The interp1d() function of scipy.interpolate package is used to interpolate a 1-D function. It takes arrays of values such as x and y to approximate some function y = f(x) and then uses interpolation to find the value of new points.
Syntax
scipy.interpolate.interp1d(x, y)
where x is a 1-D array of real values and y is an N-D array of real values. The length of y along the interpolation axis must be equal to the length of x.
Example 1
Let us consider the following example −
# Import the required libraries import matplotlib.pyplot as plt import numpy as np from scipy import interpolate # Set the figure size plt.rcParams["figure.figsize"]=[7.00, 3.50] plt.rcParams["figure.autolayout"]=True # Define the values x = np.arange(0, 10) y = np.exp(-x/5.0) # Input Data plt.subplot(1,2,1) plt.title("Input X and Y") plt.plot(x,y) # Interpolated Data plt.subplot(1,2,2) plt.title("Interpolated") f = interpolate.interp1d(x, y) x_new = np.arange(0, 7, 0.7) y_new = f(x_new) plt.plot(x_new, y_new, 's') plt.show()
Output
The above program will generate the following output −
Example 2
Let us take another example −
# Import the required libraries import matplotlib.pyplot as plt import numpy as np from scipy import interpolate # Set the figure size plt.rcParams["figure.figsize"]=[7.00, 3.50] plt.rcParams["figure.autolayout"]=True # Define the values x = np.arange(0, 10) y = np.exp(-x **2/9.0) # interpolate function f = interpolate.interp1d(x, y) xnew = np.arange(0, 9, 1.2) plt.plot(x, y, 'o', xnew) plt.show()
Output
The above program will generate the following output −