pept.tracking.GaussianDensity#

class pept.tracking.GaussianDensity(sigma=None)[source]#

Bases: Filter

Append weights according to the Gaussian distribution that best fits the samples of points.

Filter signature:

      PointData -> GaussianDensity.fit_sample -> PointData
  numpy.ndarray -> GaussianDensity.fit_sample -> PointData
list[PointData] -> GaussianDensity.fit_sample -> list[PointData]

This is treated as an optimisation problem: find the 3D location that maximises the sum of Probability Distributions (PDF) centered at each point.

Given N points p_1, p_2, ..., p_N:

          N
maximise sum( exp( -0.5 * |x - p_i|^2 / sigma^2 ) )
   x      i

Each point is then assigned a weight corresponding to its PDF - i.e. the exponential part - saved in the weight column.

Sigma controls the standard deviation of the Gaussian distribution centred at each point; this corresponds to the relative uncertainty in each point’s location. For TimeOfFlight data, leave sigma = None and it will be computed from the “spatial_resolution” attribute.

You can use Centroids afterwards to compute the weighted centroid, i.e. where the tracer is. For multiple particle tracking (or just more robustness to noise) you can use HDBSCAN + SplitLabels beforehand.

New in pept-0.4.2

__init__(sigma=None)[source]#

Methods

`__init__`([sigma])
`copy`([deep])	Create a deep copy of an instance of this class, including all inner attributes.
`fit`(samples[, executor, max_workers, verbose])	Apply self.fit_sample (implemented by subclasses) according to the execution policy.
`fit_sample`(points)
`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.

fit(samples, 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")

fit_sample(points)[source]#