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scipy.interpolate.BSpline.

# basis_element

classmethod BSpline.basis_element(t, extrapolate=True)

  Return a B-spline basis element `B(x | t[0], ..., t[k+1])`.

  Parameters:
    t ndarray, shape (k+2,)

      internal knots

    extrapolate bool or ‘periodic’, optional

      whether to extrapolate beyond the base interval, `t[0] .. t[k+1]`, or to return nans. If ‘periodic’, periodic extrapolation is used. Default is True.

  Returns:
    basis_element callable

      A callable representing a B-spline basis element for the knot vector `t`.

  Notes

  The degree of the B-spline, k, is inferred from the length of `t` as `len(t)-2`. The knot vector is constructed by appending and prepending `k+1` elements to internal knots `t`.

  Examples

  Construct a cubic B-spline:

      >>> import numpy as np
      >>> from scipy.interpolate import BSpline
      >>> b = BSpline.basis_element([0, 1, 2, 3, 4])
      >>> k = b.k
      >>> b.t[k:-k]
      array([ 0.,  1.,  2.,  3.,  4.])
      >>> k
      3

  Construct a quadratic B-spline on `[0, 1, 1, 2]`, and compare to its explicit form:

      >>> t = [0, 1, 1, 2]
      >>> b = BSpline.basis_element(t)
      >>> def f(x):
      ...     return np.where(x < 1, x*x, (2. - x)**2)

      >>> import matplotlib.pyplot as plt
      >>> fig, ax = plt.subplots()
      >>> x = np.linspace(0, 2, 51)
      >>> ax.plot(x, b(x), 'g', lw=3)
      >>> ax.plot(x, f(x), 'r', lw=8, alpha=0.4)
      >>> ax.grid(True)
      >>> plt.show()