Merge remote-tracking branch 'upstream/main'

Author: quaquel

docs/index.md

@@ -99,7 +99,7 @@ Mesa Packages <packages>
 [github issue tracker]: https://github.com/projectmesa/mesa/issues
 [matrix chat room]: https://matrix.to/#/#project-mesa:matrix.org
 [mesa]: https://github.com/projectmesa/mesa/
-[mesa introductory tutorial]: tutorials/intro_tutorial.html
-[mesa visualization tutorial]: tutorials/visualization_tutorial.html
+[mesa introductory tutorial]: tutorials/intro_tutorial
+[mesa visualization tutorial]: tutorials/visualization_tutorial
 [pypi]: https://pypi.python.org/pypi/Mesa/
 [ticket]: https://github.com/projectmesa/mesa/issues

docs/overview.md

@@ -17,8 +17,8 @@ Mesa is modular, meaning that its modeling, analysis and visualization component
 Most models consist of one class to represent the model itself; one class (or more) for agents; a scheduler to handle time (what order the agents act in), and possibly a space for the agents to inhabit and move through. These are implemented in Mesa's modeling modules:
 
 - `mesa.Model`, `mesa.Agent`
-- [mesa.time](apis/time.html)
-- [mesa.space](apis/space.html)
+- [mesa.time](apis/time)
+- [mesa.space](apis/space)
 
 The skeleton of a model might look like this:
 
@@ -56,16 +56,16 @@ model = MyModel(5)
 model.step()
 ```
 
-You should see agents 0-4, activated in random order. See the [tutorial](tutorials/intro_tutorial.html) or API documentation for more detail on how to add model functionality.
+You should see agents 0-4, activated in random order. See the [tutorial](tutorials/intro_tutorial) or API documentation for more detail on how to add model functionality.
 
 To bootstrap a new model install mesa and run `mesa startproject`
 
 ### Analysis modules
 
 If you're using modeling for research, you'll want a way to collect the data each model run generates. You'll probably also want to run the model multiple times, to see how some output changes with different parameters. Data collection and batch running are implemented in the appropriately-named analysis modules:
 
-- [mesa.datacollection](apis/datacollection.html)
-- [mesa.batchrunner](apis/batchrunner.html)
+- [mesa.datacollection](apis/datacollection)
+- [mesa.batchrunner](apis/batchrunner)
 
 You'd add a data collector to the model like this:
 

docs/tutorials/visualization_tutorial.ipynb

@@ -208,7 +208,7 @@
     "    # plt.figure(), for thread safety purpose\n",
     "    fig = Figure()\n",
     "    ax = fig.subplots()\n",
-    "    wealth_vals = [agent.wealth for agent in model.schedule.agents]\n",
+    "    wealth_vals = [agent.wealth for agent in model.agents]\n",
     "    # Note: you have to use Matplotlib's OOP API instead of plt.hist\n",
     "    # because plt.hist is not thread-safe.\n",
     "    ax.hist(wealth_vals, bins=10)\n",