NdPPoly#
- class scipy.interpolate.NdPPoly(c, x, extrapolate=None)[source]#
- Piecewise tensor product polynomial - The value at point - xp = (x', y', z', ...)is evaluated by first computing the interval indices i such that:- x[0][i[0]] <= x' < x[0][i[0]+1] x[1][i[1]] <= y' < x[1][i[1]+1] ... - and then computing: - S = sum(c[k0-m0-1,...,kn-mn-1,i[0],...,i[n]] * (xp[0] - x[0][i[0]])**m0 * ... * (xp[n] - x[n][i[n]])**mn for m0 in range(k[0]+1) ... for mn in range(k[n]+1)) - where - k[j]is the degree of the polynomial in dimension j. This representation is the piecewise multivariate power basis.- Parameters:
- cndarray, shape (k0, …, kn, m0, …, mn, …)
- Polynomial coefficients, with polynomial order kj and mj+1 intervals for each dimension j. 
- xndim-tuple of ndarrays, shapes (mj+1,)
- Polynomial breakpoints for each dimension. These must be sorted in increasing order. 
- extrapolatebool, optional
- Whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. Default: True. 
 
 - See also - PPoly
- piecewise polynomials in 1D 
 - Notes - High-order polynomials in the power basis can be numerically unstable. - Attributes:
- xtuple of ndarrays
- Breakpoints. 
- cndarray
- Coefficients of the polynomials. 
 
 - Methods - __call__(x[, nu, extrapolate])- Evaluate the piecewise polynomial or its derivative - derivative(nu)- Construct a new piecewise polynomial representing the derivative. - antiderivative(nu)- Construct a new piecewise polynomial representing the antiderivative. - integrate(ranges[, extrapolate])- Compute a definite integral over a piecewise polynomial. - integrate_1d(a, b, axis[, extrapolate])- Compute NdPPoly representation for one dimensional definite integral - construct_fast(c, x[, extrapolate])- Construct the piecewise polynomial without making checks.