class cytoflow.operations.quad.QuadOp[source]

Bases: traits.has_traits.HasStrictTraits

Apply a quadrant gate to a cytometry experiment.

Creates a new metadata column named name, with values name_1, name_2, name_3, name_4 ordered clockwise from upper-left.


The operation name. Used to name the new metadata field in the experiment that’s created by apply()


The name of the first channel to apply the range gate.


The threshold in the xchannel to gate with.


The name of the secon channel to apply the range gate.


The threshold in ychannel to gate with.



Make a little data set.

>>> import cytoflow as flow
>>> import_op = flow.ImportOp()
>>> import_op.tubes = [flow.Tube(file = "Plate01/RFP_Well_A3.fcs",
...                              conditions = {'Dox' : 10.0}),
...                    flow.Tube(file = "Plate01/CFP_Well_A4.fcs",
...                              conditions = {'Dox' : 1.0})]
>>> import_op.conditions = {'Dox' : 'float'}
>>> ex = import_op.apply()

Create and parameterize the operation.

>>> quad = flow.QuadOp(name = "Quad",
...                    xchannel = "V2-A",
...                    xthreshold = 100,
...                    ychannel = "Y2-A",
...                    ythreshold = 1000)

Show the default view

>>> qv = quad.default_view(huefacet = "Dox",
...                        xscale = 'log',
...                        yscale = 'log')
>>> qv.plot(ex)


If you want to use the interactive default view in a Jupyter notebook, make sure you say %matplotlib notebook in the first cell (instead of %matplotlib inline or similar). Then call default_view() with interactive = True:

qv = quad.default_view(huefacet = "Dox",
                       xscale = 'log',
                       yscale = 'log',
                       interactive = True)

Apply the gate and show the result

>>> ex2 = quad.apply(ex)
Quad_1    1783
Quad_2    2584
Quad_3    8236
Quad_4    7397
dtype: int64

Applies the quad gate to an experiment.

Parameters:experiment (Experiment) – the old experiment to which this op is applied
Returns:a new Experiment, the same as the old Experiment but with a new column the same as the operation name. The new column is of type Category, with values name_1, name_2, name_3, and name_4, applied to events CLOCKWISE from upper-left.
Return type:Experiment
class cytoflow.operations.quad.QuadSelection[source]

Bases: cytoflow.operations.base_op_views.Op2DView, cytoflow.views.scatterplot.ScatterplotView

Plots, and lets the user interact with, a quadrant gate.


is this view interactive? Ie, can the user set the threshold with a mouse click?

xchannel, ychannel

The channels to use for this view’s X and Y axes. If you created the view using default_view(), this is already set.

xscale, yscale

The way to scale the x axes. If you created the view using default_view(), this may be already set.

Type:{‘linear’, ‘log’, ‘logicle’}

The IOperation that this view is associated with. If you created the view using default_view(), this is already set.

xlim, ylim

Set the min and max limits of the plots’ x and y axes.

Type:(float, float)
xfacet, yfacet

Set to one of the conditions in the Experiment, and a new row or column of subplots will be added for every unique value of that condition.


Set to one of the conditions in the in the Experiment, and a new color will be added to the plot for every unique value of that condition.


How should the color scale for huefacet be scaled?

Type:{‘linear’, ‘log’, ‘logicle’}


We inherit xfacet and yfacet from cytoflow.views.ScatterplotView, but they must both be unset!


In an Jupyter notebook with %matplotlib notebook

>>> q = flow.QuadOp(name = "Quad",
...                 xchannel = "V2-A",
...                 ychannel = "Y2-A"))
>>> qv = q.default_view()
>>> qv.interactive = True
>>> qv.plot(ex2)
plot(experiment, **kwargs)[source]

Plot the underlying scatterplot and then plot the selection on top of it.

  • experiment (Experiment) – The Experiment to plot using this view.
  • title (str) – Set the plot title
  • xlabel, ylabel (str) – Set the X and Y axis labels
  • huelabel (str) – Set the label for the hue facet (in the legend)
  • legend (bool) – Plot a legend for the color or hue facet? Defaults to True.
  • sharex, sharey (bool) – If there are multiple subplots, should they share axes? Defaults to True.
  • row_order, col_order, hue_order (list) – Override the row/column/hue facet value order with the given list. If a value is not given in the ordering, it is not plotted. Defaults to a “natural ordering” of all the values.
  • height (float) – The height of each row in inches. Default = 3.0
  • aspect (float) – The aspect ratio of each subplot. Default = 1.5
  • col_wrap (int) – If xfacet is set and yfacet is not set, you can “wrap” the subplots around so that they form a multi-row grid by setting col_wrap to the number of columns you want.
  • sns_style ({“darkgrid”, “whitegrid”, “dark”, “white”, “ticks”}) – Which seaborn style to apply to the plot? Default is whitegrid.
  • sns_context ({“paper”, “notebook”, “talk”, “poster”}) – Which seaborn context to use? Controls the scaling of plot elements such as tick labels and the legend. Default is talk.
  • despine (Bool) – Remove the top and right axes from the plot? Default is True.
  • min_quantile (float (>0.0 and <1.0, default = 0.001)) – Clip data that is less than this quantile.
  • max_quantile (float (>0.0 and <1.0, default = 1.00)) – Clip data that is greater than this quantile.
  • xlim, ylim ((float, float)) – Set the range of the plot’s axis.
  • alpha (float (default = 0.25)) – The alpha blending value, between 0 (transparent) and 1 (opaque).
  • s (int (default = 2)) – The size in points^2.
  • marker (a matplotlib marker style, usually a string) – Specfies the glyph to draw for each point on the scatterplot. See matplotlib.markers for examples. Default: ‘o’