Power spectral density in SciPy - Mini Project: Build & Apply

Calculate Power Spectral Density using SciPy

📖 Scenario: You have recorded a simple signal from a sensor measuring vibrations. You want to analyze the signal to find out how its power is distributed across different frequencies. This helps in understanding the main frequency components of the signal.

🎯 Goal: Build a Python program that calculates the power spectral density (PSD) of a given signal using SciPy's signal.welch function.

📋 What You'll Learn

Create a signal as a list of numbers representing sensor data.

Set the sampling frequency of the signal.

Use SciPy's signal.welch function to calculate the PSD.

Print the frequency array and the corresponding PSD values.

💡 Why This Matters

🌍 Real World

Power spectral density analysis is used in engineering and science to find dominant frequencies in signals like vibrations, sound, or electrical signals.

💼 Career

Understanding PSD helps in roles like signal processing engineer, data analyst, and research scientist to analyze and interpret sensor data.

1

Create the signal data

Create a list called signal_data with these exact values: 0, 1, 0, -1, 0, 1, 0, -1 representing the sensor measurements.

SciPy

# Create the signal_data list with the given values
# Your code here

Need a hint?

Use square brackets to create a list and separate values with commas.

2

Set the sampling frequency

Create a variable called sampling_freq and set it to 4, representing 4 samples per second.

SciPy

signal_data = [0, 1, 0, -1, 0, 1, 0, -1]
# Set the sampling frequency to 4
# Your code here

Need a hint?

Assign the number 4 to the variable sampling_freq.

3

Calculate the power spectral density

Import signal from scipy. Then use signal.welch with signal_data and sampling_freq to calculate the frequency array freqs and power spectral density array psd.

SciPy

signal_data = [0, 1, 0, -1, 0, 1, 0, -1]
sampling_freq = 4
# Import signal from scipy and calculate freqs and psd using signal.welch
# Your code here

Need a hint?

Use from scipy import signal to import. Then call signal.welch(signal_data, fs=sampling_freq) and unpack the result into freqs and psd.

4

Print the frequency and PSD arrays

Print the variables freqs and psd on separate lines to display the frequency values and their corresponding power spectral density values.

SciPy

from scipy import signal

signal_data = [0, 1, 0, -1, 0, 1, 0, -1]
sampling_freq = 4

freqs, psd = signal.welch(signal_data, fs=sampling_freq)
# Print freqs and psd
# Your code here

Need a hint?

Use two print statements: one for freqs and one for psd.