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scipy.interpolate.Akima1DInterpolator.

# from_spline

classmethod Akima1DInterpolator.from_spline(tck, extrapolate=None)

  Construct a piecewise polynomial from a spline

  Parameters:
    tck

      A spline, as returned by `splrep` or a BSpline object.

    extrapolate bool or ‘periodic’, optional

      If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If ‘periodic’, periodic extrapolation is used. Default is True.

  Examples

  Construct an interpolating spline and convert it to a `PPoly` instance

      >>> import numpy as np
      >>> from scipy.interpolate import splrep, PPoly
      >>> x = np.linspace(0, 1, 11)
      >>> y = np.sin(2*np.pi*x)
      >>> tck = splrep(x, y, s=0)
      >>> p = PPoly.from_spline(tck)
      >>> isinstance(p, PPoly)
      True

  Note that this function only supports 1D splines out of the box.

  If the `tck` object represents a parametric spline (e.g. constructed by `splprep` or a `BSpline` with `c.ndim > 1`), you will need to loop over the dimensions manually.

      >>> from scipy.interpolate import splprep, splev
      >>> t = np.linspace(0, 1, 11)
      >>> x = np.sin(2*np.pi*t)
      >>> y = np.cos(2*np.pi*t)
      >>> (t, c, k), u = splprep([x, y], s=0)

  Note that `c` is a list of two arrays of length 11.

      >>> unew = np.arange(0, 1.01, 0.01)
      >>> out = splev(unew, (t, c, k))

  To convert this spline to the power basis, we convert each component of the list of b-spline coefficients, `c`, into the corresponding cubic polynomial.

      >>> polys = [PPoly.from_spline((t, cj, k)) for cj in c]
      >>> polys[0].c.shape
      (4, 14)

  Note that the coefficients of the polynomials polys are in the power basis and their dimensions reflect just that: here 4 is the order (degree+1), and 14 is the number of intervals—which is nothing but the length of the knot array of the original tck minus one.

  Optionally, we can stack the components into a single `PPoly` along the third dimension:

      >>> cc = np.dstack([p.c for p in polys])    # has shape = (4, 14, 2)
      >>> poly = PPoly(cc, polys[0].x)
      >>> np.allclose(poly(unew).T,     # note the transpose to match `splev`
      ...             out, atol=1e-15)
      True