Mesa implementation of Ising Model #138
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| agents_list = list(self.agents) | ||
| self._steps += 1000 | ||
| for i in range(1000): | ||
| random_spin = self.random.choice(agents_list) |
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Having to create a new list of agents from scratch and selecting one is not ideal. I know this is a limitation of AgentSet. @EwoutH any idea to do this in a performant way via AgentSet? It seems to be a tower of abstractions, that it has slowed down the benchmark for mesa-frames as well: see https://github.com/adamamer20/mesa-frames/issues/25.
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thanks for the comment. it does not feel right at all haha.
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Currently I don’t think there is. @vitorfrois could you open a new issue on the main Mesa repo describing this lack of functionality and linking to this PR as example?
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I was going to suggest you can just do:
self.agents.select(n=1000)But looking at our current implementation (here) that isn’t actually random.
What you can do though is:
self.agents.shuffle().select(n=1000)
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| What happens when the temperature slider is set very low? (This is called the "ferromagnetic" state.) Again, try this with different **Spin Up Probability** values. | ||
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| Between these two very different behaviors is a transition point. On an infinite grid, the transition point can be proved to be $2 / ln (1 + sqrt 2)$, which is about 2.27. On a large enough finite toroidal grid, the transition point is near this number. |
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We don't have means of aggregating various param sweep into a single plot of magnetization vs temperature. This is a homework for us developers to allow visualizing batch_run result easily.
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This model is very helpful in demonstrating phase transition. We have Epstein civil violence, but the Ising model is the bread and butter toy model for phase transition in statistical physics. We should aim towards being able to specify this model in code concisely. |
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Other than the 2 comments, this PR LGTM. Once you fix them, I will merge. |
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@rht comments fixed! |
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For consistency is prefer this named app.py.
It might not be the best name, but it’s what we use now.
| import solara | ||
| from ising.model import IsingModel | ||
| from ising.portrayal import portray_spin | ||
| from mesa.visualization import JupyterViz |
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You can now import SolaraViz
| from mesa.visualization import JupyterViz | |
| from mesa.visualization import SolaraViz |
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| @solara.component | ||
| def Page(): | ||
| JupyterViz( |
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| JupyterViz( | |
| SolaraViz( |
| self.state = init_state | ||
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| def neighbors(self): | ||
| return self.model.grid.iter_neighbors((self.x, self.y), True) |
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Make explicit for readability:
| return self.model.grid.iter_neighbors((self.x, self.y), True) | |
| return self.model.grid.iter_neighbors((self.x, self.y), moore=True) |
| """ | ||
| Create a cell, in the given state, at the given x, y position. | ||
| """ | ||
| super().__init__(pos, model) |
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Doesn’t this map to unique_id, model?
If intended, please add a comment, if not, update
| for i in range(1000): | ||
| random_spin = self.random.choice(agents_list) | ||
| dE = self.get_energy_change(random_spin) | ||
| if dE < 0: |
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Maybe you can do here
| if dE < 0: | |
| if dE < 0 or self.random.random() < self.boltzmann_factor(dE): |
If I understand Python correctly, the later part won’t be evaluated if the former is False already, making it just as fast, and more readable.
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| def step(self): | ||
| agents_list = list(self.agents) | ||
| self._steps += 1000 |
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If I understand this correctly, you are using _step to count the number of agent activations (you use 1000, as in the for loop). Why use _step for this?
Co-authored-by: Ewout ter Hoeven <E.M.terHoeven@student.tudelft.nl>
Ising Model (https://ccl.northwestern.edu/netlogo/models/Ising, https://en.wikipedia.org/wiki/Ising_model) implementation using new Visualization of Ising Model as issued at #136.
