TypeError：不支持的操作数类型 -：自定义类中的“builtin_function_or_method”和“float”

Question

我制作了以下课程来尝试和计时我正在运行的模拟： 编辑：

class TimeError(Exception):
"""A custom exception used to report errors in use of Timer Class"""
class simulation_timer: 
def __init__(self):
    self._simulation_start_time = None
    self._simulation_stop_time = None

def start(self):
    """start a new timer"""
    if self._simulation_start_time is not None:    # attribute
        raise TimeError(f"Timer is running.\n Use .stop() to stop it")

    self._simulation_start_time = time.perf_counter()  
def stop(self):
    """stop the time and report the elsaped time"""
    if self._simulation_start_time is None:
        raise TimeError(f"Timer is not running.\n Use .start() to start it.")

    self._simulation_stop_time = time.perf_counter
    elasped_simulation_time = self._simulation_stop_time - self._simulation_start_time  <-- Error here!
    self._simulation_start_time = None
    print(f"Elasped time: {elasped_simulation_time:0.4f} seconds")

然后我在下面的函数中使用它：

def gillespie_tau_leaping(propensity_calc, popul_num, popul_num_all, rxn_vector, tao, delta_t, epsi):
t = simulation_timer()
t.start()
while tao < tmax:
    propensity = propensity_calc(LHS, popul_num, stoch_rate)        
    a0 = (sum(propensity))
    if a0 == 0.0:
        break
    # if reaction cannot fire corresponding element in rxn_vector should be zero --> tau leaping method 
    if popul_num.any() < 0:
        break   
    lam = (propensity_calc(LHS, popul_num, stoch_rate)*delta_t)
    rxn_vector = np.random.poisson(lam)    
    if tao + delta_t > tmax:
        break
    tao += delta_t
    print("tao:\n", tao)
    # divide tao by delta_t to calculate number of leaps
    leap_counter = tao / delta_t  # should this be after the if statement below? 
    if tao >= 2/a0:     
        for j in range(len(rxn_vector)):
            state_change_lambda = np.squeeze(np.asarray(state_change_array[j])*rxn_vector[j]) 
            popul_num = popul_num + state_change_lambda
            new_propensity = propensity_calc(LHS, popul_num, stoch_rate)   # maybe inside for loop?  
        #for m in range(len(propensity)):
        for n in range(len(new_propensity)):
            propensity_check = propensity + state_change_lambda 
            if propensity_check[n] - new_propensity[n] >= epsi*a0:  
                print("The value of delta_t {} choosen is too large".format(delta_t))
                break
            else:
                popul_num = popul_num + state_change_lambda     
                popul_num_all.append(popul_num)   
                tao_all.append(tao)
                #return popul_num_all.append(popul_num), tao_all.append(tao), leap_counter
    else:
        t = np.random.exponential(1/a0)
        rxn_probability = propensity / a0   
        num_rxn = np.arange(rxn_probability.size)       
        if tao + t > tmax:      
            tao = tmax
            break
        j = stats.rv_discrete(values=(num_rxn, rxn_probability)).rvs()
        tao = tao + t
        popul_num = popul_num + np.squeeze(np.asarray(state_change_array[j]))   
        print("Simulation time:\n", t, tao)
t.stop()
return popul_num_all.append(popul_num), tao_all.append(tao), leap_counter

只有我不断收到以下错误：

TypeError: unsupported operand type(s) for -: 'builtin_function_or_method' and 'float'

我在这里查看了其他帖子，说这与重新定义已经内置的函数有关，所以我尝试重命名所有变量，但仍然没有运气！任何人都知道我该如何解决这个问题？

干杯

Answer 1

您正在尝试从函数中扣除浮点值：

elasped_simulation_time = time.perf_counter - self._simulation_start_time

perf_counter 是一个错误表示的函数

builtin_function_or_method

您可以在开始时调用它并保存值，并在停止时执行相同操作，然后扣除值。