Enhance ECG plotting with tkplot function

Added a new function, tkplot, to visualize ECG recordings and their
segmentation predictions in either an interactive or static format. This
function integrates with the project's existing data structures to
provide a clear representation of predictions according to different
datasets. It auto-configures based on the dataset, uses resampling for
data consistency, and supports custom subtitle generation with analysis
metadata. The interactive mode leverages Plotly for dynamic insights,
while the static mode offers publication-quality plots with ggplot2.
This addition streamlines diagnostic plot generation, promoting better
interpretability and analysis of ECG segmentation results.

scripts/helpers/plot_fluss.R

@@ -1,5 +1,3 @@
-
-
 plot_fluss <- function(ecg_data, type = c("data", "matrix"),
                        main = "Fast Low-cost Unipotent Semantic Segmentation", xlab = "index",
                        ylab = "", ...) {
@@ -83,3 +81,105 @@ plot_fluss <- function(ecg_data, type = c("data", "matrix"),
   }
   return(ecg_data)
 }
+
+tkplot <- function(object, interactive = FALSE, res = 50, dataset = c("afib", "magvent", "vtachy")) {
+  # fixed configurations
+  if (dataset == "afib") {
+    basedir <- here::here("inst", "extdata", "afib_regimes")
+    rsam_from <- 200
+    track <- "II"
+  } else if (dataset == "magvent") {
+    basedir <- here::here("inst", "extdata", "malignantventricular")
+    rsam_from <- 250
+    track <- "ECG1"
+  } else if (dataset == "vtachy") {
+    basedir <- here::here("inst", "extdata", "vtachyarrhythmias")
+    rsam_from <- 250
+    track <- "ECG"
+  }
+
+  ecg <- read_ecg_with_atr(glue::glue(basedir, "/", object$record), resample_from = rsam_from, resample_to = res)
+  value <- ecg[[1]][[track]]
+  prop <- 250 / res
+  mask <- seq.int(50, 100)
+  value[1:5] <- median(value[mask])
+  value[(length(value) - 5):length(value)] <- median(value[mask])
+  time <- seq(1, floor(length(value) * prop), length.out = length(value))
+  data <- tibble::tibble(time = time, value = value)
+  min_data <- min(data$value)
+  max_data <- max(data$value)
+  truth <- clean_truth(floor(attr(ecg[[1]], "regimes") * prop), floor(length(value) * prop)) # object$truth[[1]]
+  preds <- object$pred[[1]]
+
+  title <- glue::glue(
+    "Recording: {object$record} ",
+    "#truth: {length(truth)}, ",
+    "#preds: {length(preds)}, ",
+    "length: {floor(length(value)*prop)} ",
+    "FLOSS Score: {round(object$score, 3)}"
+  )
+
+  subtitle <- suppressWarnings(glue::glue(
+    "Parameters: ",
+    "MP window: {object$window_size}, ",
+    ifelse(!is.null(object$mp_threshold), "MP threshold: {object$mp_threshold}, ", ""),
+    ifelse(!is.null(object$time_constraint), "Time constraint: {object$time_constraint}, ", ""),
+    ifelse(!is.null(object$regime_threshold), "Regime threshold: {object$regime_threshold}, ", ""),
+    ifelse(!is.null(object$regime_landmark), "Regime landmark: {object$regime_landmark}", "")
+  ))
+
+  plot <- data |>
+    timetk::plot_time_series(
+      time, value,
+      .title = glue::glue(title, "<br><sup>{subtitle}</sup>"),
+      .interactive = interactive,
+      .smooth = FALSE,
+      .line_alpha = 0.3,
+      .line_size = 0.2,
+      .plotly_slider = interactive
+    )
+
+  if (interactive) {
+    plot <- plot |>
+      plotly::add_segments(
+        x = preds, xend = preds, y = min_data,
+        yend = max_data + (max_data - min_data) * 0.1,
+        line = list(width = 2.5, color = "#0108c77f"),
+        name = "Predicted"
+      ) |>
+      plotly::add_segments(
+        x = truth, xend = truth, y = min_data,
+        yend = max_data,
+        line = list(width = 2.5, color = "#ff00007f"),
+        name = "Truth"
+      )
+  } else {
+    sci <- getOption("scipen")
+    options(scipen = 999)
+    plot <- plot +
+      ggplot2::geom_segment(
+        data = tibble::tibble(pre = preds),
+        ggplot2::aes(
+          x = pre, xend = pre,
+          y = min_data, yend = max_data + (max_data - min_data) * 0.1
+        ), linewidth = 1, color = "#0108c77f"
+      ) +
+      ggplot2::geom_segment(
+        data = tibble::tibble(tru = truth),
+        ggplot2::aes(
+          x = tru, xend = tru,
+          y = min_data, yend = max_data - (max_data - min_data) * 0.1
+        ), linewidth = 1, color = "#ff00007f"
+      ) +
+      ggplot2::theme_bw() +
+      ggplot2::theme(
+        axis.ticks.y = ggplot2::element_blank(),
+        axis.text.y = ggplot2::element_blank(),
+        legend.position = "none",
+        plot.margin = ggplot2::margin(0, 0, 0, 10)
+      ) + ggplot2::scale_x_continuous(n.breaks = 6, labels = scales::label_number(scale = 0.004, suffix = "s")) +
+      ggplot2::labs(title = title, subtitle = subtitle, y = ggplot2::element_blank())
+    options(scipen = sci)
+  }
+  plot
+}