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Refactor run_simulation function
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@chraibi
chraibi committed Oct 13, 2024
1 parent b066925 commit 75c4322
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Showing 6 changed files with 1,827 additions and 141 deletions.
4 changes: 2 additions & 2 deletions app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@
if c1.button("Run Simulation"):
call_simulation(CONFIG_FILE, OUTPUT_FILE, data)

if c2.button("Visualisation"):
if c2.button("Visualization"):
output_path = Path(OUTPUT_FILE)
if output_path.exists():
trajectory_data, walkable_area = read_sqlite_file(OUTPUT_FILE)
Expand All @@ -67,7 +67,7 @@
x1 = 0.5 * (vertices[0][0] + vertices[1][0]) + width
y1 = 0.5 * (vertices[1][1] + vertices[1][1]) + width

st.info(f"{x0=}, {y0=}, {x1=}, {y1=}")
st.info(f"Motivation door: {x0=}, {y0=}, {x1=}, {y1=}")
anm = animate(
data_with_speed,
walkable_area,
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8 changes: 4 additions & 4 deletions files/inifile.json
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Original file line number Diff line number Diff line change
Expand Up @@ -10,16 +10,16 @@
"a_ped_min": 0.1,
"a_ped_max": 0.5,
"d_ped_min": 0.4,
"d_ped_max": 0.6,
"d_ped_max": 0.4,
"a_wall": 0.4,
"d_wall": 0.2,
"radius": 0.10
},
"simulation_parameters": {
"fps": 60,
"fps": 100,
"time_step": 0.001,
"number_agents": 50,
"simulation_time": 500
"number_agents": 82,
"simulation_time": 200
},
"measurement_line": {
"vertices": [
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1,656 changes: 1,643 additions & 13 deletions positions.ipynb
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213 changes: 114 additions & 99 deletions simulation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,12 @@
parse_way_points,
)
from src.logger_config import init_logger, log_debug, log_error
from src.utilities import build_geometry, distribute_and_add_agents, init_journey
from src.utilities import (
build_geometry,
distribute_and_add_agents,
init_journey,
calculate_distance,
)

# import cProfile
# import pstats
Expand Down Expand Up @@ -135,15 +140,14 @@ def init_simulation(
_trajectory_path: pathlib.Path,
from_file: bool = True,
) -> Any:
"""Initialise geometry.
"""Initialize geometry.
:param data:
:type data: str
:param time_step:
:type time_step: float
:returns:
"""

accessible_areas = parse_accessible_areas(_data)
if from_file:
geometry = from_wkt(
Expand Down Expand Up @@ -184,106 +188,103 @@ def adjust_parameter_linearly(
return min_value + (max_value - min_value) * motivation_i


def run_simulation(
def process_agent(
agent: jps.Agent,
door: List[float],
simulation: jps.Simulation,
motivation_model: mm.MotivationModel,
_simulation_time: float,
ped_ids: List[int],
_data,
msg: Any,
) -> None:
"""Run simulation logic.
a_ped_min: float,
a_ped_max: float,
d_ped_min: float,
d_ped_max: float,
default_strength: float,
default_range: float,
file_handle: _io.TextIOWrapper,
frame_to_write: int,
):
"""Process an individual agent by calculating motivation and updating model parameters."""
position = agent.position
distance = calculate_distance(position, door)

:param simulation:
:type simulation:
:param writer:
:type writer:
:returns:
params = {
"agent_id": agent.id,
"distance": distance,
"number_agents_in_simulation": simulation.agent_count(),
}

"""
# profiler = cProfile.Profile()
# profiler.enable()
# deltas = []
x_door = 0.5 * (motivation_model.door_point1[0] + motivation_model.door_point2[0])
y_door = 0.5 * (motivation_model.door_point1[1] + motivation_model.door_point2[1])
door = [x_door, y_door]
# logging.info("init initial speed")
# to generate some initial frames with speed = 0, since pedpy can not calculate 0 speeds.
a_ped, d_ped, a_wall, d_wall, a_ped_min, a_ped_max, d_ped_min, d_ped_max = (
parse_velocity_init_parameters(_data)
motivation_i = motivation_model.motivation_strategy.motivation(params)

if motivation_i > 1:
logging.error(f"{simulation.iteration_count()}: {agent.id}: {motivation_i = }")

v_0, time_gap = motivation_model.calculate_motivation_state(motivation_i, agent.id)

# Adjust agent parameters based on motivation
agent.model.strength_neighbor_repulsion = adjust_parameter_linearly(
motivation_i=motivation_i,
min_value=a_ped_min,
default_value=default_strength,
max_value=a_ped_max,
)

agent.model.range_neighbor_repulsion = adjust_parameter_linearly(
motivation_i=motivation_i,
min_value=d_ped_min,
default_value=default_range,
max_value=d_ped_max,
)

agent.model.v0 = v_0
agent.model.time_gap = time_gap

# Write the processed data to the file
write_value_to_file(
file_handle,
f"{frame_to_write}, {agent.id}, {simulation.elapsed_time()}, {motivation_i}, {position[0]}, {position[1]}",
)

for agent_id in ped_ids:
value_agent = motivation_model.motivation_strategy.get_value(agent_id=agent_id)
simulation.agent(agent_id).model.v0 *= value_agent
# TODO:
# simulation.agent(agent_id).model.strength_neighbor_repulsion = 0.22
default_range = simulation.agent(agent_id).model.range_neighbor_repulsion
default_strength = simulation.agent(agent_id).model.strength_neighbor_repulsion
# print(f"{simulation.agent(agent_id).model.strength_neighbor_repulsion =}")
# print(f"{simulation.agent(agent_id).model.range_neighbor_repulsion =}")

# print(
# value_agent,
# simulation.agent(agent_id).model.v0,
# simulation.agent(agent_id).model.strength_neighbor_repulsion,
# )
logging.info(f"{default_range=}")
with open("values.txt", "w", encoding="utf-8") as file_handle:

def run_simulation_loop(
simulation: jps.Simulation,
door: List[float],
motivation_model: mm.MotivationModel,
simulation_time: float,
a_ped_min: float,
a_ped_max: float,
d_ped_min: float,
d_ped_max: float,
default_strength: float,
default_range: float,
every_nth_frame: int,
motivation_file: pathlib.Path,
) -> None:
"""Run the main simulation loop and write motivation data to a file."""
with open(motivation_file, "w", encoding="utf-8") as file_handle:
frame_to_write = 0

while (
simulation.elapsed_time() < _simulation_time
and simulation.agent_count() > 0
simulation.elapsed_time() < simulation_time and simulation.agent_count() > 0
):
simulation.iterate()
if simulation.iteration_count() % 100 == 0:
number_agents_in_simulation = simulation.agent_count()
print(f"time: {simulation.elapsed_time():.2f}", end="\r")

if simulation.iteration_count() % every_nth_frame == 0:
for agent in simulation.agents():
position = agent.position
distance = (
(position[0] - door[0]) ** 2 + (position[1] - door[1]) ** 2
) ** 0.5
params = {
"agent_id": agent.id,
"distance": distance,
"number_agents_in_simulation": number_agents_in_simulation,
}
motivation_i = motivation_model.motivation_strategy.motivation(
params
)
if motivation_i > 1:
logging.error(
f"{simulation.iteration_count()}: {agent.id}: {motivation_i = }"
)
v_0, time_gap = motivation_model.calculate_motivation_state(
motivation_i, agent.id
)
agent.model.strength_neighbor_repulsion = adjust_parameter_linearly(
motivation_i=motivation_i,
min_value=a_ped_min,
default_value=default_strength,
max_value=a_ped_max,
)
# # D
agent.model.range_neighbor_repulsion = adjust_parameter_linearly(
motivation_i=motivation_i,
min_value=d_ped_min,
default_value=default_range,
max_value=d_ped_max,
)
agent.model.v0 = v_0
agent.model.time_gap = time_gap
# print(
# f"{ agent.model.range_neighbor_repulsion=}, {d_ped_min=}, {d_ped_max}"
# )
# if agent.id == -3:
# logging.info(
# f"{simulation.iteration_count()}, Agent={agent.id}, {agent.model.strength_neighbor_repulsion =}, {agent.model.v0 = :.2f}, {time_gap = :.2f}, {motivation_i = }, Pos: {position[0]:.2f} {position[1]:.2f}"
# )

write_value_to_file(
process_agent(
agent,
door,
simulation,
motivation_model,
a_ped_min,
a_ped_max,
d_ped_min,
d_ped_max,
default_strength,
default_range,
file_handle,
f"{position[0]} {position[1]} {motivation_i} {v_0} {time_gap} {distance}",
frame_to_write,
)
frame_to_write += 1
simulation.iterate()


def create_agent_parameters(
Expand Down Expand Up @@ -390,13 +391,18 @@ def main(
:param trajectory_file:
:returns:
"""
motivation_file = _trajectory_path.with_name(
_trajectory_path.stem + "_motivation.txt"
)
logging.info(f"{motivation_file}")
simulation = init_simulation(_data, _time_step, _fps, _trajectory_path)
a_ped, d_ped, a_wall, d_wall, a_ped_min, a_ped_max, d_ped_min, d_ped_max = (
parse_velocity_init_parameters(_data)
)
agent_parameters_list, exit_positions = create_agent_parameters(_data, simulation)
# positions = init_positions(_data, _number_agents)
positions = read_positions_from_csv(file_path="1C060_frame_3951.csv")
logging.info(f"Number of Agents {len(positions)}")
# positions = read_positions_from_csv(file_path="debug.csv")
ped_ids = distribute_and_add_agents(
simulation=simulation,
Expand All @@ -405,15 +411,24 @@ def main(
exit_positions=exit_positions,
)
motivation_model = init_motivation_model(_data, ped_ids)
x_door = 0.5 * (motivation_model.door_point1[0] + motivation_model.door_point2[0])
y_door = 0.5 * (motivation_model.door_point1[1] + motivation_model.door_point2[1])
motivation_door = [x_door, y_door]
logging.info(f"Running simulation for {len(ped_ids)} agents:")
logging.info(f"{motivation_model.motivation_strategy.width = }")
run_simulation(
simulation,
motivation_model,
_simulation_time,
ped_ids,
_data,
msg,
run_simulation_loop(
simulation=simulation,
door=motivation_door,
motivation_model=motivation_model,
simulation_time=_simulation_time,
a_ped_min=a_ped_min,
a_ped_max=a_ped_max,
d_ped_min=d_ped_min,
d_ped_max=d_ped_max,
default_strength=a_ped,
default_range=d_ped,
every_nth_frame=_data["simulation_parameters"]["fps"],
motivation_file=motivation_file,
)
logging.info(
f"Simulation completed after {simulation.iteration_count()} iterations"
Expand Down
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