Remove the info in the dt_comparison folder and just point to the docs

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 <https://github.com/mapbox/delaunator>. 
-  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