Version and benchmark update.

Author: Jaybro

CMakeLists.txt

@@ -6,7 +6,7 @@ cmake_minimum_required(VERSION 3.12)
 
 project(pico_tree
     LANGUAGES CXX
-    VERSION 0.7.2
+    VERSION 0.7.3
     DESCRIPTION "PicoTree is a C++ header only library with Python bindings for nearest neighbor searches and range searches using a KdTree."
     HOMEPAGE_URL "https://github.com/Jaybro/pico_tree")
 

README.md

@@ -13,7 +13,7 @@ See the table below to get an impression of the performance provided by the [KdT
 | [SciPy cKDTree][spck] v1.5.0        | ...       | 5.0s          | ...        | 547.2s      |
 | [Scikit-learn KDTree][skck] 0.22.2  | ...       | 12.2s         | ...        | 44.5s       |
 | [OpenCV FLANN][cvfn] 4.5.2          | 1.9s      | ...           | 4.7s       | ...         |
-| PicoTree KdTree v0.7.2              | 0.8s      | 1.0s          | 3.0s       | 3.9s        |
+| PicoTree KdTree v0.7.3              | 0.9s      | 1.1s          | 2.8s       | 3.3s        |
 
 It compares the performance of the build and query algorithms using two [LiDAR](./docs/benchmark.md) based point clouds of sizes 7733372 and 7200863. The first is used to compare build times and both are used to compare query times. All benchmarks were generated with the following parameters: `max_leaf_size=10`, `knn=1` and `OMP_NUM_THREADS=1`. Note that a different C++ back-end was used for each of the `Knn C++` and `Knn Python` benchmarks. This means that they shouldn't be compared directly. See the [Python comparison](./examples/python/kd_tree.py) for more details. A more detailed [C++ comparison](./docs/benchmark.md) of PicoTree is available with respect to [nanoflann][nano].
 

docs/benchmark.md

@@ -2,6 +2,8 @@
 
 One of the PicoTree examples is a small [benchmark](../examples/benchmark/) that compares the KdTree of this library with that of [nanoflann](https://github.com/jlblancoc/nanoflann). This page describes the benchmark output and how to reproduce the exact same input that was used for benchmarking.
 
+Results were generated on: 24-05-2021 using MinGW GCC 10.1 using PicoTree 0.7.3.
+
 # Data sets
 
 The [Robotic 3D Scan Repository](http://kos.informatik.uni-osnabrueck.de/3Dscans/) provides several 3D point clouds that have been generated using a LiDAR scanner. The following has been used for the comparison benchmark:
@@ -25,8 +27,6 @@ The running time of the benchmark was kept reasonable by using two subsets of po
 
 Both parts are 360 degree scans taken from different positions. The first is used to build a tree and the second for querying that tree. Note that each run time describes a single invocation of a build algorithm and n invocations of the others.
 
-Results were generated on: 01-05-2021 using MinGW GCC 10.1.
-
 ![Build Time](./images/benchmark_gauss_build_time.png)![Radius Search Time](./images/benchmark_gauss_radius_search_time.png)
 
 ![Knn Search Time](./images/benchmark_gauss_knn_search_time.png)

docs/images/benchmark_gauss_build_time.png

@@ -34,7 +34,7 @@ def compile_cmake_args():
 
 setup(name='pico_tree',
       # The same as the CMake project version.
-      version='0.7.2',
+      version='0.7.3',
       description='PicoTree Python Bindings',
       author='Jonathan Broere',
       url='https://github.com/Jaybro/pico_tree',