Programmatic usage¶
Shell¶
The Shell tab is a functional Python command line embedded within the program. The pipeline can be accessed directly
from the shell, and behaves like a dictionary keyed by variable names. Pylab is imported in the shell making a number of
MATLAB-style plotting and basic numeric commands accessible (see the matplotlib webpage for more docs).
One can, for example, plot a histogram of point amplitudes by executing hist(pipeline['A'])
.
Pipeline data sources can be accessed by entering pipeline.dataSources[datasource_key]
. For a list of datasource keys, type pipeline.dataSources
.
Jupyter notebook¶
PYMEVisualize can be used directly from a Jupyter notebook. At the top of the notebook, enter
from PYME.LMVis import VisGUI
, %gui wx
, and then pymevis = VisGUI.ipython_pymevisualize()
. This makes it possible to
access a PYMEVisualize instance from a notebook through the pymevis
variable. Setting pipeline=pymevis.pipeline
gives the user access to the PYMEVisualize pipeline in exactly the same way as described in Shell section.
An example of generating a point cloud, passing it to a tabular data source, and visualizing
the data source is shown in Fig. 16.

Fig. 16 An example of generating a point cloud, passing it to a tabular data source, and visualizing the data source in PYMEVisualize from a Jupyter notebook.¶
Please note that both PYME and the Jupyter kernel must be set up in the Framework build on a Mac
(see https://python-microscopy.org/doc/Installation/InstallationFromSource.html) for this to work.
To install the Jupyter kernel in the Framework build, activate your_pyme_environment
and then type PATH/TO/CONDA/ENVIRONMENT/python.app/Contents/MacOS/python -m ipykernel install --user --name your_pyme_environment
.
Plugins¶
Details on extending and writing plugins for PYMEVisualize are available at http://python-microscopy.org/doc/hacking.html. A template for extending PYMEVisualize can be found at https://github.com/python-microscopy/pyme-plugin.