| Name | ox-vox-nns JSON |
| Version |
0.5.0
JSON |
| download |
| home_page | None |
| Summary | A hybrid-ish nearest neighbour search implemented in rust, tailored towards consistent performance, especially on difficult inputs for KDTrees |
| upload_time | 2024-02-29 10:41:13 |
| maintainer | None |
| docs_url | None |
| author | Hamish Morgan <ham430@gmail.com> |
| requires_python | >=3.8 |
| license | None |
| keywords |
nearest
neighbour
pointcloud
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# OxVoxNNS - **Ox**idised **Vox**elised **N**earest **N**eighbour **S**earch
[](https://pypi.org/project/ox-vox-nns/)
[](https://github.com/hacmorgan/OxVoxNNS/actions)
A hybrid-ish nearest neighbour search implemented in rust, tailored towards consistent performance, especially on difficult inputs for KDTrees
## Installation
### Precompiled (from PyPI, recommended)
```
pip install ox_vox_nns
```
### Manual
Checkout this repo and enter a virtual environment, then run
```
maturin develop --release
```
## Usage
Basic usage, query a block of query points in **sparse** mode:
```
import numpy as np
from ox_vox_nns.ox_vox_nns import OxVoxNNS
NUM_POINTS = 100_000
TEST_POINTS = np.random.random((NUM_POINTS, 3))
indices, distances = ox_vox_nns.OxVoxNNS(
search_points=TEST_POINTS,
max_dist=0.05,
).find_neighbours(
query_points=TEST_POINTS,
num_neighbours=1000,
sparse=True,
)
```
More complex usage, using a single NNS object for multiple *exact* mode queries (e.g. to distribute the `nns` object and perform queries in parallel, or to query from a large number of query points in batches/chunks)
```
# same imports and test data as above
nns = ox_vox_nns.OxVoxNNS(TEST_POINTS, 0.1)
for query_points_chunk in query_points_chunks:
chunk_indices, chunk_distances = nns.find_neighbours(
query_points=query_points_chunk,
num_neighbours=1,
sparse=False,
)
```
## Performance
As a rough heuristic:
- Open3D will edge out OxVox on **easier data**, e.g. uniformally distributed points, though OxVox does outperform SciPy and SKLearn's KDTree implementations
- OxVox in exact mode will outperform even Open3D on harder inputs, e.g. with clusters of very dense points
- OxVox in sparse mode or with `epsilon > 0.0` will dramatically outperform KDTrees too. This is not really a fair comparison, but if you don't strictly need the exact `k` nearest neighbours it can be very helpful
See `performance_test_ox_vox_nns.py` for test code.
More thorough tests and interactive visualisations are still being developed to help a prospective user decide quickly if OxVox is worth trying
Raw data
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"description": "# OxVoxNNS - **Ox**idised **Vox**elised **N**earest **N**eighbour **S**earch\n\n[](https://pypi.org/project/ox-vox-nns/)\n[](https://github.com/hacmorgan/OxVoxNNS/actions)\n\nA hybrid-ish nearest neighbour search implemented in rust, tailored towards consistent performance, especially on difficult inputs for KDTrees\n\n\n## Installation\n### Precompiled (from PyPI, recommended)\n```\npip install ox_vox_nns\n```\n\n### Manual\nCheckout this repo and enter a virtual environment, then run\n```\nmaturin develop --release\n```\n\n\n## Usage\nBasic usage, query a block of query points in **sparse** mode:\n```\nimport numpy as np\nfrom ox_vox_nns.ox_vox_nns import OxVoxNNS\n\nNUM_POINTS = 100_000\nTEST_POINTS = np.random.random((NUM_POINTS, 3))\n\nindices, distances = ox_vox_nns.OxVoxNNS(\n search_points=TEST_POINTS,\n max_dist=0.05,\n).find_neighbours(\n query_points=TEST_POINTS,\n num_neighbours=1000,\n sparse=True,\n)\n```\n\nMore complex usage, using a single NNS object for multiple *exact* mode queries (e.g. to distribute the `nns` object and perform queries in parallel, or to query from a large number of query points in batches/chunks)\n```\n# same imports and test data as above\n\nnns = ox_vox_nns.OxVoxNNS(TEST_POINTS, 0.1)\n\nfor query_points_chunk in query_points_chunks:\n chunk_indices, chunk_distances = nns.find_neighbours(\n query_points=query_points_chunk,\n num_neighbours=1,\n sparse=False,\n )\n```\n\n\n## Performance\nAs a rough heuristic:\n\n- Open3D will edge out OxVox on **easier data**, e.g. uniformally distributed points, though OxVox does outperform SciPy and SKLearn's KDTree implementations\n- OxVox in exact mode will outperform even Open3D on harder inputs, e.g. with clusters of very dense points\n- OxVox in sparse mode or with `epsilon > 0.0` will dramatically outperform KDTrees too. This is not really a fair comparison, but if you don't strictly need the exact `k` nearest neighbours it can be very helpful\n\nSee `performance_test_ox_vox_nns.py` for test code.\n\nMore thorough tests and interactive visualisations are still being developed to help a prospective user decide quickly if OxVox is worth trying\n\n",
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