diff --git a/DESCRIPTION b/DESCRIPTION index 6643986..d3a6768 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -25,8 +25,7 @@ Imports: boot, bench, ggplot2, stats, - testthat (>= 2.1.0), - hrbrthemes + testthat (>= 2.1.0) Suggests: covr, knitr, diff --git a/docs/articles/testComplexity.html b/docs/articles/testComplexity.html index eb07ffb..8c844c3 100644 --- a/docs/articles/testComplexity.html +++ b/docs/articles/testComplexity.html @@ -282,15 +282,20 @@

These functions are for plotting the metrics returned by the quantifiers, in case the user still prefers to diagnose the obtained complexity result via the traditional method, with a visual representation of the benchmarked data. Considering the columns from the data frames returned by the quantiftying functions, the user can create plots for:

library(testComplexity)
 df <- asymptoticTimings(PeakSegOptimal::PeakSegPDPA(rpois(N, 1), max.segments = 3L), data.sizes = 10^seq(1, 4))
-plotTimings(df, titles = list("Timings", "PeakSegOptimal::PeakSegPDPA"), line.color = "#ffec1b", point.color = "#ffec1b", line.size = 1, point.size = 1.5) + hrbrthemes::theme_ft_rc()
-

- Memory Allocations vs Data sizes, via plotMemoryUsage:

+plotTimings(df, titles = list("Timings", "PeakSegOptimal::PeakSegPDPA"), line.color = "orange", point.color = "black", line.size = 1, point.size = 1.5) +

+ 
library(testComplexity)
 df <- asymptoticMemoryUsage(PeakSegOptimal::PeakSegPDPA(rpois(N, 1), max.segments = 3L), data.sizes = 10^seq(1, 4, by = 0.1))
-plotMemoryUsage(df, titles = list("Memory usage", "PeakSegOptimal::PeakSegPDPA"), line.color = "#ffec1b", point.color = "#ffec1b", line.size = 1, point.size = 1.5) + hrbrthemes::theme_ft_rc()
+plotMemoryUsage(df, titles = list("Memory usage", "PeakSegOptimal::PeakSegPDPA"), line.color = "orange", point.color = "black", line.size = 1, point.size = 1.5)

That sums up the functionality offered by testComplexity 0.0.0.9000. For more examples, please check the articles section of the website.

diff --git a/docs/articles/testComplexity_files/figure-html/unnamed-chunk-1-1.png b/docs/articles/testComplexity_files/figure-html/unnamed-chunk-1-1.png index 1aaa0d7..979b859 100644 Binary files a/docs/articles/testComplexity_files/figure-html/unnamed-chunk-1-1.png and b/docs/articles/testComplexity_files/figure-html/unnamed-chunk-1-1.png differ diff --git a/docs/articles/testComplexity_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/testComplexity_files/figure-html/unnamed-chunk-2-1.png index 5ff7f95..41aa579 100644 Binary files a/docs/articles/testComplexity_files/figure-html/unnamed-chunk-2-1.png and b/docs/articles/testComplexity_files/figure-html/unnamed-chunk-2-1.png differ diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 9494fce..290490f 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -10,5 +10,5 @@ articles: PELT_and_SegNeigh: PELT_and_SegNeigh.html substring_and_gregexpr: substring_and_gregexpr.html testComplexity: testComplexity.html -last_built: 2020-08-23T07:58Z +last_built: 2020-08-23T08:41Z diff --git a/vignettes/testComplexity.Rmd b/vignettes/testComplexity.Rmd index 5a8f208..365127f 100644 --- a/vignettes/testComplexity.Rmd +++ b/vignettes/testComplexity.Rmd @@ -202,17 +202,18 @@ The call stack (sequence of calls that lead to the error) is returned via a call ### [Plotters](https://anirban166.github.io/testComplexity/reference/index.html#section-plotters) These functions are for plotting the metrics returned by the quantifiers, in case the user still prefers to diagnose the obtained complexity result via the traditional method, with a visual representation of the benchmarked data. Considering the columns from the data frames returned by the quantiftying functions, the user can create plots for: -- Timings vs Data sizes, via `plotTimings`: +- Timings vs Data sizes, via `plotTimings`:
```{r echo=TRUE, fig.show='hold', warning=FALSE} library(testComplexity) df <- asymptoticTimings(PeakSegOptimal::PeakSegPDPA(rpois(N, 1), max.segments = 3L), data.sizes = 10^seq(1, 4)) -plotTimings(df, titles = list("Timings", "PeakSegOptimal::PeakSegPDPA"), line.color = "#ffec1b", point.color = "#ffec1b", line.size = 1, point.size = 1.5) + hrbrthemes::theme_ft_rc() +plotTimings(df, titles = list("Timings", "PeakSegOptimal::PeakSegPDPA"), line.color = "orange", point.color = "black", line.size = 1, point.size = 1.5) ``` -- Memory Allocations vs Data sizes, via `plotMemoryUsage`: + +- Memory Allocations vs Data sizes, via `plotMemoryUsage`:
```{r echo=TRUE, fig.show='hold', warning=FALSE} library(testComplexity) df <- asymptoticMemoryUsage(PeakSegOptimal::PeakSegPDPA(rpois(N, 1), max.segments = 3L), data.sizes = 10^seq(1, 4, by = 0.1)) -plotMemoryUsage(df, titles = list("Memory usage", "PeakSegOptimal::PeakSegPDPA"), line.color = "#ffec1b", point.color = "#ffec1b", line.size = 1, point.size = 1.5) + hrbrthemes::theme_ft_rc() +plotMemoryUsage(df, titles = list("Memory usage", "PeakSegOptimal::PeakSegPDPA"), line.color = "orange", point.color = "black", line.size = 1, point.size = 1.5) ``` -------------------------------------------------------------------------------------------------