# chunky3d
A 3D array-like NumPy-based data structure for large sparsely-populated volumes
## Build
[](https://travis-ci.org/K3D-tools/chunky3d)
# 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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"description": "# chunky3d\nA 3D array-like NumPy-based data structure for large sparsely-populated volumes\n\n## Build\n[](https://travis-ci.org/K3D-tools/chunky3d)\n\n# Introduction\n\nThis library provides a data structure, `Sparse`, which represents 3D volumetric data\nand supports a subset of `np.ndarray` features.\n\n## Example\n\n```\n>>> import numpy as np\n>>> from chunky3d import Sparse\n\n>>> s = Sparse(shape=(64, 64, 64))\n>>> s[0, 0, 0]\n0\n\n>>> s.dtype\nnumpy.float64\n\n>>> s.nchunks\n8\n\n>>> s.nchunks_initialized\n0\n\n>>> s[1, 2, 3] = 3\n>>> s.nchunks_initialized\n1\n\n>>> s[:2, 2, 3:5]\narray([[0., 0.],\n [3., 0.]])\n```\n\n# Features\n\n* `chunky3d.sparse_func` - a collection of functions for analyzing chunked arrays, including \n morphological operations (opening, closing), thinning, connected components\n* Fast load and save using `msgpack`\n* Operations on arrays using `.run()`, with possible acceleration using `multiprocessing`\n* `multiprocessing`-based acceleration in most of existing `sparse_func`\n* Accelerated lookup using `numba`\n* Interpolation (point probe)\n* Origin and spacing: representing 3D space with non-uniform spacing for different axes\n* Easy visualization of arrays with `dtype=np.uint8` via `chunky3d.k3d_connector.get_k3d_object()`\n\n\n",
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