feat(features): integrate time-domain feature extraction into data pipeline

- Implement FeatureExtractor class in time_domain_features.py for calculating statistical features from dataset columns.
- Create build_features.py script to automate feature extraction from processed data and save results in a structured format.
- Adjust build_features.py to read processed data, utilize FeatureExtractor, and save feature matrix.

This update supports enhanced analysis capabilities within the thesis-project structure, allowing for more sophisticated data processing and model training stages.

Closes #1

code/src/features/time_domain_features.py

@@ -3,14 +3,16 @@ import pandas as pd
 from scipy.stats import kurtosis, skew
 
 class FeatureExtractor:
-    def __init__(self, file_path):
-        # Read data from CSV file
-        self.data = pd.read_csv(file_path)
-        # Assuming the data to analyze is in the first column
-        self.x = self.data.iloc[:, 0].values
+    # integrates the feature extraction into your project's existing data processing pipeline
+    def __init__(self, data):
+        # Assuming data is a numpy array
+        self.x = data
+        # Calculate features
+        self.features = self.calculate_features()
 
         # Calculate all features
-        self.features = {
+    def calculate_features(self):
+        features = {
             'Mean': np.mean(self.x),
             'Max': np.max(self.x),
             'Peak (Pm)': np.max(np.abs(self.x)),
@@ -26,6 +28,7 @@ class FeatureExtractor:
             'Kurtosis': kurtosis(self.x, fisher=False),
             'Skewness': skew(self.x, bias=False)
         }
+        return features
 
     def __repr__(self):
         result = "Feature Extraction Results:\n"