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# numpy.interp

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

  One-dimensional linear interpolation for monotonically increasing sample points.

  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

  Warning

  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)

  See also

  `scipy.interpolate`

  Examples

      >>> import numpy as np
      >>> 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])