# scipy.interpolate.UnivariateSpline

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

  1-D 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; must be strictly increasing if s is 0.

    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 w is None, weights are all equal. Default is None.

    bbox(2,) array_like, optional

      2-sequence specifying the boundary of the approximation interval. If bbox is None, `bbox=[x[0], x[-1]]`. Default is None.

    k int, optional

      Degree of the smoothing spline. Must be 1 <= k <= 5. `k = 3` is a cubic spline. Default is 3.

    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 s is None, `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. Default is None.

    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.

      Default 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

  `BivariateSpline`

    a base class for bivariate splines.

  `SmoothBivariateSpline`

    a smoothing bivariate spline through the given points

  `LSQBivariateSpline`

    a bivariate spline using weighted least-squares fitting

  `RectSphereBivariateSpline`

    a bivariate spline over a rectangular mesh on a sphere

  `SmoothSphereBivariateSpline`

    a smoothing bivariate spline in spherical coordinates

  `LSQSphereBivariateSpline`

    a bivariate spline in spherical coordinates using weighted least-squares fitting

  `RectBivariateSpline`

    a bivariate spline over a rectangular mesh

  `InterpolatedUnivariateSpline`

    a interpolating univariate spline for a given set of data points.

  `bisplrep`

    a function to find a bivariate B-spline representation of a surface

  `bisplev`

    a function to evaluate a bivariate B-spline and its derivatives

  `splrep`

    a function to find the B-spline representation of a 1-D curve

  `splev`

    a function to evaluate a B-spline or its derivatives

  `sproot`

    a function to find the roots of a cubic B-spline

  `splint`

    a function to evaluate the definite integral of a B-spline between two given points

  `spalde`

    a function to evaluate all derivatives of a B-spline

  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
      >>> rng = np.random.default_rng()
      >>> x = np.linspace(-3, 3, 50)
      >>> y = np.exp(-x**2) + 0.1 * rng.standard_normal(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()

  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.

  validate_input |

scipy.interpolate.insert scipy.interpolate.UnivariateSpline.__call__