pept.processing.SpatialProjections#

class pept.processing.SpatialProjections(directory, start, end, dimension='x', num_divisions=500, max_distance=10, colorbar_col=-1, height=1000, width=1000, prefix='frame', **kwargs)[source]#

Bases: Reducer

Project multiple tracer passes onto a moving 2D plane along a given direction between start and end coordinates, saving each frame in directory.

Reducer signature:

       PointData -> SpatialProjections.fit -> None
 list[PointData] -> SpatialProjections.fit -> None
list[np.ndarray] -> SpatialProjections.fit -> None

Each sample in the input `PointData` is treated as a separate streamline / tracer pass. You can group passes using `Segregate + GroupBy(“label”)`.

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

The extra keyword arguments **kwargs are passed to the histogram creation routine pept.plots.histogram. You can e.g. set the YAxis limits by adding ylim = [0, 20].

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

>>> from pept.processing import SpatialProjections
>>> entrance_x = -100                   # Pipe data was aligned with X
>>> exit_x = 100
>>> SpatialProjections(
>>>     directory = "projections",      # Creates directory to save images
>>>     start = entrance_x,
>>>     end = exit_x,
>>> ).fit(streamlines)

Now the directory “projections” was created inside your current working folder, and eachc projected frame was 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(
>>>     "projections/frame*.png",
>>>     output = "projections/video.avi"
>>> )

The raw projections can also be extracted directly:

>>> sp = SpatialProjections(
>>>     directory = "projections",   # Creates directory to save images
>>>     p1 = entrance_x,
>>>     p2 = exit_x,
>>> )
>>> sp.fit(streamlines)
>>> sp.projections

Attributes

projectionslist[(N, 5), np.ndarray]: A list of frames for each division between start and end, with each frame saving 5 columns [t, x, y, z, colorbar_col].

__init__(directory, start, end, dimension='x', num_divisions=500, max_distance=10, colorbar_col=-1, height=1000, width=1000, prefix='frame', **kwargs)[source]#

Methods

`__init__`(directory, start, end[, dimension, ...])
`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.

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

fit(trajectories, executor='joblib', max_workers=None, verbose=True)[source]#

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")