From 7b5627fafc04453df7013e30fcb5147b3c8ea033 Mon Sep 17 00:00:00 2001 From: AlexBocken Date: Tue, 27 Jun 2023 22:45:40 +0200 Subject: [PATCH] fix indents, add bfs to scheduler --- model.py | 83 ++++++++++++++++++++++++++------------------------------ 1 file changed, 38 insertions(+), 45 deletions(-) diff --git a/model.py b/model.py index 4510db8..b50383c 100644 --- a/model.py +++ b/model.py @@ -107,7 +107,6 @@ class ActiveWalkerModel(Model): self.grid = MultiHexGridScalarFields(width=width, height=height, torus=True, fields=fields) if resistance_map_type is None: - print("No resistance field") self.grid.fields["res"] = np.ones((width, height)).astype(float) elif resistance_map_type == "perlin": # perlin generates anisotropic noise which may or may not be a good choice @@ -139,46 +138,6 @@ class ActiveWalkerModel(Model): for _ in range(N_f): self.grid.add_food(food_size) - - # Breadth-first-search algorithm for connectivity - # TODO: Implement pheromone B (take max of the two or sum?) - # alex: what's to say against max? - def bfs(self): - threshold = 0.0000001 #the value of A - connectivity = 0 #initial value of connectivity - connected_food_sources = list() #empty list of connected food sources - visited = list() #empty list of visited (by the algorithm) nodes - - nest = np.argwhere(self.grid.fields["nests"] == 1) #get nest location - nest = nest[0].tolist() #transforming not to have type errors - nest = tuple(nest) #transforming not to have type errors - start_node = nest #rename - - neighbours_to_check = list([start_node]) #start node gets checked first - neighbours_to_check = neighbours_to_check + self.grid.get_neighborhood(start_node) #start node neighbours get added to the to check list - - while neighbours_to_check: #as long as there is something on the to check list - current_node = neighbours_to_check[0] #the first list entry is taken - del neighbours_to_check[0] #and deleted on the to check list - - if current_node not in visited: #if it has not previously been checked - if self.grid.fields["A"][current_node] >= threshold: #and its A value is above our threshold - new_neighbors = self.grid.get_neighborhood(current_node) #then we get its neighbours - if new_neighbors not in visited: #if they have not yet been visited - neighbours_to_check = neighbours_to_check + new_neighbors #then they are also added to our to check list - visited = visited + list([current_node]) #and the current node has now been checked - - neighbours_to_check = list(dict.fromkeys(neighbours_to_check)) #only check nodes once (unique values) - - if self.grid.fields["food"][current_node] > 0: #in case the node we check is food - connectivity += 1 #then we have found a connected path to a food source - connected_food_sources = connected_food_sources + list([current_node]) #and it is added to the list of connected food sources - - return connectivity #we want the connectivity (0-5) - - self.connectivity = bfs(self) - - self.datacollector = DataCollector( # model_reporters={"agent_dens": lambda m: m.agent_density()}, model_reporters = {"pheromone_a": lambda m: m.grid.fields["A"], @@ -191,10 +150,42 @@ class ActiveWalkerModel(Model): ) self.datacollector.collect(self) # keep at end of __init___ - #def subset_agent_count(self): - # subset_agents = [agent for agent in self.schedule.agents if agent.sensitivity == self.s_0] - # count = float(len(subset_agents)) - # return count + # Breadth-first-search algorithm for connectivity + # TODO: Implement pheromone B (take max of the two or sum?) + # alex: what's to say against max? + def bfs(self): + threshold = 0.0000001 #the value of A + connectivity = 0 #initial value of connectivity + connected_food_sources = list() #empty list of connected food sources + visited = list() #empty list of visited (by the algorithm) nodes + + nest = np.argwhere(self.grid.fields["nests"] == 1) #get nest location + nest = nest[0].tolist() #transforming not to have type errors + nest = tuple(nest) #transforming not to have type errors + start_node = nest #rename + + neighbours_to_check = list([start_node]) #start node gets checked first + neighbours_to_check = neighbours_to_check + self.grid.get_neighborhood(start_node) #start node neighbours get added to the to check list + + while neighbours_to_check: #as long as there is something on the to check list + current_node = neighbours_to_check[0] #the first list entry is taken + del neighbours_to_check[0] #and deleted on the to check list + + if current_node not in visited: #if it has not previously been checked + if self.grid.fields["A"][current_node] >= threshold: #and its A value is above our threshold + new_neighbors = self.grid.get_neighborhood(current_node) #then we get its neighbours + if new_neighbors not in visited: #if they have not yet been visited + neighbours_to_check = neighbours_to_check + new_neighbors #then they are also added to our to check list + visited = visited + list([current_node]) #and the current node has now been checked + + neighbours_to_check = list(dict.fromkeys(neighbours_to_check)) #only check nodes once (unique values) + + if self.grid.fields["food"][current_node] > 0: #in case the node we check is food + connectivity += 1 #then we have found a connected path to a food source + connected_food_sources = connected_food_sources + list([current_node]) #and it is added to the list of connected food sources + + # why not normalize to 0-1 ? + return connectivity #we want the connectivity (0-5) def agent_density(self): a = np.zeros((self.grid.width, self.grid.height)) @@ -206,12 +197,14 @@ class ActiveWalkerModel(Model): def step(self): self.schedule.step() # step() and advance() all agents + self.connectivity = self.bfs(self) # apply decay rate on pheromone levels for key in ("A", "B"): field = self.grid.fields[key] self.grid.fields[key] = field - self.gamma*field + self.datacollector.collect(self) if self.schedule.steps >= self.max_steps: