如何在使用interpolate.interp2d绘制曲面后从给定的Z值获得X、Y值

import numpy as np
from scipy import interpolate

f = interpolate.interp2d(X_data, Y_data, Z_data, kind='linear')

x = np.linspace(min(X_data), max(X_data), num=100)  # 在X范围内生成100个均匀分布的点
y = np.linspace(min(Y_data), max(Y_data), num=100)  # 在Y范围内生成100个均匀分布的点
X, Y = np.meshgrid(x, y)  # 生成网格点坐标矩阵
Z = f(X, Y)  # 根据插值函数计算对应的Z值
indices = np.where(np.isclose(Z, z))  # 找到Z值最接近z的点的索引
x_values = X[indices]  # 获取对应的X值
y_values = Y[indices]  # 获取对应的Y值

import numpy as np
from scipy import interpolate

# 准备数据
X_data = np.array([1, 2, 3, 4, 5])
Y_data = np.array([1, 2, 3, 4, 5])
Z_data = np.array([[1, 2, 3, 4, 5],
                   [2, 4, 6, 8, 10],
                   [3, 6, 9, 12, 15],
                   [4, 8, 12, 16, 20],
                   [5, 10, 15, 20, 25]])

# 创建插值函数
f = interpolate.interp2d(X_data, Y_data, Z_data, kind='linear')

# 获取Z值为7的对应的X和Y值
z = 7
x = np.linspace(min(X_data), max(X_data), num=100)
y = np.linspace(min(Y_data), max(Y_data), num=100)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
indices = np.where(np.isclose(Z, z))
x_values = X[indices]
y_values = Y[indices]

print("对应Z值为{}的X值：{}".format(z, x_values))
print("对应Z值为{}的Y值：{}".format(z, y_values))