Configurable point rasterization for large scatterplots
| Název práce v češtině: | Konfigurovatelná rasterizace velkých bodových grafů |
|---|---|
| Název v anglickém jazyce: | Configurable point rasterization for large scatterplots |
| Klíčová slova: | bodové grafy|vizualizace|rasterizace|jazyk R |
| Klíčová slova anglicky: | scatterplots|visualization|rasterization|R language |
| Akademický rok vypsání: | 2021/2022 |
| Typ práce: | bakalářská práce |
| Jazyk práce: | angličtina |
| Ústav: | Katedra softwarového inženýrství (32-KSI) |
| Vedoucí / školitel: | doc. RNDr. David Hoksza, Ph.D. |
| Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 06.11.2021 |
| Datum zadání: | 08.11.2021 |
| Datum potvrzení stud. oddělením: | 26.04.2022 |
| Datum a čas obhajoby: | 29.06.2023 09:00 |
| Datum odevzdání elektronické podoby: | 09.05.2023 |
| Datum odevzdání tištěné podoby: | 09.05.2023 |
| Datum proběhlé obhajoby: | 29.06.2023 |
| Oponenti: | Mgr. Ladislav Peška, Ph.D. |
| Zásady pro vypracování |
| Scattermore is a simple R package for plotting large amounts of points in 2D scatterplots, useful mainly for its optimized plot rendering implemented in C, which vastly outperforms the standard R packages. While the package gained a considerable use in the cytometry community where large scatterplot-style data prevail, the current single-purpose implementation offers only limited plotting possibilities, leaving many related use-cases unanswered. The aim of the thesis is to improve Scattermore package to allow plotting of various new kinds of data (such as point shapes, lines, polygons) and new ways of blending and coloring the data (e.g. proper alpha blending with avoidance of overplotting, density-based coloring, etc.), possibly inspired by packages from other ecosystems (DataShader, GigaScatter.jl). As the main target, the API of the operations should allow reasonable composition of the rendering and blending routines, enabling high customizability of the processing and output. The thesis will additionally review, analyze, and possibly apply methods to make the rendering of the plots faster, e.g. by utilizing parallelism or optimizing the cache utilization of the CPU. |
| Seznam odborné literatury |
| Xian, Z., & Xiaobing, L. (2010, August). Improved DDA line drawing anti-aliasing algorithm based on embedded graphics system. In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE) (Vol. 2, pp. V2-497). IEEE.
Wang, Y., Chen, Z., Cheng, L., Li, M., & Wang, J. (2013). Parallel scanline algorithm for rapid rasterization of vector geographic data. Computers & geosciences, 59, 31-40. Zhou, C., Chen, Z., Liu, Y., Li, F., Cheng, L., Zhu, A. X., & Li, M. (2015). Data decomposition method for parallel polygon rasterization considering load balancing. Computers & Geosciences, 85, 196-209. Venables, W. N., Smith, D. M., & R Development Core Team. (2009). An introduction to R. Eddelbuettel, D., & Balamuta, J. J. (2018). Extending R with C++: A brief introduction to Rcpp. The American Statistician, 72(1), 28-36. |