ndc | Numerical differentiation leveraging convolutions

ndc is a Python library. ndc has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install ndc' or download it from GitHub, PyPI.

Differentiate signals stored as PyTorch tensors, e.g. measurements obtained from a device or simulation, where automatic differentiation can not be applied. The idea of this small repository is to use the duality between convolution, i.e., filtering, and numerical differentiation to leverage the existing functions for 1-dimensional convolution in order to compute the (time) derivatives. More often then not I received (recorded) simulation data as PyTorch tensors rather than numpy arrays. Thus, I think it is nice to have a function to differentiate measurement signals without switching the data type or computation device. Moreover, the torch.conv1d function fits perfectly for this purpose.