"""
Unit tests for the Crowd class configuration and statistical validation.
Tests cover:
- Agent population initialization count
- Anthropometric statistic validation (means, proportions)
"""
# Copyright 2025 Institute of Light and Matter, CNRS UMR 5306, University Claude Bernard Lyon 1
# Contributors: Oscar DUFOUR, Maxime STAPELLE, Alexandre NICOLAS
# This software is a computer program designed to generate a realistic crowd from anthropometric data and
# simulate the mechanical interactions that occur within it and with obstacles.
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import numpy as np
import pytest
import configuration.utils.constants as cst
from configuration.models.crowd import Crowd
from configuration.models.measures import CrowdMeasures
NUMBER_AGENTS: int = 30
REPULSION_LENGTH: float = 5.0 # (cm)
DESIRED_DIRECTION: float = 90.0 # (degrees)
RANDOM_PACKING: bool = False
AGENT_STATISTICS: dict[str, float] = {
**cst.CrowdStat,
"male_proportion": 0.4,
"male_bideltoid_breadth_mean": 70.0, # cm
"male_bideltoid_breadth_std_dev": 3.0, # cm
}
[docs]
@pytest.fixture
def crowd() -> Crowd:
"""
Fixture to create a Crowd instance with predefined measures and agents.
Returns
-------
Crowd
An instance of Crowd with agents created and packed.
"""
crowd_measures = CrowdMeasures(agent_statistics=AGENT_STATISTICS)
crowd = Crowd(measures=crowd_measures)
crowd.create_agents(number_agents=NUMBER_AGENTS)
crowd.pack_agents_with_forces()
return crowd
[docs]
def test_crowd_number_of_agents(crowd: Crowd) -> None:
"""
Test that the crowd contains the expected number of agents.
Parameters
----------
crowd : Crowd
The crowd fixture.
"""
assert crowd.get_number_agents() == NUMBER_AGENTS, f"Expected {NUMBER_AGENTS} agents, but got {crowd.get_number_agents()} agents."
[docs]
def test_crowd_statistics_means_and_proportion(crowd: Crowd) -> None:
"""
Test that measured crowd statistics are close to the expected values.
Parameters
----------
crowd : Crowd
The crowd fixture.
"""
measured_stats = crowd.get_crowd_statistics()["measures"]
for key, value in measured_stats.items():
if "mean" in key and ("pedestrian" in key or "male" in key or "female" in key):
expected = AGENT_STATISTICS[key]
assert np.isclose(value, expected, rtol=0.1), f"Expected {key} to be close to {expected}, but got {value}."
if key == "male_proportion":
expected = AGENT_STATISTICS[key]
assert np.isclose(value, expected, atol=0.4), f"Expected {key} to be close to {expected}, but got {value}."