So I am trying to fit a 2-dimensional function with polynomials.

I have some data and I have written a function to test it.

But something unexpected happens like:

ValueError: object too deep for desired array

error: Result from function call is not a proper array of floats.

Then I go to the net but found nothing like my situation. I have no idea how to flatten my data.What should I do to fix this problem?

(this is my code:

def poly(order, cov, x):

z = 0

for r in range(order + 1):

    for k in range(order + 1):

       z += cov[r * order + k] * x[0] ** r * x[1] ** k

return z

def polynomial_func(x , *cov):

order = int(np.sqrt(len(cov))) - 1 

return poly(order, cov, x)

k = int(input(‘Please type the order:’))

U = sorted(list(u))

V = sorted(list(v))[300:501]

U = U*201

V0 = []

for item in V:

V0 = V0 + [item]*151

V = V0

arr_x = np.array(U[::3])

arr_y = np.array(V[::3])

arr_z = g(arr_x,arr_y)

popt, pcov = curve_fit(polynomial_func, (arr_x,arr_y), arr_z , p0=[0.0] * (k+1)**2)

print(np.round(popt, 2))

where u and v are some random x y and g is the fuction generated by interpolation)

I have gone through the Web.
This problem seems commonly due to 2-D array, but I have no idea how to deal with them.

Is this happens because of arr_z?