Expressiveness Benchmark

def average_window(data):
  def window(value):
    return math.floor(value["time"] / 7)
  if len(data) < 1:
    return []
  data.sort(key=lambda v: v["time"])
  result = []
  current_window = window(data[0])
  total, count = data[0]["x"], 1
  for value in data[1:]:
    time_window = window(value)
    if time_window != current_window:
      result.append(total / count)
      current_window, total, count = time_window, 0, 0
    total += value["x"]
    count += 1
  result.append(total / count)
  return result

def average_window(data):
  def window(value):
    return math.floor(value["time"] / 7)
  grouped_values = [
    [point["x"] for point in data if window(point) == w]
    for w in set(map(window, data))
  ]
  return [
    sum(values) / len(values)
    for values in grouped_values
  ]

def average_window(data):
  def window(t):
    return math.floor(t / 7)
  result = (data.sort_values("time")
                .set_index("time").groupby(window).mean())
  return result['x'].tolist()

average_window <- function(data) {
  data %>% 
    group_by(floor(time / 7)) %>% 
    summarize(avg = mean(x)) %>% 
    pull(avg)
}

SELECT AVG(x) as x
FROM data
GROUP BY cast(time / 7 as int)
ORDER BY time

.decl window(w: number)
window(t/7) :- data(t, _).
.decl windowed(w: number, x: float)
windowed(t/7, x) :- data(t, x).
.decl windowed_total(w: number, x: float)
windowed_total(w, total / n) :- window(w),
  total = sum x : { windowed(w, x) },
      n = sum z : { windowed(w, x), z=1.0 }.
average_window(v) :- windowed_total(_, v).

average_window:
  (value select[<time] avg x by 7 xbar time from data) `x