Skip to main content

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

Project description


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


Build Status


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


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

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

>>> s.dtype

>>> s.nchunks

>>> s.nchunks_initialized

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

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


  • 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()

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

chunky3d-0.1.9.tar.gz (30.8 kB view hashes)

Uploaded source

Built Distribution

chunky3d-0.1.9-py3-none-any.whl (27.1 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page