#import "@preview/lilaq:0.6.0" as lq

#let epoch = datetime(year: 1970, month: 01, day: 01, hour: 0, minute: 0, second: 0)
#let unix-to-datetime(stamp) = epoch + duration(seconds: int(stamp))
#let datetime-to-unix(dt) = int((dt - epoch).seconds())

#let parse-iso-datetime(s) = {
  let parts = s.split("T")
  let date-part = parts.at(0).split("-").map(int)
  let time-part = parts.at(1).split(":").map(int)
  datetime(
    year: date-part.at(0),
    month: date-part.at(1),
    day: date-part.at(2),
    hour: time-part.at(0),
    minute: time-part.at(1),
    second: time-part.at(2)
  )
}

#let window-regions(filtered-windows, filtered-times-dt) = {
  let regions = ()
  let i = 0
  while i < filtered-windows.len() {
    let start-time = filtered-times-dt.at(i)
    let status = filtered-windows.at(i)
    let j = i
    // Find end of consecutive region with same status
    while j < filtered-windows.len() and filtered-windows.at(j) == status {
      j += 1
    }
    let end-time = if j < filtered-times-dt.len() {
      filtered-times-dt.at(j - 1)
    } else {
      filtered-times-dt.at(filtered-times-dt.len() - 1)
    }
    // Add small duration to end time to make rectangle visible
    let end-time-dt = end-time + duration(seconds: 60)

    let fill-color = if status == 0 { none } else { rgb("#e8e8e8") }

    regions.push(lq.rect(
      start-time,
      0%,
      width: end-time-dt - start-time,
      height: 100%,
      fill: fill-color,
      stroke: none,
      z-index: 1
    ))

    i = j
  }
  regions
}

#let plot_co2(path, start-date, stop-date) = [
  #let start-unix = datetime-to-unix(start-date)
  #let stop-unix = datetime-to-unix(stop-date)

  #let data = lq.load-txt(
    read(path),
    header: true,
    converters: (time: parse-iso-datetime, rest: float)
  )
  #let times = data.at("time")
  #let co2s = data.at("co2")
  #let windows = data.at("windows")
  
  // Filter data by date range
  #let filtered-indices = range(times.len()).filter(i => {
    let t = times.at(i)
    t >= start-date and t <= stop-date
  })
  
  #let filtered-times-dt = filtered-indices.map(i => times.at(i))
  #let filtered-co2s = filtered-indices.map(i => co2s.at(i))
  #let filtered-windows = filtered-indices.map(i => windows.at(i))
  #let regions = window-regions(filtered-windows, filtered-times-dt)
  
  #lq.diagram(
    width: 100%,
    height: 7cm,
    title: [CO2 Level over Time],
    xlabel: [Time],
    ylabel: [CO2 Level (ppm)],
    yaxis: (lim: (400, 1500), exponent: 0),
    xaxis: (format-ticks: lq.tick-format.datetime.with(
      format-offset: (datetimes, period: none) => none,
    )),
    ..regions,
    lq.plot(filtered-times-dt, filtered-co2s, label: [CO2 Level], color: rgb("#388e3c"))
  )
]

#let plot_temp_hum(path, start-date, stop-date) = [
  #let start-unix = datetime-to-unix(start-date)
  #let stop-unix = datetime-to-unix(stop-date)

  #let data = lq.load-txt(
    read(path),
    header: true,
    converters: (time: parse-iso-datetime, rest: float)
  )
  #let times = data.at("time")
  #let temps = data.at("temperature")
  #let hums = data.at("humidity")
  #let windows = data.at("windows")

  
  // Filter data by date range
  #let filtered-indices = range(times.len()).filter(i => {
    let t = times.at(i)
    t >= start-date and t <= stop-date
  })
  
  #let filtered-times-dt = filtered-indices.map(i => times.at(i))
  #let filtered-temps = filtered-indices.map(i => temps.at(i))
  #let filtered-hums = filtered-indices.map(i => hums.at(i))
  #let filtered-times-dt = filtered-indices.map(i => times.at(i))
  #let filtered-windows = filtered-indices.map(i => windows.at(i))
  #let regions = window-regions(filtered-windows, filtered-times-dt)

  #lq.diagram(
    width: 100%,
    height: 7cm,
    title: [Temperature and Humidity over Time],
    xlabel: [Time],
    ylabel: [Temperature (°C)],
    yaxis: (lim: (20, 35)),
    xaxis: (format-ticks: lq.tick-format.datetime.with(
      format-offset: (datetimes, period: none) => none,
    )),
    ..regions,
    lq.plot(filtered-times-dt, filtered-temps, label: [Temperature], color: rgb("#d32f2f")),
    lq.yaxis(
      position: right,
      label: [Humidity (%)],
      lim: (30, 55),
      lq.plot(filtered-times-dt, filtered-hums, label: [Humidity], color: rgb("#1976d2"))
    )
  )
]