api.task_logic

Task

Trial Generators

Warmup

Coupled

Composite

Integration Test

Trial

class aind_behavior_dynamic_foraging.task_logic.AindDynamicForagingTaskLogic(*, name: Literal['AindDynamicForaging'] = 'AindDynamicForaging', description: str = '', task_parameters: AindDynamicForagingTaskParameters, version: Literal['0.0.2-rc24'] = '0.0.2-rc24', 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-rc24']

class aind_behavior_dynamic_foraging.task_logic.AindDynamicForagingTaskParameters(*, rng_seed: float | None = None, aind_behavior_services_pkg_version: Annotated[Literal['0.13.5'], _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-]+)*))?$')] = '0.13.5', reward_size: RewardSize = RewardSize(right_value_volume=3.0, left_value_volume=3.0), trial_generator: TrialGeneratorSpec = IntegrationTestTrialGeneratorSpec(type='IntegrationTestTrialGenerator'), **extra_data: 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.

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].

reward_size: RewardSize

trial_generator: TrialGeneratorSpec

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

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.trial_models.AuditorySecondaryReinforcer(*, type: Literal['Auditory'] = 'Auditory')

Bases: BaseModel

Represents an auditory secondary reinforcer.

model_config = {}

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

type: Literal['Auditory']

type aind_behavior_dynamic_foraging.task_logic.trial_models.SecondaryReinforcer = Annotated[AuditorySecondaryReinforcer, FieldInfo(annotation=NoneType, required=True, description='Type of secondary reinforcer', discriminator='type')]

class aind_behavior_dynamic_foraging.task_logic.trial_models.Trial(*, p_reward_left: Annotated[float, Ge(ge=0), Le(le=1)] = 1.0, p_reward_right: Annotated[float, Ge(ge=0), Le(le=1)] = 1.0, reward_consumption_duration: Annotated[float, Ge(ge=0)] = 5.0, reward_delay_duration: Annotated[float, Ge(ge=0)] = 0.0, secondary_reinforcer: SecondaryReinforcer | None = None, response_deadline_duration: Annotated[float, Ge(ge=0)] = 5.0, enable_fast_retract: bool = False, quiescence_period_duration: Annotated[float, Ge(ge=0)] = 0.5, inter_trial_interval_duration: Annotated[float, Ge(ge=0.5)] = 5.0, is_auto_response_right: bool | None = None, lickspout_offset_delta: float = 0.0, extra_metadata: Annotated[Any, SerializeAsAny()] | None = None)

Bases: BaseModel

Represents a single trial that can be instantiated by the Bonsai state machine.

enable_fast_retract: bool

extra_metadata: Annotated[Any, SerializeAsAny()] | None

inter_trial_interval_duration: float

is_auto_response_right: bool | None

lickspout_offset_delta: float

model_config = {}

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

p_reward_left: float

p_reward_right: float

quiescence_period_duration: float

response_deadline_duration: float

reward_consumption_duration: float

reward_delay_duration: float

secondary_reinforcer: SecondaryReinforcer | None

class aind_behavior_dynamic_foraging.task_logic.trial_models.TrialOutcome(*, trial: Trial, is_right_choice: bool | None, is_rewarded: bool)

Bases: BaseModel

Represents the outcome of a single trial.

is_rewarded: bool

is_right_choice: bool | None

model_config = {}

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

trial: Trial

type aind_behavior_dynamic_foraging.task_logic.trial_generators.TrialGeneratorSpec = Annotated[WarmupTrialGeneratorSpec | CoupledTrialGeneratorSpec | IntegrationTestTrialGeneratorSpec | TrialGeneratorCompositeSpec[TrialGeneratorSpec], FieldInfo(annotation=NoneType, required=True, description='Type of trial generator', discriminator='type')]

class aind_behavior_dynamic_foraging.task_logic.trial_generators._base.BaseTrialGeneratorSpecModel(*, type: str)

Bases: BaseModel, ABC

Base model for trial generator specifications.

abstractmethod create_generator() → ITrialGenerator

Create a trial generator instance from the specification.

model_config = {}

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

type: str

class aind_behavior_dynamic_foraging.task_logic.trial_generators._base.ITrialGenerator(*args, **kwargs)

Bases: Protocol

Interface for trial generators.

next() → Trial | None

Return the next trial to run. Return None if there are no more trials to run.

update(outcome: TrialOutcome | str) → None

Update the trial generator with the outcome of the previous trial.

class aind_behavior_dynamic_foraging.task_logic.trial_generators.composite_trial_generator.TrialGeneratorComposite(spec: TrialGeneratorCompositeSpec[BaseTrialGeneratorSpecModel])

Bases: ITrialGenerator

A composite trial generator that concatenates multiple trial generators.

When the current generator’s next() method returns None, the composite automatically moves to the next generator in the list.

next() → Trial | None

Get the next trial from the current generator.

If the current generator returns None, automatically advance to the next generator in the list. Returns None only when all generators are exhausted.

Returns:

The next Trial, or None if all generators are exhausted

update(outcome: TrialOutcome | str) → None

Update the current active generator with the trial outcome.

Parameters:

outcome – The outcome of the last trial

class aind_behavior_dynamic_foraging.task_logic.trial_generators.composite_trial_generator.TrialGeneratorCompositeSpec(*, type: Literal['TrialGeneratorComposite'] = 'TrialGeneratorComposite', generators: Annotated[list[Annotated[_TSpec, SerializeAsAny()]], MinLen(min_length=1)])

Bases: BaseTrialGeneratorSpecModel, Generic[_TSpec]

Specification for a composite trial generator that concatenates multiple generators.

create_generator() → TrialGeneratorComposite

Create a trial generator instance from the specification.

generators: list[Annotated[_TSpec, SerializeAsAny()]]

model_config = {}

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

type: Literal['TrialGeneratorComposite']

class aind_behavior_dynamic_foraging.task_logic.trial_generators.composite_trial_generator.TrialGeneratorCompositeSpec(*, type: Literal['TrialGeneratorComposite'] = 'TrialGeneratorComposite', generators: Annotated[list[Annotated[_TSpec, SerializeAsAny()]], MinLen(min_length=1)])

Bases: BaseTrialGeneratorSpecModel, Generic[_TSpec]

Specification for a composite trial generator that concatenates multiple generators.

create_generator() → TrialGeneratorComposite

Create a trial generator instance from the specification.

generators: list[Annotated[_TSpec, SerializeAsAny()]]

model_config = {}

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

type: Literal['TrialGeneratorComposite']

type composite_trial_generator.TrialGeneratorSpec = Annotated[WarmupTrialGeneratorSpec | CoupledTrialGeneratorSpec | IntegrationTestTrialGeneratorSpec | TrialGeneratorCompositeSpec[TrialGeneratorSpec], FieldInfo(annotation=NoneType, required=True, description='Type of trial generator', discriminator='type')]

class aind_behavior_dynamic_foraging.task_logic.trial_generators.coupled_trial_generator.BehaviorStabilityParameters(*, behavior_evaluation_mode: Literal['end', 'anytime'] = 'end', behavior_stability_fraction: Annotated[float, Ge(ge=0), Le(le=1)] = 0.5, min_consecutive_stable_trials: Annotated[int, Ge(ge=0)] = 5)

Bases: BaseModel

Parameters controlling when behavior is considered stable enough to switch blocks.

behavior_evaluation_mode: Literal['end', 'anytime']

behavior_stability_fraction: float

min_consecutive_stable_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.trial_generators.coupled_trial_generator.CoupledTrialGenerationEndConditions(*, ignore_win: Annotated[int, Ge(ge=0)] = 30, ignore_ratio_threshold: Annotated[float, Ge(ge=0), Le(le=1)] = 0.8, max_trial: Annotated[int, Ge(ge=0)] = 1000, max_time: float = 4500, min_time: float = 1800)

Bases: BaseModel

Defines the conditions under which a foraging session should terminate.

ignore_ratio_threshold: float

ignore_win: int

max_time: float

max_trial: int

min_time: float

model_config = {}

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

class aind_behavior_dynamic_foraging.task_logic.trial_generators.coupled_trial_generator.CoupledTrialGenerator(spec: CoupledTrialGeneratorSpec)

Bases: BlockBasedTrialGenerator

Trial generator for a coupled block-based dynamic foraging task.

Extends BlockBasedTrialGenerator with session end conditions, baiting state management, and behavior-dependent block switching.

spec

The CoupledTrialGeneratorSpec defining task parameters.

Type:

aind_behavior_dynamic_foraging.task_logic.trial_generators.coupled_trial_generator.CoupledTrialGeneratorSpec

start_time

Timestamp recorded at initialization, used to track elapsed session time.

static compute_choice_fraction(kernel_size: int, choice_history: list[int | None])

Computes a sliding-window fraction of right choices over the trial history.

Ignores None (no-response) trials by treating them as NaN in the mean.

Parameters:

• kernel_size – Number of trials in each sliding window.

• choice_history – Trial history with 1 for right, 0 for left, and None for ignored trials.

Returns:

Array of per-window right-choice fractions, of length len(choice_history) - kernel_size + 1.

spec: CoupledTrialGeneratorSpec

update(outcome: TrialOutcome | str) → None

Updates generator state from the previous trial outcome and switches block if criteria are met.

Records choice and reward history, manages baiting state, optionally extends the block on no response, and triggers a block switch if all switch criteria are satisfied.

Parameters:

outcome – The TrialOutcome from the most recently completed trial.

class aind_behavior_dynamic_foraging.task_logic.trial_generators.coupled_trial_generator.CoupledTrialGeneratorSpec(*, type: Literal['CoupledTrialGenerator'] = 'CoupledTrialGenerator', quiescent_duration: Distribution, <aind_behavior_services.schema._SgenTypenameAnnotation object at 0x7fb76f988980>]=ExponentialDistribution(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, distribution_parameters=ExponentialDistributionParameters(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, rate=1.0), truncation_parameters=TruncationParameters(truncation_mode='exclude', min=0.0, max=1.0), scaling_parameters=None), response_duration: Annotated[float, ~annotated_types.Ge(ge=0)] = 1.0, reward_consumption_duration: Annotated[float, ~annotated_types.Ge(ge=0)] = 3.0, inter_trial_interval_duration: Distribution, <aind_behavior_services.schema._SgenTypenameAnnotation object at 0x7fb76f988980>]=ExponentialDistribution(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, distribution_parameters=ExponentialDistributionParameters(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, rate=0.5), truncation_parameters=TruncationParameters(truncation_mode='exclude', min=1.0, max=8.0), scaling_parameters=None), block_len: Distribution, <aind_behavior_services.schema._SgenTypenameAnnotation object at 0x7fb76f988980>]=ExponentialDistribution(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, distribution_parameters=ExponentialDistributionParameters(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, rate=0.05), truncation_parameters=TruncationParameters(truncation_mode='exclude', min=20.0, max=60.0), scaling_parameters=None), min_block_reward: Annotated[int, ~annotated_types.Ge(ge=0)] = 1, kernel_size: int = 2, reward_probability_parameters: RewardProbabilityParameters = RewardProbabilityParameters(base_reward_sum=0.8, reward_pairs=[[8, 1]]), is_baiting: bool = False, trial_generation_end_parameters: CoupledTrialGenerationEndConditions = CoupledTrialGenerationEndConditions(ignore_win=30, ignore_ratio_threshold=0.8, max_trial=1000, max_time=4500, min_time=1800), behavior_stability_parameters: BehaviorStabilityParameters | None = BehaviorStabilityParameters(behavior_evaluation_mode='end', behavior_stability_fraction=0.5, min_consecutive_stable_trials=5), extend_block_on_no_response: bool = True)

Bases: BlockBasedTrialGeneratorSpec

behavior_stability_parameters: BehaviorStabilityParameters | None

create_generator() → CoupledTrialGenerator

Create a trial generator instance from the specification.

extend_block_on_no_response: bool

model_config = {}

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

trial_generation_end_parameters: CoupledTrialGenerationEndConditions

type: Literal['CoupledTrialGenerator']

class aind_behavior_dynamic_foraging.task_logic.trial_generators.warmup_trial_generator.WarmupTrialGenerationEndConditions(*, min_trial: Annotated[int, Ge(ge=0)] = 50, max_choice_bias: Annotated[float, Ge(ge=0), Le(le=1)] = 0.1, min_response_rate: Annotated[float, Ge(ge=0), Le(le=1)] = 0.8, evaluation_window: Annotated[int, Ge(ge=0)] = 20)

Bases: BaseModel

evaluation_window: int

max_choice_bias: float

min_response_rate: float

min_trial: int

model_config = {}

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

class aind_behavior_dynamic_foraging.task_logic.trial_generators.warmup_trial_generator.WarmupTrialGenerator(spec: BlockBasedTrialGeneratorSpec)

Bases: BlockBasedTrialGenerator

spec: WarmupTrialGeneratorSpec

update(outcome: TrialOutcome | str) → None

Warmup switches block every update

Parameters:

outcome – trial outcome of previous trial

class aind_behavior_dynamic_foraging.task_logic.trial_generators.warmup_trial_generator.WarmupTrialGeneratorSpec(*, type: Literal['WarmupTrialGenerator'] = 'WarmupTrialGenerator', quiescent_duration: Distribution, <aind_behavior_services.schema._SgenTypenameAnnotation object at 0x7fb76f988980>]=ExponentialDistribution(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, distribution_parameters=ExponentialDistributionParameters(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, rate=1.0), truncation_parameters=TruncationParameters(truncation_mode='exclude', min=0.0, max=1.0), scaling_parameters=None), response_duration: Annotated[float, ~annotated_types.Ge(ge=0)] = 1.0, reward_consumption_duration: Annotated[float, ~annotated_types.Ge(ge=0)] = 3.0, inter_trial_interval_duration: Distribution, <aind_behavior_services.schema._SgenTypenameAnnotation object at 0x7fb76f988980>]=ExponentialDistribution(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, distribution_parameters=ExponentialDistributionParameters(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, rate=0.5), truncation_parameters=TruncationParameters(truncation_mode='exclude', min=1.0, max=8.0), scaling_parameters=None), block_len: Distribution, <aind_behavior_services.schema._SgenTypenameAnnotation object at 0x7fb76f988980>]=Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=0), truncation_parameters=None, scaling_parameters=None), min_block_reward: Literal[1] = 1, kernel_size: int = 2, reward_probability_parameters: RewardProbabilityParameters = RewardProbabilityParameters(base_reward_sum=0.8, reward_pairs=[[8, 1]]), is_baiting: Literal[True] = True, trial_generation_end_parameters: WarmupTrialGenerationEndConditions = WarmupTrialGenerationEndConditions(min_trial=50, max_choice_bias=0.1, min_response_rate=0.8, evaluation_window=20))

Bases: BlockBasedTrialGeneratorSpec

block_len: _SgenTypenameAnnotation object at 0x7fb76f988980>]

create_generator() → WarmupTrialGenerator

Create a trial generator instance from the specification.

is_baiting: Literal[True]

min_block_reward: Literal[1]

model_config = {}

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

trial_generation_end_parameters: WarmupTrialGenerationEndConditions

type: Literal['WarmupTrialGenerator']

class aind_behavior_dynamic_foraging.task_logic.trial_generators.block_based_trial_generator.Block(*, p_right_reward: Annotated[float, Ge(ge=0), Le(le=1)], p_left_reward: Annotated[float, Ge(ge=0), Le(le=1)], min_length: Annotated[int, Ge(ge=0)])

Bases: BaseModel

min_length: int

model_config = {}

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

p_left_reward: float

p_right_reward: float

class aind_behavior_dynamic_foraging.task_logic.trial_generators.block_based_trial_generator.BlockBasedTrialGenerator(spec: BlockBasedTrialGeneratorSpec)

Bases: ITrialGenerator, ABC

Abstract trial generator for block-based dynamic foraging tasks.

Manages block transitions, baiting logic, and trial generation. Subclasses must implement _are_end_conditions_met to define session termination logic.

spec

The specification used to configure this generator.

is_right_choice_history

Record of whether each trial was a right choice. None indicates no choice was made (e.g. missed trial).

Type:

list[bool | None]

reward_history

Record of whether each trial resulted in a reward.

Type:

list[bool]

block_history

Record of all completed blocks.

Type:

list[aind_behavior_dynamic_foraging.task_logic.trial_generators.block_based_trial_generator.Block]

block

The currently active block.

Type:

aind_behavior_dynamic_foraging.task_logic.trial_generators.block_based_trial_generator.Block

trials_in_block

Number of trials elapsed in the current block.

is_left_baited

Whether the left port currently has a baited reward.

Type:

bool

is_right_baited

Whether the right port currently has a baited reward.

Type:

bool

block: Block

block_history: list[Block]

is_left_baited: bool

is_right_baited: bool

is_right_choice_history: list[bool | None]

next() → Trial | None

Generates the next trial in the session.

Checks end conditions, samples timing parameters, and applies baiting logic if enabled. Returns None if the session should end.

Returns:

The next Trial, or None if end conditions are met.

reward_history: list[bool]

class aind_behavior_dynamic_foraging.task_logic.trial_generators.block_based_trial_generator.BlockBasedTrialGeneratorSpec(*, type: Literal['BlockBasedTrialGenerator'] = 'BlockBasedTrialGenerator', quiescent_duration: Distribution, <aind_behavior_services.schema._SgenTypenameAnnotation object at 0x7fb76f988980>]=ExponentialDistribution(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, distribution_parameters=ExponentialDistributionParameters(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, rate=1.0), truncation_parameters=TruncationParameters(truncation_mode='exclude', min=0.0, max=1.0), scaling_parameters=None), response_duration: Annotated[float, ~annotated_types.Ge(ge=0)] = 1.0, reward_consumption_duration: Annotated[float, ~annotated_types.Ge(ge=0)] = 3.0, inter_trial_interval_duration: Distribution, <aind_behavior_services.schema._SgenTypenameAnnotation object at 0x7fb76f988980>]=ExponentialDistribution(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, distribution_parameters=ExponentialDistributionParameters(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, rate=0.5), truncation_parameters=TruncationParameters(truncation_mode='exclude', min=1.0, max=8.0), scaling_parameters=None), block_len: Distribution, <aind_behavior_services.schema._SgenTypenameAnnotation object at 0x7fb76f988980>]=ExponentialDistribution(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, distribution_parameters=ExponentialDistributionParameters(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, rate=0.05), truncation_parameters=TruncationParameters(truncation_mode='exclude', min=20.0, max=60.0), scaling_parameters=None), min_block_reward: Annotated[int, ~annotated_types.Ge(ge=0)] = 1, kernel_size: int = 2, reward_probability_parameters: RewardProbabilityParameters = RewardProbabilityParameters(base_reward_sum=0.8, reward_pairs=[[8, 1]]), is_baiting: bool = False)

Bases: BaseTrialGeneratorSpecModel

block_len: _SgenTypenameAnnotation object at 0x7fb76f988980>]

create_generator() → BlockBasedTrialGenerator

Create a trial generator instance from the specification.

inter_trial_interval_duration: _SgenTypenameAnnotation object at 0x7fb76f988980>]

is_baiting: bool

kernel_size: int

min_block_reward: int

model_config = {}

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

quiescent_duration: _SgenTypenameAnnotation object at 0x7fb76f988980>]

response_duration: float

reward_consumption_duration: float

reward_probability_parameters: RewardProbabilityParameters

type: Literal['BlockBasedTrialGenerator']

class aind_behavior_dynamic_foraging.task_logic.trial_generators.block_based_trial_generator.RewardProbabilityParameters(*, base_reward_sum: float = 0.8, reward_pairs: list[list[float, float]] = [[8, 1]])

Bases: BaseModel

Defines the reward probability structure for a dynamic foraging task.

Reward probabilities are defined as pairs (p_left, p_right) normalized by base_reward_sum. Pairs are drawn from a family representing a difficulty level:

Family 1: [[8, 1], [6, 1], [3, 1], [1, 1]] Family 2: [[8, 1], [1, 1]] Family 3: [[1.0, 0.0], [0.9, 0.1], [0.8, 0.2], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5]] Family 4: [[6, 1], [3, 1], [1, 1]]

base_reward_sum: float

model_config = {}

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

reward_pairs: list[list[float, float]]

class aind_behavior_dynamic_foraging.task_logic.trial_generators.integration_test_trial_generator.IntegrationTestTrialGenerator(spec: IntegrationTestTrialGeneratorSpec)

Bases: ITrialGenerator

next() → Trial | None

Return the next trial to run. Return None if there are no more trials to run.

update(outcome: TrialOutcome | str) → None

Update the trial generator with the outcome of the previous trial.

class aind_behavior_dynamic_foraging.task_logic.trial_generators.integration_test_trial_generator.IntegrationTestTrialGeneratorSpec(*, type: Literal['IntegrationTestTrialGenerator'] = 'IntegrationTestTrialGenerator')

Bases: BaseTrialGeneratorSpecModel

create_generator() → IntegrationTestTrialGenerator

Create a trial generator instance from the specification.

model_config = {}

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

type: Literal['IntegrationTestTrialGenerator']