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Matrix library (`numpy.matlib`)

# numpy.interp

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

  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. fp : 1-D sequence of floats The y-coordinates of the data points, same length as xp. left : float, optional Value to return for x < xp[0], default is fp[0]. right : float, optional Value to return for x > xp[-1], default is fp[-1].
  Returns: | y : {float, ndarray} The interpolated values, same shape as x.
  Raises: | ValueError : If xp and fp have different length

  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()

  (Source code, png, pdf)