Provides build targets for physical models formulated in MMT.
To load the extension for exporting MMT model theories into python, run in the mmt shell:
extension uniformal.physics.MPDStatsExporter
and then proceed to generate statistics files corresponding to the theories with:
build MitM/Models mpd-stats
Currently, the files will contain functional cycles present in the models.
To load the extension for exporting MMT model theories into python, run in the mmt shell:
extension uniformal.physics.PythonExporter
and then proceed to generate python files corresponding to the theories with:
build MitM/Models mpd-python
As an example, a toy model was written in VanRoosbrockEg.mmt. The generated
python file is VanRoosbrockEg.py. One can immediately attempt iterative
methods like Gauss-Seidel to solve for the electric displacement cycle1:
import VanRoosbrockEg
from mpdbase import *
import numpy as np
model = VanRoosbrockEg.MPD_VanRoosbrockEg(np.linspace(0, 0.5, 10))
state = MPDState(model)
state.gauss_seidel("cycle1", epsilon=1e-3)
print(state)
In general, from the Model object, one has access to the laws of the
Model using model['law_name'] and the quantities using
model.quantity_name. For instance, one can compute evaluate the law
gauss_law and assign it to the current value of charge_density in
the state:
state.charge_density = model['gauss_law'].charge_density(state)
The model object also provides with a method to generate a rough MPD plot of the model:
model.plot_mpd_graphic()
generates:
