pept.tracking.SplitAll

class pept.tracking.SplitAll(column)

Bases: pept.base.pipelines.Reducer

Stack all samples and split them into a list according to a named / numeric column index.

Reducer signature:

LineData -> SplitAll.fit -> list[LineData]
list[LineData] -> SplitAll.fit -> list[LineData]

PointData -> SplitAll.fit -> list[PointData]
list[PointData] -> SplitAll.fit -> list[PointData]

numpy.ndarray -> SplitAll.fit -> list[numpy.ndarray]
list[numpy.ndarray] -> SplitAll.fit -> list[numpy.ndarray]

If using a LineData / PointData, you can use a columns name as a string, e.g. SplitAll("label") or a number SplitAll(4). If using a NumPy array, only numeric indices are accepted.

__init__(column)

Methods

__init__(column)
copy([deep])	Create a deep copy of an instance of this class, including all inner attributes.
fit(samples)
load(filepath)	Load a saved / pickled PEPTObject object from filepath.
save(filepath)	Save a PEPTObject instance as a binary pickle object.

copy(deep=True)

Create a deep copy of an instance of this class, including all inner attributes.

static load(filepath)

Load a saved / pickled PEPTObject object from filepath.

Most often the full object state was saved using the .save method.

Parameters

filepathfilename or file handle

If filepath is a path (rather than file handle), it is relative to where python is called.

Returns

pept.PEPTObject subclass instance

The loaded object.

Examples

Save a LineData instance, then load it back:

>>> lines = pept.LineData([[1, 2, 3, 4, 5, 6, 7]])
>>> lines.save("lines.pickle")

>>> lines_reloaded = pept.LineData.load("lines.pickle")

save(filepath)

Save a PEPTObject instance as a binary pickle object.

Saves the full object state, including inner attributes, in a portable binary format. Load back the object using the load method.

Parameters

filepathfilename or file handle

If filepath is a path (rather than file handle), it is relative to where python is called.

Examples

Save a LineData instance, then load it back:

>>> lines = pept.LineData([[1, 2, 3, 4, 5, 6, 7]])
>>> lines.save("lines.pickle")

>>> lines_reloaded = pept.LineData.load("lines.pickle")

fit(samples: collections.abc.Iterable)

pept.tracking.SplitLabels pept.tracking.Centroids