There are several general facilities available in SciPy for interpolation and smoothing for data in 1, 2, and higher dimensions. The choice of a specific interpolation routine depends on the data: whether it is one-dimensional, is given on a structured grid, or is unstructured.

The interp1d class in scipy.interpolate is a convenient method to create a function based on fixed data points, which can be evaluated anywhere within the domain defined by the given data using linear interpolation. An instance of this class is created by passing the 1 …

This example shows how to interpolate scattered 2-D data: >>> import numpy as np >>> from scipy.interpolate import RBFInterpolator >>> import matplotlib.pyplot as plt

To this end, scipy.interpolate allows constructing smoothing splines which balance how close the resulting curve, \(g(x)\), is to the data, and the smoothness of \(g(x)\). Mathematically, the task is to solve a penalized least-squares problem, where the penalty controls the smoothness of \(g(x)\) .

The interp1d class in scipy.interpolate is a convenient method to create a function based on fixed data points, which can be evaluated anywhere within the domain defined by the given data using linear interpolation. An instance of this class is created by passing the 1 …

scipy.interpolate. interpn (points, values, xi, method = 'linear', bounds_error = True, fill_value = nan) [source] # Multidimensional interpolation on regular or rectilinear grids. Strictly speaking, not all regular grids are supported - this function works on rectilinear grids, that is, a rectangular grid with even or uneven spacing.

Interpolation (scipy.interpolate)# Sub-package for objects used in interpolation. As listed below, this sub-package contains spline functions and classes, 1-D and multidimensional (univariate and multivariate) interpolation classes, Lagrange and Taylor polynomial interpolators, and wrappers for FITPACK and DFITPACK functions.

This class returns a function whose call method uses interpolation to find the value of new points. Parameters: x (npoints, ) array_like. A 1-D array of real values. y (…, npoints, …) array_like. A N-D array of real values. The length of y along the interpolation axis must be equal to the length of x. Use the axis parameter to select ...

scipy.interpolate. griddata (points, values, xi, method = 'linear', fill_value = nan, rescale = False) [source] # Interpolate unstructured D-D data. Parameters: points 2-D ndarray of floats with shape (n, D), or length D tuple of 1-D ndarrays with shape (n,). Data point coordinates. values ndarray of float or complex, shape (n,) Data values.

Different interpolators use different sets of keyword arguments to control the behavior outside of the data domain: some use extrapolate=True/False/None, some allow the fill_value keyword. Refer to the API documentation for details for each specific interpolation routine.