_cubicspline_interpolate(xi, yi, x, axis=0, bc_type='not-a-knot', extrapolate=None)
See scipy.interpolate.CubicSpline
for details.
1-d array containing values of the independent variable. Values must be real, finite and in strictly increasing order.
Array containing values of the dependent variable. It can have arbitrary number of dimensions, but the length along axis
(see below) must match the length of x
. Values must be finite.
Axis along which y is assumed to be varying. Meaning that for x[i]
the corresponding values are np.take(y, i, axis=axis)
. Default is 0.
Boundary condition type. Two additional equations, given by the boundary conditions, are required to determine all coefficients of polynomials on each segment . If bc_type
is a string, then the specified condition will be applied at both ends of a spline. Available conditions are: * 'not-a-knot' (default): The first and second segment at a curve end are the same polynomial. It is a good default when there is no information on boundary conditions. * 'periodic': The interpolated functions is assumed to be periodic of period x[-1] - x[0]
. The first and last value of y must be identical: y[0] == y[-1]
. This boundary condition will result in y'[0] == y'[-1]
and y''[0] == y''[-1]
. * 'clamped': The first derivative at curves ends are zero. Assuming a 1D y, bc_type=((1, 0.0), (1, 0.0))
is the same condition. * 'natural': The second derivative at curve ends are zero. Assuming a 1D y, bc_type=((2, 0.0), (2, 0.0))
is the same condition. If bc_type
is a 2-tuple, the first and the second value will be applied at the curve start and end respectively. The tuple values can be one of the previously mentioned strings (except 'periodic') or a tuple :None:None:`(order, deriv_values)` allowing to specify arbitrary derivatives at curve ends: * :None:None:`order`: the derivative order, 1 or 2. * :None:None:`deriv_value`: array-like containing derivative values, shape must be the same as y, excluding axis
dimension. For example, if y is 1D, then :None:None:`deriv_value` must be a scalar. If y is 3D with the shape (n0, n1, n2) and axis=2, then :None:None:`deriv_value` must be 2D and have the shape (n0, n1).
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. If None (default), extrapolate
is set to 'periodic' for bc_type='periodic'
and to True otherwise.
The result, of shape (m,)
Convenience function for cubic spline data interpolator.
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