangle), diameter, color, optional label (font, textsize, position) Bars: width, color, optional label (font, textsize, position) Graphs: stacked, square-distributed, overlay Line legend: can unite in overlay mode Zooming and scrolling Linear or logarithmic scaling (and appropriate axis) Greek characters in Latex format Compact size (23 classes) allows for easy code ripping (allowed through GPL) OpenGL support through JOGL

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