cytoflow.operations.quad

Applies a (2D) quad gate to an Experiment. quad has two classes:

QuadOp – Applies the gate, given a pair of thresholds

ScatterplotQuadSelectionView – an IView that allows you to view the quadrants and/or interactively set the thresholds on a scatterplot.

ScatterplotQuadSelectionView – an IView that allows you to view the quadrants and/or interactively set the thresholds on a density plot.

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 (upper-left quadrant), name_2 (upper-right), name_3 (lower-left), and name_4 (lower-right). This ordering is arbitrary, and was chosen to match the FACSDiva order.

name

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

Type

Str

xchannel

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

Type

Str

xthreshold

The threshold in the xchannel to gate with.

Type

Float

ychannel

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

Type

Str

ythreshold

The threshold in ychannel to gate with.

Type

Float

Examples

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)
../../_images/cytoflow-operations-quad-3.png

Note

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)
qv.plot(ex)

Apply the gate and show the result

>>> ex2 = quad.apply(ex)
>>> ex2.data.groupby('Quad').size()
Quad
Quad_1    1783
Quad_2    2584
Quad_3    8236
Quad_4    7397
dtype: int64
apply(experiment)[source]

Applies the quad gate to an experiment.

Parameters

experiment (Experiment) – the 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

Raises

CytoflowOpError – if for some reason the operation can’t be applied to this experiment. The reason is in the args attribute of CytoflowOpError.

default_view(**kwargs)[source]

Returns an IView that allows a user to view the quad selector or interactively draw it.

Parameters

density (bool, default = False) – If True, return a density plot instead of a scatterplot.

class cytoflow.operations.quad.ScatterplotQuadSelectionView[source]

Bases: cytoflow.operations.quad._QuadSelection, cytoflow.views.scatterplot.ScatterplotView

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

interactive

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

Type

Bool

xchannel

The channels to use for this view’s X axis. If you created the view using default_view, this is already set.

Type

Str

ychannel

The channels to use for this view’s Y axis. If you created the view using default_view, this is already set.

Type

Str

xscale

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

Type

{‘linear’, ‘log’, ‘logicle’}

yscale

The way to scale the y axis. If you created the view using default_view, this may be already set.

Type

{‘linear’, ‘log’, ‘logicle’}

op

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

Type

Instance(IOperation)

subset

An expression that specifies the subset of the statistic to plot. Passed unmodified to pandas.DataFrame.query.

Type

str

xfacet

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

Type

String

yfacet

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

Type

String

huefacet

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

Type

String

huescale

How should the color scale for huefacet be scaled?

Type

{‘linear’, ‘log’, ‘logicle’}

Examples

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 default view, and then draw the quad selection on top of it.

Parameters
  • experiment (Experiment) – The Experiment to plot using this view.

  • title (str) – Set the plot title

  • xlabel (str) – Set the X axis label

  • ylabel (str) – Set the Y axis label

  • 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 (bool) – If there are multiple subplots, should they share X axes? Defaults to True.

  • sharey (bool) – If there are multiple subplots, should they share Y axes? Defaults to True.

  • row_order (list) – Override the row 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.

  • col_order (list) – Override the column 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.

  • hue_order (list) – Override the 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 this 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.

  • palette (palette name, list, or dict) – Colors to use for the different levels of the hue variable. Should be something that can be interpreted by seaborn.color_palette, or a dictionary mapping hue levels to matplotlib colors.

  • 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 ((float, float)) – Set the range of the plot’s X axis.

  • ylim ((float, float)) – Set the range of the plot’s Y 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’

  • line_props (Dict) – The properties of the matplotlib.lines.Line2D that are drawn on top of the scatterplot or density view. They’re passed directly to the matplotlib.lines.Line2D constructor. Default: {color : 'black', linewidth : 2}

class cytoflow.operations.quad.DensityQuadSelectionView[source]

Bases: cytoflow.operations.quad._QuadSelection, cytoflow.views.densityplot.DensityView

Plots, and lets the user interact with, a quadrant gate on a density view

interactive

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

Type

Bool

xchannel

The channels to use for this view’s X axis. If you created the view using default_view, this is already set.

Type

Str

ychannel

The channels to use for this view’s Y axis. If you created the view using default_view, this is already set.

Type

Str

xscale

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

Type

{‘linear’, ‘log’, ‘logicle’}

yscale

The way to scale the y axis. If you created the view using default_view, this may be already set.

Type

{‘linear’, ‘log’, ‘logicle’}

op

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

Type

Instance(IOperation)

huefacet

You must leave the hue facet unset!

Type

None

subset

An expression that specifies the subset of the statistic to plot. Passed unmodified to pandas.DataFrame.query.

Type

str

xfacet

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

Type

String

yfacet

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

Type

String

huescale

How should the color scale for huefacet be scaled?

Type

{‘linear’, ‘log’, ‘logicle’}

Examples

In an Jupyter notebook with %matplotlib notebook

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

Plot the default view, and then draw the quad selection on top of it.

Parameters
  • experiment (Experiment) – The Experiment to plot using this view.

  • title (str) – Set the plot title

  • xlabel (str) – Set the X axis label

  • ylabel (str) – Set the Y axis label

  • 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 (bool) – If there are multiple subplots, should they share X axes? Defaults to True.

  • sharey (bool) – If there are multiple subplots, should they share Y axes? Defaults to True.

  • row_order (list) – Override the row 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.

  • col_order (list) – Override the column 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.

  • hue_order (list) – Override the 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 this 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.

  • palette (palette name, list, or dict) – Colors to use for the different levels of the hue variable. Should be something that can be interpreted by seaborn.color_palette, or a dictionary mapping hue levels to matplotlib colors.

  • 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 ((float, float)) – Set the range of the plot’s X axis.

  • ylim ((float, float)) – Set the range of the plot’s Y axis.

  • gridsize (int) – The size of the grid on each axis. Default = 50

  • smoothed (bool) – Should the resulting mesh be smoothed?

  • smoothed_sigma (int) – The standard deviation of the smoothing kernel. default = 1.

  • cmap (cmap) – An instance of matplotlib.colors.Colormap. By default, the viridis colormap is used

  • under_color (matplotlib color) – Sets the color to be used for low out-of-range values.

  • bad_color (matplotlib color) – Set the color to be used for masked values.

  • line_props (Dict) – The properties of the matplotlib.lines.Line2D that are drawn on top of the scatterplot or density view. They’re passed directly to the matplotlib.lines.Line2D constructor. Default: {color : 'black', linewidth : 2}