/**
 * Chart geometry, smoothing, and SVG path generation for analytics charts.
 *
 * Fixed 720x180 plot area inside a larger viewBox that also holds the axis
 * labels, so each chart SVG is self-contained; values are per-unit rates
 * (hour on the week view, day on month+).
 */

import { DAY, HOUR, MIN5, WEEK, mondayUTC } from './time.js'
import { formatCount } from './format.js'

export const CHART_W = 720
export const CHART_H = 180
export const PAD_TOP = 14 // room above the highest point
export const MARGIN_L = 56 // y tick labels + vertical axis label
export const MARGIN_B = 24 // x tick labels
export const VIEW_W = MARGIN_L + CHART_W + 8
export const VIEW_H = CHART_H + MARGIN_B

/**
 * Y always starts at 0; the max is a multiple of a 1-2-5 major step with at
 * most 5 intervals, so labeled ticks are always round and evenly divided.
 * A minimum range of 10 keeps tiny near-zero values (e.g. a single visit)
 * from being enlarged to a fractional scale; minor lines subdivide each
 * major step in five when that yields integers.
 */
export function yScale(maxValue) {
  let step = 1
  outer: for (let exp = -3; exp < 8; exp++) {
    for (const base of [1, 2, 5]) {
      step = base * 10 ** exp
      if (Math.ceil(maxValue / step) <= 5) break outer
    }
  }
  let max = Math.ceil(maxValue / step) * step
  if (max < 10) {
    max = 10
    step = 2
  }
  const minor = step >= 5 && step % 5 === 0 ? step / 5 : null
  return { max, step, minor }
}

/**
 * Edge-aware Gaussian smoothing with a fixed bandwidth. A change-point
 * detector first finds traffic-level shifts (two-unit totals compared on
 * both sides of each bucket; strong ratio + significance marks a candidate,
 * and each run of candidates keeps only its best-scoring bucket as an
 * edge). Each edge-delimited segment is then smoothed independently: every
 * bucket spreads its count with a fixed Gaussian sigma chosen so N events
 * in a single bucket peak at N events per unit, clipped to the segment and
 * renormalized so total visitor count is preserved exactly. The unit is
 * one hour on the week view and one day on the month+ views, so the
 * smoothing time scale follows the range. The raw series is drawn faintly
 * behind the curve for reference. Operates on raw counts.
 */
export function smooth(counts, binMinutes, unitMinutes, {
  detectorWindowMinutes = 2 * unitMinutes,
  // Count thresholds are defined per hour and scale with the unit, so
  // "low traffic" means the same thing on hourly and daily views
  // (5-20 events/hour = 120-480/day on the month+ ranges).
  highTrafficEvents = 10 * unitMinutes / 60,
  minRatio = 2.5,
  minSignificance = 4,
} = {}) {
  const n = counts.length
  if (!n) return counts
  const detectorWindowBins = Math.max(1, Math.round(detectorWindowMinutes / binMinutes))

  const cumsum = new Float64Array(n + 1)
  for (let i = 0; i < n; i++) cumsum[i + 1] = cumsum[i] + counts[i]

  // Detect abrupt regime changes from aggregated traffic on both sides.
  // Individual bins are deliberately ignored because even high traffic
  // produces many 0-1 count bins at five-minute resolution.
  const score = new Float64Array(n)
  const candidate = new Uint8Array(n)
  for (let i = detectorWindowBins; i < n - detectorWindowBins; i++) {
    const left = cumsum[i] - cumsum[i - detectorWindowBins]
    const right = cumsum[i + detectorWindowBins] - cumsum[i]
    const high = Math.max(left, right)
    const low = Math.min(left, right)
    if (high < highTrafficEvents) continue
    const ratio = (high + 1) / (low + 1)
    const significance = (high - low) / Math.sqrt(high + low + 1)
    if (ratio >= minRatio && significance >= minSignificance) {
      candidate[i] = 1
      score[i] = significance * Math.log(ratio)
    }
  }

  // Collapse each continuous detector region to its strongest boundary.
  const edges = []
  for (let i = 0; i < n;) {
    if (!candidate[i]) { i++; continue }
    let j = i + 1
    while (j < n && candidate[j]) j++
    let best = i
    for (let k = i + 1; k < j; k++) {
      if (score[k] > score[best]) best = k
    }
    edges.push(best)
    i = j
  }

  // Fixed sigma: N events in one bucket peak at N events per unit.
  // sigma_bins * sqrt(2*pi) = rate = unitMinutes / binMinutes.
  const sigmaBins = unitMinutes / (binMinutes * Math.sqrt(2 * Math.PI))
  const radius = Math.ceil(4 * sigmaBins)

  // Process each discontinuity-delimited regime independently so the
  // Gaussian cannot see through a detected boundary. Each input bin spreads
  // its count with the fixed sigma; the kernel is renormalized after
  // clipping to the segment, preserving total visitor count apart from
  // floating-point error.
  const bounds = [0, ...edges, n]
  const smoothed = new Float64Array(n)
  for (let b = 0; b < bounds.length - 1; b++) {
    const lo = bounds[b]
    const length = bounds[b + 1] - lo
    const segment = counts.slice(lo, lo + length)
    for (let j = 0; j < length; j++) {
      const count = segment[j]
      if (!count) continue
      const start = Math.max(0, j - radius)
      const end = Math.min(length, j + radius + 1)
      let weightSum = 0
      for (let i = start; i < end; i++) {
        const d = i - j
        weightSum += Math.exp(-0.5 * (d / sigmaBins) ** 2)
      }
      for (let i = start; i < end; i++) {
        const d = i - j
        smoothed[lo + i] += count * Math.exp(-0.5 * (d / sigmaBins) ** 2) / weightSum
      }
    }
  }
  return [...smoothed]
}

/**
 * Catmull-Rom spline through the (smoothed) points, control points clamped
 * to the plot area so the curve can never dip below zero or above the max.
 */
export function spline(pts) {
  if (pts.length < 3) {
    return `M${pts.map((p) => `${p.x},${p.y}`).join('L')}`
  }
  const clampY = (y) => Math.min(CHART_H, Math.max(PAD_TOP, y))
  let d = `M${pts[0].x},${pts[0].y}`
  for (let i = 0; i < pts.length - 1; i++) {
    const p0 = pts[i - 1] || pts[i]
    const p1 = pts[i]
    const p2 = pts[i + 1]
    const p3 = pts[i + 2] || p2
    const c1y = clampY(p1.y + (p2.y - p0.y) / 6)
    const c2y = clampY(p2.y - (p3.y - p1.y) / 6)
    d += `C${p1.x + (p2.x - p0.x) / 6},${c1y} `
       + `${p2.x - (p3.x - p1.x) / 6},${c2y} ${p2.x},${p2.y}`
  }
  return d
}

/** Build a full chart model from a series descriptor produced by time.js. */
export function buildChart(input, now = Date.now()) {
  if (!input || !input.series.length) return null
  if (input.unit === '5min') return buildDayChart(input, now)
  const { series, t0, t1, rate, binMinutes, unitMinutes, unit } = input
  // Values are per-unit rates (hour on the week view, day on month+); the
  // y max is derived from the *smoothed* curves so random single-bucket
  // spikes don't blow up the scale. Smoothing works on raw counts (its edge
  // detector thresholds are count-based), the result is scaled back to rates.
  const smoothed = series.map((s) =>
    smooth(s.points.map((p) => p.count), binMinutes, unitMinutes).map((v) => v * rate))
  // Scale from the current/primary series only; older overlay weeks are drawn
  // with the same scale and allowed to overflow if they are busier.
  const highest = Math.max(0, ...smoothed[0])
  const { max, step, minor } = yScale(highest)
  const x = (t) => ((t - t0) / (t1 - t0)) * CHART_W
  const y = (v) => PAD_TOP + (1 - Math.max(0, v) / max) * (CHART_H - PAD_TOP)
  const drawn = series.map((s, si) => {
    const pts = s.points.map((p, i) => ({ x: x(p.t), y: y(smoothed[si][i]) }))
    const line = spline(pts)
    const first = pts[0]
    const last = pts.at(-1)
    return {
      ...s,
      line,
      area: s.area ? `${line}L${last.x},${CHART_H}L${first.x},${CHART_H}Z` : null,
    }
  })
  // Major (labeled) and minor (hairline) y grid ticks.
  const majors = []
  const minors = []
  const nMajor = Math.round(max / step)
  for (let k = 0; k <= nMajor; k++) {
    const v = k * step
    majors.push({ value: v, y: y(v), label: fmtY(v) })
  }
  if (minor) {
    for (let v = minor; v < max; v += minor) {
      if (v % step !== 0) minors.push({ y: y(v) })
    }
  }
  // X ticks. Week view: weekday labels centered at midday UTC, no vertical
  // lines (day boundaries would be misleading in the viewer's timezone).
  // Month view: likewise lineless, day numbers at noon UTC with the month
  // name substituted for the 1st (marking the month change). Longer
  // ranges: boundary lines at Mondays / months / years.
  const isWeek = t1 - t0 === WEEK
  const isMonth = !isWeek && t1 - t0 <= 31 * DAY
  let xticks
  if (isWeek) {
    xticks = Array.from({ length: 7 }, (_, d) => {
      const t = t0 + d * DAY + 12 * HOUR
      return {
        x: x(t),
        label: new Date(t).toLocaleDateString(undefined, {
          weekday: 'short', timeZone: 'UTC',
        }),
        line: false,
      }
    })
  } else if (isMonth) {
    // t0 is day-aligned; label every day whose noon falls inside the range.
    xticks = []
    for (let day = t0; day + 12 * HOUR < t1; day += DAY) {
      const date = new Date(day)
      const t = day + 12 * HOUR
      xticks.push({
        x: x(t),
        label: date.getUTCDate() === 1
          ? date.toLocaleDateString(undefined, { month: 'short', timeZone: 'UTC' })
          : String(date.getUTCDate()),
        line: false,
      })
    }
  } else {
    xticks = xticksFor(t0, t1).map((t) => ({
      x: x(t), label: fmtTick(t, t1 - t0), line: true,
    }))
  }
  return { max, majors, minors, series: drawn, xticks, unit }
}

/**
 * Day view: 5-minute bars for the last 24 hours. Bars are drawn at raw
 * counts; the skyline uses a projected full-bucket value for the still-open
 * final bucket. The y scale is derived from the projected skyline maximum.
 */
export function buildDayChart(input, now = Date.now()) {
  const { series, t0, t1 } = input
  const points = series[0]?.points || []
  const n = points.length
  if (!n) return null
  const bucketMs = (t1 - t0) / n
  const bucketWidth = CHART_W / n
  const gap = 0.2
  const barWidth = Math.max(0.2, bucketWidth - gap)

  const x = (i) => i * bucketWidth + gap / 2
  const prevRaw = n > 1 ? points[n - 2].count : 0
  const projected = points.map((p, i) => {
    if (i !== n - 1) return p.count
    const bucketStart = t0 + i * bucketMs
    const elapsed = Math.max(1, Math.min(bucketMs, now - bucketStart))
    // Blend the observed partial bucket with the previous full bucket:
    // the longer the current bucket has run, the less we borrow from it.
    const share = elapsed / bucketMs
    return p.count + prevRaw * (1 - share)
  })
  const highest = Math.max(0, ...projected)
  const { max, step, minor } = yScale(highest)
  const y = (v) => PAD_TOP + (1 - Math.max(0, v) / max) * (CHART_H - PAD_TOP)

  const bars = points.map((p, i) => {
    const bx = x(i)
    const by = y(p.count)
    return {
      x: bx,
      y: by,
      width: barWidth,
      height: CHART_H - by,
      raw: p.count,
      projected: projected[i],
    }
  })

  let skyline = ''
  for (let i = 0; i < bars.length; i++) {
    const b = bars[i]
    const top = y(b.projected)
    if (i === 0) {
      skyline += `M${b.x},${top} H${b.x + b.width}`
    } else {
      skyline += ` V${top} H${b.x + b.width}`
    }
  }

  const majors = []
  const minors = []
  const nMajor = Math.round(max / step)
  for (let k = 0; k <= nMajor; k++) {
    const v = k * step
    majors.push({ value: v, y: y(v), label: fmtY(v) })
  }
  if (minor) {
    for (let v = minor; v < max; v += minor) {
      if (v % step !== 0) minors.push({ y: y(v) })
    }
  }

  const xticks = []
  const tickStep = 3 * HOUR
  const firstTick = Math.ceil(t0 / tickStep) * tickStep
  for (let t = firstTick; t < t1; t += tickStep) {
    if (t < t0) continue
    const d = new Date(t)
    xticks.push({
      x: ((t - t0) / (t1 - t0)) * CHART_W,
      label: `${String(d.getUTCHours()).padStart(2, '0')}:00`,
      line: false,
    })
  }
  return { bars, skyline: skyline.trim(), max, majors, minors, xticks, unit: '5min', series: [] }
}

/** X ticks for year/all: Monday boundaries up to a quarter, UTC month
 *  boundaries up to a few years, then years. */
export function xticksFor(t0, t1) {
  const span = t1 - t0
  const ticks = []
  if (span <= 100 * DAY) {
    for (let t = mondayUTC(t0); t <= t1; t += WEEK) {
      if (t >= t0) ticks.push(t)
    }
    return ticks
  }
  if (span <= 4 * 365 * DAY) {
    const d = new Date(t0)
    let t = Date.UTC(d.getUTCFullYear(), d.getUTCMonth() + 1, 1)
    for (; t <= t1; ) {
      ticks.push(t)
      const m = new Date(t)
      t = Date.UTC(m.getUTCFullYear(), m.getUTCMonth() + 1, 1)
    }
    return ticks
  }
  const d = new Date(t0)
  for (let yr = d.getUTCFullYear() + 1; Date.UTC(yr, 0, 1) <= t1; yr++) {
    ticks.push(Date.UTC(yr, 0, 1))
  }
  return ticks
}

export function fmtTick(t, span) {
  const d = new Date(t)
  if (span <= 100 * DAY) {
    return d.toLocaleDateString(undefined, { month: 'short', day: 'numeric', timeZone: 'UTC' })
  }
  if (span <= 4 * 365 * DAY) {
    return d.getUTCMonth() === 0
      ? d.toLocaleDateString(undefined, { year: 'numeric', timeZone: 'UTC' })
      : d.toLocaleDateString(undefined, { month: 'short', timeZone: 'UTC' })
  }
  return d.toLocaleDateString(undefined, { year: 'numeric', timeZone: 'UTC' })
}

/** Y labels use the same compact formatter as text labels. */
export function fmtY(v) {
  return formatCount(v)
}