pept.tracking.Voxelize#

class pept.tracking.Voxelize(number_of_voxels, xlim=None, ylim=None, zlim=None, set_lims=None)[source]#

Bases: LineDataFilter

Asynchronously voxelize samples of lines from a pept.LineData.

Filter signature:

LineData -> Voxelize.fit_sample -> PointData

This filter is much more memory-efficient than voxelizing all samples of LoRs at once - which often overflows the available memory. Most often this is used alongside voxel-based tracking algorithms, e.g. pept.tracking.FPI:

>>> from pept.tracking import *
>>> pipeline = pept.Pipeline([
>>>     Voxelize((50, 50, 50)),
>>>     FPI(3, 0.4),
>>>     Stack(),
>>> ])

Parameters

number_of_voxels3-tuple: A tuple-like containing exactly three integers specifying the number of voxels to be used in each dimension.
xlim(2,) list[float], optional: The lower and upper boundaries of the voxellised volume in the x-dimension, formatted as [x_min, x_max]. If undefined, it is inferred from the bounding box of each sample of lines.
ylim(2,) list[float], optional: The lower and upper boundaries of the voxellised volume in the y-dimension, formatted as [y_min, y_max]. If undefined, it is inferred from the bounding box of each sample of lines.
zlim(2,) list[float], optional: The lower and upper boundaries of the voxellised volume in the z-dimension, formatted as [z_min, z_max]. If undefined, it is inferred from the bounding box of each sample of lines.
set_lims(N, 7) numpy.ndarray or pept.LineData, optional: If defined, set the system limits upon creating the class to the bounding box of the lines in set_lims.

__init__(number_of_voxels, xlim=None, ylim=None, zlim=None, set_lims=None)[source]#

Methods

`__init__`(number_of_voxels[, xlim, ylim, ...])
`copy`([deep])	Create a deep copy of an instance of this class, including all inner attributes.
`fit`(line_data[, executor, max_workers, verbose])	Apply self.fit_sample (implemented by subclasses) according to the execution policy.
`fit_sample`(sample_lines)
`load`(filepath)	Load a saved / pickled PEPTObject object from filepath.
`save`(filepath)	Save a PEPTObject instance as a binary pickle object.
`set_lims`(lines[, set_xlim, set_ylim, set_zlim])

Attributes

`number_of_voxels`
`xlim`
`ylim`
`zlim`

set_lims(lines, set_xlim=True, set_ylim=True, set_zlim=True)[source]#

property number_of_voxels#

property xlim#

property ylim#

property zlim#

fit_sample(sample_lines)[source]#

copy(deep=True)#: Create a deep copy of an instance of this class, including all inner attributes.

fit(line_data, executor='joblib', max_workers=None, verbose=True)#: Apply self.fit_sample (implemented by subclasses) according to the execution policy. Simply return a list of processed samples. If you need a reduction step (e.g. stack all processed samples), apply it in the subclass.

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")