numpy.interp() function returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (xp, fp), evaluated at x.
Syntax : numpy.interp(x, xp, fp, left = None, right = None, period = None)
Parameters :
x : [array_like] The x-coordinates at which to evaluate the interpolated values.
xp: [1-D sequence of floats] The x-coordinates of the data points, must be increasing if the argument period is not specified. Otherwise, xp is internally sorted after normalizing the periodic boundaries with xp = xp % period.
fp : [1-D sequence of float or complex] The y-coordinates of the data points, same length as xp.
left : [optional float or complex corresponding to fp] Value to return for x < xp[0], default is fp[0].
right : [optional float or complex corresponding to fp] Value to return for x > xp[-1], default is fp[-1].
period : [None or float, optional] A period for the x-coordinates. This parameter allows the proper interpolation of angular x-coordinates. Parameters left and right are ignored if the period is specified.Return : [float or complex or ndarray] The interpolated values, same shape as x.
Code #1 :
# Python program explaining# numpy.interp() function # importing numpy as geek import numpy as geek x = 3.6xp = [2, 4, 6]fp = [1, 3, 5] gfg = geek.interp(x, xp, fp) print (gfg) |
Output :
2.6
Code #2 :
# Python program explaining# numpy.interp() function # importing numpy as geek import numpy as geek x = [0, 1, 2.5, 2.72, 3.14]xp = [2, 4, 6]fp = [1, 3, 5] gfg = geek.interp(x, xp, fp) print (gfg) |
Output :
[1. 1. 1.5 1.72 2.14]
