Python Numba函数无法从Numba修饰的生成器函数附加到列表

Numba无法在Numba修饰的生成器中执行

\uuuuuuuuuuuuuuuuuuuuu next（）

调用。错误显示UniTuple（float64 x 4）类型的

Unknown属性'\uuuuuu next\uuuuuuuu'

完整错误输出为

TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Unknown attribute '__next__' of type UniTuple(float64 x 4) generator(func=<function random_walk at 0x7fbe39806488>, args=(int64, int64, float64, float64, int64), has_finalizer=True)

File "rw_nb.py", line 47:
def random_walk_simulation(initial_position = 0, acceleration = 0,
    <source elided>
    data = []
    data.append(rw.__next__())
    ^

[1] During: typing of get attribute at /home/igor/rw_nb.py (47)

File "rw_nb.py", line 47:
def random_walk_simulation(initial_position = 0, acceleration = 0,
    <source elided>
    data = []
    data.append(rw.__next__())
    ^

TypingError:在nopython模式管道中失败（步骤：nopython前端）
UniTuple（float64 x 4）生成器（func=，args=（int64，int64，float64，float64，int64）类型的未知属性“\uuuuu next\uuuuuu”具有\u finalizer=True）
文件“rw_nb.py”，第47行：
def随机行走模拟（初始位置=0，加速度=0，
数据=[]
data.append（rw.\uuuu next\uuuu（））
^
[1] 期间：在/home/igor/rw_nb.py中键入get属性（47）
文件“rw_nb.py”，第47行：
def随机行走模拟（初始位置=0，加速度=0，
数据=[]
data.append（rw.\uuuu next\uuuu（））
^

MWE源代码如下所示

import random
import numba
import numpy as np

@numba.njit
def random_walk(s_0, a_0, pa, pb, seed=None):
    """Initial position (often 0), acceleration, 0 < pa < pb < 1"""
    if seed is not None:
        random.seed(seed)
    # Time, x-position, Velocity, Acceleration
    t, x, v, a = 0, s_0, 0, a_0
    yield (t, x, v, a)

    while True:        
        # Roll the dices
        rnd = random.random()
        if rnd <= pa:
                # Increase acceleration
                a += .005
        elif rnd <= pa+pb:
                # Reduce acceleration
                a -= .005

        # Lets avoid too much acceleration
        #lower, upper = -0.2, 0.2
        a = -0.2 if a < -0.2 else 0.2 if a > 0.2 else a

        # How much time has passed, since last update?
        dt = random.random()
        v += dt*a
        x += dt*v
        t += dt

        yield (t, x, v, a)

@numba.njit
def random_walk_simulation(initial_position = 0, acceleration = 0,
                           prob_increase=5e-3, prob_decrease=5e-3,
                           max_distance=1e5, simul_time=1e3,
                           seed=None):

    rw = random_walk(initial_position, acceleration,
                     prob_increase, prob_decrease, seed)

    # Runs the first iteraction
    data = []
    data.append(rw.__next__())

    # While there is simulation time or not too far away
    while (data[-1][0] < simul_time) and (abs(data[-1][1]) < max_distance):
        data.append(rw.__next__())

    return np.array(data)


def main():
    experiment = random_walk_simulation(seed=0)
    print(experiment.shape)

if __name__ == '__main__':
    main()

随机导入
进口麻木
将numpy作为np导入
@纳巴恩吉特
def随机游动（s_0，a_0，pa，pb，种子=无）：
“”“初始位置（通常为0），加速度，0如果rnd调用Numba中生成器的下一项，而不是使用
data.append（rw.\uuu next\uuuu（））
您可以：

data.append(next(rw))

我的天啊。太差劲了。我用错了发电机。你能写一个答案来获得正确的信用吗？