`scipy.interpolate._interpolate:PPoly`

source: /scipy/interpolate/_interpolate.py :842

Signature

class PPoly ( c , x , extrapolate = None , axis = 0 )

Members

Summary

Piecewise polynomial in the power basis.

Extended Summary

The polynomial between x[i] and x[i + 1] is written in the local power basis

S = sum(c[m, i] * (xp - x[i])**(k-m) for m in range(k+1))

where k is the degree of the polynomial.

Parameters

c : ndarray, shape (k+1, m, ...): Polynomial coefficients, degree k and m intervals.
x : ndarray, shape (m+1,): Polynomial breakpoints. Must be sorted in either increasing or decreasing order.
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.
axis : int, optional: Interpolation axis. Default is zero.

Attributes

x : ndarray: Breakpoints.
c : ndarray: Coefficients of the polynomials. They are reshaped to a 3-D array with the last dimension representing the trailing dimensions of the original coefficient array.
axis : int: Interpolation axis.

Methods

__call__
derivative
antiderivative
integrate
solve
roots
extend
from_spline
from_bernstein_basis
construct_fast

Notes

High-order polynomials in the power basis can be numerically unstable. Precision problems can start to appear for orders larger than 20-30.

Array API Standard Support

PPoly has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ⛔                   
PyTorch               ✅                     ⛔                   
JAX                   ⚠️ no JIT             ⛔                   
Dask                  ⛔                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Aliases

scipy.interpolate.PPoly