This module uses the flowPeaks algorithm to assign events to clusters in an unsupervized manner.


The operation name; determines the name of the new metadata

X Channel, Y Channel

The channels to apply the mixture model to.

X Scale, Y Scale

Re-scale the data in Channel before fitting.

h, h0

Scalar values that control the smoothness of the estimated distribution. Increasing h makes it “rougher,” while increasing h0 makes it smoother.


How readily should clusters be merged? Must be between 0 and 1.

Merge Distance

How far apart can clusters be before they are merged?


A list of metadata attributes to aggregate the data before estimating the model. For example, if the experiment has two pieces of metadata, Time and Dox, setting By to ["Time", "Dox"] will fit the model separately to each subset of the data with a unique combination of Time and Dox.

class cytoflowgui.op_plugins.flowpeaks.FlowPeaksHandler(*args: Any, **kwargs: Any)[source]

Bases: traitsui.api.

class cytoflowgui.op_plugins.flowpeaks.FlowPeaksViewHandler(*args: Any, **kwargs: Any)[source]

Bases: traitsui.api.

class cytoflowgui.op_plugins.flowpeaks.FlowPeaksPlugin[source]

Bases: envisage.plugin.Plugin, cytoflowgui.op_plugins.op_plugin_base.PluginHelpMixin

operation_id = ''
view_id = ''
short_name = 'Flow Peaks'
menu_group = 'Gates'
get_handler(model, context)[source]