"""
The methods and classes that are not defined in Python are defined in C++ py_main.cpp.
"""
from ..lib.BTLS import (
_ConfigData,
_LaneFlowComposition,
_VehicleGenNominal,
_VehModelDataNominal,
_VehicleGenGrave,
_VehModelDataGrave,
_VehicleGenGarage,
_VehModelDataGarage,
_VehClassPattern,
_VehClassAxle,
Vehicle,
_Vehicle,
)
from .lfc import LaneFlowComposition
from ..garage.read import read_garage_file
from typing import Literal, Union
from pathlib import Path
import importlib.resources as pkg_resources
__all__ = ["VehicleGenNominal", "VehicleGenGrave", "VehicleGenGarage"]
[docs]
class VehicleGenNominal:
def __init__(self, nominal_vehicle: Vehicle, COV_list: list[float], **kwargs):
"""
The VehicleGenNominal instance in Python stores the data for creating a CVehicleGenNominal instance in C++.\n
All heavy vehicles will be generated based on the intput nominal vehicle.
Parameters
----------
nominal_vehicle : Vehicle\n
Nominal vehicle.
COV_list : list[float]\n
List of COV for axle spacing and axle weight. \n
COV_list = [COV_AS, COV_AW].
Keyword Arguments
-----------------
classifier_type : str\n
Vehicle classifier type. "axle" or "pattern". Default is "pattern".
lane_eccentricity_std : float\n
Standard deviation of lane eccentricity. Default is 0.0.
kernel_type : int\n
Kernel type. 0 (Normal) or 1 (Triangle). Default is 1 (Triangle).
"""
self._tag = "Nominal"
self._config = _ConfigData()
if len(COV_list) != 2:
raise ValueError("Invalid COV list for nominal vehicle generator.")
self._nominal_vehicle = nominal_vehicle
self._COV_list = COV_list # COV_list = [COV_AS, COV_AW]
self._set_config(**kwargs)
def __getstate__(self):
attribute_dict = {}
attribute_dict["tag"] = self._tag
attribute_dict["config"] = self._config
attribute_dict["nominal_vehicle"] = self._nominal_vehicle
attribute_dict["COV_list"] = self._COV_list
return attribute_dict
def __setstate__(self, attribute_dict):
self._tag = attribute_dict["tag"]
self._config = attribute_dict["config"]
self._nominal_vehicle = attribute_dict["nominal_vehicle"]
self._COV_list = attribute_dict["COV_list"]
@property
def tag(self) -> str:
return self._tag
def _get_VC(self):
return self._config._Traffic.CLASSIFICATION
def _set_config(self, **kwargs):
classifier_type = 0 if kwargs.get("classifier_type") == "axle" else 1
lane_eccentricity_std = kwargs.get("lane_eccentricity_std", 0.0)
kernel_type = kwargs.get("kernel_type", 1)
self._config.set_veh_gen_nominal(
vehicle_classifier=classifier_type,
lane_eccentricity_std=lane_eccentricity_std, # in cm
kernel_type=kernel_type,
)
def _check_lfc(self, lfc: LaneFlowComposition):
if not lfc.flow_assigned:
raise ValueError(
"Flow data is not included in the LaneFlowComposition instance."
)
def _get_generator(
self, lfc: _LaneFlowComposition
) -> tuple[_VehicleGenNominal, _VehModelDataNominal]:
"""
Get a CVehicleGenNominal instance and a CVehModelDataNominal instance (generator and its model data).
"""
if self._config._Traffic.CLASSIFICATION == 1:
vehicle_classifier = _VehClassPattern()
else:
vehicle_classifier = _VehClassAxle()
model_data = _VehModelDataNominal(
self._config, vehicle_classifier, lfc, self._nominal_vehicle, self._COV_list
)
return _VehicleGenNominal(model_data), model_data
[docs]
class VehicleGenGrave:
def __init__(
self,
traffic_site: Literal[
"A196",
"A296",
"Angers",
"Auxerre",
"B224",
"Samaris-D",
"Samaris-D1",
"Samaris-D2",
"Samaris-D3",
"Samaris-S",
"Samaris-S1",
"Samaris-S2",
"Samaris-S3",
],
truck_track_width: float = 190.0,
**kwargs,
):
"""
The VehicleGenGrave instance in Python stores the data for creating a CVehicleGenGrave instance in C++.\n
All heavy vehicles will be generated by sampling from the pre-studied distributions of vehicle properties.
Parameters
----------
traffic_site : Literal['A196','A296','Angers','Auxerre','B224','Samaris-D','Samaris-D1','Samaris-D2','Samaris-D3','Samaris-S','Samaris-S1','Samaris-S2','Samaris-S3']\n
Traffic site. These sites are all in Europe.
truck_track_width : float optional\n
Truck track width. Default is 190.0.
Keyword Arguments
-----------------
classifier_type : str\n
Vehicle classifier type. "axle" or "pattern". Default is "pattern".
lane_eccentricity_std : float\n
Standard deviation of lane eccentricity. Default is 0.0.
"""
self._tag = "Grave"
self._config = _ConfigData()
if traffic_site not in [
"A196",
"A296",
"Angers",
"Auxerre",
"B224",
"Samaris-D",
"Samaris-D1",
"Samaris-D2",
"Samaris-D3",
"Samaris-S",
"Samaris-S1",
"Samaris-S2",
"Samaris-S3",
]:
raise ValueError("Unrecorded traffic site for Grave model.")
self._traffic_site = traffic_site
self._truck_track_width = truck_track_width
self._set_config(**kwargs)
def __getstate__(self):
attribute_dict = {}
attribute_dict["tag"] = self._tag
attribute_dict["config"] = self._config
return attribute_dict
def __setstate__(self, attribute_dict):
self._tag = attribute_dict["tag"]
self._config = attribute_dict["config"]
@property
def tag(self) -> str:
return self._tag
def _get_VC(self):
return self._config._Traffic.CLASSIFICATION
def _set_config(self, **kwargs):
classifier_type = 0 if kwargs.get("classifier_type") == "axle" else 1
lane_eccentricity_std = kwargs.get("lane_eccentricity_std", 0.0)
traffic_folder = str(
pkg_resources.files("pybtls").joinpath(
"data/GraveParameters/" + self._traffic_site
)
)
self._config.set_veh_gen_grave(
vehicle_classifier=classifier_type,
traffic_folder=traffic_folder,
truck_track_width=self._truck_track_width,
lane_eccentricity_std=lane_eccentricity_std, # in cm
)
def _check_lfc(self, lfc: LaneFlowComposition):
if not lfc.flow_assigned:
raise ValueError(
"Flow data is not included in the LaneFlowComposition instance."
)
if not lfc.truck_composition_assigned:
raise ValueError(
"Truck composition data is not included in the LaneFlowComposition instance."
)
def _get_generator(
self, lfc: _LaneFlowComposition
) -> tuple[_VehicleGenGrave, _VehModelDataGrave]:
"""
Get a CVehicleGenGrave instance and a CVehModelDataGrave instance (generator and its model data).
"""
if self._config._Traffic.CLASSIFICATION == 1:
vehicle_classifier = _VehClassPattern()
else:
vehicle_classifier = _VehClassAxle()
model_data = _VehModelDataGrave(self._config, vehicle_classifier, lfc)
return _VehicleGenGrave(model_data), model_data
[docs]
class VehicleGenGarage:
def __init__(
self,
garage: Union[Path, list[_Vehicle]],
kernel: list[list[float]],
garage_format: Literal[1, 2, 3, 4] = None,
**kwargs,
):
"""
The VehicleGenGarage instance in Python stores the data for creating a CVehicleGenGarage instance in C++.\n
This generation is a bootstrapping process.
Parameters
----------
garage : Union[Path, list[_Vehicle]]\n
The path to the garage file,
or a list of Vehicle objects.
kernel : list[list[float]]\n
The kernel is to ensure variation between the generated vehicles and garages. \n
kernel = [\n
[Mean_GVW, Std_GVW],\n
[Mean_AxleWeight, Std_AxleWeight],\n
[Mean_AxleSpacing, Std_AxleSpacing]\n
].
garage_format : Literal[1,2,3,4], optional\n
The format of the .txt garage file.\n
1: CASTOR format.\n
2: BEDIT format.\n
3: DITIS format.\n
4: MON format.
Keyword Arguments
-----------------
classifier_type : str\n
Vehicle classifier type. "axle" or "pattern". Default is "pattern".
lane_eccentricity_std : float\n
Standard deviation of lane eccentricity. Default is 0.0.
kernel_type : int\n
Kernel type. 0 (Normal) or 1 (Triangle). Default is 1 (Triangle).
"""
self._tag = "Garage"
self._config = _ConfigData()
if len(kernel) != 3 and not all(len(sublist) == 2 for sublist in kernel):
raise ValueError("Invalid kernel data for garage vehicle generator.")
if isinstance(garage, (Path, str)):
if garage_format is None:
raise ValueError("Garage format is not specified.")
self._garage = (
Path(garage).resolve()
if not isinstance(garage, Path)
else garage.resolve()
)
self._garage_format = garage_format
elif isinstance(garage, list):
if all(isinstance(vehicle, (Vehicle, _Vehicle)) for vehicle in garage):
self._garage = garage
else:
raise ValueError("Existing non-Vehicle object in the garage.")
else:
raise ValueError("Invalid garage data for garage vehicle generator.")
self._kernel = kernel # kernel = [[Mean_GVW, Std_GVW], [Mean_AW, Std_AW], [Mean_AS, Std_AS]]
self._set_config(**kwargs)
def __getstate__(self):
attribute_dict = {}
attribute_dict["tag"] = self._tag
attribute_dict["config"] = self._config
attribute_dict["garage"] = self._garage
attribute_dict["garage_format"] = self._garage_format
attribute_dict["kernel"] = self._kernel
return attribute_dict
def __setstate__(self, attribute_dict):
self._tag = attribute_dict["tag"]
self._config = attribute_dict["config"]
self._garage = attribute_dict["garage"]
self._garage_format = attribute_dict["garage_format"]
self._kernel = attribute_dict["kernel"]
@property
def tag(self) -> str:
return self._tag
def _get_VC(self):
return self._config._Traffic.CLASSIFICATION
def _set_config(self, **kwargs):
classifier_type = 0 if kwargs.get("classifier_type") == "axle" else 1
lane_eccentricity_std = kwargs.get("lane_eccentricity_std", 0.0)
kernel_type = kwargs.get("kernel_type", 1)
self._config.set_veh_gen_garage(
vehicle_classifier=classifier_type,
lane_eccentricity_std=lane_eccentricity_std, # in cm
kernel_type=kernel_type,
)
def _check_lfc(self, lfc: LaneFlowComposition):
if not lfc.flow_assigned:
raise ValueError(
"Flow data is not included in the LaneFlowComposition instance."
)
def _get_generator(
self, lfc: _LaneFlowComposition
) -> tuple[_VehicleGenGarage, _VehModelDataGarage]:
"""
Get a CVehicleGenGarage instance and a CVehModelDataGarage instance (generator and its model data).
"""
if self._config._Traffic.CLASSIFICATION == 1:
vehicle_classifier = _VehClassPattern()
else:
vehicle_classifier = _VehClassAxle()
vehicle_list = (
read_garage_file(self._garage, self._garage_format)
if isinstance(self._garage, Path)
else self._garage
)
model_data = _VehModelDataGarage(
self._config, vehicle_classifier, lfc, vehicle_list, self._kernel
)
return _VehicleGenGarage(model_data), model_data
