import math
SIMPSON_ESP = 10 ** (-8)


n = int(input())
circles = []
for _ in range(n):
    x, y, r = map(int, input().split())
    circles.append((x, y, r))

# 被积函数, 求扫描线x和所有圆相交部分的并集的长度
from functools import lru_cache
@lru_cache(typed=False, maxsize=99999999999)
def f(x):
    rr = []
    for x_val, y_val, r_val in circles:
        if x <= x_val-r_val or x >= x_val+r_val:
            continue
        y_delta = math.sqrt(r_val**2 - (x-x_val)**2)
        rr.append((y_val - y_delta, y_val + y_delta))

    # 区间合并
    rr.sort()
    tot = 0
    max_end = -0x7fffffff  # 当前已经枚举过的区间的结尾的最大值
    for a, b in rr:
        if a <= max_end:
            if b > max_end:
                tot += b - max_end
                max_end = b
        else:
            tot += b - a
            max_end = b

    return tot

# 普通simpson积分估值函数, func是被积函数，a, b是积分区间
def __simpson(func, a, b):
    return (func(a) + 4*func((a+b)/2) + func(b)) * (b-a) / 6

# 自适应simpson积分
def adaptive_simpson(func, a, b):
    mid = (a + b) / 2
    val, val1, val2 = __simpson(func, a, b), __simpson(func, a, mid), __simpson(func, mid, b)
    if abs(val - val1 - val2) < SIMPSON_ESP:
        return val1 + val2
    return adaptive_simpson(func, a, mid) + adaptive_simpson(func, mid, b)

print("%0.3f" % adaptive_simpson(f, -2100, 2100))