Convolution (convolve) in SciPy - Mini Project: Build & Apply

Convolution with scipy.convolve

📖 Scenario: Imagine you are analyzing a simple signal from a sensor that measures temperature changes over time. You want to smooth the signal to reduce noise and see the overall trend more clearly.

🎯 Goal: You will create a signal and a smoothing filter, then use scipy.signal.convolve to smooth the signal. Finally, you will display the smoothed signal.

📋 What You'll Learn

Create a 1D list called signal with exact values representing temperature readings.

Create a 1D list called filter_kernel representing a smoothing filter.

Use scipy.signal.convolve with mode='same' to smooth the signal.

Print the resulting smoothed signal as a list.

💡 Why This Matters

🌍 Real World

Smoothing sensor data is common in weather monitoring, health devices, and audio processing to reduce noise and get clearer signals.

💼 Career

Understanding convolution is important for data scientists working with time series data, signal processing, and machine learning feature engineering.

1

Create the signal data

Create a list called signal with these exact values: [2, 4, 6, 8, 6, 4, 2] representing temperature readings over time.

SciPy

# Create the signal list with temperature readings
# Your code here

Need a hint?

Use a Python list with the exact numbers given.

2

Create the smoothing filter

Create a list called filter_kernel with these exact values: [0.25, 0.5, 0.25] to act as a smoothing filter.

SciPy

signal = [2, 4, 6, 8, 6, 4, 2]
# Create the smoothing filter list
# Your code here

Need a hint?

The filter weights should add up to 1 to preserve the signal scale.

3

Apply convolution to smooth the signal

Import convolve from scipy.signal and create a variable called smoothed_signal by convolving signal with filter_kernel using mode='same'.

SciPy

signal = [2, 4, 6, 8, 6, 4, 2]
filter_kernel = [0.25, 0.5, 0.25]
# Import convolve and apply it to smooth the signal
# Your code here

Need a hint?

Use mode='same' to keep the output length equal to the input signal length.

4

Print the smoothed signal

Print the variable smoothed_signal to display the smoothed temperature readings.

SciPy

from scipy.signal import convolve

signal = [2, 4, 6, 8, 6, 4, 2]
filter_kernel = [0.25, 0.5, 0.25]

smoothed_signal = convolve(signal, filter_kernel, mode='same')
# Print the smoothed signal
# Your code here

Need a hint?

Convert the result to a list before printing to see the values clearly.