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 usingmultiprocessing multiprocessing-based acceleration in most of existingsparse_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.uint8viachunky3d.k3d_connector.get_k3d_object()
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