cytoflow.views.bar_chart#

Plot a bar chart from a statistic.

BarChartView – the IView class that makes the plot.

class cytoflow.views.bar_chart.BarChartView[source]#

Bases: Base1DStatisticsView

Plots a bar chart of some summary statistic

variable#

The condition that varies when plotting this statistic: used for the x axis of line plots, the bar groups in bar plots, etc. Must be a level in the statistic’s index.

Type:

Str

feature#

The column in the statistic to plot (often a channel name.)

Type:

Str

error_low#

The name of the column used to plot low extent error bars. If error_low is set, error_high must be set as well.

Type:

Str

error_high#

The name of the column used to plot high extent error bars. If error_high is set, error_low must be set as well.

Type:

Str

scale#

The scale applied to the data before plotting it.

Type:

{‘linear’, ‘log’, ‘logicle’}

statistic#

The statistic to plot. Must be a key in Experiment.statistics.

Type:

Str

xfacet#

Set to one of the index levels in the statistic being plotted, and a new column of subplots will be added for every unique value of that index level.

Type:

String

yfacet#

Set to one of the index levels in the statistic being plotted, and a new row of subplots will be added for every unique value of that index level.

Type:

String

huefacet#

Set to one of the index levels in the statistic being plotted, and a new colored artist (line, bar, etc) will be added for every unique value of that index level.

Type:

String

subset#

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

Type:

str

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()

Add a threshold gate

>>> ex2 = flow.ThresholdOp(name = 'Threshold',
...                        channel = 'Y2-A',
...                        threshold = 2000).apply(ex)

Add a statistic

>>> ex3 = flow.ChannelStatisticOp(name = "ByDox",
...                               channel = "Y2-A",
...                               by = ['Dox', 'Threshold'],
...                               function = len).apply(ex2)

Plot the bar chart

>>> flow.BarChartView(statistic = "ByDox",
...                   variable = "Dox",
...                   feature = 'Y2-A',
...                   huefacet = "Threshold").plot(ex3)
../../_images/cytoflow-views-bar_chart-4.png
enum_plots(experiment)[source]#

Returns an iterator over the possible plots that this View can produce. The values returned can be passed to “plot”.

plot(experiment, plot_name=None, **kwargs)[source]#

Plot a bar chart

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.

  • plot_name (str) – If this IView can make multiple plots, plot_name is the name of the plot to make. Must be one of the values retrieved from enum_plots.

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

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

  • color (a matplotlib color) – Sets the colors of all the bars, even if there is a hue facet

  • errwidth (scalar) – The width of the error bars, in points

  • errcolor (a matplotlib color) – The color of the error bars

  • capsize (scalar) – The size of the error bar caps, in points

Notes

Other kwargs are passed to matplotlib.axes.Axes.bar