pyDOF: a Python library for the design of discrete forward and inverse filters
In this work, we present pyDOF, a Python-based software library which provides a domain-specific framework for the design of symmetric, physical-space, forward as well as inverse discrete filters. pyDOF is based on a constrained optimisation framework developed in our previous work [1, 2]. This framework allows the user to impose a wide range of constraints on the discrete filter transfer-function such as monotonicity, positivity, value-fixing, gradient-smoothing etc. amongst many others. pyDOF additionally includes an adaptive filter stencil selection option, and a van Cittert-based inverse-filter design with a user-controlled reconstruction order. The filter coefficients are computed automatically, and saved to a plain text file which can be readily parsed by any programming language. pyDOF can be used to design a wide range of low-pass, high-pass, multi band-pass/band-stop etc. discrete filters. In addition, due to its generality and abstraction, pyDOF can be used to design specific filters for user-defined target filter transfer functions. Although developed primarily for application to computational fluid dynamics simulations, pyDOF can be used to design discrete filters for a wide range of signal processing applications.