cytoflow.operations.channel_stat¶

class
cytoflow.operations.channel_stat.
ChannelStatisticOp
[source]¶ Bases:
traits.has_traits.HasStrictTraits
Apply a function to subsets of a data set, and add it as a statistic to the experiment.
The
apply()
function groups the data by the variables inby
, then applies thefunction
callable to thechannel
series in each subset. The callable should take a singlepandas.Series
as an argument. The return type is arbitrary, but to be used with the rest ofcytoflow
it should probably be a numeric type or an iterable of numeric types.
name
¶ The operation name. Becomes the first element in the
Experiment.statistics
key tuple.Type: Str

channel
¶ The channel to apply the function to.
Type: Str

function
¶ The function used to compute the statistic.
function
must take apandas.Series
as its only parameter. The return type is arbitrary, but to be used with the rest ofcytoflow
it should probably be a numeric type or an iterable of numeric types. Ifstatistic_name
is unset, the name of the function becomes the second in element in theExperiment.statistics
key tuple.Warning
Be careful! Sometimes this function is called with an empty input! If this is the case, poorlybehaved functions can return
NaN
or throw an error. If this happens, it will be reported.Type: Callable

statistic_name
¶ The name of the function; if present, becomes the second element in the
Experiment.statistics
key tuple. Particularly useful iffunction
is a lambda expression.Type: Str

by
¶ A list of metadata attributes to aggregate the data before applying the function. For example, if the experiment has two pieces of metadata,
Time
andDox
, settingby = ["Time", "Dox"]
will applyfunction
separately to each subset of the data with a unique combination ofTime
andDox
.Type: List(Str)

subset
¶ A Python expression sent to
Experiment.query()
to subset the data before computing the statistic.Type: Str

fill
¶ The value to use in the statistic if a slice of the data is empty.
Type: Any (default = 0)
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.
>>> ch_op = flow.ChannelStatisticOp(name = 'MeanByDox', ... channel = 'Y2A', ... function = flow.geom_mean, ... by = ['Dox']) >>> ex2 = ch_op.apply(ex)
View the new operation
>>> print(ex2.statistics.keys()) dict_keys([('MeanByDox', 'geom_mean')])
>>> print(ex2.statistics[('MeanByDox', 'geom_mean')]) Dox 1.0 19.805601 10.0 446.981927 dtype: float64

apply
(experiment)[source]¶ Apply the operation to an
Experiment
.Parameters: experiment – The Experiment
to apply this operation to.Returns: A new Experiment
, containing a new entry inExperiment.statistics
. The key of the new entry is a tuple(name, function)
(or(name, statistic_name)
ifstatistic_name
is set.Return type: Experiment
