cupyx.scipy.interpolate.PchipInterpolator#

class cupyx.scipy.interpolate.PchipInterpolator(x, y, axis=0, extrapolate=None)[source]#

PCHIP 1-D monotonic cubic interpolation.

x and y are arrays of values used to approximate some function f, with y = f(x). The interpolant uses monotonic cubic splines to find the value of new points. (PCHIP stands for Piecewise Cubic Hermite Interpolating Polynomial).

Parameters
• x (ndarray) – A 1-D array of monotonically increasing real values. x cannot include duplicate values (otherwise f is overspecified)

• y (ndarray) – A 1-D array of real values. y’s length along the interpolation axis must be equal to the length of x. If N-D array, use axis parameter to select correct axis.

• axis (int, optional) – Axis in the y array corresponding to the x-coordinate values.

• extrapolate (bool, optional) – Whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs.

CubicHermiteSpline

Piecewise-cubic interpolator.

Akima1DInterpolator

Akima 1D interpolator.

PPoly

Piecewise polynomial in terms of coefficients and breakpoints.

Notes

The interpolator preserves monotonicity in the interpolation data and does not overshoot if the data is not smooth.

The first derivatives are guaranteed to be continuous, but the second derivatives may jump at $$x_k$$.

Determines the derivatives at the points $$x_k$$, $$f'_k$$, by using PCHIP algorithm 1.

Let $$h_k = x_{k+1} - x_k$$, and $$d_k = (y_{k+1} - y_k) / h_k$$ are the slopes at internal points $$x_k$$. If the signs of $$d_k$$ and $$d_{k-1}$$ are different or either of them equals zero, then $$f'_k = 0$$. Otherwise, it is given by the weighted harmonic mean

$\frac{w_1 + w_2}{f'_k} = \frac{w_1}{d_{k-1}} + \frac{w_2}{d_k}$

where $$w_1 = 2 h_k + h_{k-1}$$ and $$w_2 = h_k + 2 h_{k-1}$$.

The end slopes are set using a one-sided scheme 2.

References

1

F. N. Fritsch and J. Butland, A method for constructing local monotone piecewise cubic interpolants, SIAM J. Sci. Comput., 5(2), 300-304 (1984). 10.1137/0905021.

2

see, e.g., C. Moler, Numerical Computing with Matlab, 2004. 10.1137/1.9780898717952

Methods

__call__(x, nu=0, extrapolate=None)[source]#

Evaluate the piecewise polynomial or its derivative.

Parameters
• x (array_like) – Points to evaluate the interpolant at.

• nu (int, optional) – Order of derivative to evaluate. Must be non-negative.

• extrapolate ({bool, 'periodic', None}, 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. If None (default), use self.extrapolate.

Returns

y – Interpolated values. Shape is determined by replacing the interpolation axis in the original array with the shape of x.

Return type

array_like

Notes

Derivatives are evaluated piecewise for each polynomial segment, even if the polynomial is not differentiable at the breakpoints. The polynomial intervals are considered half-open, [a, b), except for the last interval which is closed [a, b].

antiderivative(nu=1)[source]#

Construct a new piecewise polynomial representing the antiderivative. Antiderivative is also the indefinite integral of the function, and derivative is its inverse operation.

Parameters

nu (int, optional) – Order of antiderivative to evaluate. Default is 1, i.e., compute the first integral. If negative, the derivative is returned.

Returns

pp – Piecewise polynomial of order k2 = k + n representing the antiderivative of this polynomial.

Return type

PPoly

Notes

The antiderivative returned by this function is continuous and continuously differentiable to order n-1, up to floating point rounding error.

If antiderivative is computed and self.extrapolate='periodic', it will be set to False for the returned instance. This is done because the antiderivative is no longer periodic and its correct evaluation outside of the initially given x interval is difficult.

classmethod construct_fast(c, x, extrapolate=None, axis=0)[source]#

Construct the piecewise polynomial without making checks. Takes the same parameters as the constructor. Input arguments c and x must be arrays of the correct shape and type. The c array can only be of dtypes float and complex, and x array must have dtype float.

derivative(nu=1)[source]#

Construct a new piecewise polynomial representing the derivative.

Parameters

nu (int, optional) – Order of derivative to evaluate. Default is 1, i.e., compute the first derivative. If negative, the antiderivative is returned.

Returns

pp – Piecewise polynomial of order k2 = k - n representing the derivative of this polynomial.

Return type

PPoly

Notes

Derivatives are evaluated piecewise for each polynomial segment, even if the polynomial is not differentiable at the breakpoints. The polynomial intervals are considered half-open, [a, b), except for the last interval which is closed [a, b].

extend(c, x)[source]#

Parameters
• c (ndarray, size (k, m, ...)) – Additional coefficients for polynomials in intervals. Note that the first additional interval will be formed using one of the self.x end points.

• x (ndarray, size (m,)) – Additional breakpoints. Must be sorted in the same order as self.x and either to the right or to the left of the current breakpoints.

classmethod from_spline(tck, extrapolate=None)[source]#

Construct a piecewise polynomial from a spline

Parameters
• tck – A spline, as a (knots, coefficients, degree) tuple 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.

integrate(a, b, extrapolate=None)[source]#

Compute a definite integral over a piecewise polynomial.

Parameters
• a (float) – Lower integration bound

• b (float) – Upper integration bound

• extrapolate ({bool, 'periodic', None}, 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. If None (default), use self.extrapolate.

Returns

ig – Definite integral of the piecewise polynomial over [a, b]

Return type

array_like

roots(discontinuity=True, extrapolate=None)[source]#

Find real roots of the piecewise polynomial.

Parameters
• discontinuity (bool, optional) – Whether to report sign changes across discontinuities at breakpoints as roots.

• extrapolate ({bool, 'periodic', None}, optional) – If bool, determines whether to return roots from the polynomial extrapolated based on first and last intervals, ‘periodic’ works the same as False. If None (default), use self.extrapolate.

Returns

roots – Roots of the polynomial(s). If the PPoly object describes multiple polynomials, the return value is an object array whose each element is an ndarray containing the roots.

Return type

ndarray

solve(y=0.0, discontinuity=True, extrapolate=None)[source]#

Find real solutions of the equation pp(x) == y.

Parameters
• y (float, optional) – Right-hand side. Default is zero.

• discontinuity (bool, optional) – Whether to report sign changes across discontinuities at breakpoints as roots.

• extrapolate ({bool, 'periodic', None}, optional) – If bool, determines whether to return roots from the polynomial extrapolated based on first and last intervals, ‘periodic’ works the same as False. If None (default), use self.extrapolate.

Returns

roots – Roots of the polynomial(s). If the PPoly object describes multiple polynomials, the return value is an object array whose each element is an ndarray containing the roots.

Return type

ndarray

Notes

This routine works only on real-valued polynomials. If the piecewise polynomial contains sections that are identically zero, the root list will contain the start point of the corresponding interval, followed by a nan value. If the polynomial is discontinuous across a breakpoint, and there is a sign change across the breakpoint, this is reported if the discont parameter is True.

At the moment, there is not an actual implementation.

__eq__(value, /)#

Return self==value.

__ne__(value, /)#

Return self!=value.

__lt__(value, /)#

Return self<value.

__le__(value, /)#

Return self<=value.

__gt__(value, /)#

Return self>value.

__ge__(value, /)#

Return self>=value.

Attributes

c#
x#
extrapolate#
axis#