Interpolation is the process of estimating values between known data points. This is frequently used in mechanics when resampling data or looking up values from tables.
NumPy’s interp function performs one-dimensional linear interpolation.
import numpy as np
import matplotlib.pyplot as plt
# Known data points
x = np.array([0, 1, 2, 3, 4, 5])
y = np.array([0, 0.8, 0.9, 0.1, -0.8, -1.0])
# Points where we want to interpolate
x_new = np.linspace(0, 5, 50)
# Linear interpolation
y_linear = np.interp(x_new, x, y)
plt.plot(x, y, 'o', label='Data')
plt.plot(x_new, y_linear, '-', label='Linear Interp')
plt.legend()
plt.show()
For more advanced interpolation, such as cubic splines, use scipy.interpolate.
from scipy.interpolate import interp1d
# Create an interpolation function (Cubic spline)
f_cubic = interp1d(x, y, kind='cubic')
# Evaluate at new points
y_cubic = f_cubic(x_new)
plt.plot(x, y, 'o', label='Data')
plt.plot(x_new, y_cubic, '--', label='Cubic Spline')
plt.legend()
plt.show()