Curve fitting (curve_fit) in SciPy - Mini Project: Build & Apply

Curve fitting with scipy curve_fit

📖 Scenario: You have collected some data points from an experiment measuring how a plant grows over days. You want to find a simple mathematical formula that fits this data well. This helps you understand the growth pattern and predict future growth.

🎯 Goal: Build a Python program that uses scipy.optimize.curve_fit to find the best fitting curve for the plant growth data. You will define a function for the curve, fit it to the data, and print the fitted parameters.

📋 What You'll Learn

Create arrays for days and plant heights

Define a simple linear function for growth

Use curve_fit to find the best parameters

Print the fitted parameters

💡 Why This Matters

🌍 Real World

Scientists and engineers often collect data and want to find a simple formula that explains it. Curve fitting helps find that formula.

💼 Career

Data scientists and analysts use curve fitting to model trends and make predictions in fields like biology, finance, and engineering.

1

Create data arrays for days and plant heights

Create two variables: days and heights. Set days to the list [1, 2, 3, 4, 5, 6, 7] and heights to the list [2.3, 2.9, 3.8, 4.5, 5.1, 5.8, 6.3].

SciPy

# Create the data lists for days and heights
# Your code here

Need a hint?

Use Python lists to store the numbers exactly as given.

2

Define a linear function for plant growth

Define a function called linear_growth that takes x, a, and b as inputs and returns a * x + b. This function models a straight line.

SciPy

days = [1, 2, 3, 4, 5, 6, 7]
heights = [2.3, 2.9, 3.8, 4.5, 5.1, 5.8, 6.3]

# Define the linear_growth function
# Your code here

Need a hint?

Use def to create the function and return the formula a * x + b.

3

Use curve_fit to find the best fitting parameters

Import curve_fit from scipy.optimize. Use curve_fit with linear_growth, days, and heights to get the fitted parameters. Save the first output as params.

SciPy

days = [1, 2, 3, 4, 5, 6, 7]
heights = [2.3, 2.9, 3.8, 4.5, 5.1, 5.8, 6.3]

def linear_growth(x, a, b):
    return a * x + b

# Import curve_fit and find params
# Your code here

Need a hint?

Use from scipy.optimize import curve_fit and call curve_fit with the function and data.

4

Print the fitted parameters

Write a print statement to display the text "Fitted parameters:" followed by the params variable.

SciPy

from scipy.optimize import curve_fit

days = [1, 2, 3, 4, 5, 6, 7]
heights = [2.3, 2.9, 3.8, 4.5, 5.1, 5.8, 6.3]

def linear_growth(x, a, b):
    return a * x + b

params, _ = curve_fit(linear_growth, days, heights)

# Print the fitted parameters
# Your code here

Need a hint?

Use print("Fitted parameters:", params) to show the result.