Система для метода Ньютона

def Newton(f, dfdx, dfdy, x, y, eps):
    xstore = []
    ystore = []
    fstore = []
    fstore1 = []
    f_value = f(x, y)
    # f_value1 = f(y)
    iteration_counter = 0
    while np.all(f_value) > eps and iteration_counter < 100:
        try:
            x = x - float(f_value) / dfdx(x)
            y = y - float(f_value) / dfdy(y)
        except ZeroDivisionError:
            print("Error! The derivative is zero for x = ", x)
            sys.exit(1)  # Abort with error
        f_value = f(x, y)
        xstore.append(x)
        fstore.append(f_value)
        ystore.append(y)
        iteration_counter += 1
 
    if abs(f_value) > eps:
        iteration_counter = -1
 
    return x, y, iteration_counter, xstore, fstore, ystore, fstore1
 
 
def f(x, y):
    return np.sin(x + y) - 1.6 * x, (1 - x**2)**0.5
 
 
def dfdx(x):
    return np.cos(x+y) * y - 1.6,  2 * x
 
 
def dfdy(y):
    return np.cos(x+y) * x, 2 * y
 
 
solution, no_iterations, no_iterations1, xvalues, yvalues, fvalues = Newton(f, dfdx, dfdy, x=-3, y=-3, eps=1.0e-4)
 
if no_iterations and no_iterations1 > 0:  # Solution found
    print("Number of function calls: %d %d" % (no_iterations, no_iterations1))
    print("Solution: x = %.4f" % (solution))
else:
    print("No solution found!")

TypeError: float() argument must be a string or a real number, not 'tuple'

import numpy as np
from matplotlib import pyplot as plt
x=np.linspace(-1, 1, 100)
y=np.linspace(-1, 1, 100)
X,Y=np.meshgrid(x,y)
F1=np.sin(X+Y)-1.6*X
F2=X*X+Y*Y
plt.contour(X,Y,F1, levels=[0])
plt.contour(X,Y, F2, levels=[1])
plt.grid()
plt.axis('scaled')
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
x = np.linspace(-np.pi, np.pi, 100)
y = np.linspace(-np.pi, np.pi, 100)
p, q = np.meshgrid(x, y)
 
f1 = lambda  x, y: np.sin(x+y)+1.6*x
f2 = lambda  x, y: x**2+y**2
z=f1(p,q)
w=f2(p,q)
 
# рисуем линии уровня f1(x,y)==0 и f2(x,y)==1
plt.contour(p, q, z , [0], colors=["k"])
plt.contour(p, q, w , [1], colors=["r"])