cytoflow.operations.polygon#

Apply a polygon gate to two channels in an Experiment. polygon has two classes:

PolygonOp – Applies the gate, given a set of vertices.

ScatterplotPolygonSelectionView – an IView that allows you to view the polygon and/or interactively set the vertices on a scatterplot.

DensityPolygonSelectionView – an IView that allows you to view the polygon and/or interactively set the vertices on a scatterplot.

class cytoflow.operations.polygon.PolygonOp[source]#

Bases: HasStrictTraits

Apply a polygon gate to a cytometry experiment.

name#

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

Type:

Str

xchannel, ychannel

The names of the x and y channels to apply the gate.

Type:

Str

xscale, yscale

The scales applied to the data before drawing the polygon.

Type:

{‘linear’, ‘log’, ‘logicle’} (default = ‘linear’)

vertices#

The polygon verticies. An ordered list of 2-tuples, representing the x and y coordinates of the vertices.

Type:

List((Float, Float))

Notes

You can set the verticies by hand, I suppose, but it’s much easier to use the interactive view you get from default_view to do so. Set ScatterplotPolygonSelectionView.interactive to True, then single-click to set vertices. Click the first vertex a second time to close the polygon. You’ll need to do this in a Jupyter notebook with %matplotlib notebook – see the Interactive Plots demo for an example.

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.

>>> p = flow.PolygonOp(name = "Polygon",
...                    xchannel = "V2-A",
...                    ychannel = "Y2-A")
>>> p.vertices = [(23.411982294776319, 5158.7027015021222),
...               (102.22182270573683, 23124.058843387455),
...               (510.94519955277201, 23124.058843387455),
...               (1089.5215641232173, 3800.3424832180476),
...               (340.56382570202402, 801.98947404942271),
...               (65.42597937575897, 1119.3133482602157)]

Show the default view.

>>> df = p.default_view(huefacet = "Dox",
...                     xscale = 'log',
...                     yscale = 'log',
...                     density = True)

>>> df.plot(ex)
../../_images/cytoflow-operations-polygon-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:

df = p.default_view(huefacet = "Dox",
                    xscale = 'log',
                    yscale = 'log',
                    interactive = True)
df.plot(ex)

Apply the gate, and show the result

>>> ex2 = p.apply(ex)
>>> ex2.data.groupby('Polygon', observed = True).size()
Polygon
False    15875
True      4125
dtype: int64

You can also get (or draw) the polygon on a density plot instead of a scatterplot:

>>> df = p.default_view(huefacet = "Dox",
...                     xscale = 'log',
...                     yscale = 'log')

>>> df.plot(ex)
../../_images/cytoflow-operations-polygon-5.png
apply(experiment)[source]#

Applies the gate to an experiment.

Parameters:

experiment (Experiment) – the old Experiment to which this op is applied

Returns:

a new ‘Experiment`, the same as experiment but with a new column of type bool with the same as the operation name. The bool is True if the event’s measurement is within the polygon, and False otherwise.

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.

default_view(**kwargs)[source]#

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

Parameters:

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

class cytoflow.operations.polygon.ScatterplotPolygonSelectionView[source]#

Bases: _PolygonSelection, ScatterplotView

Plots, and lets the user interact with, a 2D polygon selection on a scatterplot.

interactive#

is this view interactive? Ie, can the user set the polygon verticies with mouse clicks?

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)

huechannel#

If set, color the points using a normed color scale. The norm function is set by huescale, and the color palette can be changed by passing the palette parameter to plot.

Type:

Str

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 a Jupyter notebook with %matplotlib notebook

>>> s = flow.PolygonOp(xchannel = "V2-A",
...                    ychannel = "Y2-A")
>>> poly = s.default_view()
>>> poly.plot(ex2)
>>> poly.interactive = True
plot(experiment, **kwargs)[source]#

Plot the default view, and then draw the 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.

  • legend_out (bool) – Plot the legend outside of the plot or grid? 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 ({“notebook”, “paper”, “talk”, “poster”}) – Which seaborn context to use? Controls the scaling of plot elements such as tick labels and the legend. Default is notebook.

  • 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. See https://seaborn.pydata.org/tutorial/color_palettes.html for a good overview.

  • 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’

  • patch_props (Dict) – The properties of the matplotlib.patches.Patch that are drawn on top of the scatterplot. They’re passed directly to the matplotlib.patches.Patch constructor. Default: {edgecolor : 'black', linewidth : 2, fill : False}

  • selector_props (Dict) – The properties of the matplotlib.lines.Line2D that are drawn on top of the scatterplot. They’re passed directly to the matplotlib.patches.Patch constructor. Default: {color : 'black', linestyle : '-', linewidth : 2, alpha = 0.5}

class cytoflow.operations.polygon.DensityPolygonSelectionView[source]#

Bases: _PolygonSelection, DensityView

Plots, and lets the user interact with, a 2D polygon selection on a density plot.

interactive#

is this view interactive? Ie, can the user set the polygon verticies with mouse clicks?

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 a Jupyter notebook with %matplotlib notebook

>>> s = flow.PolygonOp(xchannel = "V2-A",
...                    ychannel = "Y2-A")
>>> poly = s.default_view(density = True)
>>> poly.plot(ex2)
>>> poly.interactive = True
plot(experiment, **kwargs)[source]#

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

Parameters:

  • patch_props (Dict) – The properties of the matplotlib.patches.Patch that are drawn on top of the scatterplot or density view. They’re passed directly to the matplotlib.patches.Patch constructor. Default: {edgecolor : ‘white’, linewidth : 2, fill : False}

  • selector_props (Dict) – The properties of the matplotlib.lines.Line2D that are drawn on top of the scatterplot. They’re passed directly to the matplotlib.patches.Patch constructor. Default: {color : 'white', linestyle : '-', linewidth : 2, alpha = 0.5}