benchmark added

README.md

@@ -235,8 +235,24 @@ See: <http://lastools.org/download/lascanopy_README.txt>
 `glcm_IDMN`, `glcm_IDN`, `glcm_inverseVariance`, `glcm_maxProb`,
 `glcm_sumAverage`, `glcm_sumEntropy`, `glcm_sumVariance`
 
-Requires the {ForestTools} package. ForestTools::glcm_img() function is
-used to calculate the GLCM statistics (see package manual for details)
+Requires the {ForestTools} package
+(<https://github.com/andrew-plowright/ForestTools>). ForestTools::glcm()
+function is used to calculate the GLCM statistics (see package manual
+for details)
+
+## Benchmarking
+
+The processing time required for each function in the package varies,
+sometimes significantly, depending on the calculations involved. The
+figure below presents the average processing time for each `metrics_*`
+function included in the package. This benchmark was conducted using the
+“Megaplot.laz” dataset from the `lidR` package, with iterative calls to
+the `pixel_metrics()` function. It’s important to note that the results
+of this benchmark are dependent on the workstation’s specifications and
+should be considered as a relative indication of the processing time
+required.
+
+![](README_files/figure-gfm/unnamed-chunk-3-1.png)<!-- -->
 
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README.rmd

@@ -187,12 +187,32 @@ http://lastools.org/download/lascanopy_README.txt
 
 `glcm_mean`, `glcm_variance`, `glcm_autoCorrelation`, `glcm_cProminence`, `glcm_cShade`, `glcm_cTendency`, `glcm_contrast`, `glcm_correlation`, `glcm_differenceEntropy`, `glcm_dissimilarity`, `glcm_energy`, `glcm_entropy`, `glcm_homogeneity1`, `glcm_homogeneity2`, `glcm_IDMN`, `glcm_IDN`, `glcm_inverseVariance`, `glcm_maxProb`, `glcm_sumAverage`, `glcm_sumEntropy`, `glcm_sumVariance`
 
-Requires the {ForestTools} package. ForestTools::glcm_img() function is used to calculate the GLCM statistics (see package manual for details)
+Requires the {ForestTools} package (https://github.com/andrew-plowright/ForestTools). ForestTools::glcm() function is used to calculate the GLCM statistics (see package manual for details)
 
+## Benchmarking
 
+The processing time required for each function in the package varies, sometimes significantly, depending on the calculations involved. The figure below presents the average processing time for each `metrics_*` function included in the package. This benchmark was conducted using the "Megaplot.laz" dataset from the `lidR` package, with iterative calls to the `pixel_metrics()` function. It's important to note that the results of this benchmark are dependent on the workstation's specifications and should be considered as a relative indication of the processing time required.
 
 
 
+```{r, warning=F, echo=F, message=F}
+library(tidyverse)
+library(bench)
+bm2 <- read_csv("results_benchmark.csv")
+bm2$median <- bench::as_bench_time(bm2$median)
+bm2 <- bm2 %>% mutate(id = fct_rev(factor(id)))
+
+ggplot(bm2, aes(id, median)) + 
+  geom_point() +
+  geom_segment( aes(x=id, xend=id, y=0, yend=median))+ 
+  coord_flip()+
+  bench::scale_y_bench_time()+
+  theme_light()+
+  theme(axis.title = element_blank(),
+        panel.grid = element_blank())
+```
+
+
 
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