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README.Rmd
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README.Rmd
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---
title: Enhance quantitative analysis of EPMA maps with qntmap
output: github_document
---
<!-- badges: start -->
[](https://github.com/atusy/qntmap/actions)
[](https://qntmap.atusy.net/LICENSE-text.html)
<!-- badges: end -->
```{r setup, include=FALSE, eval = TRUE}
knitr::opts_chunk$set(echo = TRUE, eval = FALSE)
library(knitr)
library(data.table)
```
# Overview
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)](https://doi.org/10.2138/am-2018-6323CCBY)
for implementations.
Current version supports data from JEOL-style EPMA.
# Installation
Copy & paste a following command to R.
```r
source("https://install-github.me/atusy/qntmap")
```
# How to
1. [EPMA analysis] (spot before map)
1. [Export data] from EPMA to PC
1. [Run qntmap on R] for data processing.
Details below.
## EPMA analysis
Conversion is performed by utilizing spot analysis data as internal standards.
Thus, [spot analysis][Spot analysis] must be done prior to [mapping][Mapping].
### Spot analysis
- Analytical conditions
- Same as those conventionally applied in your lab.
- Use wavelength-dispersive X-ray spectrometer
- Spots to be analyzed
- **20 spots per phase** in the area to be mapped (the more is better).
- It is better but not necessary to quantify grains larger than mapping probe diameter.
- Make sure at least **20 spots per element** are analyzing grains
larger than mapping probe diameter.
- Identify phases in comment
- Give same comments on the same phase with the similar compositions.
- e.g., quartz, plagioclase, garnet-core, garnet-rim, ...
- An alternative is to use external file later.
### Mapping
- Analytical conditions
- Acceralating voltage must be same as that in spot analysis.
- Probe diameter should be larger than that in spot analysis.
- Probe current is recommended to be 100 nA following Lanari et al. (2014).
- Dwell time is recommended to be 0.1 - 0.3 sec following Lanari et al. (2014).
### Example of analytical conditions
| | 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 |
## Export data {#Export}
1. **ASCII convert** mapping data into matrix format,
and save the result in the directory
where raw data is stored (e.g., `.map/1`).
1. Export whole directory of analysis containing
`.map` directory and `.qnt` directory
### Required files
#### Spot analysis
The 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`).
```{r file-qnt, eval = TRUE, echo = FALSE}
kable(fread(system.file(package = "qntmap", "extdata", "files-qnt.csv")))
```
#### Map analysis
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`).
```{r file-map, eval = TRUE, echo = FALSE}
kable(fread(system.file(package = "qntmap", "extdata", "files-map.csv")))
```
`*` indicates wild cards.
## Run qntmap on R
For data processing.
### Interactive mode
Follow instructions shown by running the following code.
```{r interactive}
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.
### Manual mode
A work-flow is available with an example dataset at
https://qntmap.atusy.net/articles/qntmap.html .
```{r manual, include = FALSE, eval = FALSE}
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.
```