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keep original timezone #4

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keep original timezone #4

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b9ed343
keep original timezone
caijun Dec 20, 2017
c4ef23e
https://github.com/twitter/AnomalyDetection/pull/98
caijun Mar 1, 2019
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13 changes: 8 additions & 5 deletions R/ts_anom_detection.R
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Original file line number Diff line number Diff line change
Expand Up @@ -83,6 +83,9 @@ AnomalyDetectionTs <- function(x, max_anoms = 0.10, direction = 'pos',
colnames(x) <- c("timestamp", "count")
}

# extract original timezone attr
tz <- attr(x$timestamp, "tzone")

if (!is.logical(na.rm)) {
stop("na.rm must be either TRUE (T) or FALSE (F)")
}
Expand Down Expand Up @@ -165,6 +168,7 @@ AnomalyDetectionTs <- function(x, max_anoms = 0.10, direction = 'pos',
if (gran == "sec") {
x <- format_timestamp(aggregate(x[2], format(x[1], "%Y-%m-%d %H:%M:00"),
eval(parse(text = "sum"))))
gran <- "min"
}

period = switch(gran,
Expand Down Expand Up @@ -323,7 +327,7 @@ AnomalyDetectionTs <- function(x, max_anoms = 0.10, direction = 'pos',
week_rng <- get_range(x_subset_week, index = 2, y_log = y_log)
day_rng <- get_range(x_subset_single_day, index = 2, y_log = y_log)
yrange <- c(min(week_rng[1], day_rng[1]), max(week_rng[2], day_rng[2]))
xgraph <- add_day_labels_datetime(xgraph, breaks = breaks, start = as.POSIXlt(min(x_subset_week[[1]]), tz = "UTC"), end = as.POSIXlt(max(x_subset_single_day[[1]]), tz = "UTC"), days_per_line = num_days_per_line)
xgraph <- add_day_labels_datetime(xgraph, breaks = breaks, start = as.POSIXlt(min(x_subset_week[[1]]), tz = tz), end = as.POSIXlt(max(x_subset_single_day[[1]]), tz = tz), days_per_line = num_days_per_line)
xgraph <- xgraph +
ggplot2::labs(x = xlabel, y = ylabel, title = plot_title)
} else {
Expand All @@ -337,7 +341,7 @@ AnomalyDetectionTs <- function(x, max_anoms = 0.10, direction = 'pos',
ggplot2::geom_line(data = x, ggplot2::aes_string(colour = color_name), alpha = alpha)
yrange <- get_range(x, index = 2, y_log = y_log)
xgraph <- xgraph +
ggplot2::scale_x_datetime(labels = function(x) ifelse(as.POSIXlt(x, tz = "UTC")$hour != 0, strftime(x, format = "%kh", tz = "UTC"), strftime(x, format = "%b %e", tz = "UTC")),
ggplot2::scale_x_datetime(labels = function(x) ifelse(as.POSIXlt(x, tz = tz)$hour != 0, strftime(x, format = "%kh", tz = tz), strftime(x, format = "%b %e", tz = tz)),
expand = c(0,0))
xgraph <- xgraph +
ggplot2::labs(x = xlabel, y = ylabel, title = plot_title)
Expand All @@ -363,15 +367,14 @@ AnomalyDetectionTs <- function(x, max_anoms = 0.10, direction = 'pos',
# Store expected values if set by user
if (e_value) {
anoms <- data.frame(timestamp = all_anoms[[1]], anoms = all_anoms[[2]],
expected_value = subset(seasonal_plus_trend[[2]], as.POSIXlt(seasonal_plus_trend[[1]], tz = "UTC") %in% all_anoms[[1]]),
expected_value = subset(seasonal_plus_trend[[2]], as.POSIXlt(seasonal_plus_trend[[1]], tz = tz) %in% all_anoms[[1]]),
stringsAsFactors = FALSE)
} else {
anoms <- data.frame(timestamp = all_anoms[[1]], anoms = all_anoms[[2]], stringsAsFactors = FALSE)
}

# Make sure we're still a valid POSIXct datetime.
# TODO: Make sure we keep original datetime format and timezone.
anoms$timestamp <- as.POSIXct(anoms$timestamp, tz = "UTC")
anoms$timestamp <- as.POSIXct(anoms$timestamp, tz = tz)

# Lastly, return anoms and optionally the plot if requested by the user
if (plot) {
Expand Down
9 changes: 4 additions & 5 deletions tests/testthat/test-edge.R
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Expand Up @@ -4,16 +4,15 @@ context("Testing edge cases")

test_that("checking for errors if time series has constant value for all values", {
data <- rep(1,1000)
expect_true({AnomalyDetectionVec(data, period=14, plot=T, direction='both'); TRUE})

expect_true({AnomalyDetectionVec(data, period = 14, plot = T, direction = 'both'); TRUE})
})

test_that("checking that midnight dates get H%M%S format applied", {
data_file <- system.file("extdata", "data.csv", package="AnomalyDetection")
data_file <- system.file("extdata", "data.csv", package = "AnomalyDetection")
data <- read.csv(data_file)
data$date <- as.POSIXct(strptime(data$date, "%Y-%m-%d %H:%M", tz = "UTC"))
anomalyDetectionResult <- AnomalyDetectionTs(data, max_anoms=0.2, threshold = "None",
direction='both', plot=FALSE,
anomalyDetectionResult <- AnomalyDetectionTs(data, max_anoms = 0.2, threshold = "None",
direction = 'both', plot = FALSE,
only_last = "day", e_value = TRUE)

expect_equal(length(anomalyDetectionResult$anoms$anoms), length(anomalyDetectionResult$anoms$expected_value))
Expand Down