api.task_logic

class aind_behavior_dynamic_foraging.task_logic.AindDynamicForagingTaskLogic(*, name: Literal['AindDynamicForaging'] = 'AindDynamicForaging', description: str = '', task_parameters: AindDynamicForagingTaskParameters, version: Literal['0.0.2-rc2'] = '0.0.2-rc2', stage_name: str | None = None)

Bases: Task

Main task logic model for the AIND Dynamic Foraging task.

This is the top-level class that encapsulates the complete task logic specification for the dynamic foraging behavioral experiment. It includes all task parameters, environment specifications, and control settings.

model_config = {'extra': 'forbid', 'str_strip_whitespace': True, 'strict': True, 'validate_assignment': True, 'validate_default': True}

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

name: Literal['AindDynamicForaging']

task_parameters: AindDynamicForagingTaskParameters

version: Literal['0.0.2-rc2']

class aind_behavior_dynamic_foraging.task_logic.AindDynamicForagingTaskParameters(*, rng_seed: float | None = None, aind_behavior_services_pkg_version: ~typing.Annotated[~typing.Literal['0.13.0-rc2'], _PydanticGeneralMetadata(pattern='^(0|[1-9]\\d*)\\.(0|[1-9]\\d*)\\.(0|[1-9]\\d*)(?:-((?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*)(?:\\.(?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\\+([0-9a-zA-Z-]+(?:\\.[0-9a-zA-Z-]+)*))?$')] = '0.13.0-rc2', block_parameters: ~aind_behavior_dynamic_foraging.task_logic.BlockParameters = BlockParameters(min=20, max=60, beta=20, min_reward=1), reward_probability: ~aind_behavior_dynamic_foraging.task_logic.RewardProbability = RewardProbability(base_reward_sum=0.8, family=1, pairs_n=1), uncoupled_reward: ~typing.Annotated[list[float] | None, ~annotated_types.MinLen(min_length=3), ~annotated_types.MaxLen(max_length=3)] = [0.1, 0.3, 0.7], randomness: ~typing.Literal['Exponential', 'Even'] = 'Exponential', delay_period: ~aind_behavior_dynamic_foraging.task_logic.DelayPeriod = DelayPeriod(min=0.0, max=1.0, beta=1.0), reward_delay: float = 0, auto_water: ~aind_behavior_dynamic_foraging.task_logic.AutoWater | None = None, inter_trial_interval: ~aind_behavior_dynamic_foraging.task_logic.InterTrialInterval = <factory>, response_time: ~aind_behavior_dynamic_foraging.task_logic.Response = Response(response_time=1.0, reward_consume_time=3.0), auto_block: ~aind_behavior_dynamic_foraging.task_logic.AutoBlock | None = None, reward_size: ~aind_behavior_dynamic_foraging.task_logic.RewardSize = RewardSize(right_value_volume=3.0, left_value_volume=3.0), warmup: ~aind_behavior_dynamic_foraging.task_logic.Warmup | None = None, no_response_trial_addition: bool = True, reward_n: ~aind_behavior_dynamic_foraging.task_logic.RewardN | None = None, lick_spout_retraction: bool | None = False, **extra_data: ~typing.Any)

Bases: TaskParameters

Complete parameter specification for the AIND Dynamic Foraging task.

This class contains all configurable parameters for the Dynamic Foraging task, including environment structure, task mode settings, operation control, and numerical updaters for dynamic parameter modification.

auto_block: AutoBlock | None

auto_water: AutoWater | None

block_parameters: BlockParameters

delay_period: DelayPeriod

inter_trial_interval: InterTrialInterval

lick_spout_retraction: bool | None

model_config = {'extra': 'allow', 'str_strip_whitespace': True, 'strict': True, 'validate_assignment': True, 'validate_default': True}

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

no_response_trial_addition: bool

randomness: Literal['Exponential', 'Even']

response_time: Response

reward_delay: float

reward_n: RewardN | None

reward_probability: RewardProbability

reward_size: RewardSize

uncoupled_reward: list[float] | None

warmup: Warmup | None

class aind_behavior_dynamic_foraging.task_logic.AutoBlock(*, advanced_block_auto: Literal['now', 'once'] = 'now', switch_thr: float = 0.5, points_in_a_row: int = 5)

Bases: BaseModel

advanced_block_auto: Literal['now', 'once']

model_config = {}

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

points_in_a_row: int

switch_thr: float

class aind_behavior_dynamic_foraging.task_logic.AutoWater(*, auto_water_type: Literal['Natural', 'Both', 'High pro'] = 'Natural', multiplier: float = 0.8, unrewarded: int = 200, ignored: int = 100, include_reward: bool = False)

Bases: BaseModel

auto_water_type: Literal['Natural', 'Both', 'High pro']

ignored: int

include_reward: bool

model_config = {}

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

multiplier: float

unrewarded: int

class aind_behavior_dynamic_foraging.task_logic.BlockParameters(*, min: int = 20, max: int = 60, beta: int = 20, min_reward: int = 1)

Bases: BaseModel

beta: int

max: int

min: int

min_reward: int

model_config = {}

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

class aind_behavior_dynamic_foraging.task_logic.DelayPeriod(*, min: float = 0.0, max: float = 1.0, beta: float = 1.0)

Bases: BaseModel

beta: float

max: float

min: float

model_config = {}

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

class aind_behavior_dynamic_foraging.task_logic.InterTrialInterval(*, min: float = 1.0, max: float = 8.0, beta: float = 2.0, increase: float = 0.0)

Bases: BaseModel

beta: float

increase: float

max: float

min: float

model_config = {}

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

class aind_behavior_dynamic_foraging.task_logic.Response(*, response_time: float = 1.0, reward_consume_time: float = 3.0)

Bases: BaseModel

model_config = {}

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

response_time: float

reward_consume_time: float

class aind_behavior_dynamic_foraging.task_logic.RewardN(*, initial_inactive_trials: int = 2)

Bases: BaseModel

initial_inactive_trials: int

model_config = {}

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

class aind_behavior_dynamic_foraging.task_logic.RewardProbability(*, base_reward_sum: float = 0.8, family: int = 1, pairs_n: int = 1)

Bases: BaseModel

base_reward_sum: float

family: int

model_config = {}

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

pairs_n: int

class aind_behavior_dynamic_foraging.task_logic.RewardSize(*, right_value_volume: float = 3.0, left_value_volume: float = 3.0)

Bases: BaseModel

left_value_volume: float

model_config = {}

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

right_value_volume: float

class aind_behavior_dynamic_foraging.task_logic.Warmup(*, min_trial: int = 50, max_choice_ratio_bias: float = 0.1, min_finish_ratio: float = 0.8, windowsize: int = 20)

Bases: BaseModel

max_choice_ratio_bias: float

min_finish_ratio: float

min_trial: int

model_config = {}

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

windowsize: int