cytoflow.views.kde_2d¶
A two-dimensional kernel density estimate – kind of like a data “topo” map.
Kde2DView
– the IView
class that makes the plot.
- 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.
- xchannel¶
The channel to view on the X axis
- Type
Str
- ychannel¶
The channel to view on the Y axis
- Type
Str
- xscale¶
The scales applied to the
xchannel
data before plotting it.- Type
{‘linear’, ‘log’, ‘logicle’} (default = ‘linear’)
- yscale¶
The scales applied to the
ychannel
data before plotting it.- Type
{‘linear’, ‘log’, ‘logicle’} (default = ‘linear’)
- subset¶
An expression that specifies the subset of the statistic to plot. Passed unmodified to
pandas.DataFrame.query
.- Type
- xfacet¶
Set to one of the
Experiment.conditions
in theExperiment
, 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 theExperiment
, 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 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)
- 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 (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 andyfacet
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 iswhitegrid
.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 istalk
.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.
xlim ((float, float)) – Set the range of the plot’s X axis.
ylim ((float, float)) – Set the range of the plot’s Y 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