Python implementation of the Fast Linear Assigment Sorting Algorithm
Project description
Fast Linear Assignment Sorting - FLAS
FLAS allows to sort 2d grids based on similarity. This directory contains the python implementation and shows how to use it.
Features
- Implementation of FLAS and DPQ (and other metrics to quantify an arrangement)
- Allows frozen fields
- Allows empty fields (holes)
- Installable via PyPI
- Easy to use python interface with numpy support
- Fast implementation in C++
Installation
TODO: Install via pypi
Usage
Given that you have N features with D-dimensions, you can sort these features based on similarity with the following code:
import numpy as np
from vc_flas import Grid, flas
N, D = 241, 7
features = np.random.random((N, D))
grid = Grid.from_features(features)
arrangement = flas(grid, wrap=True, radius_decay=0.99)
sorted_features = arrangement.get_sorted_features()
height, width, dim = sorted_features.shape
assert (height, width, dim) == (16, 16, 7)
TODO
- Explain arrangements / labels
- How to build grids (GridBuilder, Grid.from_features(), Grid.from_grid_features())
- explain flas() Parameters
- explain metrics
For more usage examples see examples TODO.
About
Kai Barthel, Nico Hezel, Klaus Jung, Bruno Schilling and Konstantin Schall
HTW Berlin, Visual Computing Group, Germany
This is an example implementation of the algorithms from the paper
Improved Evaluation and Generation of Grid Layouts using Distance Preservation Quality and Linear Assignment Sorting
Published in COMPUTER GRAPHICS Forum: (https://onlinelibrary.wiley.com/doi/full/10.1111/cgf.14718)
Reference
Reference to cite when you use any of the presented algorithms in a research paper:
@article{https://doi.org/10.1111/cgf.14718,
author = {Barthel, K. U. and Hezel, N. and Jung, K. and Schall, K.},
title = {Improved Evaluation and Generation Of Grid Layouts Using Distance Preservation Quality and Linear Assignment Sorting},
journal = {Computer Graphics Forum},
volume = {42},
number = {1},
pages = {261-276},
keywords = {interaction, user studies, visualization, information visualization, high dimensional sorting, assistive interfaces},
doi = {https://doi.org/10.1111/cgf.14718},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14718},
year = {2023}
}
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- Download URL: vc_flas-0.1.0-cp311-cp311-macosx_11_0_arm64.whl
- Upload date:
- Size: 71.3 kB
- Tags: CPython 3.11, macOS 11.0+ ARM64
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.1.1 CPython/3.12.7
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Provenance
The following attestation bundles were made for vc_flas-0.1.0-cp311-cp311-macosx_11_0_arm64.whl:
Publisher:
BuildAndPublish.yml on Visual-Computing/LAS_FLAS
-
Statement:
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Statement type:
https://in-toto.io/Statement/v1 -
Predicate type:
https://docs.pypi.org/attestations/publish/v1 -
Subject name:
vc_flas-0.1.0-cp311-cp311-macosx_11_0_arm64.whl -
Subject digest:
89daf039b566d318782dcce7e40694902027f808083c26222e006840cd3dd918 - Sigstore transparency entry: 153090056
- Sigstore integration time:
-
Permalink:
Visual-Computing/LAS_FLAS@5b4bf2a256b7b31602aa6d7fe611f6563d9f6f17 -
Branch / Tag:
refs/tags/v0.1.0 - Owner: https://github.com/Visual-Computing
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Access:
public
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Token Issuer:
https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
Publication workflow:
BuildAndPublish.yml@5b4bf2a256b7b31602aa6d7fe611f6563d9f6f17 -
Trigger Event:
push
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Statement type: