# helpers_plotting.R
library(dplyr)
library(plotly)
library(lubridate)

#' Create a placeholder plot for missing data
#' @param message Message to display
#' @return A plotly object
create_empty_plot <- function(message = "Data not available for this parameter") {
    plotly::plot_ly() %>%
        plotly::add_trace(type = "scatter", mode = "markers", marker = list(opacity = 0), showlegend = FALSE) %>%
        plotly::add_annotations(
            text = message,
            showarrow = FALSE,
            xref = "paper", yref = "paper",
            x = 0.5, y = 0.5,
            font = list(size = 16, color = "#666")
        ) %>%
        plotly::layout(
            xaxis = list(visible = FALSE),
            yaxis = list(visible = FALSE),
            margin = list(t = 50, b = 50, l = 50, r = 50)
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Generate a plot axis label with units from dataframe attributes
#'
#' @param df Dataframe with 'units' attribute
#' @param col_name Column name to look up unit for
#' @param default_label Default label text if unit is missing
#' @return Formatted label string: "Label [unit]"
get_axis_label <- function(df, col_name, default_label = NULL) {
    units <- attr(df, "units")

    # If no default label provided, generate from column name
    if (is.null(default_label)) {
        base_label <- col_name %>%
            gsub("_", " ", .) %>%
            stringr::str_to_title()
    } else {
        base_label <- default_label
    }

    # Append unit if found in the attribute map
    if (!is.null(units) && !is.null(units[[col_name]])) {
        return(paste0(base_label, " [", units[[col_name]], "]"))
    }

    return(base_label)
}


#' Split Dataframe into Continuous Chunks
#'
#' @param df Dataframe with datetime and value columns
#' @param value_col Column name to check for NAs to define breaks
#' @return List of dataframes
split_into_chunks <- function(df, value_col) {
    # If no data, return empty list
    if (nrow(df) == 0) {
        return(list())
    }

    # Identify runs of non-NA values
    is_na_vec <- is.na(df[[value_col]])

    # If all NA, return list
    if (all(is_na_vec)) {
        return(list())
    }

    rle_res <- rle(is_na_vec)

    chunk_ids <- rep(seq_along(rle_res$values), rle_res$lengths)
    df$chunk_id <- chunk_ids

    # Filter only the chunks where value is NOT NA
    valid_chunks <- df[!is_na_vec, ]

    if (nrow(valid_chunks) == 0) {
        return(list())
    }

    # Split by chunk_id
    split(valid_chunks, valid_chunks$chunk_id)
}

#' Centralized Cleaning for Météo-France Data
#'
#' Ensures all weather columns are numeric and that Météo-France-specific
#' flag values (8000, 9999, -999) are converted to NA.
#'
#' @param df Data frame to clean
#' @return Cleaned data frame
clean_weather_data <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(df)
    }

    # Weather columns to check
    weather_cols <- c("temp", "temp_min", "temp_max", "temp_min_abs", "temp_max_abs", "rh", "precip", "wind_speed", "wind_dir", "pressure", "station_pressure", "cloud_cover", "wind_gust_max", "solar_global", "sunshine_duration")

    # Aggressive base R filtering for 8000 and other flags
    for (col in weather_cols) {
        if (col %in% names(df)) {
            # Convert to numeric if not already
            if (!is.numeric(df[[col]])) {
                df[[col]] <- suppressWarnings(as.numeric(as.character(df[[col]])))
            }
            # Catch all variants of 8000, 9999, -999
            bad_idx <- is.na(df[[col]]) | df[[col]] >= 7999 | df[[col]] <= -998
            df[[col]][bad_idx] <- NA_real_
        }
    }

    # Parameter-specific safety bounds (Physical sanity checks)
    if ("temp" %in% names(df)) df$temp[df$temp < -90 | df$temp > 60] <- NA_real_
    if ("temp_min" %in% names(df)) df$temp_min[df$temp_min < -90 | df$temp_min > 60] <- NA_real_
    if ("temp_max" %in% names(df)) df$temp_max[df$temp_max < -90 | df$temp_max > 60] <- NA_real_
    if ("temp_min_abs" %in% names(df)) df$temp_min_abs[df$temp_min_abs < -90 | df$temp_min_abs > 60] <- NA_real_
    if ("temp_max_abs" %in% names(df)) df$temp_max_abs[df$temp_max_abs < -90 | df$temp_max_abs > 60] <- NA_real_
    if ("temp_min_ground" %in% names(df)) df$temp_min_ground[df$temp_min_ground < -90 | df$temp_min_ground > 70] <- NA_real_
    if ("temp_min_50cm" %in% names(df)) df$temp_min_50cm[df$temp_min_50cm < -90 | df$temp_min_50cm > 60] <- NA_real_

    if ("rh" %in% names(df)) df$rh[df$rh < 0 | df$rh > 100] <- NA_real_
    if ("rh_min" %in% names(df)) df$rh_min[df$rh_min < 0 | df$rh_min > 100] <- NA_real_
    if ("rh_max" %in% names(df)) df$rh_max[df$rh_max < 0 | df$rh_max > 100] <- NA_real_

    if ("precip" %in% names(df)) df$precip[df$precip < 0 | df$precip > 500] <- NA_real_
    if ("precip_duration" %in% names(df)) df$precip_duration[df$precip_duration < 0 | df$precip_duration > 1440] <- NA_real_ # Max 24h in minutes
    if ("pressure" %in% names(df)) df$pressure[df$pressure < 800 | df$pressure > 1100] <- NA_real_
    if ("station_pressure" %in% names(df)) df$station_pressure[df$station_pressure < 500 | df$station_pressure > 1100] <- NA_real_
    if ("solar_global" %in% names(df)) df$solar_global[df$solar_global < 0 | df$solar_global > 3500] <- NA_real_
    if ("cloud_cover" %in% names(df)) df$cloud_cover[df$cloud_cover < 0 | df$cloud_cover > 9] <- NA_real_
    if ("snow_depth" %in% names(df)) df$snow_depth[df$snow_depth < 0 | df$snow_depth > 1000] <- NA_real_
    if ("snow_fresh" %in% names(df)) df$snow_fresh[df$snow_fresh < 0 | df$snow_fresh > 500] <- NA_real_
    if ("etp" %in% names(df)) df$etp[df$etp < 0 | df$etp > 600] <- NA_real_

    return(df)
}

#' Calculate Dew Point
#' Approximation using Magnus formula
calculate_dew_point <- function(temp, rh) {
    if (is.null(temp) | is.null(rh)) {
        return(NA)
    }
    # Constants
    a <- 17.625
    b <- 243.04
    alpha <- log(rh / 100) + (a * temp) / (b + temp)
    dp <- (b * alpha) / (a - alpha)
    return(dp)
}

#' Create Daily Summary Helper
create_daily_summary <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(NULL)
    }
    # Centralized cleaning
    df <- clean_weather_data(df)

    # Check if we already have daily min/max (from daily Météo-France data)
    has_daily_minmax <- "temp_min" %in% names(df) && "temp_max" %in% names(df)

    if (has_daily_minmax) {
        # Use existing daily min/max columns directly (Météo-France daily data: TN, TX)
        df %>%
            mutate(date = as.Date(datetime)) %>%
            group_by(date) %>%
            summarise(
                Tmax = if (all(is.na(temp_max))) NA else max(temp_max, na.rm = TRUE),
                Tmin = if (all(is.na(temp_min))) NA else min(temp_min, na.rm = TRUE),
                .groups = "drop"
            )
    } else {
        # Calculate from hourly temp data (for hourly datasets)
        df %>%
            mutate(date = as.Date(datetime)) %>%
            group_by(date) %>%
            summarise(
                Tmax = if (all(is.na(temp))) NA else max(temp, na.rm = TRUE),
                Tmin = if (all(is.na(temp))) NA else min(temp, na.rm = TRUE),
                .groups = "drop"
            )
    }
}

#' Create Weather Trends Multi-Panel Plot
#'
#' @param df Data frame with parsed GHCNh data
#' @return A plotly subplot object
#' @export
create_weather_trends_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available for the selected period"))
    }

    # Centralized cleaning
    df <- clean_weather_data(df)

    # Enhancement: Calculate Dew Point if not present
    if (!"dew_point" %in% names(df) && "rh" %in% names(df)) {
        df$dew_point <- calculate_dew_point(df$temp, df$rh)
        # Clean dew point too
        if (!is.null(df$dew_point)) {
            df$dew_point[is.na(df$dew_point) | df$dew_point < -90 | df$dew_point > 60] <- NA_real_
        }
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    date_range_vals <- c(min(df$datetime), max(df$datetime))

    # Panel 1: Temperature and Dew Point
    has_temp <- "temp" %in% names(df) && any(!is.na(df$temp))
    has_dew <- "dew_point" %in% names(df) && any(!is.na(df$dew_point))

    if (has_temp || has_dew) {
        p1 <- plotly::plot_ly(type = "scatter", mode = "none")
        if (has_temp) {
            p1 <- p1 %>% plotly::add_lines(
                data = df %>% dplyr::filter(!is.na(temp)),
                x = ~datetime,
                y = ~temp, name = "Temperature",
                line = list(color = "#e53935", width = 2),
                hovertemplate = "Temp: %{y:.1f}°C<extra></extra>",
                type = "scatter", mode = "lines",
                showlegend = TRUE
            )
        }
        if (has_dew) {
            p1 <- p1 %>% plotly::add_lines(
                data = df %>% dplyr::filter(!is.na(dew_point)),
                x = ~datetime,
                y = ~dew_point, name = "Dew Point",
                line = list(color = "#1e88e5", width = 1.5, dash = "dot"),
                hovertemplate = "Dew Pt: %{y:.1f}°C<extra></extra>",
                type = "scatter", mode = "lines",
                showlegend = TRUE
            )
        }
        p1 <- p1 %>% plotly::layout(yaxis = list(title = "Temp/Dew (°C)"))
    } else {
        p1 <- plotly::plot_ly(type = "scatter", mode = "none") %>%
            plotly::add_trace(type = "scatter", mode = "markers", marker = list(opacity = 0), name = "N/A", showlegend = FALSE) %>%
            plotly::add_annotations(text = "Temperature Not Available", showarrow = FALSE, font = list(color = "#999")) %>%
            plotly::layout(xaxis = list(title = "", type = "date"), yaxis = list(visible = FALSE, title = "Temp"))
    }

    # Panel 2: Wind Speed and Gusts
    has_wind <- "wind_speed" %in% names(df) && any(!is.na(df$wind_speed))
    has_gust <- "wind_gust_max" %in% names(df) && any(!is.na(df$wind_gust_max))

    if (has_wind || has_gust) {
        p2 <- plotly::plot_ly(type = "scatter", mode = "none")
        if (has_wind) {
            p2 <- p2 %>% plotly::add_lines(
                data = df %>% dplyr::filter(!is.na(wind_speed)),
                x = ~datetime,
                y = ~wind_speed, name = "Wind Speed", fill = "tozeroy",
                line = list(color = "#43a047", width = 1),
                hovertemplate = "Wind: %{y:.1f} m/s<extra></extra>",
                type = "scatter", mode = "lines",
                showlegend = TRUE
            )
        }
        if (has_gust) {
            p2 <- p2 %>% plotly::add_markers(
                data = df %>% dplyr::filter(!is.na(wind_gust_max)),
                x = ~datetime,
                y = ~wind_gust_max, name = "Gust (Max)",
                marker = list(color = "#2e7d32", size = 4),
                hovertemplate = "Gust: %{y:.1f} m/s<extra></extra>",
                type = "scatter", mode = "markers",
                showlegend = TRUE
            )
        }
        p2 <- p2 %>% plotly::layout(yaxis = list(title = "Wind (m/s)"))
    } else {
        p2 <- plotly::plot_ly(type = "scatter", mode = "none") %>%
            plotly::add_trace(type = "scatter", mode = "markers", marker = list(opacity = 0), name = "N/A", showlegend = FALSE) %>%
            plotly::add_annotations(text = "Wind Not Available", showarrow = FALSE, font = list(color = "#999")) %>%
            plotly::layout(xaxis = list(title = "", type = "date"), yaxis = list(visible = FALSE, title = "Wind"))
    }

    # Panel 3: Pressure
    has_msl <- "pressure" %in% names(df) && any(!is.na(df$pressure))
    has_station_p <- "station_pressure" %in% names(df) && any(!is.na(df$station_pressure))

    if (has_msl || has_station_p) {
        p3 <- plotly::plot_ly(type = "scatter", mode = "none")
        if (has_msl) {
            p3 <- p3 %>%
                plotly::add_lines(
                    data = df %>% dplyr::filter(!is.na(pressure)),
                    x = ~datetime,
                    y = ~pressure, name = "Sea Level",
                    line = list(color = "#7b1fa2", width = 1.5),
                    hovertemplate = "MSL: %{y:.1f} hPa<extra></extra>",
                    type = "scatter", mode = "lines",
                    showlegend = TRUE
                )
        }
        if (has_station_p) {
            p3 <- p3 %>%
                plotly::add_lines(
                    data = df %>% dplyr::filter(!is.na(station_pressure)),
                    x = ~datetime,
                    y = ~station_pressure, name = "Pressure (Station)",
                    line = list(color = "#AB47BC", width = 1.5, dash = "dot"),
                    hovertemplate = "Stn: %{y:.1f} hPa<extra></extra>",
                    type = "scatter", mode = "lines",
                    showlegend = TRUE
                )
        }
        p3 <- p3 %>%
            plotly::add_lines(
                x = date_range_vals,
                y = c(1013.25, 1013.25),
                name = "Standard (1013.25)",
                line = list(color = "rgba(100, 100, 100, 0.5)", width = 1, dash = "dash"),
                showlegend = TRUE,
                hoverinfo = "skip",
                inherit = FALSE,
                type = "scatter", mode = "lines"
            ) %>%
            plotly::layout(yaxis = list(title = "Pressure (hPa)"))
    } else {
        p3 <- plotly::plot_ly(type = "scatter", mode = "none") %>%
            plotly::add_trace(type = "scatter", mode = "markers", marker = list(opacity = 0), name = "N/A", showlegend = FALSE) %>%
            plotly::add_annotations(text = "Pressure Not Available", showarrow = FALSE, font = list(color = "#999")) %>%
            plotly::layout(xaxis = list(title = "", type = "date"), yaxis = list(visible = FALSE, title = "Pressure"))
    }

    # Panel 4: Cloud Cover & Humidity
    has_cloud <- "cloud_cover" %in% names(df) && any(!is.na(df$cloud_cover))
    has_rh <- "rh" %in% names(df) && any(!is.na(df$rh))

    if (has_cloud || has_rh) {
        p4 <- plotly::plot_ly(type = "scatter", mode = "none")
        if (has_cloud) {
            # Use markers instead of lines to handle sparse data correctly (avoid misleading interpolation)
            p4 <- p4 %>% plotly::add_trace(
                data = df %>% dplyr::filter(!is.na(cloud_cover)),
                x = ~datetime,
                y = ~cloud_cover, name = "Cloud Cover",
                marker = list(color = "#757575", size = 4, symbol = "circle"),
                hovertemplate = "Cloud: %{y} /8 octas<extra></extra>",
                type = "scatter", mode = "markers"
            )
        }
        if (has_rh) {
            p4 <- p4 %>% plotly::add_lines(
                data = df %>% dplyr::filter(!is.na(rh)),
                x = ~datetime,
                y = ~rh, name = "Humidity",
                line = list(color = "#00bcd4", width = 1.5, dash = "dot"),
                hovertemplate = "Humidity: %{y:.1f}%<extra></extra>",
                yaxis = "y2"
            )
        }
        p4 <- p4 %>% plotly::layout(
            yaxis = list(title = "Cloud (octas)", range = c(0, 8.5)),
            yaxis2 = list(title = "Humidity (%)", overlaying = "y", side = "right", range = c(0, 105), showgrid = FALSE)
        )
    } else {
        p4 <- plotly::plot_ly(type = "scatter", mode = "none") %>%
            plotly::add_trace(type = "scatter", mode = "markers", marker = list(opacity = 0), name = "N/A", showlegend = FALSE) %>%
            plotly::add_annotations(text = "Cloud/Humidity Not Available", showarrow = FALSE, font = list(color = "#999")) %>%
            plotly::layout(xaxis = list(title = "", type = "date"), yaxis = list(visible = FALSE, title = "Cloud"))
    }

    # Combine all panels
    plotly::subplot(p1, p2, p3, p4, nrows = 4, shareX = TRUE, titleY = TRUE, margin = 0.04) %>%
        plotly::layout(
            title = list(text = paste("Weather Overview:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date", range = date_range_vals),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.1),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Precipitation Plot
#'
#' Creates a stacked subplot for all available precipitation intervals.
#'
#' @param df Data frame with parsed GHCNh data
#' @return A plotly subplot object
#' @export
create_precipitation_plot <- function(df, resolution = "hourly") {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available for the selected period"))
    }

    # Centralized cleaning
    df <- clean_weather_data(df)

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    # Valid precipitation columns (smallest interval to largest)
    label_suffix <- if (resolution == "daily") "Daily" else "1h"
    precip_cols <- c(
        "precip" = paste0("Precip (", label_suffix, ")"),
        "snow_fresh" = "Fresh Snow (mm)",
        "snow_depth" = "Snow Depth (cm)"
    )

    plot_list <- list()

    # Iterate through columns and create a plot if data exists
    for (col_name in names(precip_cols)) {
        if (col_name %in% names(df) && any(!is.na(df[[col_name]]))) {
            label <- precip_cols[[col_name]]

            # Prepare data
            # Use standard evaluation with .data or base syntax to avoid rlang issues if package not loaded
            p_data <- df[!is.na(df[[col_name]]), ]

            if (nrow(p_data) > 0) {
                # Keep non-zero for bar visibility, or all for timeline?
                # User code kept all.

                # Check rlang dependency for !!sym. If not available, use dynamic formula or base.
                # Simplest: use plot_ly(x=~datetime, y=as.formula(paste0("~", col_name)))

                # Initialize empty plot to avoid inheriting 'mode' attributes for bar plots
                p <- plotly::plot_ly(data = p_data, x = ~datetime) %>%
                    plotly::add_bars(
                        y = as.formula(paste0("~", col_name)),
                        name = paste("Precip", label),
                        marker = list(color = "#0277bd"),
                        hovertemplate = paste0(label, ": %{y:.1f} mm<extra></extra>"),
                        showlegend = TRUE
                    ) %>%
                    plotly::layout(
                        yaxis = list(title = paste0(label, " (mm)"))
                    )

                plot_list[[length(plot_list) + 1]] <- p
            }
        }
    }

    if (length(plot_list) == 0) {
        return(create_empty_plot("No precipitation data available"))
    }

    # Combine plots vertically
    plotly::subplot(plot_list, nrows = length(plot_list), shareX = TRUE, titleY = TRUE, margin = 0.04) %>%
        plotly::layout(
            title = list(text = paste("Precipitation:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            hovermode = "x unified",
            hoverlabel = list(),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}


#' Create Wind Rose Plot
#'
#' Creates a polar bar chart showing wind direction and speed distribution.
#'
#' @param df Data frame with parsed GHCNh data
#' @return A plotly polar bar chart
#' @export
create_wind_rose_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available for the selected period"))
    }

    # Centralized cleaning
    df <- clean_weather_data(df)

    if (!all(c("wind_speed", "wind_dir") %in% names(df))) {
        return(create_empty_plot("Wind data not available for this station"))
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    wind_df <- df %>%
        dplyr::filter(!is.na(wind_speed), !is.na(wind_dir)) %>%
        dplyr::mutate(
            dir_bin = round(wind_dir / 22.5) * 22.5,
            dir_bin = ifelse(dir_bin == 360, 0, dir_bin),
            speed_cat = cut(
                wind_speed,
                breaks = c(-0.1, 2, 4, 6, 8, 10, 12, 14, 16, Inf),
                labels = c("0-2", "2-4", "4-6", "6-8", "8-10", "10-12", "12-14", "14-16", "16+")
            )
        ) %>%
        dplyr::group_by(dir_bin, speed_cat) %>%
        dplyr::summarise(count = dplyr::n(), .groups = "drop")

    total_obs <- sum(wind_df$count)
    wind_df <- wind_df %>%
        dplyr::mutate(percentage = count / total_obs * 100)

    if (nrow(wind_df) == 0) {
        return(create_empty_plot("No wind data available for the selected period"))
    }

    compass <- data.frame(
        dir_bin = seq(0, 337.5, by = 22.5),
        label = c(
            "N", "NNE", "NE", "ENE", "E", "ESE", "SE", "SSE",
            "S", "SSW", "SW", "WSW", "W", "WNW", "NW", "NNW"
        )
    )

    p <- plotly::plot_ly(type = "barpolar")
    speed_levels <- levels(wind_df$speed_cat)
    # Meteorological scale: 9 distinct bins (green → teal → blue → yellow → orange → red)
    colors <- c("#a5d6a7", "#66bb6a", "#26a69a", "#0288d1", "#ffeb3b", "#ffc107", "#ff9800", "#f44336", "#b71c1c")

    for (i in seq_along(speed_levels)) {
        lvl <- speed_levels[i]
        lvl_df <- wind_df %>% dplyr::filter(speed_cat == lvl)
        if (nrow(lvl_df) > 0) {
            p <- p %>%
                plotly::add_trace(
                    data = lvl_df, type = "barpolar",
                    r = ~percentage, theta = ~dir_bin,
                    name = lvl, marker = list(color = colors[i])
                )
        }
    }

    p %>%
        plotly::layout(
            title = list(text = paste("Wind Rose:", date_range_str), font = list(size = 14)),
            polar = list(
                angularaxis = list(
                    rotation = 90, direction = "clockwise",
                    tickmode = "array", tickvals = compass$dir_bin, ticktext = compass$label,
                    tickfont = list(size = 12)
                ),
                radialaxis = list(
                    ticksuffix = "%",
                    tickfont = list(size = 10)
                )
            ),
            showlegend = TRUE,
            hoverlabel = list(),
            legend = list(
                title = list(text = "Wind Speed (m/s)", side = "top"),
                orientation = "h",
                x = 0.5,
                xanchor = "center",
                y = -0.2
            ),
            margin = list(t = 50, b = 60, l = 20, r = 20),
            modebar = list(orientation = "h"),
            annotations = list(
                list(
                    x = 0, y = 1.15, xref = "paper", yref = "paper",
                    text = "&#9432;", # info symbol
                    showarrow = FALSE,
                    font = list(size = 18, color = "#666"),
                    xanchor = "left",
                    hovertext = "Wind rose shows the frequency distribution of wind direction and speed",
                    hoverinfo = "text",
                    # Added name to select it in JS later
                    name = "info_annotation"
                )
            )
        ) %>%
        plotly::config(displaylogo = FALSE)
}


#' Create Weathergami Plot
#'
#' Creates a heatmap showing the frequency of daily max/min temperature
#' combinations. This visualization helps identify climate patterns.
#'
#' @param df Data frame with parsed GHCNh data
#' @return A plotly heatmap
#' @export
create_weathergami_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available for the selected period"))
    }

    # Clean data first
    df <- clean_weather_data(df)

    # Check if daily min/max columns exist (required for weathergami)
    has_daily_minmax <- "temp_min" %in% names(df) && "temp_max" %in% names(df)

    if (!has_daily_minmax) {
        return(create_empty_plot("Weathergami requires daily data with Tmin/Tmax.<br>Please select daily resolution."))
    }

    # Work directly with temp_min and temp_max columns (no aggregation needed)
    # Filter to valid data points
    daily_temps <- df %>%
        filter(!is.na(temp_min), !is.na(temp_max)) %>%
        mutate(
            date = as.Date(datetime),
            tmax_bin = round(temp_max),
            tmin_bin = round(temp_min)
        )

    if (nrow(daily_temps) == 0) {
        return(create_empty_plot("No valid temperature min/max data available"))
    }

    date_range_str <- paste(
        format(min(daily_temps$datetime), "%d %b %Y"), "-",
        format(max(daily_temps$datetime), "%d %b %Y")
    )

    # Count occurrences of each max/min combination
    freq_table <- daily_temps %>%
        dplyr::group_by(tmax_bin, tmin_bin) %>%
        dplyr::summarise(count = dplyr::n(), .groups = "drop")

    # Create the heatmap using plotly - use add_heatmap for proper trace handling
    p <- plotly::plot_ly(type = "heatmap") %>%
        plotly::add_heatmap(
            data = freq_table,
            x = ~tmax_bin,
            y = ~tmin_bin,
            z = ~count,
            colors = c("#f7fbff", "#c6dbef", "#6baed6", "#2171b5", "#08519c", "#08306b"),
            colorbar = list(title = "Days"),
            hovertemplate = paste0(
                "Tmax: %{x}°C<br>",
                "Tmin: %{y}°C<br>",
                "Days: %{z}<extra></extra>"
            )
        )

    # Add diagonal
    tmin_r <- range(freq_table$tmin_bin)
    tmax_r <- range(freq_table$tmax_bin)
    diag_range <- seq(min(tmin_r, tmax_r) - 2, max(tmin_r, tmax_r) + 2)

    p <- p %>%
        plotly::add_lines(
            x = diag_range,
            y = diag_range,
            line = list(color = "rgba(150, 150, 150, 0.5)", width = 1, dash = "dash"),
            name = "Tmax = Tmin",
            showlegend = TRUE,
            hoverinfo = "skip",
            inherit = FALSE
        )

    p %>%
        plotly::layout(
            title = list(
                text = paste("Weathergami:", date_range_str),
                font = list(size = 14)
            ),
            xaxis = list(
                title = "Daily Maximum Temperature (°C)",
                zeroline = FALSE
            ),
            yaxis = list(
                title = "Daily Minimum Temperature (°C)",
                zeroline = FALSE,
                scaleanchor = "x",
                scaleratio = 1
            ),
            hovermode = "closest",
            hoverlabel = list()
        ) %>%
        plotly::config(displaylogo = FALSE)
}


#' Create Diurnal Temperature Cycle Plot
#'
#' Creates a plot showing temperature variation by hour of day.
#'
#' @param df Data frame with parsed GHCNh data
#' @return A plotly line chart
#' @export
create_diurnal_plot <- function(df, offset_hours = 0) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available for the selected period"))
    }

    # Basic Diurnal Plot (UTC for now, offset not fully piped in from UI yet)
    df_diurnal <- df %>%
        dplyr::filter(!is.na(temp)) %>%
        dplyr::mutate(
            local_time = datetime + (offset_hours * 3600),
            date = as.Date(local_time),
            hour_val = lubridate::hour(local_time)
        )

    if (nrow(df_diurnal) == 0) {
        return(create_empty_plot("No temperature data available for diurnal plot"))
    }

    median_cycle <- df_diurnal %>%
        dplyr::group_by(hour_val) %>%
        dplyr::summarise(median_temp = median(temp, na.rm = TRUE), .groups = "drop")

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    # Sample background lines
    unique_dates <- sort(unique(df_diurnal$date))
    sampled_dates <- unique_dates[seq(1, length(unique_dates), length.out = min(length(unique_dates), 100))] # Capp at 100 lines for perf

    df_bg <- df_diurnal %>% filter(date %in% sampled_dates)

    plotly::plot_ly(type = "scatter", mode = "none", showlegend = FALSE, hoverinfo = "none") %>%
        plotly::add_lines(
            data = df_bg,
            x = ~hour_val,
            y = ~temp,
            split = ~date,
            line = list(color = "rgba(150, 150, 150, 0.2)", width = 0.5),
            hoverinfo = "none",
            showlegend = FALSE,
            type = "scatter", mode = "lines"
        ) %>%
        plotly::add_lines(
            data = median_cycle,
            x = ~hour_val,
            y = ~median_temp,
            name = "Median Cycle",
            line = list(color = "#d32f2f", width = 4),
            hovertemplate = "Hour: %{x}:00<br>Median: %{y:.1f}°C<extra></extra>",
            type = "scatter", mode = "lines"
        ) %>%
        plotly::layout(
            title = list(text = paste("Diurnal Cycle:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "Hour (UTC+Offset)"),
            yaxis = list(title = "Temperature (°C)"),
            showlegend = TRUE,
            hoverlabel = list()
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Solar Plot (Matching DWD Style)
#'
#' @param df Cleaned data frame
#' @return A plotly subplot object
#' @export
create_solar_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available for the selected period"))
    }

    # Centralized cleaning
    df <- clean_weather_data(df)

    # Detect Resolution
    is_hourly <- TRUE
    if (nrow(df) > 1) {
        # Check average time difference
        head_df <- df[1:min(10, nrow(df)), ]
        dt <- mean(as.numeric(difftime(head_df$datetime[2:nrow(head_df)], head_df$datetime[1:(nrow(head_df) - 1)], units = "hours")), na.rm = TRUE)
        if (!is.na(dt) && dt > 20) is_hourly <- FALSE
    } else {
        # Fallback for single row: assume hourly or strictly follow data format?
        # If it has AAAAMMJJ only and datetime is 00:00, it's daily.
        # But here we only have datetime posix.
        # Let's default to hourly view unless we know better.
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    # Prepare Data
    plot_data <- df

    # Check availability
    has_global <- "solar_global" %in% names(df) && any(!is.na(df$solar_global))
    has_sun <- "sunshine_duration" %in% names(df) && any(!is.na(df$sunshine_duration))
    has_etp <- "etp" %in% names(df) && any(!is.na(df$etp))

    if (!has_global && !has_sun && !has_etp) {
        return(create_empty_plot("No solar/sun/etp data in selected time range"))
    }

    # --- Panel 1: Global Radiation ---
    if (has_global) {
        p1 <- plotly::plot_ly(
            data = plot_data,
            x = ~datetime,
            y = ~solar_global,
            name = if (is_hourly) "Global Radiation" else "Daily Radiation",
            type = "scatter",
            mode = "lines",
            fill = "tozeroy",
            line = list(color = "rgba(255, 179, 0, 0.9)", width = 1),
            fillcolor = "rgba(255, 179, 0, 0.3)",
            hovertemplate = "Solar: %{y:,.0f} J/cm²<extra></extra>"
        ) %>%
            plotly::layout(yaxis = list(title = "Rad (J/cm²)"))
    } else {
        p1 <- create_empty_plot("No Radiation Data") %>%
            plotly::layout(yaxis = list(title = "Rad"))
    }

    # --- Panel 2: Sunshine Duration ---
    if (has_sun) {
        if (!is_hourly) {
            # Convert minutes to hours for daily view
            plot_data <- plot_data %>% mutate(sun_display = sunshine_duration / 60)
            y_title <- "Sun (h)"
            hv_temp <- "Sunshine: %{y:.1f} h<extra></extra>"
            y_range <- NULL # Auto
        } else {
            plot_data <- plot_data %>% mutate(sun_display = sunshine_duration)
            y_title <- "Sun (min)"
            hv_temp <- "Sunshine: %{y:.0f} min<extra></extra>"
            y_range <- c(0, 65)
        }

        p2 <- plotly::plot_ly(
            data = plot_data,
            x = ~datetime,
            y = ~sun_display,
            name = if (is_hourly) "Sunshine" else "Daily Sunshine",
            type = "bar",
            marker = list(color = "rgba(0, 0, 0, 0.3)", line = list(color = "#FFD700", width = 1.5)),
            hovertemplate = hv_temp
        ) %>%
            plotly::layout(yaxis = list(title = y_title, range = y_range))
    } else {
        p2 <- create_empty_plot("No Sunshine Data") %>%
            plotly::layout(yaxis = list(title = "Sun"))
    }

    # --- Panel 3: ETP (if available) ---
    p3 <- NULL
    if (has_etp) {
        p3 <- plotly::plot_ly(
            data = plot_data,
            x = ~datetime,
            y = ~etp,
            name = "ETP",
            type = "scatter",
            mode = "lines",
            line = list(color = "#795548", width = 1.5, dash = "dash"),
            hovertemplate = "ETP: %{y:.1f} mm<extra></extra>"
        ) %>%
            plotly::layout(yaxis = list(title = "ETP (mm)"))
    }

    # Combine
    plot_list <- list(p1, p2)
    rows_cnt <- 2
    if (!is.null(p3)) {
        plot_list[[3]] <- p3
        rows_cnt <- 3
    }

    plotly::subplot(plot_list, nrows = rows_cnt, shareX = TRUE, titleY = TRUE, margin = 0.04) %>%
        plotly::layout(
            title = list(text = paste("Solar Radiation & Sunshine:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            margin = list(t = 60, b = 60, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Temperature Plot (Single Panel)
#'
#' Creates a standalone temperature plot for the overview section.
#' For daily data, includes Tmin and Tmax.
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_temperature_single_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available for Temperature"))
    }

    df <- clean_weather_data(df)

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    has_temp <- "temp" %in% names(df) && any(!is.na(df$temp))
    has_tmin <- "temp_min" %in% names(df) && any(!is.na(df$temp_min))
    has_tmax <- "temp_max" %in% names(df) && any(!is.na(df$temp_max))
    has_tmin_abs <- "temp_min_abs" %in% names(df) && any(!is.na(df$temp_min_abs))
    has_tmax_abs <- "temp_max_abs" %in% names(df) && any(!is.na(df$temp_max_abs))

    if (!has_temp && !has_tmin && !has_tmax) {
        return(create_empty_plot("No data available for Temperature"))
    }

    p <- plotly::plot_ly(type = "scatter", mode = "lines")

    # --- Monthly Resolution Tiered Visualisation (Absolute Extremes) ---
    if (has_tmin_abs && has_tmax_abs) {
        # 1. Outer Band (Absolute Extremes) - Lightest
        # Lower bound of outer ribbon (Absolute Min)
        p <- p %>% plotly::add_trace(
            data = df %>% dplyr::filter(!is.na(temp_min_abs)),
            x = ~datetime,
            y = ~temp_min_abs,
            type = "scatter", mode = "lines",
            name = "Abs Min",
            line = list(color = "rgba(211, 47, 47, 0.3)", width = 1),
            hovertemplate = "Abs Min: %{y:.1f}°C<extra></extra>",
            showlegend = FALSE
        )
        # Upper bound of outer ribbon (Absolute Max) - filled to previous line
        p <- p %>% plotly::add_trace(
            data = df %>% dplyr::filter(!is.na(temp_max_abs)),
            x = ~datetime,
            y = ~temp_max_abs,
            type = "scatter", mode = "lines",
            fill = "tonexty",
            name = "Absolute Range",
            line = list(color = "rgba(211, 47, 47, 0.3)", width = 1),
            fillcolor = "rgba(211, 47, 47, 0.1)",
            hovertemplate = "Abs Max: %{y:.1f}°C<extra></extra>",
            showlegend = TRUE
        )
    }

    # --- Daily/Monthly Average Extremes Visualisation ---
    if (has_tmin && has_tmax) {
        # Split data into continuous chunks to correctly handle gaps with ribbons
        # NOTE: `split_into_chunks` is a helper function assumed to be defined elsewhere.
        # It should take a dataframe and a column name, and return a list of dataframes,
        # where each dataframe represents a continuous sequence of non-NA values in the specified column.
        chunks <- split_into_chunks(df, "temp_min")

        for (i in seq_along(chunks)) {
            chunk <- chunks[[i]]
            if (nrow(chunk) < 1) next

            # Only show legend for the first chunk
            show_leg <- (i == 1)

            # Lower bound (Tmin)
            p <- p %>% plotly::add_trace(
                data = chunk,
                x = ~datetime,
                y = ~temp_min,
                type = "scatter", mode = "lines",
                name = "Min/Max Range",
                line = list(color = "rgba(211, 47, 47, 0.4)", width = 1),
                hovertemplate = "Tmin: %{y:.1f}°C<extra></extra>",
                showlegend = FALSE,
                legendgroup = "daily_range"
            )

            # Upper bound (Tmax) - filled to previous
            p <- p %>% plotly::add_trace(
                data = chunk,
                x = ~datetime,
                y = ~temp_max,
                type = "scatter", mode = "lines",
                fill = "tonexty",
                name = "Min/Max Range",
                line = list(color = "rgba(211, 47, 47, 0.4)", width = 1),
                fillcolor = "rgba(211, 47, 47, 0.2)",
                hovertemplate = "Tmax: %{y:.1f}°C<extra></extra>",
                showlegend = show_leg,
                legendgroup = "daily_range"
            )
        }
    } else {
        # Fallback: Plot lines individually if only one extreme is available
        if (has_tmin) {
            p <- p %>% plotly::add_lines(
                data = df %>% dplyr::filter(!is.na(temp_min)),
                x = ~datetime,
                y = ~temp_min, name = "Mean Min",
                line = list(color = "#1976d2", width = 2),
                hovertemplate = "Mean Min: %{y:.1f}°C<extra></extra>",
                showlegend = TRUE
            )
        }
        if (has_tmax) {
            p <- p %>% plotly::add_lines(
                data = df %>% dplyr::filter(!is.na(temp_max)),
                x = ~datetime,
                y = ~temp_max, name = "Mean Max",
                line = list(color = "#d32f2f", width = 2),
                hovertemplate = "Mean Max: %{y:.1f}°C<extra></extra>",
                showlegend = TRUE
            )
        }
    }

    # Mean temperature (hourly temp or daily mean) - Always on top
    if (has_temp) {
        p <- p %>% plotly::add_lines(
            data = df %>% dplyr::filter(!is.na(temp)),
            x = ~datetime,
            y = ~temp, name = if (has_tmin || has_tmax) "Tmean" else "Temperature",
            line = list(
                color = "#b71c1c",
                width = 2,
                dash = if (has_tmin || has_tmax) "solid" else "solid"
            ),
            hovertemplate = paste0(if (has_tmin || has_tmax) "Tmean" else "Temp", ": %{y:.1f}°C<extra></extra>"),
            type = "scatter", mode = "lines",
            showlegend = TRUE
        )
    }

    p %>%
        plotly::layout(
            title = list(text = paste("Temperature:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = get_axis_label(df, "temp", "Temperature")),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Humidity Plot (Single Panel)
#'
#' Creates a standalone plot for dew point and relative humidity.
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_humidity_single_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available for Humidity"))
    }

    # Centralized cleaning
    df <- clean_weather_data(df)

    # Calculate Dew Point if not present
    if (!"dew_point" %in% names(df) && "rh" %in% names(df) && "temp" %in% names(df)) {
        df$dew_point <- calculate_dew_point(df$temp, df$rh)
        if (!is.null(df$dew_point)) {
            df$dew_point[is.na(df$dew_point) | df$dew_point < -90 | df$dew_point > 60] <- NA_real_
        }
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    has_dew <- "dew_point" %in% names(df) && any(!is.na(df$dew_point))
    has_rh <- "rh" %in% names(df) && any(!is.na(df$rh))

    if (!has_dew && !has_rh) {
        return(create_empty_plot("No data available for Humidity"))
    }

    p <- plotly::plot_ly(type = "scatter", mode = "none")

    if (has_dew) {
        p <- p %>% plotly::add_lines(
            data = df %>% dplyr::filter(!is.na(dew_point)),
            x = ~datetime,
            y = ~dew_point, name = "Dew Point",
            line = list(color = "#1e88e5", width = 2),
            hovertemplate = "Dew Pt: %{y:.1f}°C<extra></extra>",
            type = "scatter", mode = "lines",
            showlegend = TRUE
        )
    }

    if (has_rh) {
        p <- p %>% plotly::add_lines(
            data = df %>% dplyr::filter(!is.na(rh)),
            x = ~datetime,
            y = ~rh, name = "Relative Humidity",
            line = list(color = "#43a047", width = 1.5, dash = "dot"),
            hovertemplate = "RH: %{y:.0f}%<extra></extra>",
            type = "scatter", mode = "lines",
            yaxis = "y2",
            showlegend = TRUE
        )
    }

    # Layout with dual y-axis if both are present
    layout_args <- list(
        title = list(text = paste("Humidity:", date_range_str), font = list(size = 14)),
        xaxis = list(title = "", type = "date"),
        yaxis = list(title = get_axis_label(df, "dew_point", "Dew Point")),
        hovermode = "x unified",
        hoverlabel = list(),
        legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
        margin = list(t = 50, b = 80, l = 60, r = 60),
        modebar = list(orientation = "h")
    )

    if (has_rh) {
        layout_args$yaxis2 <- list(
            title = get_axis_label(df, "rh", "Relative Humidity"),
            overlaying = "y",
            side = "right",
            range = c(0, 100)
        )
    }

    do.call(plotly::layout, c(list(p), layout_args)) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Solar Radiation Plot (Individual)
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_solar_radiation_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    has_global <- "solar_global" %in% names(df) && any(!is.na(df$solar_global))
    if (!has_global) {
        return(create_empty_plot("No solar radiation data available"))
    }

    # Detect resolution
    is_hourly <- TRUE
    if (nrow(df) > 1) {
        head_df <- df[1:min(10, nrow(df)), ]
        dt <- mean(as.numeric(difftime(head_df$datetime[2:nrow(head_df)], head_df$datetime[1:(nrow(head_df) - 1)], units = "hours")), na.rm = TRUE)
        if (!is.na(dt) && dt > 20) is_hourly <- FALSE
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    plotly::plot_ly(
        data = df,
        x = ~datetime,
        y = ~solar_global,
        name = if (is_hourly) "Global Radiation" else "Daily Radiation",
        type = "scatter",
        mode = "lines",
        fill = "tozeroy",
        line = list(color = "rgba(255, 179, 0, 0.9)", width = 1),
        fillcolor = "rgba(255, 179, 0, 0.3)",
        hovertemplate = "Solar: %{y:,.0f} J/cm²<extra></extra>"
    ) %>%
        plotly::layout(
            title = list(text = paste("Solar Radiation:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = get_axis_label(df, "solar_global", "Radiation")),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Sunshine Duration Plot (Individual)
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_sunshine_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    has_sun <- "sunshine_duration" %in% names(df) && any(!is.na(df$sunshine_duration))
    if (!has_sun) {
        return(create_empty_plot("No sunshine duration data available"))
    }

    # Detect resolution
    is_hourly <- TRUE
    if (nrow(df) > 1) {
        head_df <- df[1:min(10, nrow(df)), ]
        dt <- mean(as.numeric(difftime(head_df$datetime[2:nrow(head_df)], head_df$datetime[1:(nrow(head_df) - 1)], units = "hours")), na.rm = TRUE)
        if (!is.na(dt) && dt > 20) is_hourly <- FALSE
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    # Convert to appropriate units
    if (!is_hourly) {
        df <- df %>% mutate(sun_display = sunshine_duration / 60)
        y_title <- "Sunshine (h)"
        hv_temp <- "Sunshine: %{y:.1f} h<extra></extra>"
        y_range <- NULL
    } else {
        df <- df %>% mutate(sun_display = sunshine_duration)
        y_title <- "Sunshine (min)"
        hv_temp <- "Sunshine: %{y:.0f} min<extra></extra>"
        y_range <- c(0, 65)
    }

    plotly::plot_ly(
        data = df,
        x = ~datetime,
        y = ~sun_display,
        name = if (is_hourly) "Sunshine" else "Daily Sunshine",
        type = "bar",
        marker = list(color = "rgba(0, 0, 0, 0.3)", line = list(color = "#FFD700", width = 1.5)),
        hovertemplate = hv_temp
    ) %>%
        plotly::layout(
            title = list(text = paste("Sunshine Duration:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = y_title, range = y_range),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create ETP Plot (Individual)
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_etp_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    has_etp <- "etp" %in% names(df) && any(!is.na(df$etp))
    if (!has_etp) {
        return(create_empty_plot("No ETP data available"))
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    plotly::plot_ly(
        data = df,
        x = ~datetime,
        y = ~etp,
        name = "ETP",
        type = "scatter",
        mode = "lines",
        line = list(color = "#795548", width = 1.5),
        hovertemplate = "ETP: %{y:.1f} mm<extra></extra>"
    ) %>%
        plotly::layout(
            title = list(text = paste("Evapotranspiration:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = get_axis_label(df, "etp", "ETP")),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Precipitation Plot (Individual - precipitation only)
#'
#' @param df Data frame with parsed weather data
#' @param resolution Resolution string ("hourly" or "daily")
#' @return A plotly object
#' @export
create_precipitation_only_plot <- function(df, resolution = "hourly") {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    has_precip <- "precip" %in% names(df) && any(!is.na(df$precip))
    if (!has_precip) {
        return(create_empty_plot("No precipitation data available"))
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    label_suffix <- if (resolution == "daily") "Daily" else "1h"
    axis_title <- get_axis_label(df, "precip", paste0("Precipitation (", label_suffix, ")"))

    if (resolution != "daily") {
        alias_map <- attr(df, "preferred_alias_map")
        precip_source <- NA_character_
        if (!is.null(alias_map) && length(alias_map) > 0) {
            precip_alias <- alias_map["precipitation_amount"]
            rain_alias <- alias_map["rainfall_amount"]
            if (!is.null(precip_alias) && length(precip_alias) == 1 && !is.na(precip_alias)) {
                precip_source <- precip_alias
            } else if (!is.null(rain_alias) && length(rain_alias) == 1 && !is.na(rain_alias)) {
                precip_source <- rain_alias
            }
        }

        is_1h_accum <- is.character(precip_source) &&
            length(precip_source) == 1 &&
            !is.na(precip_source) &&
            grepl("^precip_1h($|_)", precip_source, ignore.case = TRUE)

        # Fallback: infer 1h accumulation by matching the plotted 'precip' series
        # against known 1h source columns when alias metadata is unavailable.
        if (!is_1h_accum && "precip" %in% names(df)) {
            one_h_cols <- names(df)[grepl(
                "^(precip_1h($|_)|rainfall_amount(_lvl[^_]+)?_1h$)",
                names(df),
                ignore.case = TRUE
            )]
            if (length(one_h_cols) > 0) {
                p_vals <- suppressWarnings(as.numeric(df$precip))
                for (col_name in one_h_cols) {
                    c_vals <- suppressWarnings(as.numeric(df[[col_name]]))
                    ok <- is.finite(p_vals) & is.finite(c_vals)
                    if (sum(ok) >= 3) {
                        max_diff <- suppressWarnings(max(abs(p_vals[ok] - c_vals[ok]), na.rm = TRUE))
                        if (is.finite(max_diff) && max_diff < 1e-8) {
                            is_1h_accum <- TRUE
                            break
                        }
                    }
                }
            }
        }

        dt_mins <- NA_real_
        if ("datetime" %in% names(df) && nrow(df) > 1) {
            dt_series <- df$datetime[order(df$datetime)]
            dt_vals <- as.numeric(difftime(dt_series[2:nrow(df)], dt_series[1:(nrow(df) - 1)], units = "mins"))
            dt_vals <- dt_vals[is.finite(dt_vals) & dt_vals > 0]
            if (length(dt_vals) > 0) {
                dt_mins <- suppressWarnings(stats::median(dt_vals, na.rm = TRUE))
            }
        }
        updated_every_10m <- is.finite(dt_mins) && dt_mins >= 9 && dt_mins <= 11

        if (is_1h_accum && updated_every_10m) {
            axis_title <- "Precipitation (1h accumulation,<br>updated every 10 min)"
        }
    }

    plotly::plot_ly(
        data = df,
        x = ~datetime,
        y = ~precip,
        name = paste0("Precip (", label_suffix, ")"),
        type = "bar",
        marker = list(color = "#0277bd"),
        hovertemplate = paste0("Precip: %{y:.1f} mm<extra></extra>")
    ) %>%
        plotly::layout(
            title = list(text = paste("Precipitation:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = axis_title),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Snow Plot (Individual - fresh snow and snow depth)
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_snow_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    has_fresh <- "snow_fresh" %in% names(df) && any(!is.na(df$snow_fresh))
    has_depth <- "snow_depth" %in% names(df) && any(!is.na(df$snow_depth))

    if (!has_fresh && !has_depth) {
        return(create_empty_plot("No snow data available"))
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    # Use explicit range to preserve the full timeline
    time_range <- c(min(df$datetime), max(df$datetime))

    # Initialize plot
    p <- plotly::plot_ly(x = ~datetime)

    # Add Fresh Snow (Left Axis, Y1) - plotted first (behind)
    if (has_fresh) {
        p <- p %>% plotly::add_bars(
            data = df %>% filter(!is.na(snow_fresh)),
            y = ~snow_fresh,
            name = "Fresh Snow",
            marker = list(color = "#B0BEC5"),
            hovertemplate = "Fresh: %{y:.1f} cm<extra></extra>",
            yaxis = "y"
        )
    }

    # Add Snow Depth (Right Axis, Y2) - plotted second (on top) with some transparency if needed
    if (has_depth) {
        p <- p %>% plotly::add_bars(
            data = df %>% filter(!is.na(snow_depth)),
            y = ~snow_depth,
            name = "Snow Depth",
            marker = list(color = "#90CAF9", opacity = 0.7),
            hovertemplate = "Depth: %{y:.1f} cm<extra></extra>",
            yaxis = "y2"
        )
    }

    # Configure Layout
    layout_args <- list(
        title = list(text = paste("Snow:", date_range_str), font = list(size = 14)),
        xaxis = list(title = "", type = "date", range = time_range),
        yaxis = list(
            title = get_axis_label(df, "snow_fresh", "Fresh Snow"),
            titlefont = list(color = "#000000"),
            tickfont = list(color = "#000000")
        ),
        # Default Y2 settings (hidden if not used, but we configure it just in case)
        yaxis2 = list(
            title = get_axis_label(df, "snow_depth", "Snow Depth"),
            titlefont = list(color = "#000000"),
            tickfont = list(color = "#000000"),
            overlaying = "y",
            side = "right"
        ),
        hovermode = "x unified",
        margin = list(t = 50, b = 80, l = 60, r = 60),
        legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
        barmode = "overlay" # Important for dual axis bars
    )

    # If only one variable, adjust titles to remove confusion
    if (!has_fresh) {
        layout_args$yaxis$title <- ""
        layout_args$yaxis$showticklabels <- FALSE
        layout_args$yaxis$range <- c(0, 0.1) # Dummy range to hide
    }
    if (!has_depth) {
        layout_args$yaxis2$title <- ""
        layout_args$yaxis2$showticklabels <- FALSE
        layout_args$yaxis2$overlaying <- NULL # Don't overlay if not using
    }

    p <- do.call(plotly::layout, c(list(p), layout_args))

    p %>% plotly::config(displaylogo = FALSE)
}

#' Create Pressure Plot (Individual)
#'
#' Display Sea Level Pressure and/or Station Pressure.
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_pressure_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    # Check for variables (including the newly separated ones)
    has_sea <- "pressure_sea" %in% names(df) && any(!is.na(df$pressure_sea))
    has_stat <- "pressure_station" %in% names(df) && any(!is.na(df$pressure_station))

    # Fallback to generic 'pressure' column if specific ones are missing but generic exists
    # (generic usually stores PMERM/Sea Level by default logic in helpers.R)
    if (!has_sea && !has_stat && "pressure" %in% names(df) && any(!is.na(df$pressure))) {
        has_sea <- TRUE
        df$pressure_sea <- df$pressure
    }

    if (!has_sea && !has_stat) {
        return(create_empty_plot("No pressure data available"))
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    p <- plotly::plot_ly(type = "scatter", mode = "lines")

    if (has_sea) {
        p <- p %>% plotly::add_lines(
            data = df %>% filter(!is.na(pressure_sea)),
            x = ~datetime,
            y = ~pressure_sea,
            name = "Mean Sea Level Pressure",
            line = list(color = "#AB47BC", width = 1.5),
            hovertemplate = "MSL Pressure: %{y:.1f} hPa<extra></extra>"
        )
    }

    if (has_stat) {
        p <- p %>% plotly::add_lines(
            data = df %>% filter(!is.na(pressure_station)),
            x = ~datetime,
            y = ~pressure_station,
            name = "Station Pressure",
            line = list(color = "#7B1FA2", width = 1.5, dash = "dot"),
            hovertemplate = "Station Pressure: %{y:.1f} hPa<extra></extra>"
        )
    }

    p %>%
        plotly::layout(
            title = list(text = paste("Atmospheric Pressure:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = get_axis_label(df, "pressure_sea", "Pressure")),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Wind Time Series Plot
#'
#' Display Wind Speed (Average) and Wind Gusts over time.
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_wind_time_series_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    has_speed <- "wind_speed" %in% names(df) && any(!is.na(df$wind_speed))
    has_gust <- "wind_gust" %in% names(df) && any(!is.na(df$wind_gust))

    if (!has_speed && !has_gust) {
        return(create_empty_plot("No wind data available"))
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    p <- plotly::plot_ly(type = "scatter", mode = "lines")

    if (has_speed) {
        p <- p %>% plotly::add_lines(
            data = df %>% filter(!is.na(wind_speed)),
            x = ~datetime,
            y = ~wind_speed,
            name = "Wind Speed (Avg)",
            line = list(color = "#43a047", width = 1),
            fill = "tozeroy",
            hovertemplate = "Wind: %{y:.1f} m/s<extra></extra>"
        )
    }

    if (has_gust) {
        p <- p %>% plotly::add_markers(
            data = df %>% filter(!is.na(wind_gust)),
            x = ~datetime,
            y = ~wind_gust,
            name = "Gust (Max)",
            marker = list(color = "#2e7d32", size = 4),
            hovertemplate = "Gust: %{y:.1f} m/s<extra></extra>"
        )
    }

    p %>%
        plotly::layout(
            title = list(text = paste("Wind Speed & Gusts:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = get_axis_label(df, "wind_speed", "Speed")),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Cloud Cover Plot
#'
#' Display Cloud Cover in Oktas (0-8).
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_cloud_cover_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    has_cloud <- "cloud_cover" %in% names(df) && any(!is.na(df$cloud_cover))

    if (!has_cloud) {
        return(create_empty_plot("No cloud cover data available"))
    }

    # Aggregate to Daily Mean
    # This smoothing provides a more representative view for sparse data
    daily_cloud <- df %>%
        filter(!is.na(cloud_cover)) %>%
        mutate(date = as.Date(datetime)) %>%
        group_by(date) %>%
        summarise(
            mean_cloud = mean(cloud_cover, na.rm = TRUE),
            n_obs = n(),
            .groups = "drop"
        )

    if (nrow(daily_cloud) == 0) {
        return(create_empty_plot("No valid cloud cover data found"))
    }

    date_range_str <- paste(
        format(min(daily_cloud$date), "%d %b %Y"), "-",
        format(max(daily_cloud$date), "%d %b %Y")
    )

    plotly::plot_ly(
        data = daily_cloud,
        x = ~date,
        y = ~mean_cloud,
        name = "Daily Mean Cloud Cover",
        type = "bar",
        marker = list(color = "#78909C", line = list(color = "#546E7A", width = 1)),
        hovertemplate = "Date: %{x}<br>Mean Cloud: %{y:.1f}/8<br>Obs: %{customdata} hours<extra></extra>",
        customdata = ~n_obs
    ) %>%
        plotly::layout(
            title = list(text = paste("Daily Mean Cloud Cover:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = "Mean Cloud Cover (0-8)", range = c(0, 8.5), tickvals = 0:8),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Visibility Plot
#'
#' Display Visibility (m or km).
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_visibility_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    has_vis <- "visibility" %in% names(df) && any(!is.na(df$visibility))

    if (!has_vis) {
        return(create_empty_plot("No visibility data available"))
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    # Convert to km for readability if max visibility is large (> 2000m)
    max_vis <- max(df$visibility, na.rm = TRUE)
    is_km <- max_vis > 2000

    if (is_km) {
        df <- df %>% mutate(vis_display = visibility / 1000)
        y_lab <- "Visibility (km)"
        h_temp <- "Vis: %{y:.1f} km<extra></extra>"
    } else {
        df <- df %>% mutate(vis_display = visibility)
        y_lab <- "Visibility (m)"
        h_temp <- "Vis: %{y:.0f} m<extra></extra>"
    }

    plotly::plot_ly(
        data = df,
        x = ~datetime,
        y = ~vis_display,
        name = "Visibility",
        type = "scatter",
        mode = "lines",
        line = list(color = "#5D4037", width = 1.5),
        hovertemplate = h_temp
    ) %>%
        plotly::layout(
            title = list(text = paste("Visibility:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = y_lab),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Ground Temperature Plot
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_ground_temp_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    has_ground <- "temp_min_ground" %in% names(df) && any(!is.na(df$temp_min_ground))
    has_50cm <- "temp_min_50cm" %in% names(df) && any(!is.na(df$temp_min_50cm))

    if (!has_ground && !has_50cm) {
        return(create_empty_plot("No ground temperature data available"))
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    p <- plotly::plot_ly(type = "scatter", mode = "lines")

    if (has_ground) {
        p <- p %>% plotly::add_lines(
            data = df %>% filter(!is.na(temp_min_ground)),
            x = ~datetime,
            y = ~temp_min_ground,
            name = "Ground Surface Min (TNSOL)",
            line = list(color = "#388E3C", width = 1.5),
            hovertemplate = "Ground Min: %{y:.1f}°C<extra></extra>"
        )
    }

    if (has_50cm) {
        p <- p %>% plotly::add_lines(
            data = df %>% filter(!is.na(temp_min_50cm)),
            x = ~datetime,
            y = ~temp_min_50cm,
            name = "Soil Min (-50cm)",
            line = list(color = "#795548", width = 1.5, dash = "dot"),
            hovertemplate = "Soil (-50cm): %{y:.1f}°C<extra></extra>"
        )
    }

    # Add Freezing Line
    p <- p %>% plotly::add_lines(
        x = c(min(df$datetime), max(df$datetime)),
        y = c(0, 0),
        name = "Freezing (0°C)",
        line = list(color = "rgba(100,100,200,0.5)", width = 1, dash = "dash"),
        showlegend = FALSE, hoverinfo = "skip"
    )

    p %>%
        plotly::layout(
            title = list(text = paste("Ground Temperatures:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = get_axis_label(df, "temp_min_ground", "Temperature")),
            hovermode = "x unified",
            hoverlabel = list(),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            margin = list(t = 50, b = 80, l = 60, r = 20),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Soil Temperature Plot
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_soil_temp_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    # Check for variables
    vars <- c("soil_temp_10cm", "soil_temp_20cm", "soil_temp_50cm", "soil_temp_100cm")
    has_any <- any(sapply(vars, function(v) v %in% names(df) && any(!is.na(df[[v]]))))

    if (!has_any) {
        return(create_empty_plot("No soil temperature data available"))
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    p <- plotly::plot_ly(type = "scatter", mode = "lines")

    # Colors for depths: Lighter to Darker Brown
    colors <- c(
        "soil_temp_10cm" = "#D7CCC8", "soil_temp_20cm" = "#A1887F",
        "soil_temp_50cm" = "#795548", "soil_temp_100cm" = "#3E2723"
    )

    names <- c(
        "soil_temp_10cm" = "Soil -10cm", "soil_temp_20cm" = "Soil -20cm",
        "soil_temp_50cm" = "Soil -50cm", "soil_temp_100cm" = "Soil -100cm"
    )

    for (v in vars) {
        if (v %in% names(df) && any(!is.na(df[[v]]))) {
            p <- p %>% plotly::add_lines(
                data = df %>% filter(!is.na(.data[[v]])),
                x = ~datetime,
                y = as.formula(paste0("~", v)),
                name = names[[v]],
                line = list(color = colors[[v]], width = 1.5),
                hovertemplate = paste0(names[[v]], ": %{y:.1f}°C<extra></extra>")
            )
        }
    }

    # Add Freezing Line
    p <- p %>% plotly::add_lines(
        x = c(min(df$datetime), max(df$datetime)),
        y = c(0, 0),
        name = "Freezing (0°C)",
        line = list(color = "rgba(100,100,200,0.5)", width = 1, dash = "dash"),
        showlegend = FALSE, hoverinfo = "skip"
    )

    p %>%
        plotly::layout(
            title = list(text = paste("Soil Temperatures:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = get_axis_label(df, "soil_temp_10cm", "Temperature")),
            hovermode = "x unified",
            margin = list(t = 50, b = 80, l = 60, r = 20),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}

#' Create Wind 2m and Instant Gust Plot
#'
#' @param df Data frame with parsed weather data
#' @return A plotly object
#' @export
create_wind_2m_gust_plot <- function(df) {
    if (is.null(df) || nrow(df) == 0) {
        return(create_empty_plot("No data available"))
    }

    df <- clean_weather_data(df)

    has_FF2 <- "wind_speed_2m" %in% names(df) && any(!is.na(df$wind_speed_2m))
    has_FXI <- "wind_gust_inst" %in% names(df) && any(!is.na(df$wind_gust_inst))

    if (!has_FF2 && !has_FXI) {
        return(create_empty_plot("No Wind 2m / Instant Gust data available"))
    }

    date_range_str <- paste(
        format(min(df$datetime), "%d %b %Y"), "-",
        format(max(df$datetime), "%d %b %Y")
    )

    p <- plotly::plot_ly(type = "scatter", mode = "lines")

    if (has_FF2) {
        p <- p %>% plotly::add_lines(
            data = df %>% filter(!is.na(wind_speed_2m)),
            x = ~datetime,
            y = ~wind_speed_2m,
            name = "Wind Speed (2m)",
            line = list(color = "#81C784", width = 1.5), # Light Green
            hovertemplate = "Wind (2m): %{y:.1f} m/s<extra></extra>"
        )
    }

    if (has_FXI) {
        p <- p %>% plotly::add_markers(
            data = df %>% filter(!is.na(wind_gust_inst)),
            x = ~datetime,
            y = ~wind_gust_inst,
            name = "Instant Gust (max)",
            marker = list(color = "#D32F2F", size = 3, opacity = 0.6), # Red dots
            hovertemplate = "Inst. Gust: %{y:.1f} m/s<extra></extra>"
        )
    }

    p %>%
        plotly::layout(
            title = list(text = paste("Wind (2m) & Instant Gusts:", date_range_str), font = list(size = 14)),
            xaxis = list(title = "", type = "date"),
            yaxis = list(title = get_axis_label(df, "wind_speed_2m", "Speed")),
            hovermode = "x unified",
            margin = list(t = 50, b = 80, l = 60, r = 20),
            legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.25),
            modebar = list(orientation = "h")
        ) %>%
        plotly::config(displaylogo = FALSE)
}