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liveplot is a system for minimal hassle, on-the-fly, dataset visualization in
python. If you want maximum customizability, or plot types other than line plots
and image plots, look elsewhere. But if you just want to see your data as it
comes in to your script, with minimal effort, and without the possibility of
graphics bugs crashing your script, this might be for you.
Liveplot works in two
processes, one of which is a pyqt application hosting the window, the other is
your script, which sends data to the window over a named pipe. The intended
workflow is to open the window once (per session) and keep it open, rather than
restarting it for every run of the script. Ideally, multiple scripts can
communicate with the same window, and data remains available in the window until
it is overwritten.
pyqtgraph will be installed automatically from PyPI if not found
Basic Usage
Install from PyPI
pip install liveplot
or from the source directory
python setup.py install
and start the window
python -m liveplot
on windows, if you have py2exe, build the executable
python setup.py
which should produce dist/liveplot.exe
If the window has been successfully started, open a client and plot
fromliveplotimportLivePlotClientimportnumpyasnpplotter=LivePlotClient()
xs=np.linspace(0, 10, 100)
plotter.plot_xy('my test data', xs, np.sin(xs))
Individual plots are specified by their name, which can be any unique string.
Attempting to create two different types of plot with the same name is currently
an error. See more examples with the test suite,
python liveplot_test.py
Several methods of plotting are supported, including cumulative, parametric, and 2D-Image.
OS X Setup
By default Macs restrict the size of shared memory that can be allocated to a
single process. To overcome this, copy sysctl.conf to /etc, or append it if
the file is already present. Reboot your system to apply the changes.
GUI Features
In addition to the many wonderful features of native pyqtgraph widgets we have,
Double click on plots to bring up cross-hair marker
Cross-hair displays cross-section cuts for image plots
Restore closed plots by double-clicking the name in the plot list
Focus on a single plot by maximizing
Right click on image plots
toggle histogram & levels scale
enable/disable auto-rescaling of levels when image is updated
About
visualize your python data as it comes in, with minimal intrusion