"""
Tests the behaviour as an agent rotates and gradually relaxes to a stationary state (no motion), due to the fluid-like torque.
Tests cover:
- Time and position continuity
- Angular velocity (omega) near zero during the whole simulation
- Translational velocity vx positive or near zero during the whole simulation
- Translation velocity vy near zero during the whole simulation
- Orientation (theta) near constant during the whole simulation
- Stationary phase (with near constant x, y, theta and near zero vx, vy, omega)
"""
# 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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# of free software. You can use, modify and/ or redistribute the software under the terms of the CeCILL-B
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import subprocess
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from configuration.backup import xml_to_Chaos
#: Tolerance for the constancy of the decisional time step used throughout the simulation (s).
TIME_TOL = 1e-4
#: Maximum allowed spatial jump (m) between consecutive time steps for the agent.
MAX_SPATIAL_JUMP = 1
#: Opposite of the minimum allowed velocity along x during the whole simulation (m/s).
VX_TOL = 1e-2
#: Maximum allowed velocity along y during the whole simulation (m/s).
VY_TOL = 1e-2
#: Maximum allowed angular velocity during the whole simulation (rad/s).
OMEGA_TOL = 1e-2
#: Maximum allowed range for orientation (theta) during the whole simulation (radians).
DELTA_THETA_TOL = 1e-2
#: Maximum allowed range for x during the stationary phase (m).
DELTA_Y_TOL = 1e-2
#: Maximum allowed range for y during the stationary phase (m).
DELTA_X_TOL = 1e-2
[docs]
@pytest.fixture(scope="session")
def df() -> pd.DataFrame:
"""
Export to CSV the XML files and load the time series once per test session.
Returns
-------
pd.DataFrame
DataFrame containing all time series.
"""
subprocess.run(
["uv", "run", "python", "run_simulation.py"],
check=True,
)
filenameCSV = "all_trajectories.csv" # Name of the final CSV file we’ll generate
PathXML = Path("inputXML") # Folder path where the XML files are located
PathCSV = Path("inputCSV") # Folder path where CSV files will be saved
PathCSV.mkdir(parents=True, exist_ok=True) # Create directories if it doesn't exist
xml_to_Chaos.export_XML_to_CSV(PathCSV, PathXML)
return pd.read_csv(PathCSV / filenameCSV)
[docs]
def test_time_and_position_continuity(df: pd.DataFrame) -> None:
"""
Test time and position continuity for each agent.
Parameters
----------
df : pd.DataFrame
DataFrame containing all time series.
"""
required_cols = {"ID", "t", "x", "y"}
missing = required_cols - set(df.columns)
assert not missing, f"Missing expected columns: {missing}"
# agent IDs with irregular time steps
violations_missing_time: list[int] = []
# (agent_id, list of jump distances > MAX_SPATIAL_JUMP)
violations_big_jump: list[tuple[int, list[float]]] = []
for agent_id, g in df.sort_values("t").groupby("ID"):
t = g["t"].to_numpy()
dt = np.diff(t)
ddt = np.diff(dt)
if not np.all(np.abs(ddt) < TIME_TOL):
violations_missing_time.append(int(agent_id))
x = g["x"].to_numpy()
y = g["y"].to_numpy()
dist = np.sqrt(np.diff(x) ** 2 + np.diff(y) ** 2)
bad_jump_idx = np.where(dist > MAX_SPATIAL_JUMP)[0]
if bad_jump_idx.size > 0:
violations_big_jump.append((int(agent_id), dist[bad_jump_idx].tolist()))
assert not violations_missing_time, f"Irregular time steps: {violations_missing_time}"
assert not violations_big_jump, f"Large spatial jumps: {violations_big_jump}"
[docs]
def test_omega_vx_vy_over_simulation(df: pd.DataFrame) -> None:
"""
Omega ~ 0, vx >= ~0 and vy ~ 0, theta ~ const during the whole simulation.
Parameters
----------
df : pd.DataFrame
DataFrame containing all time series.
"""
required_cols = {"ID", "vx", "vy", "omega", "theta"}
missing = required_cols - set(df.columns)
assert not missing, f"Missing expected columns: {missing}"
violations_omega: list[tuple[int, float]] = []
violations_vx: list[tuple[int, float]] = []
violations_vy: list[tuple[int, float]] = []
violations_theta: list[tuple[int, float]] = []
for agent_id, g in df.groupby("ID"):
omega = g["omega"].to_numpy()
vx = g["vx"].to_numpy()
vy = g["vy"].to_numpy()
theta = g["theta"].to_numpy()
# omega should stay near zero
max_abs_omega = float(np.abs(omega).max())
if max_abs_omega > OMEGA_TOL:
violations_omega.append((agent_id, max_abs_omega))
# vx should be positive or near zero; allow small negative values within VX_TOL
if np.any(vx < -VX_TOL):
min_vx = float(vx.min())
violations_vx.append((agent_id, min_vx))
# vy should stay near zero
max_abs_vy = float(np.abs(vy).max())
if max_abs_vy > VY_TOL:
violations_vy.append((agent_id, max_abs_vy))
# theta should stay nearly constant
theta_range = float(theta.max() - theta.min())
if theta_range > DELTA_THETA_TOL:
violations_theta.append((agent_id, theta_range))
assert not violations_omega, f"omega not ~0 for some agents: {violations_omega}"
assert not violations_vx, f"vx significantly negative for some agents: {violations_vx}"
assert not violations_vy, f"vy not ~0 for some agents: {violations_vy}"
assert not violations_theta, f"theta not constant for some agents: {violations_theta}"
[docs]
def test_stationary_phase(df: pd.DataFrame) -> None:
"""
Last 5% of the simulation is stationary: x, y, theta ~ const; vx, vy, omega ~ 0.
Parameters
----------
df : pd.DataFrame
DataFrame containing all time series.
"""
required_cols = {"ID", "t", "x", "y", "theta", "vx", "vy", "omega"}
missing = required_cols - set(df.columns)
assert not missing, f"Missing expected columns: {missing}"
violations_pos: list[tuple[int, float, float]] = []
violations_theta: list[tuple[int, float]] = []
violations_vel: list[tuple[int, float, float]] = []
violations_omega: list[tuple[int, float]] = []
for agent_id, g in df.groupby("ID"):
t_max = g["t"].max()
stationary = g[g["t"] >= t_max * 0.95]
x = stationary["x"].to_numpy()
y = stationary["y"].to_numpy()
theta = stationary["theta"].to_numpy()
vx = stationary["vx"].to_numpy()
vy = stationary["vy"].to_numpy()
omega = stationary["omega"].to_numpy()
x_range = float(x.max() - x.min())
y_range = float(y.max() - y.min())
if x_range > DELTA_X_TOL or y_range > DELTA_Y_TOL:
violations_pos.append((agent_id, x_range, y_range))
theta_range = float(theta.max() - theta.min())
if theta_range > DELTA_THETA_TOL:
violations_theta.append((agent_id, theta_range))
max_vx = float(np.abs(vx).max())
max_vy = float(np.abs(vy).max())
if max_vx > VX_TOL or max_vy > VY_TOL:
violations_vel.append((agent_id, max_vx, max_vy))
max_abs_omega = float(np.abs(omega).max())
if max_abs_omega > OMEGA_TOL:
violations_omega.append((agent_id, max_abs_omega))
assert not violations_pos, f"x or y not constant in stationary phase: {violations_pos}"
assert not violations_theta, f"theta not constant in stationary phase: {violations_theta}"
assert not violations_vel, f"vx or vy not ~0 in stationary phase: {violations_vel}"
assert not violations_omega, f"omega not ~0 in stationary phase: {violations_omega}"