| Name | chunky3d JSON |
| Version |
0.1.13
JSON |
| download |
| home_page | |
| Summary | A 3D array-like NumPy-based data structure for large sparsely-populated volumes |
| upload_time | 2023-11-10 23:32:38 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | |
| license | MIT |
| keywords |
3d
array
chunked
sparse
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
|
| coveralls test coverage |
No coveralls.
|
# 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()`
Raw data
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