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)
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 calldefault_view()
withinteractive = 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
) – theExperiment
to which this op is applied- Returns
a new
Experiment
, the same as oldExperiment
but with a new column of typebool
with the same as the operation name. The bool isTrue
if the event’s measurement inchannel
is greater thanlow
and less thanhigh
; it isFalse
otherwise.- Return type
- 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 usingdefault_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
- 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
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 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.
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 thematplotlib.lines.Line2D
constructor. Default:{color : 'black', linewidth : 2}