---
title: "`r params$storm_name` `r params$storm_year` STORM REPORT"
subtitle: "Generated on: `r Sys.Date()`"
format:
  pdf:
    theme: cosmo
execute:
  warning: false
  message: false
params:
    storm_basin: AL
    storm_year: 1900
    storm_name: GALVESTON
---

```{r setup}
#| echo: false

library(ggplot2)
library(sf)
library(rnaturalearth)
library(dplyr)

source(file = "queries.R")

storm <- list(
    storm_basin = params$storm_basin,
    storm_year = params$storm_year,
    storm_name = params$storm_name
)

unique_lfs <- get_unique_lf_ids(storm)

storm_track <- get_hurdat_track(storm)
```

```{r track_map}
#| echo: false
#| fig-width: 10
#| fig-height: 8

  # Add color and category classification
  storm_track <- storm_track %>%
    mutate(
      hurricane_category = case_when(
        storm_status == "HU" & windspeed >= 64 & windspeed <= 82 ~ 1,
        storm_status == "HU" & windspeed >= 83 & windspeed <= 95 ~ 2,
        storm_status == "HU" & windspeed >= 96 & windspeed <= 112 ~ 3,
        storm_status == "HU" & windspeed >= 113 & windspeed <= 136 ~ 4,
        storm_status == "HU" & windspeed >= 137 ~ 5,
      ),
      status_label = case_when(
        storm_status == "TD" ~ "Tropical Depression (TD)",
        storm_status == "TS" ~ "Tropical Storm (TS)",
        hurricane_category == 1 ~ "Hurricane Category 1",
        hurricane_category == 2 ~ "Hurricane Category 2",
        hurricane_category == 3 ~ "Hurricane Category 3",
        hurricane_category == 4 ~ "Hurricane Category 4",
        hurricane_category == 5 ~ "Hurricane Category 5",
        storm_status == "EX" ~ "Extratropical Cyclone (EX)",
        storm_status == "SD" ~ "Subtropical Depression (SD)",
        storm_status == "SS" ~ "Subtropical Storm (SS)",
        storm_status %in% c("LO", "WV", "DB") ~ "Low/Wave/Disturbance",
        TRUE ~ "Missing Data"
      ),
      line_color = case_when(
        storm_status == "TD" ~ "#2AFF00",
        storm_status == "TS" ~ "#FFD020",
        hurricane_category == 1 ~ "#FF4343",
        hurricane_category == 2 ~ "#FF6FFF",
        hurricane_category == 3 ~ "#FF23D3",
        hurricane_category == 4 ~ "#C916FF",
        hurricane_category == 5 ~ "#FFFFFF",
        storm_status == "EX" ~ "#202020",
        storm_status == "SD" ~ "#0055FF",
        storm_status == "SS" ~ "#6CE2FF",
        storm_status %in% c("LO", "WV", "DB") ~ "#A1A1A1",
        TRUE ~ "#FF5C00"
      )
    ) %>%
    arrange(datetime)

  # Create ordered factor for legend
  storm_track$status_label <- factor(
    storm_track$status_label,
    levels = c(
      "Tropical Depression (TD)",
      "Tropical Storm (TS)",
      "Hurricane Category 1",
      "Hurricane Category 2",
      "Hurricane Category 3",
      "Hurricane Category 4",
      "Hurricane Category 5",
      "Extratropical Cyclone (EX)",
      "Subtropical Depression (SD)",
      "Subtropical Storm (SS)",
      "Low/Wave/Disturbance",
      "Missing Data"
    )
  )

  world <- ne_countries(scale = "medium", returnclass = "sf")
  states <- ne_states(returnclass = "sf")

  min_lon_range <- range(-100, -40)
  min_lat_range <- range(10, 50)

  #lon_range <- range(storm_track$lon)
  #lat_range <- range(storm_track$lat)
  #lon_padding <- diff(lon_range) * 0.1
  #lat_padding <- diff(lat_range) * 0.1

  p <- ggplot() +
    geom_sf(data = world, fill = "#E5E5E5", color = "#999999", size = 0.3) +
    geom_sf(data = states, fill = NA, color = "#CCCCCC", size = 0.2) +
    #coord_sf(
      #xlim = c(lon_range[1] - lon_padding, lon_range[2] + lon_padding),
      #ylim = c(lat_range[1] - lat_padding, lat_range[2] + lat_padding),
      #expand = FALSE
    #) +
    coord_sf(
      xlim = c(min_lon_range[1], min_lon_range[2]),
      ylim = c(min_lat_range[1], min_lat_range[2]),
      expand = FALSE
    ) +
    theme_minimal() +
    theme(
      panel.background = element_rect(fill = "#D4E6F1"),
      panel.grid.major = element_line(color = "#BBBBBB", size = 0.2),
      legend.position = "right",
      legend.key.size = unit(0.4, "cm"),
      legend.text = element_text(size = 8)
    )

  if (nrow(storm_track) >= 2) {
    for (i in 1:(nrow(storm_track) - 1)) {
      segment_data <- storm_track[i:(i + 1), ]
      p <- p +
        geom_path(
          data = segment_data,
          aes(x = lon, y = lat),
          color = storm_track$line_color[i],
          size = 1
        )
    }
  }

  p <- p +
    geom_point(
      data = storm_track,
      aes(x = lon, y = lat, color = status_label),
      size = 1.5
    )

  landfall_data <- storm_track %>%
    filter(record_identifier == "L")

  if (nrow(landfall_data) > 0) {
    landfall_data <- landfall_data %>%
      mutate(rmw_degrees = rmw_meters / 111320)

    p <- p +
      geom_point(
        data = landfall_data,
        aes(x = lon, y = lat),
        color = landfall_data$line_color,
        size = 4,
        shape = 19
      ) +
      geom_point(
        data = landfall_data,
        aes(x = lon, y = lat),
        color = landfall_data$line_color,
        size = landfall_data$rmw_degrees * 100,
        shape = 1,
        stroke = 1.5,
        alpha = 0.5
      )
  }

  color_values <- c(
    "Tropical Depression (TD)" = "#2AFF00",
    "Tropical Storm (TS)" = "#FFD020",
    "Hurricane Category 1" = "#FF4343",
    "Hurricane Category 2" = "#FF6FFF",
    "Hurricane Category 3" = "#FF23D3",
    "Hurricane Category 4" = "#C916FF",
    "Hurricane Category 5" = "#FFFFFF",
    "Extratropical Cyclone (EX)" = "#202020",
    "Subtropical Depression (SD)" = "#0055FF",
    "Subtropical Storm (SS)" = "#6CE2FF",
    "Low/Wave/Disturbance" = "#A1A1A1",
    "Missing Data" = "#FF5C00"
  )

  p <- p +
    scale_color_manual(
      name = "Storm Status",
      values = color_values,
      breaks = names(color_values),
      drop = FALSE
    ) +
    labs(
      title = paste(params$storm_name, params$storm_year),
      x = "Longitude",
      y = "Latitude"
    )

  print(p)
```