All public pept subroutines are fully documented here, along with copy-pastable examples. The base functionality is summarised below; the rest of the library is organised into submodules, which you can access on the left. You can also use the Search bar in the top left to go directly to what you need.

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Base Functions#

pept.read_csv(filepath_or_buffer[, ...])

Read a given number of lines from a file and return a numpy array of the values.


Load a binary saved / pickled object from filepath., obj)

Save an object obj instance as a binary file at filepath.

Base Classes#

pept.LineData(lines[, sample_size, overlap, ...])

A class for PEPT LoR data iteration, manipulation and visualisation.

pept.PointData(points[, sample_size, ...])

A class for general PEPT point-like data iteration, manipulation and visualisation.

pept.Pixels(pixels_array, xlim, ylim, **kwargs)

A class managing a 2D pixel space with physical dimensions, including tools for pixel manipulation and visualisation.

pept.Voxels(voxels_array, xlim, ylim, zlim, ...)

A class managing a 3D voxel space with physical dimensions, including tools for voxel manipulation and visualisation.


A PEPT processing pipeline, chaining multiple Filter and Reducer for efficient, parallel execution.



Define a sample_size as a fixed time window / slice.

pept.AdaptiveWindow(window[, max_elems])

Define a sample_size as a time window with a maximum limit of elements.