  Microwave Imaging is an essential technique for reconstructing the electrical
properties of an inaccessible medium. Many approaches have been proposed
employing algorithms to solve the Electromagnetic Inverse Scattering Problem
associated with this technique. In addition to the algorithm, one needs to
implement adequate structures to represent the problem domain, the input data,
the results of the adopted metrics, and experimentation routines. We introduce
an open-source Python library that offers a modular and standardized framework
for implementing and evaluating the performance of algorithms for the problem.
Based on the implementation of fundamental components for the execution of
algorithms, this library aims to facilitate the development and discussion of
new methods. Through a modular structure organized into classes, researchers
can design their case studies and benchmarking experiments relying on features
such as test randomization, specific metrics, and statistical comparison. To
the best of the authors' knowledge, it is the first time that such tools for
benchmarking and comparison are introduced for microwave imaging algorithms. In
addition, two new metrics for location and shape recovery are presented. In
this work, we introduce the principles for the design of the problem components
and provide studies to exemplify the main aspects of this library. It is freely
distributed through a Github repository that can be accessed from
https://andre-batista.github.io/eispy2d/.
