Spline interpolation in SciPy - Mini Project: Build & Apply

Spline interpolation

📖 Scenario: You have some data points from a sensor measuring temperature at different times. The data is not smooth, and you want to estimate the temperature at times between the measurements smoothly.

🎯 Goal: You will create a spline interpolation to estimate temperature values at new time points smoothly using scipy.

📋 What You'll Learn

Create a dictionary with exact time and temperature data points

Create a variable for new time points to estimate temperature

Use scipy.interpolate.make_interp_spline to create a spline interpolation

Calculate interpolated temperature values at new time points

Print the interpolated temperature values

💡 Why This Matters

🌍 Real World

Spline interpolation is used in sensor data smoothing, animation paths, and estimating missing data points in many fields like weather forecasting and engineering.

💼 Career

Data scientists and engineers use spline interpolation to create smooth models from discrete data, improving predictions and visualizations.

1

Create the original data points

Create a dictionary called data_points with these exact entries: 1: 15.0, 2: 18.5, 3: 21.0, 4: 19.5, 5: 22.0 representing time in hours and temperature in Celsius.

SciPy

# Create the dictionary data_points with exact time and temperature values
# Your code here

Need a hint?

Use curly braces to create a dictionary with keys as time and values as temperature.

2

Create new time points for interpolation

Create a list called new_times with these exact values: [1.5, 2.5, 3.5, 4.5] representing the times where you want to estimate temperature.

SciPy

data_points = {1: 15.0, 2: 18.5, 3: 21.0, 4: 19.5, 5: 22.0}
# Create the list new_times with exact values
# Your code here

Need a hint?

Use square brackets to create a list with the exact values.

3

Create spline interpolation and calculate new values

Import make_interp_spline from scipy.interpolate. Create variables times and temps from data_points keys and values. Use make_interp_spline(times, temps) to create a spline called spline. Calculate interpolated temperatures at new_times and store in interpolated_temps.

SciPy

data_points = {1: 15.0, 2: 18.5, 3: 21.0, 4: 19.5, 5: 22.0}
new_times = [1.5, 2.5, 3.5, 4.5]
# Import make_interp_spline and create spline interpolation
# Your code here

Need a hint?

Use list() to get keys and values from the dictionary. Then create spline and call it with new times.

4

Print the interpolated temperature values

Write a print statement to display the variable interpolated_temps.

SciPy

from scipy.interpolate import make_interp_spline

data_points = {1: 15.0, 2: 18.5, 3: 21.0, 4: 19.5, 5: 22.0}
new_times = [1.5, 2.5, 3.5, 4.5]

times = list(data_points.keys())
temps = list(data_points.values())

spline = make_interp_spline(times, temps)
interpolated_temps = spline(new_times)
# Print the interpolated_temps variable
# Your code here

Need a hint?

Use print(interpolated_temps) to show the result.