pept.processing.LaceyColorsLinear#

class pept.processing.LaceyColorsLinear(directory, p1, p2, xlim=None, ylim=None, num_divisions=50, max_distance=10, resolution=(8, 8), height=1000, width=1000, prefix='frame')[source]#

Bases: Reducer

Apply the LaceyColors mixing algorithm at num_divisions equidistant points between p1 and p2, saving images at each step in directory.

Reducer signature:

       PointData -> LaceyColors.fit -> (height, width, 3) np.ndarray
 list[PointData] -> LaceyColors.fit -> (height, width, 3) np.ndarray
list[np.ndarray] -> LaceyColors.fit -> (height, width, 3) np.ndarray

For details about the mixing algorithm itself, check the LaceyColors documentation.

The generated images (saved in directory with height x width pixels) can be stitched into a video using pept.plots.make_video.

New in pept-0.5.1

Examples

Consider a pipe-flow experiment, with tracers moving from side to side in multiple passes / streamlines. First locate the tracers, then split their trajectories into each individual pass:

>>> import pept
>>> from pept.tracking import *
>>>
>>> split_pipe = pept.Pipeline([
>>>     Segregate(window = 10, max_distance = 20),  # Appends label column
>>>     GroupBy("label"),                           # Splits into samples
>>>     Reorient(),                                 # Align with X axis
>>>     Center(),                                   # Center points at 0
>>>     Stack(),
>>> ])
>>> streamlines = split_pipe.fit(trajectories)

Now each sample in streamlines corresponds to a single tracer pass, e.g. streamlines[0] is the first pass, streamlines[1] is the second. The passes were reoriented and centred such that the pipe is aligned with the X axis.

Now the LaceyColorsLinear reducer can be used to create images of the mixing between the pipe entrance and exit:

>>> from pept.processing import LaceyColorsLinear
>>> entrance = [-100, 0, 0]     # Pipe data was aligned with X and centred
>>> exit = [100, 0, 0]
>>> LaceyColorsLinear(
>>>     directory = "lacey",    # Creates directory and saves images there
>>>     p1 = entrance,
>>>     p2 = exit,
>>> ).fit(streamlines)

Now the directory “lacey” was created inside your current working folder, and all Lacey images saved there as “frame0000.png”, “frame0001.png”, etc. You can stitch all images together into a video using pept.plots.make_video:

>>> import pept
>>> pept.plots.make_video("lacey/frame*.png", output = "lacey/video.avi")

__init__(directory, p1, p2, xlim=None, ylim=None, num_divisions=50, max_distance=10, resolution=(8, 8), height=1000, width=1000, prefix='frame')[source]#

Methods

`__init__`(directory, p1, p2[, xlim, ylim, ...])
`copy`([deep])	Create a deep copy of an instance of this class, including all inner attributes.
`fit`(trajectories[, executor, max_workers, ...])
`load`(filepath)	Load a saved / pickled PEPTObject object from filepath.
`save`(filepath)	Save a PEPTObject instance as a binary pickle object.

fit(trajectories, executor='joblib', max_workers=None, verbose=True)[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")