Why does my function crash the second time it's called from within a python while loop?

Question

I'm trying to simulate Gillespies algorithm for cellular systems in python 3.8 using a simple system with the following entities Enzyme, Substrate, Enzyme-Substrate complex, Product.

I have the following code that calculates propensity functions for a series of reactions which are represented as rows of an array:

propensity = np.zeros(len(LHS))

def propensity_calc(LHS, popul_num, stoch_rate):
    for row in range(len(LHS)):     
            a = stoch_rate[row]        
            for i in range(len(popul_num)):      
                if (popul_num[i] >= LHS[row, i]):             
                    binom_rxn = (binom(popul_num[i], LHS[row, i]))    
                    a = a*binom_rxn            
                else: 
                    a = 0   
                    break 
            propensity[row] = a
    return propensity.astype(float)

The input arrays are as follows:

popul_num = np.array([200, 100, 0, 0])
LHS = np.array([[1,1,0,0], [0,0,1,0], [0,0,1,0]])
stoch_rate = np.array([0.0016, 0.0001, 0.1000])

The function works as expected, until I try to call it from within the following while loop:

while tao < tmax: 
propensity_calc(LHS, popul_num, stoch_rate)
a0 = sum(propensity)
if a0 <= 0:
    break
else:
    t = np.random.exponential(a0)   
    print(t)   
    # sample time system stays in given state from exponential distribution of the propensity sum. 
    if tao + t > tmax: 
        tao = tmax
        break
j = stats.rv_discrete(name="Reaction index", values=(num_rxn, rxn_probability)).rvs()   # array of reactions increasing by 1 until they get to the same length/size as rxn_probability
print(j)
tao = tao + t       
popul_num = popul_num + state_change_matrix[j]      # update state of system/ popul_num

The other variables in the while loop are as follows:

a0 = sum(propensity)

def prob_rxn_fires(propensity, a0):
prob = propensity/a0   
return prob 
rxn_probability = (prob_rxn_fires(propensity, a0))

num_rxn = np.arange(1, rxn_probability.size + 1).reshape(rxn_probability.shape)

When I run the code calling the calc_propensity function from within the while loop, it makes it through the first iteration of the while loop with the following error:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

The error is first thrown on the following line of the calc_propensity function:

if (popul_num[i] >= LHS[row, i]):

but for some reason the code keeps running until it reaches the same line in the calc_propensity function but in the second function call (the while loop) and I dont understand why?

cheers

Answer 1

It appears that the values in the if statement are not to be used in the if conditional in the first place. You should use the.any() or.all() functions. Here is a link to illustrate what the ValueError stands for:

https://sopython.com/canon/119/the-truth-value-of-an-array-with-more-than-one-element-is-ambiguous/