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 …

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.

class scipy.interpolate. CubicSpline (x, y, axis = 0, bc_type = 'not-a-knot', extrapolate = None) [source] # Cubic spline data interpolator. Interpolate data with a piecewise cubic polynomial which is twice continuously differentiable . The result is represented as a PPoly instance with breakpoints matching the given data. Parameters: x array ...

class scipy.interpolate. interp1d (x, y, kind = 'linear', axis =-1, copy = True, bounds_error = None, fill_value = nan, assume_sorted = False) [source] # Interpolate a 1-D function.

class scipy.interpolate. interp2d (x, y, z, kind = 'linear', copy = True, bounds_error = False, fill_value = None) [source] ¶ Interpolate over a 2-D grid. x , y and z are arrays of values used to approximate some function f: z = f(x, y) which returns a scalar value z .

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 \(g(x)\) .

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

In fact, we will use the inverse interpolation: we interpolate the values of \(x\) versus \(у\). This way, solving the original equation becomes simply an evaluation of the interpolated function at zero \(y\) argument.

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 …