scaleSequential = (await import("https://cdn.jsdelivr.net/npm/d3@7/+esm")).scaleSequential

// a format for dates
formatDate = d3.utcFormat("%b %d, %Y")  // e.g., "Apr 18, 2025"

// counties

//counties_file = await d3.json("https://cdn.jsdelivr.net/npm/us-atlas@3/counties-10m.json");
us = await d3.json("https://sustainable-fsa.github.io/fsa-counties-dd17/fsa-counties-dd17.topojson");
//us = await d3.json("assets/fsa-counties-dd17-albers.topojson");
counties = topojson.feature(us, us.objects.counties)

// states, but just the boundaries between them
//states_file = FileAttachment("assets/fsa_states.topojson").json()
states = topojson.mesh(us, us.objects.states, (a, b) => a !== b)

// normal grazing period data, simplified
data = FileAttachment("assets/fsa-normal-grazing-period-simple.csv").csv({typed: true})

// Load color palettes
romaO = FileAttachment("assets/colors.json").json()
color = scaleSequential()
.domain([1, 366])
.interpolator(t => romaO[Math.floor(t * (romaO.length - 1))])

filtered = data.filter(d => d.year === year && d.type === type)

// Create a lookup table keyed by `id`
valueById = Object.fromEntries(filtered.map(d => [String(d.id).padStart(5, "0"), d[variable]]))

// Merge values into counties.features
countiesFiltered = {
  // Build lookup from filtered CSV: id → full row
  const rowById = Object.fromEntries(
        filtered.map(d => [String(d.id).padStart(5, "0"), d])
  );

  // Rebuild counties with joined properties
  return {
    type: "FeatureCollection",
    features: counties.features.map(f => {
      const id = String(f.id);
      const row = rowById[id];

      return {
        ...f,
        properties: {
          id: f.id,
          ...f.properties,
          ...(row ?? {}),
          value: row ? row[variable] : null  // explicitly add value for coloring
        }
      };
    })
  };
}