Mastering SciPy

Interpolation

We have three different implementation methodologies to deal with interpolation problems:

• A procedural mode that computes a set of data points (in the form of ndarray with the required dimension) representing the actual solution.
• In a few special cases, a functional mode that provides us with numpy functions representing the solutions.
• An object-oriented mode that creates classes for interpolation problems. Different classes have different methods, depending on the operations that the particular kinds of interpolants enjoy. The advantage of this mode is that, through these methods, we can request more information from the solutions: not only evaluation or representation, but also relevant operations like searching for roots, computing ...