Starting The Rogue PyDM Timeplot GUI

The Rogue timeplot GUI is a focused PyDM application for plotting changing values over time. It is a better fit than the full debug GUI when the main job is watching trends, correlations, or slowly changing control values rather than browsing the whole tree.

The stock launcher uses python/pyrogue/pydm/TimePlotTop.py, which embeds a single TimePlotter widget bound to rogue://0/root.

Command-Line Launch

Start the timeplot GUI with:

python -m pyrogue timeplot

As with the main GUI, the command-line default target is localhost:9099. To connect to a specific server, pass --server:

python -m pyrogue --server localhost:9099 timeplot

You can also provide multiple servers:

python -m pyrogue --server localhost:9099,otherhost1:9099 timeplot

The session still exposes multiple server indices through the Rogue channel plugin, but the stock timeplot top-level is built around rogue://0/root. Multi-server sessions therefore become more useful when you build a custom plotting screen that intentionally binds channels across indices.

How It Fits With The Main GUI

The timeplot GUI uses the same Rogue PyDM plugin and the same rogue:// channel scheme as the standard debug GUI. The difference is only in the top level screen that Rogue launches.

Use the timeplot GUI when:

  • You want to focus on a small set of changing Variables.

  • Trend viewing matters more than tree browsing.

  • Operators or developers need a lighter-weight plotting view.

Use the standard GUI when:

  • You also need Commands, system controls, configuration actions, or full tree browsing.

Choosing Good Channels To Plot

The timeplot GUI is most useful with Variables that update through polling, callbacks, or periodic software refresh. In practice that usually means status and monitoring values rather than one-shot configuration registers.

When building your own plotting screens, the same Rogue widget and channel URL rules still apply. This page is only about the stock timeplot launcher.

Variables with a useful /disp string or stable scalar numeric values tend to be the easiest to work with. Very large arrays or configuration values that rarely change are usually a poor fit for the stock plotting workflow.

What To Explore Next