import scipy
from scipy.optimize import minimize


lista=[0.002,0.006,0.003,0.02,0.008,0.006,0.05]

def assign(k):
    return list(map(lambda x:(k*0.005 if x in np.arange(0,k*0.0025,0.001)
    else k*0.01 if x in np.arange(0.0025,k*0.005,0.001) else k*0.025 if x in
    np.arange(0.005,k*0.0125,0.001) else k*0.05), lista))

def constraint(k):
    return sum(assign(k))-0.32

def fun(k):
    return k

k0=0
bnds=[(0,10)]
cons={'type':'eq','fun':constraint}
res=minimize(fun,k0,bounds=bnds,method='SLSQP',constraints=cons,options={'maxiter':2000})
print(res)

message: 'Iteration limit exceeded'

lambda x:(k*0.005 if x in np.arange(0,k*0.0025,0.001)
else k*0.01 if x in np.arange(0.0025,k*0.005,0.001) else k*0.025 if x in
np.arange(0.005,k*0.0125,0.001) else k*0.05)

def assign1(x, k):
    if x in np.arange(0, k * 0.0025, 0.001):
        return k * .01
    if x in np.arange(0.0025, k * 0.005, 0.001):
        return k * .025
    ...

def assign1(x, k):
    if 0 <= x < k * .0025:
        return k * .01
    if k * .0025 <= x < k * .0050:
        return k * .025
    ...