knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of the DS401 R package is to assess quality of 3d topographics scans in form of x3p images.
You can install the development version of DS401 from GitHub with:
# install.packages("devtools") devtools::install_github("heike/DS401")
library(DS401) ## basic example code library(x3ptools)
x3p_image(fau277_bb_l2, file = "man/figures/fau277_bb_l2.png")
knitr::include_graphics("man/figures/fau277_bb_l2.png")
x3p_image(fau001_ba_l1, file = "man/figures/fau001_ba_l1.png")
knitr::include_graphics("man/figures/fau001_ba_l1.png")
The DS401 package combines a set of functions assessing the quality of scans.
extract_na calculates the percentage of values that are missing in the surface matrix of the scan. For the scans shown above, the percentages are quite high for the bad scan and low for the good scan
extract_na(fau277_bb_l2) extract_na(fau001_ba_l1)
The col_na function's goal is to distinguish between good and bad scans based on the proportion of columns with large proportions of missing values. It takes in three parameters, which are an x3p object, a percentage to be used as the number acceptable percentage of missing values in a column, and the proportion that bad columns that is the threshold for a good scan.
assess_col_na(fau277_bb_l2) assess_col_na(fau001_ba_l1)
'assess_median_na_proportion' calculates the median proportion of NA values present in the middle of a 3d scan from a specific number of different y values. Specifically this variable is calculating the amount of feathering occuring in the 3d scans.
assess_median_na_proportion(fau277_bb_l2)# This scan seems to have a problem with feathering assess_median_na_proportion(fau001_ba_l1)# good scan
assess_bottomempty(fau277_bb_l2) assess_bottomempty(fau001_ba_l1)
assess_middle_na_proportion(fau277_bb_l2) assess_middle_na_proportion(fau001_ba_l1)
assess_percentile_na_proportion(fau277_bb_l2) assess_percentile_na_proportion(fau001_ba_l1)
assess_rotation(fau277_bb_l2) assess_rotation(fau001_ba_l1)
extract_median_na_proportion(fau277_bb_l2) extract_median_na_proportion(fau001_ba_l1)
predict_quality(list(fau277_bb_l2, fau001_ba_l1), c(1,1))
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