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: traits.has_traits.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: cytoflow.operations.base_op_views.Op1DView, cytoflow.operations.base_op_views.AnnotatingView, cytoflow.views.histogram.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.

  • 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.

  • 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.