Coverage for .tox/p311/lib/python3.11/site-packages/scicom/historicalletters/model.py: 95%

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1"""The model class for HistoricalLetters.""" 

2import random 

3from pathlib import Path 

4 

5import mesa 

6import mesa_geo as mg 

7import networkx as nx 

8import pandas as pd 

9from numpy import mean 

10from shapely import contains 

11from tqdm import tqdm 

12 

13from scicom.historicalletters.agents import RegionAgent, SenderAgent 

14from scicom.historicalletters.space import Nuts2Eu 

15from scicom.historicalletters.utils import createData 

16from scicom.utilities.statistics import prune 

17 

18 

19def getPrunedLedger(model: mesa.Model) -> pd.DataFrame: 

20 """Model reporter for simulation of archiving. 

21 

22 Returns statistics of ledger network of model run 

23 and various iterations of statistics of pruned networks. 

24 

25 The routine assumes that the network contains fields of sender, 

26 receiver and step information. 

27 """ 

28 if model.runPruning is True: 

29 ledgerColumns = ["sender", "receiver", "sender_location", "receiver_location", "topic", "step"] 

30 modelparams = { 

31 "population": model.population, 

32 "moveRange": model.moveRange, 

33 "letterRange": model.letterRange, 

34 "useActivation": model.useActivation, 

35 "useSocialNetwork": model.useSocialNetwork, 

36 "similarityThreshold": model.similarityThreshold, 

37 "longRangeNetworkFactor": model.longRangeNetworkFactor, 

38 "shortRangeNetworkFactor": model.shortRangeNetworkFactor, 

39 } 

40 result = prune( 

41 modelparameters=modelparams, 

42 network=model.letterLedger, 

43 columns=ledgerColumns, 

44 iterations=3, 

45 delAmounts=(0.1, 0.25, 0.5, 0.75, 0.9), 

46 delTypes=("unif", "exp", "beta", "log_normal1", "log_normal2", "log_normal3"), 

47 delMethod=("agents", "regions", "time"), 

48 rankedVals=(True, False), 

49 ) 

50 else: 

51 result = model.letterLedger 

52 return result 

53 

54 

55def getComponents(model: mesa.Model) -> int: 

56 """Model reporter to get number of components. 

57 

58 The MultiDiGraph is converted to undirected, 

59 considering only edges that are reciprocal, ie. 

60 edges are established if sender and receiver have 

61 exchanged at least a letter in each direction. 

62 """ 

63 newg = model.socialNetwork.to_undirected(reciprocal=True) 

64 return nx.number_connected_components(newg) 

65 

66 

67def getScaledLetters(model: mesa.Model) -> float: 

68 """Return relative number of send letters.""" 

69 return len(model.letterLedger)/model.schedule.time 

70 

71 

72def getScaledMovements(model: mesa.Model) -> float: 

73 """Return relative number of movements.""" 

74 return model.movements/model.schedule.time 

75 

76 

77class HistoricalLetters(mesa.Model): 

78 """A letter sending model with historical informed initital positions. 

79 

80 Each agent has an initial topic vector, expressed as a RGB value. The 

81 initial positions of the agents is based on a weighted random draw 

82 based on data from [1]. 

83 

84 Each step, agents generate two neighbourhoods for sending letters and 

85 potential targets to move towards. The probability to send letters is 

86 a self-reinforcing process. During each sending the internal topic of 

87 the sender is updated as a random rotation towards the receivers topic. 

88 

89 [1] J. Lobo et al, Population-Area Relationship for Medieval European Cities, 

90 PLoS ONE 11(10): e0162678. 

91 """ 

92 

93 def __init__( 

94 self, 

95 population: int = 100, 

96 moveRange: float = 0.05, 

97 letterRange: float = 0.2, 

98 similarityThreshold: float = 0.2, 

99 longRangeNetworkFactor: float = 0.3, 

100 shortRangeNetworkFactor: float = 0.4, 

101 regionData: str = Path(Path(__file__).parent.parent.resolve(), "data/NUTS_RG_60M_2021_3857_LEVL_2.geojson"), 

102 populationDistributionData: str = Path(Path(__file__).parent.parent.resolve(), "data/pone.0162678.s003.csv"), 

103 *, 

104 useActivation: bool = False, 

105 useSocialNetwork: bool = False, 

106 runPruning: bool = False, 

107 debug: bool = False, 

108 ) -> None: 

109 """Initialize a HistoricalLetters model.""" 

110 super().__init__() 

111 

112 # Parameters for agents 

113 self.population = population 

114 self.moveRange = moveRange 

115 self.letterRange = letterRange 

116 # Parameters for model 

117 self.runPruning = runPruning 

118 self.useActivation = useActivation 

119 self.similarityThreshold = similarityThreshold 

120 self.useSocialNetwork = useSocialNetwork 

121 self.longRangeNetworkFactor = longRangeNetworkFactor 

122 self.shortRangeNetworkFactor = shortRangeNetworkFactor 

123 # Initialize social network 

124 self.socialNetwork = nx.MultiDiGraph() 

125 # Output variables 

126 self.letterLedger = [] 

127 self.movements = 0 

128 # Internal variables 

129 self.schedule = mesa.time.RandomActivation(self) 

130 self.scaleSendInput = {} 

131 self.updatedTopicsDict = {} 

132 self.space = Nuts2Eu() 

133 self.debug = debug 

134 

135 ####### 

136 # Initialize region agents 

137 ####### 

138 

139 # Set up the grid with patches for every NUTS region 

140 # Create region agents 

141 ac = mg.AgentCreator(RegionAgent, model=self) 

142 self.regions = ac.from_file( 

143 regionData, 

144 unique_id="NUTS_ID", 

145 ) 

146 # Add regions to Nuts2Eu geospace 

147 self.space.add_regions(self.regions) 

148 

149 ####### 

150 # Initialize sender agents 

151 ####### 

152 

153 # Draw initial geographic positions of agents 

154 initSenderGeoDf = createData( 

155 population, 

156 populationDistribution=populationDistributionData, 

157 ) 

158 

159 # Calculate mean of mean distances for each agent. 

160 # This is used as a measure for the range of exchanges. 

161 meandistances = [] 

162 for idx in initSenderGeoDf.index.to_numpy(): 

163 name = initSenderGeoDf.loc[idx, "unique_id"] 

164 geom = initSenderGeoDf.loc[idx, "geometry"] 

165 otherAgents = initSenderGeoDf.query(f"unique_id != '{name}'").copy() 

166 geometries = otherAgents.geometry.to_numpy() 

167 distances = [geom.distance(othergeom) for othergeom in geometries] 

168 meandistances.append(mean(distances)) 

169 self.meandistance = mean(meandistances) 

170 

171 # Populate factors dictionary 

172 self.factors = { 

173 "similarityThreshold": similarityThreshold, 

174 "moveRange": moveRange, 

175 "letterRange": letterRange, 

176 } 

177 

178 # Set up agent creator for senders 

179 ac_senders = mg.AgentCreator( 

180 SenderAgent, 

181 model=self, 

182 agent_kwargs=self.factors, 

183 ) 

184 

185 # Create agents based on random coordinates generated 

186 # in the createData step above, see util.py file. 

187 senders = ac_senders.from_GeoDataFrame( 

188 initSenderGeoDf, 

189 unique_id="unique_id", 

190 ) 

191 

192 # Create random set of initial topic vectors. 

193 topics = [ 

194 tuple( 

195 [random.random() for x in range(3)], 

196 ) for x in range(self.population) 

197 ] 

198 

199 # Setup senders 

200 for idx, sender in enumerate(senders): 

201 # Add to social network 

202 self.socialNetwork.add_node( 

203 sender.unique_id, 

204 numLettersSend=0, 

205 numLettersReceived=0, 

206 ) 

207 # Give sender topic 

208 sender.topicVec = topics[idx] 

209 # Add current topic to dict 

210 self.updatedTopicsDict.update( 

211 {sender.unique_id: topics[idx]}, 

212 ) 

213 # Set random activation weight 

214 if useActivation is True: 

215 sender.activationWeight = random.random() 

216 # Add sender to its region 

217 regionID = [ 

218 x.unique_id for x in self.regions if contains(x.geometry, sender.geometry) 

219 ] 

220 try: 

221 self.space.add_sender(sender, regionID[0]) 

222 except IndexError as exc: 

223 text = f"Problem finding region for {sender.geometry}." 

224 raise IndexError(text) from exc 

225 # Add sender to schedule 

226 self.schedule.add(sender) 

227 

228 # Create social network 

229 if useSocialNetwork is True: 

230 for agent in self.schedule.agents: 

231 if isinstance(agent, SenderAgent): 

232 self._createSocialEdges(agent, self.socialNetwork) 

233 

234 self.datacollector = mesa.DataCollector( 

235 model_reporters={ 

236 "Ledger": getPrunedLedger, 

237 "Letters": getScaledLetters , 

238 "Movements": getScaledMovements, 

239 "Clusters": getComponents, 

240 }, 

241 ) 

242 

243 def _createSocialEdges(self, agent: SenderAgent, graph: nx.MultiDiGraph) -> None: 

244 """Create social edges with the different wiring factors. 

245 

246 Define a close range by using the moveRange parameter. Among 

247 these neighbors, create a connection with probability set by 

248 the shortRangeNetworkFactor. 

249 

250 For all other agents, that are not in this closeRange group, 

251 create a connection with the probability set by the longRangeNetworkFactor. 

252 """ 

253 closerange = [x for x in self.space.get_neighbors_within_distance( 

254 agent, 

255 distance=self.moveRange * self.meandistance, 

256 center=False, 

257 ) if isinstance(x, SenderAgent)] 

258 for neighbor in closerange: 

259 if neighbor.unique_id != agent.unique_id: 

260 connect = random.choices( 

261 population=[True, False], 

262 weights=[self.shortRangeNetworkFactor, 1 - self.shortRangeNetworkFactor], 

263 k=1, 

264 ) 

265 if connect[0] is True: 

266 graph.add_edge(agent.unique_id, neighbor.unique_id, step=0) 

267 longrange = [x for x in self.schedule.agents if x not in closerange and isinstance(x, SenderAgent)] 

268 for neighbor in longrange: 

269 if neighbor.unique_id != agent.unique_id: 

270 connect = random.choices( 

271 population=[True, False], 

272 weights=[self.longRangeNetworkFactor, 1 - self.longRangeNetworkFactor], 

273 k=1, 

274 ) 

275 if connect[0] is True: 

276 graph.add_edge(agent.unique_id, neighbor.unique_id, step=0) 

277 

278 def step(self) -> None: 

279 """One simulation step with data collection.""" 

280 self.step_no_data() 

281 self.datacollector.collect(self) 

282 

283 def step_no_data(self) -> None: 

284 """One simulation step without data collection.""" 

285 self.scaleSendInput.update( 

286 **{x.unique_id: x.numLettersReceived for x in self.schedule.agents}, 

287 ) 

288 # Update the currently held topicVec for each agent, based 

289 # on potential previouse communication events. 

290 for agent in self.schedule.agents: 

291 agent.topicVec = self.updatedTopicsDict[agent.unique_id] 

292 self.schedule.step() 

293 

294 def run(self, n:int) -> None: 

295 """Run the model for n steps. 

296 

297 Data collection is only run at the end of n steps. 

298 This is useful for batch runs accross different 

299 parameters. 

300 """ 

301 if self.debug is True: 

302 for _ in tqdm(range(n)): 

303 self.step_no_data() 

304 else: 

305 for _ in range(n): 

306 self.step_no_data() 

307 self.datacollector.collect(self)