Pass every value from a 3D array into a function with a loop

Here is the situation:
I have a function which needs 8 Parameters:

def BlackScholes(typ, cp ,S0, K, r, q, sigma, T): 
    d1  = (np.log(S0 / K) + (r - q  + sigma**2 / 2) * T) / (sigma * np.sqrt(T))
    d2  = d1 - (sigma * np.sqrt(T))
    if typ=='value':
        return cp * np.exp(-q * T) *  S0 * ss.norm.cdf(cp * d1) - cp * K * np.exp(-r * T) * ss.norm.cdf(cp* d2))

Now I call the def function in another script with:

K = WU
while K-BlackScholes('value', -1, S0, K, 0, 0, sigma, T ) < WU:
   K = K+0.01
strikes = [K]

With the above mentioned function I get one Value for the strike. But I have a sigma array with a shape (8,500,1)

I want to iterate over the function with every value from the sigma array. So that I get a array of strikes with the same shape of the sigma array . Can I do it with a simple loop or do I have tu unpack the array in the function?

I already tried it with:

for i in sigma[1,i,:]:
   while K-BlackScholes('value', -1, S0, K, 0, 0, sigma[1,i,:], T ) < WU:
      K = K+0.01
      i = i + 1
strikes = [K]

But it does not work either

I get the following error message:

IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices

If I insert one single sigma value I get one strike. But I want to iterate over the array and insert every single sigma value into the function. So I get a array of strikes corresponding to the list of sigma.

sigma.shape(8,500,1) –> strike.shape(8,500,1)

for i in range(sigma.shape[1]):
    K = WU
    while K - BlackScholes('value', -1, S0, K, 0, 0, sigma[1,i,:], T ) < WU:
        K = K + 0.01
strikes = [K]

K = np.empty_like(sigma)
for j in range(sigma.shape[0]):
    for i in range(sigma.shape[1]):
        K[j, i, :] = WU
        while K - BlackScholes('value', -1, S0, K[j, i, :], 0, 0, sigma[j,i,:], T ) < WU:
            K[j, i, :] += 0.01
strikes = K # not sure what this is for.

strikes = np.ndarray(sigma.shape)
for i in range(sigma.shape[0]):       # or for i in range(8)
    for j in range(sigma.shape[1]):   # ibidem...
      # eventually for k in range...
        K = WU
        while K-BlackScholes('value', -1, S0, K, 0, 0, sigma[i, j, 1], T ) < WU:
           K = K+0.01
        strikes[i, j, 1] = K