R/graph_ma.R
In deandevl/RtsaPkg: Helpful functions in times series analysis
Defines functions
graph_ma
Documented in
graph_ma
#' Function focuses on calculating and plotting the moving average of an observed time series.
#'
#'
#' Function returns either an overlapped or multi-paneled plot of the observed time series,
#' along with the plot for the moving average. The actual moving average values are also returned in a data.frame.
#'
#' The function uses the \href{https://github.com/deandevl/RplotterPkg}{RplotterPkg::multi_panel_grid}
#' package to draw the multi-paneled plot.
#'
#' If \code{display_plot} is TRUE then the plots will be displayed. If \code{display_plot} is FALSE then
#' the function returns a named list that includes a plot object which can be displayed from the console by entering:
#' \enumerate{
#' \item \code{grid::grid.newpage()}
#' \item \code{grid::grid.draw(plot object)}
#' }
#'
#' @param df A data frame with variables for times and corresponding values.
#' @param time_col Names the column from \code{df} for the time values. Values can
#' be numeric or Date/POSIXct.
#' @param value_col Names the value column from \code{df}.
#' @param window_n An integer that controls the backward window length of the moving average.
#' @param ma_type A string that sets the type of moving average. Accepted values are "sma" simple, "tri" triangular,
#' "wma" weighted, "exp" exponential, "mod" modified, and "spe" Spencer weighted 15 point average.
#' @param overlap A logical which if \code{TRUE} overlaps both the observed and the moving average series'. If \code{FALSE} the
#' plots are in separate panels.
#' @param title A string that sets the overall title to the plots.
#' @param ma_caption A string that sets the caption for the ma plot.
#' @param ob_caption A string that sets the caption for the observed plot.
#' @param x_title A string that defines the x axis title.
#' @param y_title A string that defines the y axis title.
#' @param x_limits A Date/POSIXct 2 element vector that sets the minimum and maximum for the x axis.
#' Use NA to refer to the existing minimum and maximum.
#' @param x_major_breaks A Date/POSIXct vector or function that defines the exact major tic locations along the x axis.
#' @param x_major_date_breaks For Date/POSIXct, a string containing the number and date unit for major breaks.
#' Examples: \code{"1 year"}, \code{"4 sec"}, \code{"3 month"}, \code{"2 week"}.
#' @param x_date_labels For Date/POSIXct, a string containing the format codes for the x axis date format.
#' This can be a strftime format for each x axis tic date label.
#' Examples: \code{"\%Y-\%m"}, \code{"\%Y/\%b/\%d"}, \code{"\%H-\%M-\%S"}.
#' @param y_limits A numeric 2 element vector that sets the minimum and maximum for the y axis.
#' The default is \code{c(1,10)}.
#' @param y_major_breaks A numeric vector or function that defines the exact major tic locations for the moving average y axis'.
#' @param show_pts A logical which if FALSE will plot only the lines.
#' @param show_major_grids A logical that controls the appearance of major grids.
#' @param show_minor_grids A logical that controls the appearance of minor grids.
#' @param show_observe A logical that controls the appearance of the observed time series.
#' @param col_width An numeric that sets the width of each plot in centimeters.
#' @param row_height A numeric that sets the height of each plot in centimeters.
#' @param display_plot A logical that if TRUE displays the plot.
#' @param png_file_path A character string with the directory and file name to produce
#' a png image of the plot.
#'
#' @return Returning a named list with:
#' \enumerate{
#' \item "ma_df" -- A data.frame/data.table with column variables for time "DateTime" and the moving average values "Value" and
#' source of the values "Source".
#' \item "plots" -- A multi-panelled/overlapped TableGrob object plotting the observed series and the moving averages.
#' Use \code{grid::grid.draw(plots)} to display the plots.
#' }
#'
#' @author Rick Dean
#'
#' @importFrom data.table data.table
#' @importFrom grid gpar
#' @importFrom grid unit
#' @importFrom grid grid.newpage
#' @importFrom grid grid.draw
#' @importFrom gtable gtable
#' @importFrom gtable gtable_add_grob
#' @importFrom rlang sym
#' @importFrom RplotterPkg create_scatter_plot
#' @importFrom RplotterPkg multi_panel_grid
#' @import ggplot2
#' @importFrom ggplot2 ggsave
#'
#' @export
graph_ma <- function(
df = NULL,
time_col = NULL,
value_col = NULL,
window_n = 4,
ma_type = "sma",
overlap = TRUE,
title = NULL,
ma_caption = NULL,
ob_caption = NULL,
x_title = NULL,
y_title = NULL,
x_limits = NULL,
x_major_breaks = waiver(),
x_major_date_breaks = waiver(),
x_date_labels = waiver(),
y_limits = NULL,
y_major_breaks = waiver(),
show_major_grids = TRUE,
show_minor_grids = TRUE,
show_pts = TRUE,
show_observe = TRUE,
col_width = 18,
row_height = 5,
display_plot = TRUE,
png_file_path = NULL
){
if(is.null(time_col) | is.null(value_col)) {
stop("Both time_col and value_col are required")
}
if(is.null(x_title)){
x_title <- time_col
}
if(is.null(y_title)){
y_title <- value_col
}
dates <- df[[time_col]]
values <- df[[value_col]]
row_heights = NULL
if(is.null(y_limits)){
y_limits <- c(min(values), max(values))
}
get_simple_ma <- function(values, window_n){
values_n <- length(values)
simple_ma <- numeric(values_n)
for (k in 1:(window_n-1)) {
simple_ma[k] <- mean(values[1:k])
}
for (k in window_n:values_n){
simple_ma[k] <- mean(values[(k - window_n + 1):k])
}
return(simple_ma)
}
if(ma_type == "sma"){
ma_name <- "Simple Moving Average"
ma = get_simple_ma(values = values, window_n = window_n)
}else if(ma_type == "tri"){
ma_name <- "Triangular Moving Average"
win_n <- ceiling((window_n + 1)/2)
ma_1 <- get_simple_ma(values = values, window_n = win_n)
ma <- get_simple_ma(values = ma_1, window_n = win_n)
}else if(ma_type == "wma"){
ma_name <- "Weighted Moving Average"
values_n <- length(values)
ma <- numeric(values_n)
for(k in 1:(window_n-1)) {
divisor <- (k * (k + 1)) / 2
ma[k] <- sum((k:1) * values[k:1]) / divisor
}
divisor <- (window_n * (window_n + 1)) / 2
for(k in window_n:values_n){
vec <- (window_n:1) * values[k:(k - window_n + 1)]
ma[k] <- sum(vec) / divisor
}
}else if(ma_type == "exp"){
ma_name <- "Exponential Moving Average"
values_n <- length(values)
ma <- numeric(values_n)
wt <- 2 / (window_n + 1)
ma[1] <- values[1]
for(k in 2:values_n) ma[k] <- wt * values[k] + (1 - wt) * ma[k-1]
}else if(ma_type == "mod"){
ma_name <- "Modified Moving Average"
values_n <- length(values)
ma <- numeric(values_n)
ma[1] <- values[1]
for(k in 2:values_n) ma[k] <- ma[k-1] + (values[k] - ma[k-1])/window_n
}else if(ma_type == "spe"){
ma_name <- "Spencer Moving Average"
values_n <- length(values) - 15
dates <- dates[1:values_n]
ma <- numeric(values_n)
weights <- c(-3, -6, -5, 3, 21, 46, 67, 74, 67, 46, 21, 3, -5, -6, -3)/320
for(k in 1:values_n){
vals <- values[k:(k + 14)]
ma[k] <- sum(vals * weights, na.rm = TRUE)
}
}
ma_dt <- data.table(
datetime = dates,
value = ma
)
if(!show_observe){
row_heights <- row_height
a_plot <- RplotterPkg::create_scatter_plot(
df = ma_dt,
aes_x = "datetime",
aes_y = "value",
caption = ma_caption,
x_title = x_title,
y_title = y_title,
x_limits = x_limits,
x_major_breaks = x_major_breaks,
x_major_date_breaks = x_major_date_breaks,
x_date_labels = x_date_labels,
y_limits = y_limits,
y_major_breaks = y_major_breaks,
show_pts = show_pts,
show_major_grids = show_major_grids,
show_minor_grids = show_minor_grids,
connect = TRUE
)
if(!is.null(png_file_path)){
n_columns <- 1
n_rows <- 1
ggplot2::ggsave(
filename = png_file_path,
plot = a_plot,
device = "png",
width = col_width * n_columns * 1700,
height = row_height[1] * n_rows * 1700,
units = "px",
scale = .05,
dpi = 72
)
}
if(display_plot){
grid::grid.newpage()
grid::grid.draw(a_plot)
}else{
return(list(
ma_df = ma_dt,
plots = a_plot
))
}
}else if(!overlap){
row_heights <- c(row_height, row_height + .5)
#create a line plot of the observed series
obsv_plot <- RplotterPkg::create_scatter_plot(
df = df,
aes_x = time_col,
aes_y = value_col,
rot_y_tic_label = TRUE,
caption = ob_caption,
hide_x_tics = TRUE,
y_title = y_title,
x_limits = x_limits,
x_major_breaks = x_major_breaks,
x_major_date_breaks = x_major_date_breaks,
x_date_labels = x_date_labels,
y_limits = y_limits,
y_major_breaks = y_major_breaks,
show_pts = show_pts,
show_major_grids = show_major_grids,
show_minor_grids = show_minor_grids,
connect = TRUE
)
# create a line plot of the ma series
ma_plot <- RplotterPkg::create_scatter_plot(
df = ma_dt,
aes_x = "datetime",
aes_y = "value",
rot_y_tic_label = TRUE,
caption = ma_caption,
x_title = x_title,
y_title = y_title,
x_limits = x_limits,
x_major_breaks = x_major_breaks,
x_major_date_breaks = x_major_date_breaks,
x_date_labels = x_date_labels,
y_limits = y_limits,
y_major_breaks = y_major_breaks,
show_pts = show_pts,
show_major_grids = show_major_grids,
show_minor_grids = show_minor_grids,
connect = TRUE
)
# put both plots in a list to display them in separate panels
plots <- list(obsv_plot,ma_plot)
# display the plots in a multipanel
n_columns <- 1
cols <- c()
for(i in seq_along(plots)){
val <- i %% n_columns
if(val == 0){
cols <- c(cols, n_columns)
}else {
cols <- c(cols,val)
}
}
n_rows <- ceiling(length(plots)/n_columns)
rows <- c()
for(i in seq(1, n_rows, by = 1)){
for(ii in seq(1, n_columns, by = 1)){
rows <- c(rows, i)
}
}
multi_layout <- list(
plots = plots,
rows = rows,
cols = cols
)
multi_plot <- RplotterPkg::multi_panel_grid(
layout = multi_layout,
col_widths = rep(col_width, n_columns),
row_heights = row_heights,
title = title,
display_plot = FALSE
)
if(!is.null(png_file_path)){
ggplot2::ggsave(
filename = png_file_path,
plot = multi_plot,
device = "png",
width = col_width * n_columns * 1700,
height = row_height * n_rows * 1700,
units = "px",
scale = .05,
dpi = 72
)
}
if(display_plot){
grid::grid.newpage()
grid::grid.draw(multi_plot)
}else{
return(list(
ma_df = ma_dt,
plots = multi_plot
))
}
}else{
observe_dt <- data.table(
datetime = df[[time_col]],
value = df[[value_col]],
source = "Observed"
)
ma_dt[, source := ma_name]
plot_df <- rbind(observe_dt, ma_dt)
a_plot <- RplotterPkg::create_scatter_plot(
df = plot_df,
aes_x = "datetime",
aes_y = "value",
aes_color = "source",
subtitle = title,
x_title = x_title,
y_title = y_title,
x_limits = x_limits,
x_major_breaks = x_major_breaks,
x_major_date_breaks = x_major_date_breaks,
x_date_labels = x_date_labels,
y_limits = y_limits,
y_major_breaks = y_major_breaks,
show_pts = show_pts,
show_major_grids = show_major_grids,
show_minor_grids = show_minor_grids,
connect = TRUE
)
if(!is.null(png_file_path)){
n_columns <- 1
n_rows <- 1
ggplot2::ggsave(
filename = png_file_path,
plot = a_plot,
device = "png",
width = col_width * n_columns * 1700,
height = row_height * n_rows * 1700,
units = "px",
scale = .05,
dpi = 72
)
}
if(display_plot){
grid::grid.draw(a_plot)
}else{
return(list(
ma_df = ma_dt,
plots = a_plot
))
}
}
}
deandevl/RtsaPkg documentation built on Oct. 5, 2023, 5:23 p.m.
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Defines functions graph_ma
Documented in graph_ma
#' Function focuses on calculating and plotting the moving average of an observed time series.
#'
#'
#' Function returns either an overlapped or multi-paneled plot of the observed time series,
#' along with the plot for the moving average. The actual moving average values are also returned in a data.frame.
#'
#' The function uses the \href{https://github.com/deandevl/RplotterPkg}{RplotterPkg::multi_panel_grid}
#' package to draw the multi-paneled plot.
#'
#' If \code{display_plot} is TRUE then the plots will be displayed. If \code{display_plot} is FALSE then
#' the function returns a named list that includes a plot object which can be displayed from the console by entering:
#' \enumerate{
#' \item \code{grid::grid.newpage()}
#' \item \code{grid::grid.draw(plot object)}
#' }
#'
#' @param df A data frame with variables for times and corresponding values.
#' @param time_col Names the column from \code{df} for the time values. Values can
#' be numeric or Date/POSIXct.
#' @param value_col Names the value column from \code{df}.
#' @param window_n An integer that controls the backward window length of the moving average.
#' @param ma_type A string that sets the type of moving average. Accepted values are "sma" simple, "tri" triangular,
#' "wma" weighted, "exp" exponential, "mod" modified, and "spe" Spencer weighted 15 point average.
#' @param overlap A logical which if \code{TRUE} overlaps both the observed and the moving average series'. If \code{FALSE} the
#' plots are in separate panels.
#' @param title A string that sets the overall title to the plots.
#' @param ma_caption A string that sets the caption for the ma plot.
#' @param ob_caption A string that sets the caption for the observed plot.
#' @param x_title A string that defines the x axis title.
#' @param y_title A string that defines the y axis title.
#' @param x_limits A Date/POSIXct 2 element vector that sets the minimum and maximum for the x axis.
#' Use NA to refer to the existing minimum and maximum.
#' @param x_major_breaks A Date/POSIXct vector or function that defines the exact major tic locations along the x axis.
#' @param x_major_date_breaks For Date/POSIXct, a string containing the number and date unit for major breaks.
#' Examples: \code{"1 year"}, \code{"4 sec"}, \code{"3 month"}, \code{"2 week"}.
#' @param x_date_labels For Date/POSIXct, a string containing the format codes for the x axis date format.
#' This can be a strftime format for each x axis tic date label.
#' Examples: \code{"\%Y-\%m"}, \code{"\%Y/\%b/\%d"}, \code{"\%H-\%M-\%S"}.
#' @param y_limits A numeric 2 element vector that sets the minimum and maximum for the y axis.
#' The default is \code{c(1,10)}.
#' @param y_major_breaks A numeric vector or function that defines the exact major tic locations for the moving average y axis'.
#' @param show_pts A logical which if FALSE will plot only the lines.
#' @param show_major_grids A logical that controls the appearance of major grids.
#' @param show_minor_grids A logical that controls the appearance of minor grids.
#' @param show_observe A logical that controls the appearance of the observed time series.
#' @param col_width An numeric that sets the width of each plot in centimeters.
#' @param row_height A numeric that sets the height of each plot in centimeters.
#' @param display_plot A logical that if TRUE displays the plot.
#' @param png_file_path A character string with the directory and file name to produce
#' a png image of the plot.
#'
#' @return Returning a named list with:
#' \enumerate{
#' \item "ma_df" -- A data.frame/data.table with column variables for time "DateTime" and the moving average values "Value" and
#' source of the values "Source".
#' \item "plots" -- A multi-panelled/overlapped TableGrob object plotting the observed series and the moving averages.
#' Use \code{grid::grid.draw(plots)} to display the plots.
#' }
#'
#' @author Rick Dean
#'
#' @importFrom data.table data.table
#' @importFrom grid gpar
#' @importFrom grid unit
#' @importFrom grid grid.newpage
#' @importFrom grid grid.draw
#' @importFrom gtable gtable
#' @importFrom gtable gtable_add_grob
#' @importFrom rlang sym
#' @importFrom RplotterPkg create_scatter_plot
#' @importFrom RplotterPkg multi_panel_grid
#' @import ggplot2
#' @importFrom ggplot2 ggsave
#'
#' @export
graph_ma <- function(
df = NULL,
time_col = NULL,
value_col = NULL,
window_n = 4,
ma_type = "sma",
overlap = TRUE,
title = NULL,
ma_caption = NULL,
ob_caption = NULL,
x_title = NULL,
y_title = NULL,
x_limits = NULL,
x_major_breaks = waiver(),
x_major_date_breaks = waiver(),
x_date_labels = waiver(),
y_limits = NULL,
y_major_breaks = waiver(),
show_major_grids = TRUE,
show_minor_grids = TRUE,
show_pts = TRUE,
show_observe = TRUE,
col_width = 18,
row_height = 5,
display_plot = TRUE,
png_file_path = NULL
){
if(is.null(time_col) | is.null(value_col)) {
stop("Both time_col and value_col are required")
}
if(is.null(x_title)){
x_title <- time_col
}
if(is.null(y_title)){
y_title <- value_col
}
dates <- df[[time_col]]
values <- df[[value_col]]
row_heights = NULL
if(is.null(y_limits)){
y_limits <- c(min(values), max(values))
}
get_simple_ma <- function(values, window_n){
values_n <- length(values)
simple_ma <- numeric(values_n)
for (k in 1:(window_n-1)) {
simple_ma[k] <- mean(values[1:k])
}
for (k in window_n:values_n){
simple_ma[k] <- mean(values[(k - window_n + 1):k])
}
return(simple_ma)
}
if(ma_type == "sma"){
ma_name <- "Simple Moving Average"
ma = get_simple_ma(values = values, window_n = window_n)
}else if(ma_type == "tri"){
ma_name <- "Triangular Moving Average"
win_n <- ceiling((window_n + 1)/2)
ma_1 <- get_simple_ma(values = values, window_n = win_n)
ma <- get_simple_ma(values = ma_1, window_n = win_n)
}else if(ma_type == "wma"){
ma_name <- "Weighted Moving Average"
values_n <- length(values)
ma <- numeric(values_n)
for(k in 1:(window_n-1)) {
divisor <- (k * (k + 1)) / 2
ma[k] <- sum((k:1) * values[k:1]) / divisor
}
divisor <- (window_n * (window_n + 1)) / 2
for(k in window_n:values_n){
vec <- (window_n:1) * values[k:(k - window_n + 1)]
ma[k] <- sum(vec) / divisor
}
}else if(ma_type == "exp"){
ma_name <- "Exponential Moving Average"
values_n <- length(values)
ma <- numeric(values_n)
wt <- 2 / (window_n + 1)
ma[1] <- values[1]
for(k in 2:values_n) ma[k] <- wt * values[k] + (1 - wt) * ma[k-1]
}else if(ma_type == "mod"){
ma_name <- "Modified Moving Average"
values_n <- length(values)
ma <- numeric(values_n)
ma[1] <- values[1]
for(k in 2:values_n) ma[k] <- ma[k-1] + (values[k] - ma[k-1])/window_n
}else if(ma_type == "spe"){
ma_name <- "Spencer Moving Average"
values_n <- length(values) - 15
dates <- dates[1:values_n]
ma <- numeric(values_n)
weights <- c(-3, -6, -5, 3, 21, 46, 67, 74, 67, 46, 21, 3, -5, -6, -3)/320
for(k in 1:values_n){
vals <- values[k:(k + 14)]
ma[k] <- sum(vals * weights, na.rm = TRUE)
}
}
ma_dt <- data.table(
datetime = dates,
value = ma
)
if(!show_observe){
row_heights <- row_height
a_plot <- RplotterPkg::create_scatter_plot(
df = ma_dt,
aes_x = "datetime",
aes_y = "value",
caption = ma_caption,
x_title = x_title,
y_title = y_title,
x_limits = x_limits,
x_major_breaks = x_major_breaks,
x_major_date_breaks = x_major_date_breaks,
x_date_labels = x_date_labels,
y_limits = y_limits,
y_major_breaks = y_major_breaks,
show_pts = show_pts,
show_major_grids = show_major_grids,
show_minor_grids = show_minor_grids,
connect = TRUE
)
if(!is.null(png_file_path)){
n_columns <- 1
n_rows <- 1
ggplot2::ggsave(
filename = png_file_path,
plot = a_plot,
device = "png",
width = col_width * n_columns * 1700,
height = row_height[1] * n_rows * 1700,
units = "px",
scale = .05,
dpi = 72
)
}
if(display_plot){
grid::grid.newpage()
grid::grid.draw(a_plot)
}else{
return(list(
ma_df = ma_dt,
plots = a_plot
))
}
}else if(!overlap){
row_heights <- c(row_height, row_height + .5)
#create a line plot of the observed series
obsv_plot <- RplotterPkg::create_scatter_plot(
df = df,
aes_x = time_col,
aes_y = value_col,
rot_y_tic_label = TRUE,
caption = ob_caption,
hide_x_tics = TRUE,
y_title = y_title,
x_limits = x_limits,
x_major_breaks = x_major_breaks,
x_major_date_breaks = x_major_date_breaks,
x_date_labels = x_date_labels,
y_limits = y_limits,
y_major_breaks = y_major_breaks,
show_pts = show_pts,
show_major_grids = show_major_grids,
show_minor_grids = show_minor_grids,
connect = TRUE
)
# create a line plot of the ma series
ma_plot <- RplotterPkg::create_scatter_plot(
df = ma_dt,
aes_x = "datetime",
aes_y = "value",
rot_y_tic_label = TRUE,
caption = ma_caption,
x_title = x_title,
y_title = y_title,
x_limits = x_limits,
x_major_breaks = x_major_breaks,
x_major_date_breaks = x_major_date_breaks,
x_date_labels = x_date_labels,
y_limits = y_limits,
y_major_breaks = y_major_breaks,
show_pts = show_pts,
show_major_grids = show_major_grids,
show_minor_grids = show_minor_grids,
connect = TRUE
)
# put both plots in a list to display them in separate panels
plots <- list(obsv_plot,ma_plot)
# display the plots in a multipanel
n_columns <- 1
cols <- c()
for(i in seq_along(plots)){
val <- i %% n_columns
if(val == 0){
cols <- c(cols, n_columns)
}else {
cols <- c(cols,val)
}
}
n_rows <- ceiling(length(plots)/n_columns)
rows <- c()
for(i in seq(1, n_rows, by = 1)){
for(ii in seq(1, n_columns, by = 1)){
rows <- c(rows, i)
}
}
multi_layout <- list(
plots = plots,
rows = rows,
cols = cols
)
multi_plot <- RplotterPkg::multi_panel_grid(
layout = multi_layout,
col_widths = rep(col_width, n_columns),
row_heights = row_heights,
title = title,
display_plot = FALSE
)
if(!is.null(png_file_path)){
ggplot2::ggsave(
filename = png_file_path,
plot = multi_plot,
device = "png",
width = col_width * n_columns * 1700,
height = row_height * n_rows * 1700,
units = "px",
scale = .05,
dpi = 72
)
}
if(display_plot){
grid::grid.newpage()
grid::grid.draw(multi_plot)
}else{
return(list(
ma_df = ma_dt,
plots = multi_plot
))
}
}else{
observe_dt <- data.table(
datetime = df[[time_col]],
value = df[[value_col]],
source = "Observed"
)
ma_dt[, source := ma_name]
plot_df <- rbind(observe_dt, ma_dt)
a_plot <- RplotterPkg::create_scatter_plot(
df = plot_df,
aes_x = "datetime",
aes_y = "value",
aes_color = "source",
subtitle = title,
x_title = x_title,
y_title = y_title,
x_limits = x_limits,
x_major_breaks = x_major_breaks,
x_major_date_breaks = x_major_date_breaks,
x_date_labels = x_date_labels,
y_limits = y_limits,
y_major_breaks = y_major_breaks,
show_pts = show_pts,
show_major_grids = show_major_grids,
show_minor_grids = show_minor_grids,
connect = TRUE
)
if(!is.null(png_file_path)){
n_columns <- 1
n_rows <- 1
ggplot2::ggsave(
filename = png_file_path,
plot = a_plot,
device = "png",
width = col_width * n_columns * 1700,
height = row_height * n_rows * 1700,
units = "px",
scale = .05,
dpi = 72
)
}
if(display_plot){
grid::grid.draw(a_plot)
}else{
return(list(
ma_df = ma_dt,
plots = a_plot
))
}
}
}
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