pept.tracking.Minpoints#

class pept.tracking.Minpoints(num_lines, max_distance, cutoffs=None, append_indices=False)[source]#

Bases: LineDataFilter

Transform LoRs (a pept.LineData instance) into minpoints (a pept.PointData instance) for clustering, in parallel.

Given a sample of lines, the minpoints are the minimum distance points (MDPs) for every possible combination of num_lines lines that satisfy the following conditions:

Are within the cutoffs.
Are closer to all the constituent LoRs than max_distance.

Under typical usage, the Minpoints class is initialised with a pept.LineData instance, automatically calculating the minpoints from the samples of lines. The Minpoints class inherits from pept.PointData, such that once the cutpoints have been computed, all the methods from the parent class pept.PointData can be used on them (such as visualisation functionality).

For more control over the operations, pept.tracking.peptml.find_minpoints can be used - it receives a generic numpy array of LoRs (one ‘sample’) and returns a numpy array of cutpoints.

See also

pept.LineData: Encapsulate LoRs for ease of iteration and plotting.
pept.tracking.peptml.HDBSCANClusterer: Efficient, parallel HDBSCAN-based clustering of cutpoints.
pept.scanners.ParallelScreens: Read in and initialise a pept.LineData instance from parallel screens PET/PEPT detectors.
pept.utilities.read_csv: Fast CSV file reading into numpy arrays.

Examples

Compute the minpoints for a LineData instance for all triplets of lines that are less than 0.1 from those lines:

>>> line_data = pept.LineData(example_data)
>>> minpts = peptml.Minpoints(line_data, 3, 0.1)

Compute the minpoints for a single sample:

>>> sample = line_data[0]
>>> cutpts_sample = peptml.find_minpoints(sample, 3, 0.1)

Attributes

line_datainstance of pept.LineData: The LoRs for which the cutpoints will be computed. It must be an instance of pept.LineData.
num_linesint: The number of lines in each combination of LoRs used to compute the MDP. This function considers every combination of num_lines from the input sample_lines. It must be smaller or equal to the number of input lines sample_lines.
max_distancefloat: The maximum allowed distance between an MDP and its constituent lines. If any distance from the MDP to one of its lines is larger than max_distance, the MDP is thrown away. A good starting value would be 0.1 mm for small tracers and/or clean data, or 0.2 mm for larger tracers and/or noisy data.
cutoffslist-like of length 6: A list (or equivalent) of the cutoff distances for every axis, formatted as [x_min, x_max, y_min, y_max, z_min, z_max]. Only the minpoints which fall within these cutoff distances are considered. The default is None, in which case they are automatically computed using pept.tracking.peptml.get_cutoffs.

__init__(num_lines, max_distance, cutoffs=None, append_indices=False)[source]#

Minpoints class constructor.

Parameters

num_linesint: The number of lines in each combination of LoRs used to compute the MDP. This function considers every combination of num_lines from the input sample_lines. It must be smaller or equal to the number of input lines sample_lines.
max_distancefloat: The maximum allowed distance between an MDP and its constituent lines. If any distance from the MDP to one of its lines is larger than max_distance, the MDP is thrown away. A good starting value would be 0.1 mm for small tracers and/or clean data, or 0.2 mm for larger tracers and/or noisy data.
cutoffslist-like of length 6, optional: A list (or equivalent) of the cutoff distances for every axis, formatted as [x_min, x_max, y_min, y_max, z_min, z_max]. Only the minpoints which fall within these cutoff distances are considered. The default is None, in which case they are automatically computed using pept.tracking.peptml.get_cutoffs.
append_indicesbool, default False: If set to True, the indices of the individual LoRs that were used to compute each minpoint are also appended to the returned array.

Raises

TypeError: If line_data is not an instance of pept.LineData.
ValueError: If 2 <= num_lines <= len(sample_lines) is not satisfied.
ValueError: If cutoffs is not a one-dimensional array with values formatted as [min_x, max_x, min_y, max_y, min_z, max_z].

Methods

`__init__`(num_lines, max_distance[, cutoffs, ...])	Minpoints class constructor.
`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.

Attributes

`append_indices`
`cutoffs`
`max_distance`
`num_lines`

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")

property num_lines#

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")

property max_distance#

property cutoffs#

property append_indices#

fit_sample(sample_lines)[source]#