Interpolate data points using NumPy and SciPy's Interpolate module

Last update on December 21 2024 07:36:59 (UTC/GMT +8 hours)

NumPy: Integration with SciPy Exercise-4 with Solution

Write a NumPy program to generate a set of data points using NumPy and perform interpolation using SciPy's interpolate module.

Sample Solution:

Python Code:

# Import necessary libraries
import numpy as np
from scipy import interpolate
import matplotlib.pyplot as plt

# Generate a set of data points using NumPy
x = np.linspace(0, 10, num=11, endpoint=True)
y = np.sin(x)

# Perform interpolation using SciPy's interpolate module
f = interpolate.interp1d(x, y, kind='cubic')

# Generate new x values for interpolation
x_new = np.linspace(0, 10, num=100, endpoint=True)
y_new = f(x_new)

# Plot the original data points and the interpolated data
plt.plot(x, y, 'o', label='Original data points')
plt.plot(x_new, y_new, '-', label='Interpolated data')
plt.legend()
plt.show()

Output:

Explanation:

• Import necessary libraries:
• Import NumPy, SciPy's interpolate module, and Matplotlib for plotting.
• Generate a set of data points using NumPy:
• Create x values evenly spaced between 0 and 10, and corresponding y values using the sine function.
• Perform interpolation using SciPy's interpolate module:
• Use SciPy's interp1d function with cubic interpolation.
• Generate new x values for interpolation:
• Create a finer set of x values for smoother interpolation.
• Plot the original data points and the interpolated data:
• Use Matplotlib to visualize the original and interpolated data.

Python-Numpy Code Editor: