knitr::opts_chunk$set(echo = TRUE, eval = FALSE) library(knitr) library(data.table)
This package generates mass concentration maps and phase distribution maps based on X-ray mapping data and spot analysis data from EPMA.
See "[How to]" for a usage and Yasumoto et al. (2018) for implementations.
Current version supports data from JEOL-style EPMA.
Copy & paste a following command to R.
source("https://install-github.me/atusy/qntmap")
Details below.
Conversion is performed by utilizing spot analysis data as internal standards. Thus, [spot analysis][Spot analysis] must be done prior to [mapping][Mapping].
| | Spot | Map | Comment | |:--------------------|------:|--------:|:--------------------------------| |Acceralating Voltage | 15 kV | 15 kV | Must be same in spot and map | |Probe diameter | 3 μm | 20 μm | Must be smaller in spot than map| |Probe current | 10 nA | 100 nA | | |Peak dwell | 10 sec| 120 msec| | |Background dwell | 5 sec| NA | No need to analyze in map |
.map/1
)..map
directory and .qnt
directoryThe exported data are stored in a directory named by
.qnt
in most environments.
If using JXA-8230, a directory's name is {PROJECT}_{#}_QNT
where
{PROJECT}
is name of a project's name defined by user or "PROJECT" if undefined,
and{#}
is a variable integer (e.g., PROJECT_0001_QNT
).
kable(fread(system.file(package = "qntmap", "extdata", "files-qnt.csv")))
The exported data are stored in a directory .map/{#}
where
{#}
is a variable integer in most environments (e.g., .map/1
).
If using JXA-8230, a directory name is
{PROJECT}_{#1}_MAP_{#2}_csv
where
{PROJECT}
is name of a project's name defined by user or "PROJECT" if undefined,
and{#1}
and {#2}
are variable integers (e.g., PROJECT_0001_MAP_0001_csv
).
kable(fread(system.file(package = "qntmap", "extdata", "files-map.csv")))
*
indicates wild cards.
For data processing.
Follow instructions shown by running the following code.
library(qntmap) qntmap()
As a result,
phase identification result is saved in "clustering
" directory and
mass concentration data as csv files in "qntmap
" directory
both under the directory contaning mapping data.
Note that interactive mode has limited functions. Use [manual mode][Manual mode] for full functionality.
A work-flow is available with an example dataset at https://qntmap.atusy.net/articles/qntmap.html .
library(qntmap) # Required parameters wd <- '.' # path to the working directory dir_map <- '.map/1' # relative/absolute path to the directory containing ascii converted X-ray map files (1_map.txt, 2_map.txt, and so on)" dir_qnt <- '.qnt' # relative/absolute path to the directory containing .qnt files (pkint.qnt, net.qnt, and so on)" # Optional parameters ## A character vector to specify phases tend to be smaller than mapping probe diameter fine_phase <- NULL ## A csv file indicating name of the phase of n-th quantitative point analysis. ## The file path is absolute or relative to `dir_qnt`. ## If NULL, names are assumed to be specified in comments during EPMA analysis. phase_list <- NULL # Run analysis # Set working directory setwd(wd) # Load mapping data # Change value of DT (dead time in nanoseconds) depending on EPMA. # 1100 ns is a value applied by JEOL JXA-8105. xmap <- read_xmap(wd = dir_map, DT = 1100) # Compile quantitative data qnt <- read_qnt(wd = dir_qnt, phase_list = phase_list, renew = TRUE) ## Check 'phase_list0.csv' under 'dir_qnt' to see if name of phases are provided properly. ## If not, modify the csv file and specify the path of modified one to `phase_list` in "Optional parameters" section and rerun the above code. # Determine initial cluster centers centers <- find_centers(xmap = xmap, qnt = qnt, fine_phase = fine_phase) ## Check 'centers0.csv' under the `wd` and modify on demand. ## If modified, assign content of the modified csv file by running ## centers <- data.table::fread('path to the modified csv file') # Phase identification # Assign group_cluster = TRUE if you want to integrate same phases subgrouped by suffix after '_' # (e.g., garnet_a and garnet_b are integrated to garnet if TRUE) cls <- cluster_xmap(xmap = xmap, centers = centers, group_cluster = FALSE) # Quantify X-ray maps qmap <- quantify( xmap = xmap, qnt = qnt, cluster = cls, fine_phase = fine_phase ) ## Resulting files are saved in `qntmap` directory` under `dir_map`. # Summarize result summary(qmap) ## This shows minimum, lower quantile, median, mean, upper quantile, and maximum values of variables.
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