In signal processing, signal recovery refers to the process of reconstructing or restoring a degraded signal to its original form. The scipy library in Python provides various functions and tools for signal recovery, including filtering, interpolation, and noise removal. In this blog post, we will explore some of the techniques and methods available in scipy for signal recovery.

Filtering

Filtering is a commonly used technique in signal processing to remove unwanted noise or artifacts from a signal. scipy provides several filter functions that can be used for signal recovery. One of the commonly used filters is the Butterworth filter.

import scipy.signal as signal

# Create a Butterworth filter
order = 4  # Filter order
cutoff = 0.5  # Cutoff frequency
b, a = signal.butter(order, cutoff, 'low', analog=False)

# Apply the filter to a signal
filtered_signal = signal.lfilter(b, a, original_signal)

In the code snippet above, we create a 4th order low-pass Butterworth filter with a cutoff frequency of 0.5. We then apply this filter to the original_signal using the lfilter function.

Interpolation

Interpolation is used to estimate the values of a signal at unobserved points based on the observed data. scipy provides various interpolation techniques that can be useful for signal recovery. One such technique is spline interpolation.

from scipy.interpolate import UnivariateSpline

# Create a spline object
spline = UnivariateSpline(x, y)

# Interpolate the signal at specific points
interp_signal = spline(new_x)

In the code snippet above, we create a spline object using the observed data (x, y). We can then use this spline object to interpolate the signal at new points defined by new_x.

Noise Removal

Noise removal is crucial in signal recovery to eliminate unwanted noise components from the signal. scipy provides various functions for denoising signals, such as the wavelet denoising technique.

from scipy import ndimage

# Apply wavelet denoising
denoised_signal = ndimage.median_filter(noisy_signal, size=3)

In the code snippet above, we use the median_filter function from scipy.ndimage to apply wavelet denoising to the noisy_signal. The size parameter determines the size of the filter window.

Conclusion

The scipy library in Python offers a range of functions and tools for signal recovery. In this blog post, we explored some of the techniques and methods available for filtering, interpolation, and noise removal. By using these techniques, you can effectively recover and restore degraded signals in your Python projects.