Summary

DescriptionShepard interpolation 1 dimension.png	English: This is a result from the shepard method applied to a 1 dimension function.
Date	10 May 2012
Source	At IST (Lisbon, PT) with ubuntu+python+numpy+matplotlib   This plot was created with Matplotlib.
Author	Fmafunenga

Licensing

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Source code

The python code (numpy archive copy at the Wayback Machine, matplotlib) used to create this image:

import numpy as np
import matplotlib.pyplot as plt

def weight( pointA, pointB ):
	p = 2.0 #this is the power parameter
	rst = ( 1.0/( abs(pointA-pointB)**p ) )
	return rst

def idw( xi, ui ):
	xidw = np.arange(-4.0, 9.0, 0.01)
	uidw = np.arange(-4.0, 9.0, 0.01)
	for idx in range(len(xidw)):
		denum = 0.0 ; num = 0.0
		for i in range(len(xi)):
			w      = weight( xidw[idx], xi[i] )
			denum += w
			num   += w*ui[i]
		uidw[idx] = num / denum
	return xidw, uidw

if __name__ == "__main__":
	xi = [0.0, 1.0, 4.0, 5.0]
	yi = [1.0, 2.0, 2.0, 1.0]
	xidwr, yidwr = idw( xi, yi )
	
	plt.plot( xi, yi, "ok",ms=10, lw=2, label="Samples" )
	plt.plot( xidwr, yidwr, "-", label="IDW" )
	plt.title( r"Shepard's interpolation in 1 dimension: $p=2$" )
	plt.xlim((-4.1,9.1)) ; plt.xlabel(r"X")
	plt.ylim((0.9,2.1)) ; plt.ylabel(r"U")
	plt.legend() ; plt.grid() ; plt.show()