gistfile1.txt

def min_max(data):
  min_approx = np.zeros(data.shape)
  max_approx = np.zeros(data.shape)

  for i, row in enumerate(data):
    for j, _ in enumerate(row):
      col = data[:, j]
      vec = np.concatenate([row, col])

      q1 = np.percentile(vec, 25, axis=0, keepdims=False)
      q3 = np.percentile(vec, 75, axis=0, keepdims=False)
      iqr = q3 - q1

      lower_whisker = q1 - 1.5 * iqr
      upper_whisker = q3 + 1.5 * iqr

      min_approx[i, j] = np.maximum(lower_whisker, np.min(row))
      max_approx[i, j] = np.minimum(upper_whisker, np.max(row))

  return min_approx, max_approx

# np.random.seed(0)  # for reproducibility
employees = 5
days = 7

mins = [
    [2, 8, 2, 2, 2, 2, 2],
    [2, 2, 2, 2, 2, 2, 2],
    [2, 2, 2, 2, 2, 2, 2],
    [2, 2, 2, 2, 2, 2, 2],
    [2, 2, 2, 2, 2, 2, 2],
]

maxs = [
    [8, 16, 8, 8, 8, 8, 8],
    [8, 8, 8, 8, 8, 8, 8],
    [8, 8, 8, 8, 8, 8, 8],
    [8, 8, 8, 8, 8, 8, 8],
    [8, 8, 8, 8, 8, 8, 8],
]

working_hours = np.random.uniform(mins, maxs, size=(employees, days)).astype(int)

min_approx, max_approx = min_max(working_hours)

print("Original working hours matrix:")
print(working_hours)

print("\nApproximated minimum working hours:")
print(min_approx)

print("\nApproximated maximum working hours:")
print(max_approx)