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SciPy Interpolation

  • Last Updated : 21 Apr, 2021

In this article, we will learn Interpolation using the SciPy module in Python. First, we will discuss interpolation and its types with implementation.

Interpolation and Its Types

Interpolation is a technique of constructing data points between given data points. The scipy.interpolate is a module in Python SciPy consisting of classes, spline functions, and univariate and multivariate interpolation classes. Interpolation is done in many ways some of them are :

  • 1-D Interpolation
  • Spline Interpolation
  • Univariate Spline Interpolation
  • RBF Interpolation

Let’s discuss all the methods one by one and visualize the results.

1-D Interpolation

To create a function based on fixed data points, scipy.interpolate.interp1d is used. It takes data points x and y and returns a function that can be called with new x and returns the corresponding y point.

Syntax: scipy.interpolate.interp1d(x , y , kind , axis , copy , bounds_error , fill_value , assume_sorted)


# Import the required Python libraries
import matplotlib.pyplot as plt
from scipy import interpolate
import numpy as np
# Initialize input values x and y
x = np.arange(0, 10)
y = x**2
# Interpolation
temp = interpolate.interp1d(x, y)
xnew = np.arange(0, 9, 0.2)
ynew = temp(xnew)
plt.title("1-D Interpolation")
plt.plot(x, y, '*', xnew, ynew, '-', color="green")



Spline Interpolation

In spline interpolation, a spline representation of the curve is computed, and then the spline is computed at the desired points. The function splrep is used to find the spline representation of a curve in a two-dimensional plane.

  • To find the B-spline representation of a 1-D curve, scipy.interpolate.splrep is used.

Syntax: scipy.interpolate.splrep(x, y, w, xb, xe, k, task, s, t, full_output, per, quiet)

  • To compute a B-spline or its derivatives, scipy.interpolate.splev is used.

Syntax: scipy.interpolate.splev(x , tck , der , ext)


# Import the required Python libraries
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
# Initialize the input values
x = np.arange(0, 10)
y = np.cos(x**3)
# Interpolation
# To find the spline representation of a 
# curve in a 2-D plane using the function 
# splrep
temp = interpolate.splrep(x, y, s=0)
xnew = np.arange(0, np.pi**2, np.pi/100)
ynew = interpolate.splev(xnew, temp, der=0)
plt.plot(x, y, '*', xnew, ynew, xnew, np.cos(xnew),
         x, y, 'b', color="green")
plt.legend(['Linear', 'Cubic Spline', 'True'])
plt.axis([-0.1, 6.5, -1.1, 1.1])
plt.title('Cubic-spline Interpolation in Python')


Univariate Spline

It is a 1-D smoothing spline that fits a given group of data points. The scipy.interpolate.UnivariateSpline is used to fit a spline y = spl(x) of degree k to the provided x, y data. s specifies the number of knots by specifying a smoothing condition. The scipy.interpolate.UnivariateSpline. set_smoothing_factor: Spline computation with the given smoothing factor s and with the knots found at the last call.

Syntax: scipy.interpolate.UnivariateSpline( x, y, w, bbox, k, s, ext)


# Import the required libraries
import matplotlib.pyplot as plt
from scipy.interpolate import UnivariateSpline
x = np.linspace(-3, 3, 50)
y = np.exp(-x**2) + 0.1 * np.random.randn(50)
plt.title("Univariate Spline")
plt.plot(x, y, 'g.', ms=8)
# Using the default values for the 
# smoothing parameter
spl = UnivariateSpline(x, y)
xs = np.linspace(-3, 3, 1000)
plt.plot(xs, spl(xs), 'green', lw=3)
# Manually change the amount of smoothing
plt.plot(xs, spl(xs), color='black', lw=3)


Radial basis function for Interpolation

The scipy.interpolate.Rbf is used for interpolating scattered data in n-dimensions. The radial basis function is defined as corresponding to a fixed reference data point. The scipy.interpolate.Rbf is a class for radial basis function interpolation of functions from N-D scattered data to an M-D domain.

Syntax: scipy.interpolate.Rbf(*args)


# Import the required libraries
import numpy as np
from scipy.interpolate import Rbf
import matplotlib.pyplot as plt
# setup the data values
x = np.linspace(0, 10, 9)
y = np.cos(x/2)
xi = np.linspace(0, 10, 110)
# Interpolation using RBF
rbf = Rbf(x, y)
fi = rbf(xi)
plt.subplot(2, 1, 2)
plt.plot(x, y, '*', color="green")
plt.plot(xi, fi, 'green')
plt.plot(xi, np.sin(xi), 'black')
plt.title('Radial basis function Interpolation')


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