cytoflow.views.violin

A violin plot is a facetted set of kernel density estimates.

ViolinPlotView – the IView class that makes the plot.

class cytoflow.views.violin.ViolinPlotView[source]

Bases: cytoflow.views.base_views.Base1DView

Plots a violin plot – a facetted set of kernel density estimates.

variable

the main variable by which we’re faceting

Type

Str

channel

The channel to view

Type

Str

scale

The scale applied to the data before plotting it.

Type

{‘linear’, ‘log’, ‘logicle’}

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

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()

Plot a violin plot

>>> flow.ViolinPlotView(channel = 'Y2-A',
...                     scale = 'log',
...                     variable = 'Dox').plot(ex)
../../_images/cytoflow-views-violin-2.png
plot(experiment, **kwargs)[source]

Plot a violin plot of a variable

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’})

  • bw ({{‘scott’, ‘silverman’, float}}, optional) – Either the name of a reference rule or the scale factor to use when computing the kernel bandwidth. The actual kernel size will be determined by multiplying the scale factor by the standard deviation of the data within each bin.

  • scale_plot ({{“area”, “count”, “width”}}, optional) – The method used to scale the width of each violin. If area, each violin will have the same area. If count, the width of the violins will be scaled by the number of observations in that bin. If width, each violin will have the same width.

  • scale_hue (bool, optional) – When nesting violins using a hue variable, this parameter determines whether the scaling is computed within each level of the major grouping variable (scale_hue=True) or across all the violins on the plot (scale_hue=False).

  • gridsize (int, optional) – Number of points in the discrete grid used to compute the kernel density estimate.

  • inner ({{“box”, “quartile”, None}}, optional) – Representation of the datapoints in the violin interior. If box, draw a miniature boxplot. If quartiles, draw the quartiles of the distribution. If point or stick, show each underlying datapoint. Using None will draw unadorned violins.

  • split (bool, optional) – When using hue nesting with a variable that takes two levels, setting split to True will draw half of a violin for each level. This can make it easier to directly compare the distributions.