mean(qmap, index = i)
#> Element #000000 #FFFFFF
diff --git a/docs/articles/read-incomplete-data.html b/docs/articles/read-incomplete-data.html
index 7396aba..599ee3f 100644
--- a/docs/articles/read-incomplete-data.html
+++ b/docs/articles/read-incomplete-data.html
@@ -99,7 +99,7 @@
Read incomplete data
YASUMOTO Atsushi
- 2019-01-17
+ 2019-01-18
Source: vignettes/read-incomplete-data.Rmd
read-incomplete-data.Rmd
@@ -203,7 +203,7 @@
Mapping data
-Mapping data are expected to be saved as ASCII converted files (*.txt or *.csv) and as condition files (*.cnd) (Tab. 3). Asterisks are expected to be filled by the format of #_map or by data# where sharps are integers. File names of ASCII converted files and condition files must be paired (e.g, 1_map.txt and 1_map.cnd). In case of irregular file names or missing condition files, prepare a csv file describing element name, file path, instrument, dwell time, probe current, X-Y coordinates of a starting pixel, step size, map size, and dead time (Tab. 4).
+Mapping data are expected to be saved as ASCII converted files (*.txt or *.csv) and as condition files (*.cnd) (Tab. 3). Asterisks are expected to be filled by the format of #_map or by data# where sharps are integers. File names of ASCII converted files and condition files must be paired (e.g, 1_map.txt and 1_map.cnd). In case of irregular file names or missing condition files, prepare a csv file describing element name, file path, instrument, dwell time, probe current, X-Y coordinates of a starting pixel, step size, map size, and dead time (Tab. 4). Then, following code will read spot analysis data.
xmap <- read_qnt(conditions = "conditions_xmap.csv")
# "conditions" argument is a path to a user-prepared csv file.
@@ -214,12 +214,12 @@
-*_map.txt
-ASCII converted mapping data
+*_map.txt or data*.csv
+ASCII converted mapping data (e.g., 1_map.txt, 2_map.txt,… or data001.csv, data002.csv, …)
*.cnd
-Analytical conditions: element name, dwell time, probe current, step size, pixel size, and coordinates
+Analytical conditions: element name, dwell time, probe current, step size, pixel size, and coordinates. File names must corresponds to mapping data (e.g, 1_map.cnd, data001.cnd)
diff --git a/docs/index.html b/docs/index.html
index 14e0f9e..075d96b 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -310,7 +310,7 @@
Run QntMap on R
-For data processing
+For data processing.
Interactive mode
@@ -323,60 +323,7 @@
Manual mode
-A work-flow is available with an example dataset at https://atusy.github.io/qntmap/articles/basic.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.
+A work-flow is available with an example dataset at https://atusy.github.io/qntmap/articles/qntmap.html .
@@ -420,7 +367,7 @@ Dev status
