import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import r2_score

def plot_density_ned(data, degree=2, save_path="density_ned_fit.png"):
    """
    根据输入二维数组 (density, NED)，拟合并可视化趋势。

    参数:
        data: 二维数组或列表 [[density1, ned1], [density2, ned2], ...]
        degree: 拟合多项式阶数 (默认2阶)，二阶多项式拟合
        save_path: 保存路径 (默认为 'density_ned_fit.png')

    输出:
        保存拟合图像，并返回拟合系数与R²值。
    """
    # 转为numpy数组
    data = np.array(data)
    X = data[:, 0].reshape(-1, 1)  # 文字密度
    y = data[:, 1]                 # NED指标

    # 多项式特征转换
    poly = PolynomialFeatures(degree=degree)
    X_poly = poly.fit_transform(X)

    # 拟合回归模型
    model = LinearRegression()
    model.fit(X_poly, y)

    # 预测与R²
    y_pred = model.predict(X_poly)
    r2 = r2_score(y, y_pred)

    # 平滑曲线点
    X_plot = np.linspace(X.min(), X.max(), 200).reshape(-1, 1)
    X_plot_poly = poly.transform(X_plot)
    y_plot = model.predict(X_plot_poly)

    # 绘图
    plt.figure(figsize=(8, 6))
    plt.scatter(X, y, color='royalblue', alpha=0.7, label='Data points')
    plt.plot(X_plot, y_plot, color='crimson', linewidth=2.5, label=f'Polynomial Fit (deg={degree})')

    plt.xlabel("Text Density", fontsize=14)
    plt.ylabel("NED", fontsize=14)
    plt.title("Relationship between Text Density and NED", fontsize=16, pad=15)
    plt.grid(alpha=0.3)
    plt.legend(fontsize=12)
    plt.tight_layout()

    # 保存高分辨率图片
    plt.savefig(save_path, dpi=300)
    plt.close()

    print(f"✅ 拟合完成，图片已保存至：{save_path}")
    print(f"📈 拟合公式系数: {model.coef_}")
    print(f"📊 R² = {r2:.4f}")

    return model.coef_, r2


# 示例使用
if __name__ == "__main__":
    data = [
        [0.1, 0.12],
        [0.2, 0.18],
        [0.3, 0.25],
        [0.4, 0.32],
        [0.5, 0.45],
        [0.6, 0.51],
        [0.7, 0.62],
    ]
    plot_density_ned(data, degree=2, save_path="/vol/density_ned_fit.png")