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bundles / numpy latest / numpy / interp

_ArrayFunctionDispatcher

numpy:interp

source: /dev/numpy/build-install/usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py :1538

Signature

def   interp ( x ,  xp ,  fp ,  left = None ,  right = None ,  period = None )

Summary

One-dimensional linear interpolation for monotonically increasing sample points.

Extended Summary

Returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (xp, fp), evaluated at x.

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 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 period is specified.

Returns

y : float or complex (corresponding to fp) or ndarray

The interpolated values, same shape as x.

Raises

: ValueError

If xp and fp have different length If xp or fp are not 1-D sequences If period == 0

Warnings

The x-coordinate sequence is expected to be increasing, but this is not explicitly enforced. However, if the sequence xp is non-increasing, interpolation results are meaningless.

Note that, since NaN is unsortable, xp also cannot contain NaNs.

A simple check for xp being strictly increasing is 

np.all(np.diff(xp) > 0)

Examples

import numpy as np
xp = [1, 2, 3]
fp = [3, 2, 0]
✓
np.interp(2.5, xp, fp)
✗
np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
UNDEF = -99.0
✓
np.interp(3.14, xp, fp, right=UNDEF)
✗
Plot an interpolant to the sine function: 
x = np.linspace(0, 2*np.pi, 10)
y = np.sin(x)
xvals = np.linspace(0, 2*np.pi, 50)
yinterp = np.interp(xvals, x, y)
import matplotlib.pyplot as plt
plt.plot(x, y, 'o')
plt.plot(xvals, yinterp, '-x')
plt.show()
✓
fig-3c763ccf50f93055.png
Interpolation with periodic x-coordinates: 
x = [-180, -170, -185, 185, -10, -5, 0, 365]
xp = [190, -190, 350, -350]
fp = [5, 10, 3, 4]
np.interp(x, xp, fp, period=360)
✓
Complex interpolation: 
x = [1.5, 4.0]
xp = [2,3,5]
fp = [1.0j, 0, 2+3j]
np.interp(x, xp, fp)
✓

See also

scipy.interpolate

Aliases

  • numpy.interp

Referenced by

Other packages