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- Add `NetworkEpidemicModel` backend to `math_explorer/src/epidemiology/networks.rs` - Generate random geometric graphs based on threshold distance. - Implement step simulation with $\beta$ (transmission) and $\gamma$ (recovery). - Expose node states (Susceptible, Infected, Recovered). - Add `NetworkPropagationTool` to `math_explorer_gui/src/tabs/epidemiology/network_propagation.rs`. - Render nodes, edges, and statistics dynamically using `egui`. - Encapsulate parameters natively through the new tool struct. - Mark 'Network Propagation' as completed in `todo_gui.md`. Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
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…agation - Change the `step` method loop to use `next_states.iter_mut().enumerate()` to adhere to strict CI clippy standards. Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
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Pull request overview
Implements the requested “Network Propagation” feature in the Epidemiology GUI by adding a new tool that visualizes and steps a simple network-based epidemic simulation, backed by new network-generation/propagation logic in math_explorer.
Changes:
- Add
NetworkPropagationToolto the Epidemiology tab and mark the roadmap item complete. - Introduce
NetworkEpidemicModel+NodeStateinmath_explorer::epidemiology::networksfor geometric graph initialization and S/I/R propagation steps. - Minor formatting-only changes in unrelated GUI files (imports / line wrapping).
Reviewed changes
Copilot reviewed 6 out of 6 changed files in this pull request and generated 5 comments.
Show a summary per file
| File | Description |
|---|---|
todo_gui.md |
Marks “Network Propagation” feature as completed. |
math_explorer_gui/src/tabs/epidemiology/network_propagation.rs |
New GUI tool: controls + network visualization + stepping the backend model. |
math_explorer_gui/src/tabs/epidemiology/mod.rs |
Registers the new tool in the Epidemiology tab tool list. |
math_explorer/src/epidemiology/networks.rs |
Adds the backend network epidemic model and node state enum. |
math_explorer_gui/src/tabs/battery_degradation/lifetime_estimator.rs |
Import formatting only. |
math_explorer_gui/src/tabs/ai/attention_maps.rs |
Formatting only (line wrapping). |
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| pub struct NetworkEpidemicModel { | ||
| pub num_nodes: usize, | ||
| pub states: Vec<NodeState>, | ||
| pub positions: Vec<[f32; 2]>, | ||
| pub adjacency: Vec<Vec<usize>>, | ||
| pub beta: f64, | ||
| pub gamma: f64, | ||
| } |
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| impl NetworkEpidemicModel { | ||
| pub fn new(num_nodes: usize, beta: f64, gamma: f64) -> Self { | ||
| let mut model = Self { | ||
| num_nodes, | ||
| states: vec![NodeState::Susceptible; num_nodes], | ||
| positions: vec![[0.0, 0.0]; num_nodes], | ||
| adjacency: vec![vec![]; num_nodes], | ||
| beta, | ||
| gamma, | ||
| }; | ||
| model.initialize_geometric_graph(); | ||
| model | ||
| } |
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| let connection_radius = 60.0; | ||
| for i in 0..self.num_nodes { | ||
| for j in (i + 1)..self.num_nodes { | ||
| let dx = self.positions[i][0] - self.positions[j][0]; | ||
| let dy = self.positions[i][1] - self.positions[j][1]; | ||
| let dist = (dx * dx + dy * dy).sqrt(); | ||
| if dist < connection_radius { | ||
| self.adjacency[i].push(j); | ||
| self.adjacency[j].push(i); | ||
| } | ||
| } | ||
| } |
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| pub fn initialize_geometric_graph(&mut self) { | ||
| let mut rng = rand::thread_rng(); | ||
| self.states = vec![NodeState::Susceptible; self.num_nodes]; | ||
| self.positions = vec![[0.0, 0.0]; self.num_nodes]; | ||
| self.adjacency = vec![vec![]; self.num_nodes]; | ||
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| // Random geometric graph | ||
| let radius = 200.0; | ||
| for i in 0..self.num_nodes { | ||
| let angle = rng.r#gen_range(0.0..TAU); | ||
| let r = radius * rng.r#gen_range(0.0f32..1.0f32).sqrt(); | ||
| self.positions[i] = [r * angle.cos(), r * angle.sin()]; | ||
| } | ||
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| let connection_radius = 60.0; | ||
| for i in 0..self.num_nodes { | ||
| for j in (i + 1)..self.num_nodes { | ||
| let dx = self.positions[i][0] - self.positions[j][0]; | ||
| let dy = self.positions[i][1] - self.positions[j][1]; | ||
| let dist = (dx * dx + dy * dy).sqrt(); | ||
| if dist < connection_radius { | ||
| self.adjacency[i].push(j); | ||
| self.adjacency[j].push(i); | ||
| } | ||
| } | ||
| } | ||
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| // Start with one infected | ||
| if self.num_nodes > 0 { | ||
| let start_idx = rng.r#gen_range(0..self.num_nodes); | ||
| self.states[start_idx] = NodeState::Infected; | ||
| } | ||
| } | ||
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| pub fn step(&mut self) { | ||
| let mut next_states = self.states.clone(); | ||
| let mut rng = rand::thread_rng(); | ||
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| for (i, next_state) in next_states.iter_mut().enumerate().take(self.num_nodes) { | ||
| match self.states[i] { | ||
| NodeState::Susceptible => { | ||
| // Check infected neighbors | ||
| let infected_neighbors = self.adjacency[i] | ||
| .iter() | ||
| .filter(|&&j| self.states[j] == NodeState::Infected) | ||
| .count(); | ||
| for _ in 0..infected_neighbors { | ||
| if rng.r#gen::<f64>() < self.beta { | ||
| *next_state = NodeState::Infected; | ||
| break; | ||
| } | ||
| } | ||
| } | ||
| NodeState::Infected => { | ||
| if rng.r#gen::<f64>() < self.gamma { | ||
| *next_state = NodeState::Recovered; | ||
| } | ||
| } | ||
| NodeState::Recovered => {} | ||
| } | ||
| } | ||
| self.states = next_states; | ||
| } |
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| #[derive(Clone, Copy, PartialEq, Debug)] | ||
| pub enum NodeState { | ||
| Susceptible, | ||
| Infected, | ||
| Recovered, | ||
| } | ||
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| pub struct NetworkEpidemicModel { | ||
| pub num_nodes: usize, | ||
| pub states: Vec<NodeState>, | ||
| pub positions: Vec<[f32; 2]>, | ||
| pub adjacency: Vec<Vec<usize>>, | ||
| pub beta: f64, | ||
| pub gamma: f64, | ||
| } |
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This PR fulfills the "Network Propagation" feature request from
todo_gui.mdwhile adhering to the Glassblower persona's strict architectural directives:NetworkEpidemicModelwithinmath_explorer.NetworkPropagationTool) cleanly implementsEpidemiologyTool, importing backend states for its UI.gencalls to prevent 2024 rust edition keywords bugs, keeping types strongly coupled.PR created automatically by Jules for task 377825193403949821 started by @fderuiter