openworld_radio_twin.batch

Functions

build_batch_plan(payload)
generate_batch_dataset(payload, plan, ...[, ...])
slugify(value, fallback)

Classes

BatchCaseSpace(**data)
BatchDatasetRequest(**data)
BatchOutputSpec(**data)	Case artifacts retained after a successful simulation.
BatchPlan(**data)
BatchSceneSpec(**data)
ExpandedCase(**data)
FloatRange(**data)
IntegerRange(**data)
PlannedScene(**data)

class openworld_radio_twin.batch.FloatRange(**data)

Bases: BaseModel

minimum: float

maximum: float

ordered()

Return type:

FloatRange

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class openworld_radio_twin.batch.IntegerRange(**data)

Bases: BaseModel

minimum: int

maximum: int

ordered()

Return type:

IntegerRange

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class openworld_radio_twin.batch.BatchOutputSpec(**data)

Bases: BaseModel

Case artifacts retained after a successful simulation.

Case metadata is mandatory because it is the provenance and resume-completion record. Empty metric lists therefore provide a metadata-only output profile.

array_metrics: list[Literal['path_gain', 'rss', 'sinr']]

image_metrics: list[Literal['path_gain', 'rss', 'sinr']]

association_image: bool

classmethod unique_ordered_metrics(metrics)

Return type:

list[Literal['path_gain', 'rss', 'sinr']]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class openworld_radio_twin.batch.BatchCaseSpace(**data)

Bases: BaseModel

strategy: Literal['fixed', 'random']

count: int

engine: EngineName

engine_config: dict[str, str | int | float | bool]

transmitter_count: IntegerRange

frequency_ghz: FloatRange

power_dbm: FloatRange

altitude_m: FloatRange

offset_radius_m: FloatRange

azimuth_deg: FloatRange

downtilt_deg: FloatRange

antenna_patterns: list[Literal['sector', 'isotropic']]

resolutions_m: list[int]

max_depths: list[int]

samples_per_tx: list[int]

association_metrics: list[Literal['path_gain', 'rss', 'sinr']]

validate_space()

Return type:

BatchCaseSpace

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class openworld_radio_twin.batch.BatchSceneSpec(**data)

Bases: BaseModel

label: str

scene_mode: Literal['geospatial', 'empty']

latitude: float

longitude: float

radius_m: int

receiver_height_m: float

voxel_pitch_m: float

include_buildings: bool

building_source: str

include_terrain: bool

material_profile: Literal['itu', 'uniform']

terrain_resolution_m: float

cases: BatchCaseSpace

fit_transmitters_in_scene()

Return type:

BatchSceneSpec

property include_surface_materials: bool

Return whether semantic surface classes are required by this scene.

property include_environment_grid: bool

Return whether a gridded ground (DEM or flat) is compiled for this scene.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class openworld_radio_twin.batch.BatchDatasetRequest(**data)

Bases: BaseModel

dataset_name: str

random_seed: int

continue_on_error: bool

output: BatchOutputSpec

scenes: list[BatchSceneSpec]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class openworld_radio_twin.batch.ExpandedCase(**data)

Bases: BaseModel

name: str

seed: int

request: SimulationRequest

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class openworld_radio_twin.batch.PlannedScene(**data)

Bases: BaseModel

name: str

label: str

specification: BatchSceneSpec

cases: list[ExpandedCase]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class openworld_radio_twin.batch.BatchPlan(**data)

Bases: BaseModel

schema_version: int

dataset_name: str

dataset_slug: str

random_seed: int

generator: str

total_scenes: int

total_cases: int

total_receiver_cells: int

total_initial_ray_samples: int

scenes: list[PlannedScene]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

openworld_radio_twin.batch.slugify(value, fallback)

Return type:

str

openworld_radio_twin.batch.build_batch_plan(payload)

Return type:

BatchPlan

async openworld_radio_twin.batch.generate_batch_dataset(payload, plan, output_root, provider, building_limit, sionna_lock, worker, progress, resume=False)

Return type:

Path