Ishaan Desai

Currently the Micro Manager accepts one micro simulation file (with Python or C++ code) and initializes all micro simulation objects with it. In certain applications, a user may want to...

In https://github.com/openjournals/joss-reviews/issues/5842#issuecomment-1758196669, @mbkumar suggested that the Micro Manager could provide an abstract class for the micro simulation code, which the users could subclass or inherit, and the give the name...

For some cases, the following warning is observed from the file `micro_manager/adaptivity/adaptivity.py`: ```bash RuntimeWarning: invalid value encountered in true_divide relative = np.nan_to_num((pointwise_diff / np.maximum(data[np.newaxis, :], data[:, np.newaxis]))) ``` This needs...

As suggested in https://github.com/openjournals/joss-reviews/issues/5842#issuecomment-1781849246 there needs to be a check to ensure that the dependencies are installed correctly when the Micro Manager is installed via pip.

Currently micro simulations are only allowed to pass data which is to be sent to the macro side. However, for the adaptivity calculation, the micro simulations may have data related...

Currently the manager requires that every rank must at least one micro simulation. However, this may not be the case for bigger simulations. The manager should be able properly handle...

Currently there is no check if the package deployed to PyPI actually works. Such a check should be done once a package is deployed after a release. Running just the...

It would be good to check test coverage for every change via a pull request. As the Micro Manager is written in Python, any solution would involve using [Coverage.py](https://coverage.readthedocs.io/en/7.2.7/). There...

There needs to be an investigation if quantities passed to adaptivity for similarity calculation can have weights so that user can control which quantities are *more important* when it comes...