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scipy.interpolate.UnivariateSpline.__call__

# scipy.interpolate.UnivariateSpline

class `scipy.interpolate.``UnivariateSpline`(x, y, w=None, bbox=[None, None], k=3, s=None, ext=0, check_finite=False)

  One-dimensional smoothing spline fit to a given set of data points.

  Fits a spline y = spl(x) of degree k to the provided x, y data. `s` specifies the number of knots by specifying a smoothing condition.

  Parameters:

  x : (N,) array_like

  > 1-D array of independent input data. Must be increasing.

  y : (N,) array_like

  > 1-D array of dependent input data, of the same length as x.

  w : (N,) array_like, optional

  > Weights for spline fitting. Must be positive. If None (default), weights are all equal.

  bbox : (2,) array_like, optional

  > 2-sequence specifying the boundary of the approximation interval. If None (default), `bbox=[x[0], x[-1]]`.

  k : int, optional

  > Degree of the smoothing spline. Must be <= 5. Default is k=3, a cubic spline.

  s : float or None, optional

  > Positive smoothing factor used to choose the number of knots. Number of knots will be increased until the smoothing condition is satisfied:

  >     sum((w[i] * (y[i]-spl(x[i])))**2, axis=0) <= s

  > If None (default), `s = len(w)` which should be a good value if `1/w[i]` is an estimate of the standard deviation of `y[i]`. If 0, spline will interpolate through all data points.

  ext : int or str, optional

  > Controls the extrapolation mode for elements not in the interval defined by the knot sequence.

  > - if ext=0 or ‘extrapolate’, return the extrapolated value.
  > - if ext=1 or ‘zeros’, return 0
  > - if ext=2 or ‘raise’, raise a ValueError
  > - if ext=3 of ‘const’, return the boundary value.

  > The default value is 0.

  check_finite : bool, optional

  > Whether to check that the input arrays contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination or non-sensical results) if the inputs do contain infinities or NaNs. Default is False.

  See also

  `InterpolatedUnivariateSpline`
    Subclass with smoothing forced to 0
  `LSQUnivariateSpline`
    Subclass in which knots are user-selected instead of being set by smoothing condition
  `splrep`
    An older, non object-oriented wrapping of FITPACK

  `splev`, `sproot`, `splint`, `spalde`

  `BivariateSpline`
    A similar class for two-dimensional spline interpolation

  Notes

  The number of data points must be larger than the spline degree k.

  NaN handling: If the input arrays contain `nan` values, the result is not useful, since the underlying spline fitting routines cannot deal with `nan` . A workaround is to use zero weights for not-a-number data points:

      >>> from scipy.interpolate import UnivariateSpline
      >>> x, y = np.array([1, 2, 3, 4]), np.array([1, np.nan, 3, 4])
      >>> w = np.isnan(y)
      >>> y[w] = 0.
      >>> spl = UnivariateSpline(x, y, w=~w)

  Notice the need to replace a `nan` by a numerical value (precise value does not matter as long as the corresponding weight is zero.)

  Examples

      >>> import matplotlib.pyplot as plt
      >>> from scipy.interpolate import UnivariateSpline
      >>> x = np.linspace(-3, 3, 50)
      >>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
      >>> plt.plot(x, y, 'ro', ms=5)

  Use the default value for the smoothing parameter:

      >>> spl = UnivariateSpline(x, y)
      >>> xs = np.linspace(-3, 3, 1000)
      >>> plt.plot(xs, spl(xs), 'g', lw=3)

  Manually change the amount of smoothing:

      >>> spl.set_smoothing_factor(0.5)
      >>> plt.plot(xs, spl(xs), 'b', lw=3)
      >>> plt.show()

  (Source code)

  Methods

  `__call__`(x[, nu, ext]) | Evaluate spline (or its nu-th derivative) at positions x.
  `antiderivative`([n]) | Construct a new spline representing the antiderivative of this spline.
  `derivative`([n]) | Construct a new spline representing the derivative of this spline.
  `derivatives`(x) | Return all derivatives of the spline at the point x.
  `get_coeffs`() | Return spline coefficients.
  `get_knots`() | Return positions of interior knots of the spline.
  `get_residual`() | Return weighted sum of squared residuals of the spline approximation.
  `integral`(a, b) | Return definite integral of the spline between two given points.
  `roots`() | Return the zeros of the spline.
  `set_smoothing_factor`(s) | Continue spline computation with the given smoothing factor s and with the knots found at the last call.