Micro Manager changelog

latest

Add information about adaptivity tuning parameters #131
Put computation of counting active steps inside the adaptivity variant if condition #130

Use absolute values to calculate normalizing factor for relative norms in adaptivity #125
Add option to use only one micro simulation object in the snapshot computation #123
Explicitly check if time window has converged using the API function is_time_window_complete() #118
Add MicroManagerSnapshot enabling snapshot computation and storage of microdata in HDF5 format #101
Make sklearn an optional dependency
Move the config variable micro_dt from the coupling parameters section to the simulation parameters section #114
Set time step of micro simulation in the configuration, and use it in the coupling #112
Add a base class called MicroManager with minimal API and member function definitions, rename the existing MicroManager class to MicroManagerCoupling #111
Handle calling initialize() function of micro simulations written in languages other than Python #110
Check if initial data returned from the micro simulation is the data that the adaptivity computation requires #109
Use executable micro-manager-precice by default, and stop using the script run_micro_manager.py #105
Make initialize() method of the MicroManager class public #105
Optionally use initial macro data to initialize micro simulations #104
Use pyproject.toml instead of setup.py to configure the build. Package name is now micro_manager_precice #84
Add handling of crashing micro simulations #85
Add switch to turn adaptivity on and off in configuration #93

Add note in the cpp-dummy that pickling support does not work due to no good way to pass the sim id to the new micro simulation instance commit
Reintroduce initialize function in the micro simulation API #79
Use Allgatherv instead of allgather when collecting number of micro simulations on each rank in initialization #81
Remove the callable function initialize() from the micro simulation API commit
Pass an ID to the micro simulation object so that it is aware of its own uniqueness #66
Resolve bug which led to an error when global adaptivity was used with unequal number of simulations on each rank #78
Make the initialize() method of the MicroManager class private #77
Add reference paper via a CITATION.cff file commit
Add JOSS DOI badge commit
Update pyprecice API calls to their newer variants #51

Add global variant to adaptivity (still experimental) #42
Add norm-based (L1 and L2) support for functions in similarity distance calculation with absolute and relative variants #40
New domain decomposition strategy based on user input of number of processors along each axis #41
Add pickling support for C++ solver dummy #30
Add C++ solver dummy to show how a C++ micro simulation can be controlled by the Micro Manager #22
Add local adaptivity #21

Change package from micro-manager to micro-manager-precice and upload to PyPI.

First release of Micro Manager prototype. Important features: Micro Manager can run in parallel, capability to handle bi-directional implicit coupling