Question

随着时间的推移，我具有以下功能来模拟生化系统。代码背后的想法是对时间步进行采样，然后将其添加到当前时间。

我有一个全局定义的开始时间：tao = 0.0

和以下功能：

def gillespie_tau_leaping(propensity_calc, popul_num, popul_num_all, tao_all, rxn_vector, tao, epsi):
    t = simulation_timer()
    t.start()
    while tao < tmax:
        evaluate_propensity = propensity_calc(LHS, popul_num, stoch_rate)        
        a0 = (sum(propensity))  # a0 is a numpy.float64
        if a0 == 0.0: 
            break   
        if popul_num.any() < 0: # This isnt working
            print("Number of molecules {} is too small for reaction to fire".format(popul_num))
            break
        # code equation 26 here! 
        for x in range(len(popul_num)):
            # equation 22:
            expctd_net_change = a0*state_change_array[x]
            # equation 24:
            part_diff = derivative(evaluate_propensity, popul_num[x]) 
            # find the partial derivative of propensity with respect to popul_num (number of discrete molecules)
        # need to find a a way to SELECT delta_t before I can code it!
        # equation 26:
        t_step = epsi*a0 / sum(expctd_net_change*part_diff)
        delta_t = optimize.fmin(t_step, 0.00012) 
        print (delta_t)
        lam = (evaluate_propensity*delta_t) 
        rxn_vector = np.random.poisson(lam) # probability of a reaction firing in the given time period    
        if tao + delta_t > tmax:
            break    
        tao = tao + delta_t 
        leap_counter = tao / delta_t 
        if tao >= 2/a0:     # if tao is big enough
            for j in range(len(rxn_vector)):  
                state_change_lambda = np.squeeze(np.asarray(state_change_array[j])*rxn_vector[j])   
                #new_propensity = evaluate_propensity
                propensity_check = evaluate_propensity.copy()
                propensity_check[0] += state_change_lambda[0]
                propensity_check[1:] += state_change_lambda  
                for n in range(len(propensity_check)): 
                    if propensity_check[n] - evaluate_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) 
        else:
            t = np.random.exponential(1/a0)
            rxn_probability = evaluate_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]))  
            popul_num_all.append(popul_num)   
            tao_all.append(tao)
    print("tao:\n", tao)
    print("Molecule numbers:\n", popul_num)
    print("Number of leaps taken:\n", leap_counter)
    t.stop()
    return popul_num_all.append(popul_num), tao_all.append(tao)

打印（gillespie_tau_leaping（propensity_calc，popul_num，popul_num_all，tao_all，rxn_vector，tao，epsi））

只有我收到错误：UnboundLocalError: local variable 'leap_counter' referenced before assignment 尝试打印或返回模拟过程中采取的时间步数时，保存在变量jump_counter中，我不知道为什么。我已经尝试过更改缩进量，但这是行不通的。怎么回事，如何解决？

编辑：以下代码在全局环境中指定delta_t的值。我已经尝试在上面进行更新，因此我在每次迭代中都计算了delta_t的值。但是后来我遇到了跳数计数器的问题。

def gillespie_tau_leaping(propensity_calc, popul_num, popul_num_all, tao_all, rxn_vector, tao, delta_t, epsi):
    t = simulation_timer()
    t.start()
    while tao < tmax:
        propensity = propensity_calc(LHS, popul_num, stoch_rate)       
        print("propensity:\n", type(propensity)) 
        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:
            print("Number of molecules {} is too small for reaction to fire".format(popul_num))
            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)
        print("Molecule numbers:\n", popul_num)
        # divide tao by delta_t to calculate number of leaps
        leap_counter = tao / delta_t   
        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)   
            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)
        else:
            next_t = np.random.exponential(1/a0)
            rxn_probability = propensity / a0   
            num_rxn = np.arange(rxn_probability.size)       
            if tao + next_t > tmax:      
                tao = tmax
                break
            j = stats.rv_discrete(values=(num_rxn, rxn_probability)).rvs()
            tao = tao + next_t
            popul_num = popul_num + np.squeeze(np.asarray(state_change_array[j]))   
    t.stop()
    return popul_num_all.append(popul_num), tao_all.append(tao), leap_counter

此代码完全按照我想要的方式工作，我无法理解的是第一个功能突然没了

欢呼

Answer 1

leap_counter仅在while循环内可见（因此可访问），请尝试在循环外（最好使用合理的默认值）创建它。

Python为什么我的变量jump_counter返回UnboundLocalError错误消息？

1 个答案: