"""
Utility Functions Module for Physiological Signal Processing
This module provides comprehensive capabilities for physiological
signal processing including ECG, PPG, EEG, and other vital signs.
Author: vitalDSP Team
Date: 2025-01-27
Version: 1.0.0
Key Features:
- Multiple processing methods and functions
- NumPy integration for numerical computations
Examples:
---------
Basic usage:
>>> import numpy as np
>>> from vitalDSP.signal_processing.normalization import Normalization
>>> signal = np.random.randn(1000)
>>> processor = Normalization(signal)
>>> result = processor.process()
>>> print(f'Processing result: {result}')
"""
import numpy as np
[docs]
def z_score_normalization(signal):
"""
Normalize the signal using Z-score normalization.
Parameters
----------
signal : numpy.ndarray
The input signal to be normalized.
Returns
-------
normalized_signal : numpy.ndarray
The Z-score normalized signal.
Examples
--------
>>> signal = np.array([1, 2, 3, 4, 5])
>>> normalized_signal = z_score_normalization(signal)
>>> print(normalized_signal)
[-1.26491106 -0.63245553 0. 0.63245553 1.26491106]
"""
mean = np.mean(signal)
std = np.std(signal)
if std == 0:
# Constant signal: z-score undefined — return zero-centered signal
return signal - mean
normalized_signal = (signal - mean) / std
return normalized_signal
[docs]
def min_max_normalization(signal, min_value=0, max_value=1):
"""
Normalize the signal using Min-Max scaling.
Parameters
----------
signal : numpy.ndarray
The input signal to be normalized.
min_value : float, optional (default=0)
The minimum value of the desired range.
max_value : float, optional (default=1)
The maximum value of the desired range.
Returns
-------
normalized_signal : numpy.ndarray
The Min-Max normalized signal.
Examples
--------
>>> signal = np.array([1, 2, 3, 4, 5])
>>> normalized_signal = min_max_normalization(signal, min_value=-1, max_value=1)
>>> print(normalized_signal)
[-1. -0.5 0. 0.5 1. ]
"""
min_signal = np.min(signal)
max_signal = np.max(signal)
# Handle the case where min and max are the same to avoid division by zero
if min_signal == max_signal:
return np.full_like(signal, (min_value + max_value) / 2.0, dtype=float)
normalized_signal = (signal - min_signal) / (max_signal - min_signal)
normalized_signal = normalized_signal * (max_value - min_value) + min_value
return normalized_signal