import numpy as np
import pandas as pd
from scipy.stats import kurtosis, skew

class FeatureExtractor:
    # integrates the feature extraction into the 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
    def calculate_features(self):
        features = {
            'Mean': np.mean(self.x),
            'Max': np.max(self.x),
            'Peak (Pm)': np.max(np.abs(self.x)),
            'Peak-to-Peak (Pk)': np.max(self.x) - np.min(self.x),
            'RMS': np.sqrt(np.mean(self.x**2)),
            'Variance': np.var(self.x, ddof=0),
            'Standard Deviation': np.std(self.x, ddof=1),
            'Power': np.mean(self.x**2),
            'Crest Factor': np.max(np.abs(self.x)) / np.sqrt(np.mean(self.x**2)),
            'Form Factor': np.sqrt(np.mean(self.x**2)) / np.mean(self.x),
            'Pulse Indicator': np.max(np.abs(self.x)) / np.mean(self.x),
            'Margin': np.max(np.abs(self.x)) / (np.mean(np.sqrt(np.abs(self.x)))),
            'Kurtosis': kurtosis(self.x, fisher=False),
            'Skewness': skew(self.x, bias=False)
        }
        return features

    def __repr__(self):
        result = "Feature Extraction Results:\n"
        for feature, value in self.features.items():
            result += f"{feature}: {value:.4f}\n"
        return result

# Usage
# Assume you have a CSV file with numerical data in the first column
# Create an instance of the class and pass the path to your CSV file
# For example:
# extractor = FeatureExtractor('path_to_your_data.csv')
# When you call the variable in a notebook or in the interpreter, it will print the results
# print(extractor)