cytoflow.operations.range

Applies a (1D) range gate to an Experiment. range has two classes:

RangeOp – Applies the gate, given a pair of thresholds

RangeSelection – an IView that allows you to view the range and/or interactively set the thresholds.

class cytoflow.operations.range.RangeOp[source]

Bases: traits.has_traits.HasStrictTraits

Apply a range 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

channel

The name of the channel to apply the range gate.

Type

Str

low

The lowest value to include in this gate.

Type

Float

high

The highest value to include in this gate.

Type

Float

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.

>>> range_op = flow.RangeOp(name = 'Range',
...                         channel = 'Y2-A',
...                         low = 2000,
...                         high = 10000)

Plot a diagnostic view

>>> rv = range_op.default_view(scale = 'log')
>>> rv.plot(ex)
../../_images/cytoflow-operations-range-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:

rv = range_op.default_view(scale = 'log',
                           interactive = True)
rv.plot(ex)

Apply the gate, and show the result

>>> ex2 = range_op.apply(ex)
>>> ex2.data.groupby('Range').size()
Range
False    16042
True      3958
dtype: int64
apply(experiment)[source]

Applies the range gate to an experiment.

Parameters

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

Returns

a new Experiment, the same as old 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 in channel is greater than low and less than high; it is False otherwise.

Return type

Experiment

default_view(**kwargs)[source]
class cytoflow.operations.range.RangeSelection[source]

Bases: cytoflow.operations.base_op_views.Op1DView, cytoflow.views.histogram.HistogramView

Plots, and lets the user interact with, a selection on the X axis.

interactive

is this view interactive? Ie, can the user set min and max with a mouse drag?

Type

Bool

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’}

Examples

In an IPython notebook with %matplotlib notebook

>>> r = RangeOp(name = "RangeGate",
...             channel = 'Y2-A')
>>> rv = r.default_view()
>>> rv.interactive = True
>>> rv.plot(ex2)
>>> ### draw a range on the plot ###
>>> print r.low, r.high
plot(experiment, **kwargs)[source]

Plot the underlying histogram and then plot 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.

  • 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).

  • line_props (Dict) – The properties of the matplotlib.lines.Line2D that are drawn on top of the histogram. They’re passed directly to the matplotlib.lines.Line2D constructor. Default: {color : 'black', linewidth : 2}