numpy.interp(x, xp, fp, left=None, right=None, period=None)
[source]
One-dimensional linear interpolation.
Returns the one-dimensional piecewise linear interpolant to a function with given values at discrete data-points.
Parameters: |
x : array_like The x-coordinates of the interpolated values. xp : 1-D sequence of floats The x-coordinates of the data points, must be increasing if argument fp : 1-D sequence of float or complex The y-coordinates of the data points, same length as left : optional float or complex corresponding to fp Value to return for right : optional float or complex corresponding to fp Value to return for period : None or float, optional A period for the x-coordinates. This parameter allows the proper interpolation of angular x-coordinates. Parameters New in version 1.10.0. |
---|---|
Returns: |
y : float or complex (corresponding to fp) or ndarray The interpolated values, same shape as |
Raises: |
ValueError If |
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)
>>> 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()
(Source code, png, pdf)
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])
()
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https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.interp.html