conway doc+minor fixes

docs/examples/conway.md

@@ -0,0 +1,21 @@
+# Conway's Game of Life
+
+An implementation of Conway's cellular automata.
+
+![Conway](./img/conway.gif)
+
+{{ include_snippet("./docs/examples/src.md", show_filename=False) }}
+
+## Inputs
+
+The only inputs are the grid size (2d) and the proportion of cells that are alive initially.
+
+## Implementation
+
+This example uses the `StateGrid` class for efficient neighbour counting, requiring only a few lines of code to evolve a generation (see below). The initial alive cells are sampled randomly according to the proportion set. In each new generation, alive cells with less than 2 or more than 3 neighbours die; empty cells with 3 live neighbours come alive. Dead cells are coloured black, live cells are coloured according to how long they have been alive, from white (youngest) to brown (oldest).
+
+{{ include_snippet("examples/conway/conway.py", "step") }}
+
+## Outputs
+
+The sole output is an animation as shown above.

docs/examples/schelling.md

@@ -8,11 +8,11 @@ An implementation of Schelling's segregation model [[7]](../references.md), whic
 
 ## Inputs
 
-In the above example, the similarity threshold is 50% and the cells states are (approximately): 36% empty, 12% red, 12% blue and 39% green, on a 640 x 480 grid. The initial population is randomly constructed using the model's Monte-Carlo engine, the process of moving agents uses randomly swaps unsatisfied agents with empty cells.
+In this example, the similarity threshold is 60% and the cells states are: 36% empty, 12% red, 12% blue and 40% green, on a 480 x 360 grid. The initial population is randomly constructed using the model's Monte-Carlo engine, the process of moving agents randomly swaps unsatisfied agents with empty cells. The boundaries are "sinks", i.e. there are no neighbouring cells
 
 ## Implementation
 
-The key features that this example uses are the `StateGrid` class for efficient neighbour counting and the use of a conditional halting: an open-ended timeline and a call to the `Model.halt()` method when a certain state is achieved.
+The key features used in this example are the `StateGrid` class for efficient neighbour counting and the use of a conditional halting: an open-ended timeline and a call to the `Model.halt()` method when a certain state is achieved.
 
 Since the key output for this model is graphical, the visualisation code sits within the model. The model reaches a steady state when there are no unsatisfied agents remaining and there is nothing to be gained by continuing, so when this happens the `neworder.Model.halt()` method is called, at the end of the `step()` implementation:
 
@@ -29,15 +29,15 @@ The `StateGrid.count_neighbours` takes a function argument that filters the stat
 The output is an animation as shown above. Log messages also record the timestep and the proportion of the population that remains unsatisfied:
 
 ```text
-[py 0/1] step 0 42.6660% unsatisfied
-[py 0/1] step 1 39.5765% unsatisfied
-[py 0/1] step 2 37.5599% unsatisfied
-[py 0/1] step 3 36.2454% unsatisfied
-[py 0/1] step 4 35.2279% unsatisfied
+[py 0/1] step 0 43.1493% unsatisfied
+[py 0/1] step 1 39.1400% unsatisfied
+[py 0/1] step 2 36.9196% unsatisfied
+[py 0/1] step 3 35.3113% unsatisfied
+[py 0/1] step 4 33.9259% unsatisfied
 ...
-[py 0/1] step 458 0.0003% unsatisfied
-[py 0/1] step 459 0.0003% unsatisfied
-[py 0/1] step 460 0.0003% unsatisfied
-[py 0/1] step 461 0.0003% unsatisfied
-[py 0/1] step 462 0.0000% unsatisfied
+[py 0/1] step 133 0.0017% unsatisfied
+[py 0/1] step 134 0.0012% unsatisfied
+[py 0/1] step 135 0.0012% unsatisfied
+[py 0/1] step 136 0.0006% unsatisfied
+[py 0/1] step 137 0.0000% unsatisfied
 ```

examples/conway/conway.py

@@ -26,31 +26,30 @@ def __init__(self, nx, ny, n, edge=no.Domain.WRAP):
 
     self.fig, self.g = self.__init_visualisation()
 
+  # !step!
   def step(self):
-
     n = self.domain.count_neighbours(lambda x: x > 0)
 
     deaths = np.logical_or(n < 2, n > 3)
     births = n == 3
 
     self.domain.state = self.domain.state * ~deaths + births
 
+    self.__update_visualisation()
+  # !step!
+
+  def check(self):
     # # randomly place a glider (not across edge)
     # if self.timeline.index() == 0:
     #   x = self.mc.raw() % (self.domain.state.shape[0] - 2)
     #   y = self.mc.raw() % (self.domain.state.shape[1] - 2)
     #   self.domain.state[x:x+3, y:y+3] = np.rot90(Conway.__glider, self.mc.raw() % 4)
-
-    self.__update_visualisation()
-
-  def check(self):
     return True
 
   def __init_visualisation(self):
-
     plt.ion()
     cmap = colors.ListedColormap(['black', 'white', 'purple', 'blue', 'green', 'yellow', 'orange', 'red', 'brown'])
-    fig = plt.figure(constrained_layout=True, figsize=(12,9))
+    fig = plt.figure(constrained_layout=True, figsize=(8,6))
     g = plt.imshow(self.domain.state, cmap=cmap, vmax=9)
     plt.axis("off")
 
@@ -60,6 +59,9 @@ def __init_visualisation(self):
     return fig, g
 
   def __update_visualisation(self):
-
     self.g.set_data(self.domain.state)
+    # plt.savefig("/tmp/conway%04d.png" % self.timeline.index(), dpi=80)
+    # if self.timeline.index() > 100:
+    #   self.halt()
+
     self.fig.canvas.flush_events()

examples/schelling/model.py

@@ -5,11 +5,11 @@
 #neworder.verbose()
 
 # category 0 is empty
-gridsize = [640,480]
-categories = np.array([0.56, 0.19, 0.19, 0.6])
-# normalise
-categories = categories / sum(categories)
-similarity = 0.5
+gridsize = [480,360]
+categories = np.array([0.36, 0.12, 0.12, 0.4])
+# normalise if necessary
+# categories = categories / sum(categories)
+similarity = 0.6
 
 # open-ended timeline with arbitrary timestep
 # the model halts when all agents are satisfied, rather than at a specific time

examples/schelling/schelling.py

@@ -81,6 +81,5 @@ def __init_visualisation(self):
 
   def __update_visualisation(self):
     self.img.set_array(self.domain.state.T)
-    # if not self.timeline.index() % 5:
-    #   plt.savefig("/tmp/schelling%04d.png" % (self.timeline.index()//5), dpi=80)
+    # plt.savefig("/tmp/schelling%04d.png" % self.timeline.index(), dpi=80)
     self.fig.canvas.flush_events()

mkdocs.yml

@@ -48,6 +48,7 @@ nav:
     - RiskPaths: examples/riskpaths.md
     - People: ./examples/people.md
     - Option: examples/option.md
+    - Conway: examples/conway.md
     - Schelling: examples/schelling.md
     - Wolf-Sheep: examples/wolf-sheep.md
     - N-body: examples/n-body.md