https://github.com/killiansheriff/atomisticreversemontecarlo - v_0.0.3

From PR description:

What?

• Store trajectory in modifier
• Input validation and optional max iteration counter
• Add seed for random number generation
• Output key properties in graphs

Why?

• Multiple matrix user inputs are error prone so input validation should be helpful to users
• Max iteration to prevent unintentional infinite loops
• Remove limitation of only having the last frame of the MC available
• Random number seed allows for reproducible results

How?

• Use the data_cache of the custom modifier / pipeline system to calculate the MC trajectory when necessary. When skipping through the the resulting trajectory only cached results are shown. Therefore, after the initial calculation visualization is really fast.
• The notifytrajectorylength_changed() mechanism is used to inform the pipeline system about the current trajectory length.
• Key metrics like WC parameters and error are shown in table / graph form to visualize convergence. See OVITO DataTable for additional details.
• Random number seeding is implemented using np.random.default_rng()

Known limitations

• The initial system needs to have the correct number of species -> if you give 3x3 target WC parameters there need to be 3 different species in the initial system.
• New calculations can only be started from frame 0.
• Former terminal output is only visible the first time the modifier is run making it rather useless. I removed this "print" based output since all information is available in the data tables.

Screenshot

- Python
Published by killiansheriff over 2 years ago

https://github.com/killiansheriff/atomisticreversemontecarlo - v_0.0.1

Initial release.

- Python
Published by killiansheriff over 2 years ago