cytoflow.views.kde_2d¶
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class
cytoflow.views.kde_2d.
Kde2DView
[source]¶ Bases:
cytoflow.views.base_views.Base2DView
Plots a 2-d kernel-density estimate. Sort of like a smoothed histogram. The density is visualized with a set of isolines.
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xchannel, ychannel
The channels to view
Type: Str
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xscale, yscale
The scales applied to the data before plotting it.
Type: {‘linear’, ‘log’, ‘logicle’} (default = ‘linear’)
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xlim, ylim
Set the min and max limits of the plots’ x and y axes.
Type: (float, float)
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xfacet, yfacet
Set to one of the
conditions
in theExperiment
, and a new row or column of subplots will be added for every unique value of that condition.Type: String
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huefacet
¶ Set to one of the
conditions
in the in theExperiment
, and a new color will be added to the plot for every unique value of that condition.Type: String
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 density plot
>>> flow.Kde2DView(xchannel = 'V2-A', ... xscale = 'log', ... ychannel = 'Y2-A', ... yscale = 'log', ... huefacet = 'Dox').plot(ex)
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plot
(experiment, **kwargs)[source]¶ Plot a faceted 2d kernel density estimate
Parameters: - experiment (Experiment) – The
Experiment
to plot using this view. - title (str) – Set the plot title
- xlabel, ylabel (str) – Set the X and Y axis labels
- 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, sharey (bool) – If there are multiple subplots, should they share axes? Defaults to True.
- row_order, col_order, hue_order (list) – Override the row/column/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 col_wrap 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.
- 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.
- xlim, ylim ((float, float)) – Set the range of the plot’s axis.
- shade (bool) – Shade the interior of the isoplot? (default = False)
- min_alpha, max_alpha (float) – The minimum and maximum alpha blending values of the isolines, between 0 (transparent) and 1 (opaque).
- n_levels (int) – How many isolines to draw? (default = 10)
- bw (str or float) – The bandwidth for the gaussian kernel, controls how lumpy or smooth the kernel estimate is. Choices are:
- gridsize (int) – How many times to compute the kernel on each axis? (default: 100)
Notes
Other
kwargs
are passed to matplotlib.axes.Axes.contour- experiment (Experiment) – The
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