larray.interp

larray.interp(*args, **kwargs)

One-dimensional linear interpolation.

larray specific variant of numpy.interp.

Documentation from numpy:

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

Parameters

xarray_like

The x-coordinates at which to evaluate the interpolated values.

xp1-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.

fp1-D sequence of float or complex

The y-coordinates of the data points, same length as xp.

leftoptional float or complex corresponding to fp

Value to return for x < xp[0], default is fp[0].

rightoptional float or complex corresponding to fp

Value to return for x > xp[-1], default is fp[-1].

periodNone 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.

New in version 1.10.0.

Returns

yfloat 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

Notes

Does not check that the x-coordinate sequence xp is increasing. If xp is not increasing, the results are nonsense. A simple check for increasing is:

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

Examples

>>> xp = [1, 2, 3]
>>> fp = [3, 2, 0]
>>> np.interp(2.5, xp, fp)
1.0
>>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
array([ 3. ,  3. ,  2.5 ,  0.56,  0. ])
>>> UNDEF = -99.0
>>> np.interp(3.14, xp, fp, right=UNDEF)
-99.0

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')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(xvals, yinterp, '-x')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.show()

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)
array([7.5, 5., 8.75, 6.25, 3., 3.25, 3.5, 3.75])

Complex interpolation:

>>> x = [1.5, 4.0]
>>> xp = [2,3,5]
>>> fp = [1.0j, 0, 2+3j]
>>> np.interp(x, xp, fp)
array([ 0.+1.j ,  1.+1.5j])