Interpolation Methods
Estimate values between known data points.
SciPy Interpolation
1from scipy import interpolate
2import numpy as np
3
4x = np.array([0, 1, 2, 3, 4])
5y = np.array([0, 1, 4, 9, 16])
6
7# Linear
8f_linear = interpolate.interp1d(x, y, kind='linear')
9
10# Cubic spline
11f_cubic = interpolate.interp1d(x, y, kind='cubic')
12
13# Evaluate
14x_new = np.linspace(0, 4, 100)
15y_new = f_cubic(x_new)
2D Interpolation
1# 2D regular grid
2f = interpolate.interp2d(x, y, z, kind='cubic')
3z_new = f(x_new, y_new)
4
5# 2D scattered data
6f = interpolate.Rbf(x, y, z)
7z_new = f(x_new, y_new)
Further Reading
Related Snippets
- Numerical Differentiation
Finite differences and automatic differentiation - Numerical Integration
Trapezoidal rule, Simpson's rule, Gaussian quadrature - Optimization Methods
Gradient descent, Newton's method, BFGS - Regularization Techniques
L1, L2, Tikhonov, and elastic net regularization - Root Finding Methods
Newton's method, bisection, and secant method - Solving Linear Systems
LU decomposition and iterative methods