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A 3D array-like NumPy-based data structure for large sparsely-populated volumes

Project description

chunky3d

A 3D array-like NumPy-based data structure for large sparsely-populated volumes

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Introduction

This library provides a data structure, Sparse, which represents 3D volumetric data and supports a subset of np.ndarray features.

Example

>>> import numpy as np
>>> from chunky3d import Sparse

>>> s = Sparse(shape=(64, 64, 64))
>>> s[0, 0, 0]
0

>>> s.dtype
numpy.float64

>>> s.nchunks
8

>>> s.nchunks_initialized
0

>>> s[1, 2, 3] = 3
>>> s.nchunks_initialized
1

>>> s[:2, 2, 3:5]
array([[0., 0.],
       [3., 0.]])

Features

  • chunky3d.sparse_func - a collection of functions for analyzing chunked arrays, including morphological operations (opening, closing), thinning, connected components
  • Fast load and save using msgpack
  • Operations on arrays using .run(), with possible acceleration using multiprocessing
  • multiprocessing-based acceleration in most of existing sparse_func
  • Accelerated lookup using numba
  • Interpolation (point probe)
  • Origin and spacing: representing 3D space with non-uniform spacing for different axes
  • Easy visualization of arrays with dtype=np.uint8 via chunky3d.k3d_connector.get_k3d_object()

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