"""
Tests the force tangential to the contact surface, representing a damped spring interaction between two agents.
Tests cover:
- Time and position continuity
- Positive or near zero angular velocity (omega) during the whole simulation
"""
# Copyright 2025 Institute of Light and Matter, CNRS UMR 5306, University Claude Bernard Lyon 1
# Contributors: Oscar DUFOUR, Maxime STAPELLE, Alexandre NICOLAS
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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 each agent.
MAX_SPATIAL_JUMP = 1
#: Tolerance for the positivity of the angular velocity (rad/s) during the whole simulation.
OMEGA_CONTACT_TOL = 0.1
[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_positive_or_near_zero(df: pd.DataFrame) -> None:
"""
Angular velocity should be positive or near zero for all agents over the whole simulation.
Parameters
----------
df : pd.DataFrame
DataFrame containing all time series.
"""
required_cols = {"ID", "omega"}
missing = required_cols - set(df.columns)
assert not missing, f"Missing expected columns: {missing}"
violations: list[tuple[int, float]] = []
for agent_id, g in df.groupby("ID"):
omega = g["omega"].to_numpy()
if np.any(omega < -OMEGA_CONTACT_TOL):
violations.append((agent_id, float(omega.min())))
assert not violations, f"omega has significantly negative values: {violations}"