Coverage for .tox/p313/lib/python3.13/site-packages/scicom/historicalletters/model.py: 97%
111 statements
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1"""The model class for HistoricalLetters."""
2import random
3from pathlib import Path
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
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
19def getPrunedLedger(model: mesa.Model) -> pd.DataFrame:
20 """Model reporter for simulation of archiving.
22 Returns statistics of ledger network of model run
23 and various iterations of statistics of pruned networks.
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
55def getComponents(model: mesa.Model) -> int:
56 """Model reporter to get number of components.
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)
67def getScaledLetters(model: mesa.Model) -> float:
68 """Return relative number of send letters."""
69 return len(model.letterLedger)/model.steps if model.steps > 0 else 0
72def getScaledMovements(model: mesa.Model) -> float:
73 """Return relative number of movements."""
74 return model.movements/model.steps if model.steps > 0 else 0
77class HistoricalLetters(mesa.Model):
78 """A letter sending model with historical informed initital positions.
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].
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.
89 [1] J. Lobo et al, Population-Area Relationship for Medieval European Cities,
90 PLoS ONE 11(10): e0162678.
91 """
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__()
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.scaleSendInput = {}
130 self.updatedTopicsDict = {}
131 self.updatedPositionDict = {}
132 self.space = Nuts2Eu()
133 self.debug = debug
135 #######
136 # Initialize region agents
137 #######
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 )
145 # Add regions to Nuts2Eu geospace
146 self.space.add_regions(self.regions)
148 #######
149 # Initialize sender agents
150 #######
152 # Draw initial geographic positions of agents
153 initSenderGeoDf = createData(
154 population,
155 populationDistribution=populationDistributionData,
156 )
158 # Calculate mean of mean distances for each agent.
159 # This is used as a measure for the range of exchanges.
160 meandistances = []
161 for idx in initSenderGeoDf.index.to_numpy():
162 geom = initSenderGeoDf.loc[idx, "geometry"]
163 otherAgents = initSenderGeoDf.query("index != @idx").copy()
164 geometries = otherAgents.geometry.to_numpy()
165 distances = [geom.distance(othergeom) for othergeom in geometries]
166 meandistances.append(mean(distances))
167 self.meandistance = mean(meandistances)
169 # Populate factors dictionary
170 self.factors = {
171 "similarityThreshold": similarityThreshold,
172 "moveRange": moveRange,
173 "letterRange": letterRange,
174 }
176 # Set up agent creator for senders
177 ac_senders = mg.AgentCreator(
178 SenderAgent,
179 model=self,
180 agent_kwargs=self.factors,
181 )
183 # Create agents based on random coordinates generated
184 # in the createData step above, see util.py file.
185 senders = ac_senders.from_GeoDataFrame(
186 initSenderGeoDf,
187 )
189 # Create random set of initial topic vectors.
190 topics = [
191 tuple(
192 [random.random() for x in range(3)],
193 ) for x in range(self.population)
194 ]
196 # Setup senders
197 for idx, sender in enumerate(senders):
198 # Add to social network
199 self.socialNetwork.add_node(
200 sender.unique_id,
201 numLettersSend=0,
202 numLettersReceived=0,
203 )
204 # Give sender topic
205 sender.topicVec = topics[idx]
206 # Add current topic to dict
207 self.updatedTopicsDict.update(
208 {sender.unique_id: topics[idx]},
209 )
210 # Set random activation weight
211 if useActivation is True:
212 sender.activationWeight = random.random()
213 # Add sender to its region
214 regionID = [
215 x.unique_id for x in self.regions if contains(x.geometry, sender.geometry)
216 ]
217 try:
218 self.space.add_sender(sender, regionID[0])
219 except IndexError as exc:
220 text = f"Problem finding region for {sender.geometry}."
221 raise IndexError(text) from exc
222 # Prepopulate positions dict
223 self.updatedPositionDict.update(
224 {sender.unique_id: [sender.geometry, regionID[0]]},
225 )
227 # Create social network
228 if useSocialNetwork is True:
229 for agent in self.agents_by_type[SenderAgent]:
230 self._createSocialEdges(agent, self.socialNetwork)
232 self.datacollector = mesa.DataCollector(
233 model_reporters={
234 "Ledger": getPrunedLedger,
235 "Letters": getScaledLetters ,
236 "Movements": getScaledMovements,
237 "Clusters": getComponents,
238 },
239 )
241 def _createSocialEdges(self, agent: SenderAgent, graph: nx.MultiDiGraph) -> None:
242 """Create social edges with the different wiring factors.
244 Define a close range by using the moveRange parameter. Among
245 these neighbors, create a connection with probability set by
246 the shortRangeNetworkFactor.
248 For all other agents, that are not in this closeRange group,
249 create a connection with the probability set by the longRangeNetworkFactor.
250 """
251 closerange = [x for x in self.space.get_neighbors_within_distance(
252 agent,
253 distance=self.moveRange * self.meandistance,
254 center=False,
255 ) if isinstance(x, SenderAgent)]
256 for neighbor in closerange:
257 if neighbor.unique_id != agent.unique_id:
258 connect = random.choices(
259 population=[True, False],
260 weights=[self.shortRangeNetworkFactor, 1 - self.shortRangeNetworkFactor],
261 k=1,
262 )
263 if connect[0] is True:
264 graph.add_edge(agent.unique_id, neighbor.unique_id, step=0)
265 longrange = [x for x in self.agents_by_type[SenderAgent] if x not in closerange]
266 for neighbor in longrange:
267 if neighbor.unique_id != agent.unique_id:
268 connect = random.choices(
269 population=[True, False],
270 weights=[self.longRangeNetworkFactor, 1 - self.longRangeNetworkFactor],
271 k=1,
272 )
273 if connect[0] is True:
274 graph.add_edge(agent.unique_id, neighbor.unique_id, step=0)
276 def step(self) -> None:
277 """One simulation step with data collection."""
278 self.step_no_data()
279 self.datacollector.collect(self)
281 def step_no_data(self) -> None:
282 """One simulation step without data collection."""
283 self.scaleSendInput.update(
284 **{str(x.unique_id): x.numLettersReceived for x in self.agents_by_type[SenderAgent]},
285 )
286 # Update the currently held topicVec for each agent, based
287 # on potential previouse communication events and the position
288 # based on a previous movement.
289 for agent in self.agents_by_type[SenderAgent]:
290 agent.topicVec = self.updatedTopicsDict[agent.unique_id]
291 newGeom = self.updatedPositionDict[agent.unique_id]
292 if newGeom[0] != agent.geometry:
293 self.space.move_sender(agent, newGeom[0], newGeom[1])
294 self.agents_by_type[SenderAgent].shuffle_do("step")
296 def run(self, n:int) -> None:
297 """Run the model for n steps.
299 Data collection is only run at the end of n steps.
300 This is useful for batch runs accross different
301 parameters.
302 """
303 if self.debug is True:
304 for _ in tqdm(range(n)):
305 self.step_no_data()
306 else:
307 for _ in range(n):
308 self.step_no_data()
309 self.datacollector.collect(self)