diff --git a/exp1_basic.py b/exp1_basic.py
index 06c7e9d..49bf475 100644
--- a/exp1_basic.py
+++ b/exp1_basic.py
@@ -9,31 +9,115 @@
     - RC 模型: 使用 ESN 等 RC 模型，调优储备池大小、谱半径等参数。
     - 训练方法: 监督学习 (例如 Ridge 回归)。
 评估指标:
-    - 降噪指标: SNR 提升、均方根误差 (RMSE)。
+    - 降噪指标: SNR 提升、均方根误差 (RMSE)、相关系数、峰值信噪比。
     - 动力学特性恢复指标: 吸引子重构质量、Lyapunov 指数估计误差、分形维数估计误差、预测精度。
 结果展示:
     - 可视化时间序列、吸引子重构、PSD 对比图和表格展示指标结果。
 """
 
-def load_data():
-    # TODO: 生成并加载带噪及干净混沌系统数据
-    pass
+import numpy as np
+import matplotlib.pyplot as plt
 
-def build_esn_model():
-    # TODO: 构建 RC/ESN 模型并设置关键参数
-    pass
+import reservoirpy as rpy
+import reservoirpy.nodes as rpn
+rpy.verbosity(0)
+rpy.set_seed(42)
 
-def train_model():
-    # TODO: 利用训练集进行训练，使用监督学习方法
-    pass
+from tools import load_data
 
-def evaluate_results():
-    # TODO: 计算 SNR, RMSE, 吸引子重构、Lyapunov 指数估计误差等指标
-    pass
+def build_esn_model(units, lr, sr, reg, output_dim, reservoir_node=None):
+    
+    if reservoir_node is None:
+        reservoir_node = rpn.Reservoir(units=units, lr=lr, sr=sr)
+    output_node = rpn.Ridge(output_dim=output_dim, ridge=reg)
+    
+    model = reservoir_node >> output_node
+    return model, reservoir_node
 
+def train_model(model, clean_data, noisy_data, warmup=1000):
+    model.fit(noisy_data, clean_data, warmup=warmup)
+    prediction = model.run(noisy_data)
+    return model, prediction
+
+def evaluate_results(model, clean_data, noisy_data, warmup=0, vis_data=True, vis_psd=False):
+    # 预测
+    prediction = model.run(noisy_data)
+    # 计算各种指标
+    snr = 10 * np.log10(np.mean(clean_data**2) / np.mean((clean_data - prediction)**2))
+    rmse = np.sqrt(np.mean((clean_data - prediction)**2))
+    psnr = 20 * np.log10(np.max(clean_data) / rmse)
+    corr_coef = np.corrcoef(clean_data, prediction)[0, 1]
+
+    # 3个维度分别绘制 noisy vs clean | prediction vs clean
+    if vis_data:
+        plt.figure(figsize=(12, 6))
+        for i in range(3):
+            plt.subplot(3, 2, 2*i+1)
+            plt.plot(clean_data[warmup:, i], label='clean')
+            plt.plot(noisy_data[warmup:, i], label='noisy')
+            plt.title(f"Dim {i+1} (Noisy vs Clean)")
+            plt.legend()
+            plt.subplot(3, 2, 2*i+2)
+            plt.plot(clean_data[warmup:, i], label='clean')
+            plt.plot(prediction[warmup:, i], label='prediction')
+            plt.title(f"Dim {i+1} (Prediction vs Clean)")
+            plt.legend()
+    # PSD 对比
+    if vis_psd:
+        plt.figure(figsize=(12, 6))
+        for i in range(3):
+            plt.subplot(3, 1, i+1)
+            plt.psd(clean_data[warmup:, i], NFFT=1024, label='clean')
+            plt.psd(noisy_data[warmup:, i], NFFT=1024, label='noisy')
+            plt.psd(prediction[warmup:, i], NFFT=1024, label='prediction')
+            plt.title(f"Dim {i+1} PSD Comparison")
+            plt.legend()
+        
+    return snr, rmse, psnr, corr_coef, prediction
+
+def run():
+    # 数据加载与预处理
+    clean_data, noisy_data = load_data(system='lorenz', noise='gaussian', 
+                                       intensity=0.5, init=[1, 1, 1], 
+                                       n_timesteps=40000, transient=10000, h=0.01)
+    # 分为训练集和测试集 按百分比
+    train_size = int(len(clean_data) * 0.8)
+    train_clean_data = clean_data[:train_size]
+    train_noisy_data = noisy_data[:train_size]
+    test_clean_data = clean_data[train_size:]
+    test_noisy_data = noisy_data[train_size:]
+    
+    # LV1
+    model_lv1, reservoir_node = build_esn_model(units=1000, lr=0.1, sr=0.9, reg=1e-5, output_dim=3)
+    model_lv1, lv1_train_pred = train_model(model_lv1, train_clean_data, train_noisy_data)
+    snr, rmse, psnr, corr_coef, lv1_pred = evaluate_results(model_lv1, test_clean_data, test_noisy_data)
+    print(f"SNR: {snr:.6f} RMSE: {rmse:.6f} PSNR: {psnr:.6f} CorrCoef: {corr_coef:.6f}")
+    
+    # LV2
+    model_lv2, _ = build_esn_model(units=1000, lr=0.2, sr=0.9, reg=1e-5, output_dim=3, reservoir_node=reservoir_node)
+    model_lv2, lv2_train_pred = train_model(model_lv2, train_clean_data, lv1_train_pred)
+    snr, rmse, psnr, corr_coef, lv2_pred = evaluate_results(model_lv2, test_clean_data, lv1_pred)
+    print(f"SNR: {snr:.6f} RMSE: {rmse:.6f} PSNR: {psnr:.6f} CorrCoef: {corr_coef:.6f}")
+    
+    # LV3
+    model_lv3, _ = build_esn_model(units=1000, lr=0.3, sr=0.9, reg=1e-5, output_dim=3, reservoir_node=reservoir_node)
+    model_lv3, lv3_train_pred = train_model(model_lv3, train_clean_data, lv2_train_pred)
+    snr, rmse, psnr, corr_coef, lv3_pred = evaluate_results(model_lv3, test_clean_data, lv2_pred)
+    print(f"SNR: {snr:.6f} RMSE: {rmse:.6f} PSNR: {psnr:.6f} CorrCoef: {corr_coef:.6f}")
+    
+    # LV4
+    model_lv4, _ = build_esn_model(units=1000, lr=0.4, sr=0.9, reg=1e-4, output_dim=3, reservoir_node=reservoir_node)
+    model_lv4, lv4_train_pred = train_model(model_lv4, train_clean_data, lv3_train_pred)
+    snr, rmse, psnr, corr_coef, lv4_pred = evaluate_results(model_lv4, test_clean_data, lv3_pred)
+    print(f"SNR: {snr:.6f} RMSE: {rmse:.6f} PSNR: {psnr:.6f} CorrCoef: {corr_coef:.6f}")
+    
+    # LV5
+    model_lv5, _ = build_esn_model(units=1000, lr=0.5, sr=0.9, reg=1e-3, output_dim=3, reservoir_node=reservoir_node)
+    model_lv5, lv5_train_pred = train_model(model_lv5, train_clean_data, lv4_train_pred)
+    snr, rmse, psnr, corr_coef, lv5_pred = evaluate_results(model_lv5, test_clean_data, lv4_pred)
+    print(f"SNR: {snr:.6f} RMSE: {rmse:.6f} PSNR: {psnr:.6f} CorrCoef: {corr_coef:.6f}")
+    
+    plt.show()
+    
 if __name__ == "__main__":
-    # 主实验流程
-    load_data()
-    build_esn_model()
-    train_model()
-    evaluate_results()
+    run()
\ No newline at end of file
diff --git a/exp4_noise.py b/exp4_noise.py
index 5ab9ef4..c349bf3 100644
--- a/exp4_noise.py
+++ b/exp4_noise.py
@@ -1,7 +1,7 @@
 """
 实验4: 不同噪声类型实验 (鲁棒性分析，可选但加分)
 目标:
-    评估 RC 降噪方法在不同噪声类型下的表现，包括高斯白噪声、色噪声 (1/f噪声)、脉冲噪声和混合噪声，
+    评估 RC 降噪方法在不同噪声类型下的表现，包括高斯白噪声、色噪声 (1/f噪声)、脉冲噪声和正弦噪声，
     分析不同噪声条件下的降噪效果及动力学特性恢复能力。
 实验设置:
     - 保持混沌系统、数据划分等设置不变，仅改变噪声类型。
diff --git a/tools.py b/tools.py
index e69de29..0783e99 100644
--- a/tools.py
+++ b/tools.py
@@ -0,0 +1,201 @@
+from reservoirpy import datasets
+import numpy as np
+
+def add_noise(data, noise_type='gaussian', intensity=0.1, **kwargs):
+    """
+    为输入数据添加噪声。
+
+    参数:
+        data: numpy 数组, 原始数据.
+        noise_type: str, 噪声类型，可选 'gaussian'（高斯白噪声）、'colored' 或 '1/f'（色噪声）、'impulse'（脉冲噪声）、'sine'（正弦噪声）。
+        intensity: float, 噪声强度.
+        kwargs: 针对某些噪声类型的额外参数，例如:
+                - 对于 'impulse': prob (脉冲发生概率, 默认 0.01)
+                - 对于 'sine'   : freq (正弦信号频率, 默认 5)
+    返回:
+        添加噪声后的数据.
+    """
+    noise_type = noise_type.lower()
+    
+    # 确保噪声与数据形状匹配，处理多维数据
+    if data.ndim > 1:
+        n_samples, n_dimensions = data.shape
+    else:
+        n_samples = len(data)
+        n_dimensions = 1
+    
+    if noise_type in ['gaussian', 'gaussian white']:
+        noise = intensity * np.random.randn(*data.shape)
+        return data + noise
+
+    elif noise_type in ['colored', '1/f']:
+        # 对于多维数据，为每个维度单独生成1/f噪声
+        if data.ndim > 1:
+            noise = np.zeros_like(data)
+            for dim in range(n_dimensions):
+                f = np.fft.fftfreq(n_samples)
+                f[0] = 1e-10
+                noise_complex = np.random.randn(n_samples) + 1j * np.random.randn(n_samples)
+                factor = intensity / np.sqrt(np.abs(f))
+                noise[:, dim] = np.fft.ifft(noise_complex * factor).real
+        else:
+            f = np.fft.fftfreq(n_samples)
+            f[0] = 1e-10
+            noise_complex = np.random.randn(n_samples) + 1j * np.random.randn(n_samples)
+            factor = intensity / np.sqrt(np.abs(f))
+            noise = np.fft.ifft(noise_complex * factor).real
+        return data + noise
+
+    elif noise_type == 'impulse':
+        noise = np.zeros_like(data)
+        prob = kwargs.get('prob', 0.01)
+        mask = np.random.rand(*data.shape) < prob
+        impulse = intensity * (2 * np.random.rand(*data.shape) - 1)
+        noise[mask] = impulse[mask]
+        return data + noise
+
+    elif noise_type == 'sine':
+        t = np.arange(n_samples)
+        freq = kwargs.get('freq', 5)
+        
+        # 对于多维数据，确保正弦波形状正确
+        if data.ndim > 1:
+            sine_wave = intensity * np.sin(2 * np.pi * freq * t / n_samples).reshape(-1, 1)
+            # 扩展到与数据相同的维度
+            sine_wave = np.tile(sine_wave, (1, n_dimensions))
+        else:
+            sine_wave = intensity * np.sin(2 * np.pi * freq * t / n_samples)
+        return data + sine_wave
+
+    else:
+        raise ValueError("未知的噪声类型。可选项：'gaussian', 'colored' (1/f), 'impulse', 'sine'")
+
+def load_data(system='lorenz', init='random', noise=None, intensity=0.1, h=0.01, n_timesteps=10000, transient=1000, normlization=True, **kwargs):
+    """
+    加载混沌系统数据.
+
+    参数:
+        system: str, 混沌系统类型, 可选 'lorenz', 'rossler', 'multiscroll', 'kuramoto_sivashinsky'。
+        init: 初始值. 如果为 'random' 则随机初始化，否则期望传入数组形式的初始值.
+        noise: None, str 或 str 列表, 指定要添加的噪声类型. 如果是列表，则混合各噪声 (取平均) 作为混合噪声.
+        -- 可选 'gaussian'（高斯白噪声）、'colored' 或 '1/f'（色噪声）、'impulse'（脉冲噪声）、'sine'（正弦噪声）
+        intensity: float, 噪声强度.
+        h: float, 时间步长 (TimeDelta).
+        n_timesteps: int, 时间步数.
+        transient: int, 需丢弃的时间步数.
+        normlization: bool, 是否对数据进行归一化处理.
+        kwargs: 其他系统参数.
+    返回:
+        (clean_data, noisy_data): 两个 numpy 数组, 分别为干净数据和添加噪声后的数据.
+        
+    各系统默认值:
+        - lorenz: rho=28, sigma=10, beta=8/3, h=0.03, x0=[1, 1, 1]
+        - rossler: a=0.2, b=0.2, c=5.7, h=0.1, x0=[-0.1, 0, 0.02]
+        - multiscroll: a=40, b=3, c=28, h=0.01, x0=[-0.1, 0.5, -0.6]
+        - kuramoto_sivashinsky: N=128, M=16, h=0.25, x0=None
+    """
+    system = system.lower()
+    if init == 'random':
+        if system in ['lorenz', 'rossler', 'multiscroll']:
+            # 默认3维初始值
+            state = np.random.rand(3)
+        elif system in ['kuramoto_sivashinsky']:
+            state = np.random.rand(1)
+        else:
+            raise ValueError("未知的混沌系统类型。")
+    else:
+        state = np.array(init, dtype=float)
+
+    if system == 'lorenz':
+        '''
+        (function) def lorenz(
+            n_timesteps: int,
+            rho: float = 28,
+            sigma: float = 10,
+            beta: float = 8 / 3,
+            x0: list | ndarray = [1, 1, 1],
+            h: float = 0.03,
+            **kwargs: Any
+        ) -> ndarray
+        '''
+        sigma = kwargs.get('sigma', 10.0)
+        rho   = kwargs.get('rho', 28.0)
+        beta  = kwargs.get('beta', 8/3)
+        clean_data = datasets.lorenz(n_timesteps=n_timesteps, h=h, sigma=sigma, rho=rho, beta=beta, x0=state)
+
+    elif system == 'rossler':
+        '''
+        (function) def rossler(
+            n_timesteps: int,
+            a: float = 0.2,
+            b: float = 0.2,
+            c: float = 5.7,
+            x0: list | ndarray = [-0.1, 0, 0.02],
+            h: float = 0.1,
+            **kwargs: Any
+        ) -> ndarray
+        '''
+        a = kwargs.get('a', 0.2)
+        b = kwargs.get('b', 0.2)
+        c = kwargs.get('c', 5.7)
+        clean_data = datasets.rossler(n_timesteps=n_timesteps, h=h, a=a, b=b, c=c, x0=state)
+
+    elif system == 'multiscroll':
+        '''
+        (function) def multiscroll(
+            n_timesteps: int,
+            a: float = 40,
+            b: float = 3,
+            c: float = 28,
+            x0: list | ndarray = [-0.1, 0.5, -0.6],
+            h: float = 0.01,
+            **kwargs: Any
+        ) -> ndarray
+        '''
+        a = kwargs.get('a', 40)
+        b = kwargs.get('b', 3)
+        c = kwargs.get('c', 28)
+        clean_data = datasets.multiscroll(n_timesteps=n_timesteps, h=h, x0=state, a=a, b=b, c=c)
+
+    elif system == 'kuramoto_sivashinsky':
+        '''
+        (function) def kuramoto_sivashinsky(
+            n_timesteps: int,
+            warmup: int = 0,
+            N: int = 128,
+            M: float = 16,
+            x0: list | ndarray = None,
+            h: float = 0.25
+        ) -> ndarray
+        '''
+        clean_data = datasets.kuramoto_sivashinsky(n_timesteps=n_timesteps, h=h, **kwargs)
+
+    else:
+        raise ValueError("未知的混沌系统类型。可选项: 'lorenz', 'rossler', 'multiscroll', 'kuramoto_sivashinsky'")
+
+    if normlization:
+        clean_data = (clean_data - clean_data.mean(axis=0)) / clean_data.std(axis=0) # Z-score 归一化
+    clean_data = clean_data[transient:]
+    
+    # 添加噪声
+    if noise is not None:
+        if isinstance(noise, list):
+            noise_sum = np.zeros_like(clean_data)
+            for nt in noise:
+                noise_sum += add_noise(np.zeros_like(clean_data), noise_type=nt, intensity=intensity, **kwargs)
+            mixed_noise = noise_sum / len(noise)
+            noisy_data = clean_data + mixed_noise
+        elif isinstance(noise, str):
+            noisy_data = add_noise(clean_data, noise_type=noise, intensity=intensity, **kwargs)
+        else:
+            raise ValueError("噪声参数 noise 必须为字符串或字符串列表。")
+    else:
+        noisy_data = clean_data.copy()
+
+    return clean_data, noisy_data
+
+if __name__ == "__main__":
+    # 测试数据加载和噪声添加
+    clean_data, noisy_data = load_data(system='lorenz', noise=['gaussian', 'colored'], intensity=0.1, n_timesteps=10000, transient=1000)
+    print("Clean Data Shape:", clean_data.shape)
+    print("Noisy Data Shape:", noisy_data.shape)
\ No newline at end of file
diff --git a/well_pattern_config.py b/well_pattern_config.py
new file mode 100644
index 0000000..e69de29