diff --git a/s2generator/__init__.py b/s2generator/__init__.py
index 1323b70..26f5949 100644
--- a/s2generator/__init__.py
+++ b/s2generator/__init__.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 
-__version__ = "0.0.8"
+__version__ = "0.0.9"
 
 __all__ = [
     "Node",
diff --git a/s2generator/augmentation/__init__.py b/s2generator/augmentation/__init__.py
index 8538e6c..47df9db 100644
--- a/s2generator/augmentation/__init__.py
+++ b/s2generator/augmentation/__init__.py
@@ -9,7 +9,7 @@
 __all__ = [
     "amplitude_modulation",
     "censor_augmentation",
-    "empirical_model_modulation",
+    "empirical_mode_modulation",
     "frequency_perturbation",
     "spike_injection",
     "wiener_filter",
@@ -23,8 +23,8 @@
 # Import the censoring augmentation function
 from ._censor_augmentation import censor_augmentation
 
-# Import the empirical model modulation function
-from ._empirical_model_modulation import empirical_model_modulation
+# Import the empirical mode modulation function
+from ._empirical_mode_modulation import empirical_mode_modulation
 
 # Import the frequency perturbation function
 from ._frequency_perturbation import frequency_perturbation
diff --git a/s2generator/augmentation/_amplitude_modulation.py b/s2generator/augmentation/_amplitude_modulation.py
index e35142d..cf908da 100644
--- a/s2generator/augmentation/_amplitude_modulation.py
+++ b/s2generator/augmentation/_amplitude_modulation.py
@@ -100,4 +100,4 @@ def amplitude_modulation(
     # Apply the modulation trend to the original time series
     modulated_series = time_series * modulation_trend
 
-    return modulated_series, np.array(modulation_trend)
+    return modulated_series
diff --git a/s2generator/base.py b/s2generator/base.py
index 8c78568..69586b8 100644
--- a/s2generator/base.py
+++ b/s2generator/base.py
@@ -376,9 +376,9 @@ def val_diff(self, xs: ndarray, deterministic: Optional[bool] = True) -> ndarray
         if xs.ndim > 1:
             # For multivariate case, keep other dimensions constant
             x_uniform_input = np.tile(np.mean(xs, axis=0), (n_integration_points, 1))
-            x_uniform_input[
-                :, 0
-            ] = x_uniform  # Replace first dimension with uniform grid
+            x_uniform_input[:, 0] = (
+                x_uniform  # Replace first dimension with uniform grid
+            )
         else:
             x_uniform_input = x_uniform.reshape(-1, 1)  # Ensure 2D array for val method
 
diff --git a/setup.py b/setup.py
index 4de574f..075db71 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 setuptools.setup(
     name="S2Generator",
     packages=setuptools.find_packages(),
-    version="0.0.8",
+    version="0.0.9",
     description="A series-symbol (S2) dual-modality data generation mechanism, enabling the unrestricted creation of high-quality time series data paired with corresponding symbolic representations.",  # 包的简短描述
     url="https://github.com/wwhenxuan/S2Generator",
     author="whenxuan, johnfan12, changewam",
diff --git a/tests/test_augmentation.py b/tests/test_augmentation.py
index 158f19d..55d0048 100644
--- a/tests/test_augmentation.py
+++ b/tests/test_augmentation.py
@@ -12,7 +12,11 @@
 from s2generator.augmentation import (
     amplitude_modulation,
     censor_augmentation,
+    empirical_mode_modulation,
     frequency_perturbation,
+    wiener_filter,
+    add_linear_trend,
+    time_series_mixup,
 )
 
 from s2generator.augmentation._frequency_perturbation import sample_random_perturbation
@@ -60,7 +64,11 @@ def test_frequency_perturbation(self) -> None:
         r = 0.3
 
         perturbed_series = frequency_perturbation(
-            series=series, min_alpha=min_alpha, max_alpha=max_alpha, r=r, rng=self.rng
+            time_series=series,
+            min_alpha=min_alpha,
+            max_alpha=max_alpha,
+            r=r,
+            rng=self.rng,
         )
 
         # Check that the output has the same length as the input
@@ -113,13 +121,12 @@ def test_amplitude_modulation(self) -> None:
         t = np.linspace(0, 1, 100)
         series = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.normal(size=100)
 
-        min_modulation = 0.5
-        max_modulation = 1.5
+        amplitude_mean, amplitude_variation = 1.0, 1.0
 
         modulated_series = amplitude_modulation(
             time_series=series.copy(),
-            min_modulation=min_modulation,
-            max_modulation=max_modulation,
+            amplitude_mean=amplitude_mean,
+            amplitude_variation=amplitude_variation,
             rng=self.rng,
         )