pept.processing.DynamicProbability2D#

class pept.processing.DynamicProbability2D(diameter, column, dimensions='xy', resolution=(512, 512), xlim=None, ylim=None, max_workers=None, verbose=True)[source]#

Bases: Reducer

Compute the 2D probability distribution of some tracer quantity (eg velocity in each cell).

Reducer signature:

      PointData -> DynamicProbability2D.fit -> Pixels
list[PointData] -> DynamicProbability2D.fit -> Pixels
  numpy.ndarray -> DynamicProbability2D.fit -> Pixels

This reducer calculates the average value of the tracer quantity in each cell of a 2D pixel grid; it uses the full projected tracer area for the pixelization step, so the distribution is accurate for arbitrarily fine resolutions.

Parameters

diameterfloat: The diameter of the imaged tracer.
columnstr or int: The PointData column used to compute the probability distribution, given as a name (str) or index (int).
dimensionsstr or list[int], default “xy”: The tracer coordinates used to rasterize its trajectory, given as a string (e.g. “xy” projects the points onto the XY plane) or a list with two column indices (e.g. [1, 3] for XZ).
resolutiontuple[int, int], default (512, 512): The number of pixels used for the rasterization grid in the X and Y dimensions.
xlimtuple[float, float], optional: The physical limits in the X dimension of the pixel grid. If unset, it is automatically computed to contain all tracer positions (default).
ylimtuple[float, float], optional: The physical limits in the y dimension of the pixel grid. If unset, it is automatically computed to contain all tracer positions (default).
max_workersint, optional: The maximum number of workers (threads, processes or ranks) to use by the parallel executor; if 1, it is sequential (and produces the clearest error messages should they happen). If unset, the os.cpu_count() is used.
verbosebool or str default True: If True, time the computation and print the state of the execution.

Examples

Compute the velocity probability distribution of a single tracer trajectory having a column named “v” corresponding to the tracer velocity:

>>> trajectories = pept.load(...)
>>> pixels_vel = DynamicProbability2D(1.2, "v", "xy").fit(trajectories)

Plot the pixel grid:

>>> from pept.plots import PlotlyGrapher2D
>>> PlotlyGrapher2D().add_pixels(pixels_vel).show()

For multiple tracer trajectories, you can use Segregate then SplitAll('label') before calling this reducer to rasterize each trajectory separately:

>>> vel_pipeline = pept.Pipeline([
>>>     Segregate(20, 10),
>>>     SplitAll("label"),
>>>     DynamicProbability2D(1.2, "v", "xy")
>>> ])
>>> pixels_vel = vel_pipeline.fit(trajectories)

__init__(diameter, column, dimensions='xy', resolution=(512, 512), xlim=None, ylim=None, max_workers=None, verbose=True)[source]#

Methods

`__init__`(diameter, column[, dimensions, ...])
`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.

fit(samples)[source]#

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")