From 8c2e10d45c2bcdfc1fe4f77ac04028a9964a3e72 Mon Sep 17 00:00:00 2001 From: Hugo Ledoux Date: Wed, 23 Oct 2024 11:41:43 +0200 Subject: [PATCH] Remove the info in the `dt_comparison` folder and just point to the docs --- dt_comparisons/readme.md | 35 +---------------------------------- 1 file changed, 1 insertion(+), 34 deletions(-) diff --git a/dt_comparisons/readme.md b/dt_comparisons/readme.md index 6c4264f..89b4ce5 100644 --- a/dt_comparisons/readme.md +++ b/dt_comparisons/readme.md @@ -1,40 +1,7 @@ # Comparison of startinpy with other Python libraries -Rough benchmarks of various Delaunay triangulation libraries against random points, real-world point clouds, and a real world DEM. - -## Python library tested - - 1. [Delaunator-py](https://github.com/HakanSeven12/Delaunator-Python): pure Python port of a proven fast triangulator written original in JavaScript . - 2. [SciPy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.Delaunay.html): wrapper around [Qhull](http://qhull.org/), written in C. Using the batch construction in 2D. - 3. [SciPy-inc](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.Delaunay.html): wrapper around [Qhull](http://qhull.org/), written in C. Using the incremental insertion (as startinpy does). - 4. [Triangle](https://pypi.org/project/triangle/): wrapper around the [fast and robust C library](https://www.cs.cmu.edu/~quake/triangle.html) that performs constrained DT and meshing - 5. [PDAL](https://pdal.io): general point data library that reads, filters, and writes point clouds. Has a [Delaunay triangulation implementation](https://pdal.io/en/2.7.2/stages/filters.delaunay.html) based on [delaunator-cpp](https://github.com/delfrrr/delaunator-cpp). - - -## Datasets - - 1. __random_10k__: 10,000 points randomly generated in a unit square - 2. __random_50k__: 50,000 points randomly generated in a unit square - 3. __LAZ_2M__: a real-world subset of the [AHN4 dataset](https://www.ahn.nl/) (covering completely the Neterlands). The sub-tile [04GN2_21](https://geotiles.citg.tudelft.nl/AHN4_T/04GN2_21.LAZ) contains 2,144,049 points. - 4. __LAZ_33M__: a real-world subset of the [AHN4 dataset](https://www.ahn.nl/). The sub-tile [69EZ1_21.LAZ](https://geotiles.citg.tudelft.nl/AHN4_T/69EZ1_21.LAZ) contains 33,107,889 points. - 5. __dem.tiff__: the GeoTIFF file in `/data/` is a 550x505 gridded terrain. We take the centre of each cell, reading row-by-row and column-by-column, and this creates a 277,750 dataset of points that are collinear and cocircular with many others (degenerate cases for the DT). - - -## Results - -Those results were obtained on a MacBook Pro, M3 Pro, 36GB of RAM, running macOS v14.5 - - -| |random_10k|random_50k|dem.tiff|LAZ_2M|LAZ_33M| -|---------------|----------|----------|--------|------|-------| -| Delaunator-py | 0.219 | 0.840 | 3.550 | 49.2 | 898.1 | -|Delaunator-pdal| 0.003 | 0.014 | 27.409 | 1.5 | 27.4 | -| SciPy | 0.026 | 0.120 | 1.563 | 9.9 | 650.3 | -| SciPy-inc | 0.021 | 0.136 | X | X | X | -| triangle | 0.004 | 0.018 | 0.179 | 0.9 | 16.0 | -| startinpy | 0.017 | 0.175 | 0.437 | 3.9 | 41.2 | - +For results, see [this page of the documentation](https://startinpy.rtfd.io). ## To replicate