cytoflow.operations.binning#

binning has two classes:

BinningOp – divides events in a channel into bins of equal width (after applying an optional scale)

BinningView – a default view to display the bins.

class cytoflow.operations.binning.BinningOp[source]#

Bases: HasStrictTraits

Bin data along an axis.

This operation creates equally spaced bins (in linear or log space) along an axis and adds a condition assigning each event to a bin. The value of the event’s condition is the left end of the bin’s interval in which the event is located.

name#

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

Type:

Str

channel#

The name of the channel along which to bin.

Type:

Str

scale#

Make the bins equidistant along what scale?

Type:

{“linear”, “log”, “logicle”}

bin_width#

The width of the bins. If scale is log, bin_width is in log-10 units; if scale is logicle, an error is thrown because the units are ill-defined.

Type:

Float

Examples

Create a small experiment:

>>> import cytoflow as flow
>>> import_op = flow.ImportOp()
>>> import_op.tubes = [flow.Tube(file = "tasbe/rby.fcs")]
>>> ex = import_op.apply()

Create and parameterize the operation

>>> bin_op = flow.BinningOp()
>>> bin_op.name = "Bin"
>>> bin_op.channel = "FITC-A"
>>> bin_op.scale = "log"
>>> bin_op.bin_width = 0.2

Apply the operation to the experiment

>>> ex2 = bin_op.apply(ex)

Plot the result

>>> bin_op.default_view().plot(ex2)
../../_images/cytoflow-operations-binning-4.png
apply(experiment)[source]#

Applies the binning to an experiment.

Parameters:

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

Returns:

A new experiment with a condition column named name, which contains the location of the left-most edge of the bin that the event is in.

Return type:

Experiment

default_view(**kwargs)[source]#

Returns a diagnostic plot to check the binning.

Returns:

An view instance, call plot() to plot the bins.

Return type:

IView

class cytoflow.operations.binning.BinningView[source]#

Bases: Op1DView, AnnotatingView, HistogramView

Plots a histogram of the current binning op. By default, the different bins are shown in different colors.

channel#

The channel this view is viewing. If you created the view using default_view, this is already set.

Type:

Str

scale#

The way to scale the x axes. 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’}

plot(experiment, **kwargs)[source]#

Plot the histogram.

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.

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

  • orientation ({‘vertical’, ‘horizontal’})

  • num_bins (int) – The number of bins to plot in the histogram. Clipped to [100, 1000]

  • histtype ({‘stepfilled’, ‘step’, ‘bar’}) – The type of histogram to draw. stepfilled is the default, which is a line plot with a color filled under the curve.

  • density (bool) – If True, re-scale the histogram to form a probability density function, so the area under the histogram is 1.

  • linewidth (float) – The width of the histogram line (in points)

  • linestyle ([‘-’ | ‘–’ | ‘-.’ | ‘:’ | “None”]) – The style of the line to plot

  • alpha (float (default = 0.5)) – The alpha blending value, between 0 (transparent) and 1 (opaque).

  • color (matplotlib color) – The color to plot the annotations. Overrides the default color cycle.