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‎benchmarks/global_benchmark.py‎

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from configurations import configurations
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from mesa.experimental.devs.simulator import ABMSimulator
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# Generic function to initialize and run a model
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def run_model(model_class, seed, parameters):
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Returns:
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startup time and run time
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"""
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uses_simulator = ["WolfSheep"]
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# Explicitly collect garbage before the run to ensure a clean memory state
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gc.collect()
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# Disable GC during timed runs to avoid random slowdowns
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gc.disable()
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start_init = time.perf_counter()
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if model_class.__name__ in uses_simulator:
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simulator = ABMSimulator()
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model = model_class(simulator=simulator, rng=seed, **parameters)
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else:
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model = model_class(rng=seed, **parameters)
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model = model_class(rng=seed, **parameters)
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end_init_start_run = time.perf_counter()
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if model_class.__name__ in uses_simulator:
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simulator.run_for(config["steps"])
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else:
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for _ in range(config["steps"]):
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model.step()
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model.run_for(config["steps"])
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end_run = time.perf_counter()
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gc.enable() # Re-enable GC after benchmarking

‎docs/apis/experimental.md‎

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@@ -4,11 +4,6 @@ This namespace contains experimental features. These are under development, and
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## Devs
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```{eval-rst}
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.. automodule:: experimental.devs.eventlist
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:members:
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```
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```{eval-rst}
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.. automodule:: experimental.devs.simulator
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:members:

‎docs/apis/time.md‎

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# Time
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```{eval-rst}
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.. automodule:: mesa.time
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:members:
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:inherited-members:
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```

‎docs/migration_guide.md‎

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Non-breaking changes aren't included, for those see our [Release history](https://github.com/mesa/mesa/releases).
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## Mesa 3.5.0
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### Event scheduling and time advancement
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Mesa 3.5 introduces public methods for event scheduling and time advancement directly on `Model`, replacing the need for `Simulator` classes.
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#### Time-based advancement replaces step loops
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```python
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# Old
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for _ in range(10):
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model.step()
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# New
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model.run_for(10) # Functionally equivalent for standard ABMs
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model.run_until(10) # You can now also run until a specific time
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```
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`run_for(1)` produces identical results to `step()` for traditional models.
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#### One-off event scheduling
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```python
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# Schedule a single event at or after a specific time
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model.schedule_event(callback, at=50.0) # Absolute time
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model.schedule_event(callback, after=5.0) # Relative time
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# Cancel if needed
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event = model.schedule_event(callback, at=100.0)
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event.cancel()
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```
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#### Recurring event scheduling
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```python
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from mesa.time import Schedule
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# Schedule an event every 10 time units
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model.schedule_recurring(func, Schedule(interval=10)) # By default, starting after 1 interval (t=10 in this case)
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model.schedule_recurring(func, Schedule(interval=10, start=0)) # Start immediately or at any other time
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# Save the event and stop it when needed
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gen = model.schedule_recurring(func, Schedule(interval=5.0))
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gen.stop()
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# Limit executions
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model.schedule_recurring(func, Schedule(interval=1.0, count=10))
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```
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#### Replacing Simulator classes
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The experimental Simulator classes are now also deprecated.
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```python
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# Old - ABMSimulator
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from mesa.experimental.devs.simulator import ABMSimulator
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simulator = ABMSimulator()
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simulator.setup(model)
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simulator.run_for(100)
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# New
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model.run_for(100)
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```
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```python
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# Old - DEVSimulator
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from mesa.experimental.devs.simulator import DEVSimulator
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simulator = DEVSimulator()
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simulator.setup(model)
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simulator.schedule_event_absolute(callback, time=10.5)
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simulator.run_for(50)
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# New
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model.schedule_event(callback, at=10.5)
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model.run_for(50)
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```
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Mesa 3.5 doesn't introduce any breaking changes. Mesa 4 will clean up many deprecated components and thus will break unmodified models.
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* Ref: [Discussion #2921](https://github.com/mesa/mesa/discussions/2921), [PR #3266](https://github.com/projectmesa/mesa/pull/3266)
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### AgentSet sequence behavior
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The Sequence behavior (indexing and slicing) on `AgentSet` is deprecated and will be removed in Mesa 4.0. Use the new `to_list()` method instead.

‎docs/tutorials/0_first_model.ipynb‎

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"for _ in range(100):\n",
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" # Run the model\n",
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" model = MoneyModel(10)\n",
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" for _ in range(30):\n",
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" model.step()\n",
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" model.run_for(30)\n",
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"\n",
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" # Store the results\n",
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" for agent in model.agents:\n",

‎docs/tutorials/2_collecting_data.ipynb‎

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"outputs": [],
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"source": [
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"model = MoneyModel(100, 10, 10)\n",
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"for _ in range(20):\n",
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" model.step()\n",
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"model.run_for(20)\n",
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"# Let's make sure it worked\n",
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"print(len(model.agents))"
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]
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"outputs": [],
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"source": [
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"model = MoneyModel(100, 10, 10)\n",
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"for _ in range(100):\n",
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" model.step()"
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"model.run_for(100)"
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]
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},
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{

‎docs/tutorials/3_agentset.ipynb‎

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"outputs": [],
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"source": [
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"model = MoneyModel(100)\n",
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"for _ in range(20):\n",
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" model.step()\n",
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"model.run_for(20)\n",
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"\n",
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"\n",
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"data = model.datacollector.get_agent_vars_dataframe()\n",

‎docs/tutorials/4_visualization_basic.ipynb‎

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"source": [
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"# Lets make sure the model works\n",
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"model = MoneyModel(n=100, width=100, height=10, seed=10)\n",
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"for _ in range(20):\n",
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" model.step()\n",
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"model.run_for(20)\n",
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"\n",
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"\n",
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"data = model.datacollector.get_agent_vars_dataframe()\n",

‎docs/tutorials/5_visualization_dynamic_agents.ipynb‎

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"source": [
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"# Lets make sure the model works\n",
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"model = MoneyModel(100, 10, 10)\n",
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"for _ in range(20):\n",
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" model.step()\n",
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"model.run_for(20)\n",
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"\n",
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"\n",
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"data = model.datacollector.get_agent_vars_dataframe()\n",

‎docs/tutorials/6_visualization_rendering_with_space_renderer.ipynb‎

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"source": [
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"# Lets make sure the model works\n",
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"model = MoneyModel(100, 10, 10)\n",
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"for _ in range(20):\n",
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" model.step()\n",
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"model.run_for(20)\n",
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"\n",
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"\n",
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"data = model.datacollector.get_agent_vars_dataframe()\n",

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