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To determine the area of a function that is implicitly defined using Python, you can make use of numerical integration techniques such as the trapezoidal rule or Simpson's rule. Here is an example using the trapezoidal rule:

1. Define the function as an expression involving x and y:

import numpy as np
from scipy.integrate import trapz

# Define the implicit function
def f(x, y):
    return x**2 + y**2 - 1

1. Create a 2D grid of x and y values and evaluate f on the grid:

# Create grid of x and y values
x = np.linspace(-1, 1, num=100)
y = np.linspace(-1, 1, num=100)
X, Y = np.meshgrid(x, y)

# Evaluate f on the grid
Z = f(X, Y)

1. Use the trapezoidal rule to estimate the area enclosed by the curve f=0:

# Find indices of points where f changes sign
indices = np.where(np.diff(np.sign(Z)))[0]

# Find x and y values where f=0 using linear interpolation between grid points
x_intersect = []
for index in indices:
    x1, x2 = x[index], x[index+1]
    y1, y2 = y[np.where(Z[:, index]==0)], y[np.where(Z[:, index+1]==0)]
    x_intersect.append((x1*y2 - x2*y1)/(y2 - y1))

# Estimate the area using the trapezoidal rule
area = trapz(x_intersect, dx=(x[-1]-x[0])/(len(x)-1))
print(area)

This code finds the points where the implicit function changes sign along each row of the grid and uses linear interpolation to estimate the x values where the function is equal to zero. The trapezoidal rule is then used to estimate the area enclosed by the curve f=0 along the x-axis.