api.task_logic

class aind_behavior_iso_force.task_logic.Action(value)

Bases: IntFlag

Defines the action types

LEFT = 1

NONE = 0

PULL = 8

PUSH = 4

PUSH_PULL = 12

RIGHT = 2

RIGHT_LEFT = 3

pydantic model aind_behavior_iso_force.task_logic.AindIsoForceTaskLogic

Bases: AindBehaviorTaskLogicModel

Config:

• extra: str = forbid

• validate_assignment: bool = True

• validate_default: bool = True

• strict: bool = True

• str_strip_whitespace: bool = True

Fields:

• name (Literal['AindIsoForce'])

• task_parameters (aind_behavior_iso_force.task_logic.AindIsoForceTaskParameters)

• version (Literal['0.0.0-rc0'])

Validators:

• coerce_version » version

field name: Literal['AindIsoForce'] = 'AindIsoForce'

Name of the task logic

field task_parameters: AindIsoForceTaskParameters [Required]

Parameters of the task logic

field version: Literal['0.0.0-rc0'] = '0.0.0-rc0'

Validated by:

• coerce_version

pydantic model aind_behavior_iso_force.task_logic.AindIsoForceTaskParameters

Bases: TaskParameters

Config:

• extra: str = allow

• validate_assignment: bool = True

• validate_default: bool = True

• strict: bool = True

• str_strip_whitespace: bool = True

Fields:

• environment (aind_behavior_iso_force.task_logic.Environment)

• operation_control (aind_behavior_iso_force.task_logic.OperationControl)

Validators:

field environment: Environment [Required]

Environment settings

field operation_control: OperationControl = OperationControl(force=ForceOperationControl(left=LoadCellInput(channel=0, is_inverted=False), right=LoadCellInput(channel=0, is_inverted=True), push=LoadCellInput(channel=1, is_inverted=False), pull=LoadCellInput(channel=1, is_inverted=True)))

Operation control

pydantic model aind_behavior_iso_force.task_logic.Block

Bases: BaseModel

Fields:

• mode (Literal[aind_behavior_iso_force.task_logic.BlockStatisticsMode.BLOCK])

• repeat_count (int | None)

• shuffle (bool)

• trials (List[aind_behavior_iso_force.task_logic.Trial])

field mode: Literal[BlockStatisticsMode.BLOCK] = BlockStatisticsMode.BLOCK

field repeat_count: int | None = 0

Number of times to repeat the block. If null, the block will be repeated indefinitely

field shuffle: bool = False

Whether to shuffle the trials in the block

field trials: List[Trial] = []

List of trials in the block

pydantic model aind_behavior_iso_force.task_logic.BlockGenerator

Bases: BaseModel

Fields:

• block_size (Distribution)

• mode (Literal[aind_behavior_iso_force.task_logic.BlockStatisticsMode.BLOCK_GENERATOR])

• trial_template (aind_behavior_iso_force.task_logic.Trial)

field block_size: Distribution = UniformDistribution(family=<DistributionFamily.UNIFORM: 'Uniform'>, distribution_parameters=UniformDistributionParameters(family=<DistributionFamily.UNIFORM: 'Uniform'>, min=50.0, max=60.0), truncation_parameters=None, scaling_parameters=None)

Size of the block

field mode: Literal[BlockStatisticsMode.BLOCK_GENERATOR] = BlockStatisticsMode.BLOCK_GENERATOR

field trial_template: Trial [Required]

Statistics of the trials in the block

class aind_behavior_iso_force.task_logic.BlockStatisticsMode(value)

Bases: str, Enum

Defines the mode of the environment

BLOCK = 'Block'

BLOCK_GENERATOR = 'BlockGenerator'

pydantic model aind_behavior_iso_force.task_logic.CrossingOutcome

Bases: BaseModel

Fields:

• action (aind_behavior_iso_force.task_logic.Action)

• duration (float)

• end (aind_behavior_iso_force.task_logic.Timestamped[ThresholdedJoystickForce])

• is_reward_action (bool)

• is_valid_duration (bool)

• start (aind_behavior_iso_force.task_logic.Timestamped[ThresholdedJoystickForce])

field action: Action [Required]

Action that was taken

field duration: float [Required]

Duration of the crossing

field end: Timestamped[ThresholdedJoystickForce] [Required]

End of the crossing

field is_reward_action: bool [Required]

Whether the crossing is a reward action

field is_valid_duration: bool [Required]

Whether the crossing duration is valid

field start: Timestamped[ThresholdedJoystickForce] [Required]

Start of the crossing

pydantic model aind_behavior_iso_force.task_logic.Environment

Bases: BaseModel

Fields:

• block_statistics (List[BlockStatistics])

• repeat_count (int | None)

• shuffle (bool)

field block_statistics: List[BlockStatistics] [Required]

Statistics of the environment

field repeat_count: int | None = 0

Number of times to repeat the environment. If null, the environment will be repeated indefinitely

field shuffle: bool = False

Whether to shuffle the blocks

pydantic model aind_behavior_iso_force.task_logic.ForceOperationControl

Bases: BaseModel

Fields:

• left (aind_behavior_iso_force.task_logic.LoadCellInput)

• pull (aind_behavior_iso_force.task_logic.LoadCellInput)

• push (aind_behavior_iso_force.task_logic.LoadCellInput)

• right (aind_behavior_iso_force.task_logic.LoadCellInput)

field left: LoadCellInput = LoadCellInput(channel=0, is_inverted=False)

Left load cell input

field pull: LoadCellInput = LoadCellInput(channel=1, is_inverted=True)

Pull load cell input

field push: LoadCellInput = LoadCellInput(channel=1, is_inverted=False)

Push load cell input

field right: LoadCellInput = LoadCellInput(channel=0, is_inverted=True)

Right load cell input

pydantic model aind_behavior_iso_force.task_logic.ForceThreshold

Bases: BaseModel

Fields:

• left (float | None)

• pull (float | None)

• push (float | None)

• right (float | None)

field left: float | None = None

Left force threshold

field pull: float | None = None

Pull force threshold

field push: float | None = None

Push force threshold

field right: float | None = None

Right force threshold

from_action(action: Action) → float | None

Returns the force threshold for the given action.

Parameters:

action (Action) – The action to get the force threshold for.

Returns:

The force threshold for the action, or None if no threshold is set.

Return type:

Optional[float]

pydantic model aind_behavior_iso_force.task_logic.JoystickForce

Bases: BaseModel

Fields:

• left (float)

• pull (float)

• push (float)

• push_pull (float)

• right (float)

• right_left (float)

field left: float [Required]

Force applied to the left axis

field pull: float [Required]

Force applied to the pull axis

field push: float [Required]

Force applied to the push axis

field push_pull: float [Required]

Signed force applied to the push and pull axes combined

field right: float [Required]

Force applied to the right axis

field right_left: float [Required]

Signed force applied to the right and left axes combined

pydantic model aind_behavior_iso_force.task_logic.LoadCellInput

Bases: BaseModel

Fields:

• channel (int)

• is_inverted (bool)

field channel: Annotated[int, FieldInfo(annotation=NoneType, required=True, description='Load cell channel number available', metadata=[Ge(ge=0), Le(le=7)])] [Required]

Load cell channel number

Constraints:

• ge = 0

• le = 7

field is_inverted: bool = False

Whether the load cell is inverted

pydantic model aind_behavior_iso_force.task_logic.OperantReward

Bases: Reward

Fields:

• reward_type (Literal['Operant'])

• time_to_collect (Distribution)

field reward_type: Literal['Operant'] = 'Operant'

field time_to_collect: Distribution = Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=0.5), truncation_parameters=None, scaling_parameters=None)

Time to collect the reward

pydantic model aind_behavior_iso_force.task_logic.OperationControl

Bases: BaseModel

Fields:

• force (aind_behavior_iso_force.task_logic.ForceOperationControl)

field force: ForceOperationControl = ForceOperationControl(left=LoadCellInput(channel=0, is_inverted=False), right=LoadCellInput(channel=0, is_inverted=True), push=LoadCellInput(channel=1, is_inverted=False), pull=LoadCellInput(channel=1, is_inverted=True))

Operation control for force sensor

pydantic model aind_behavior_iso_force.task_logic.QuiescencePeriod

Bases: BaseModel

Defines a quiescence settings

Fields:

• duration (Distribution)

• force_threshold (aind_behavior_iso_force.task_logic.ForceThreshold)

field duration: Distribution = Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=0.5), truncation_parameters=None, scaling_parameters=None)

Duration force has to stay below threshold to start the trial.

field force_threshold: ForceThreshold = ForceThreshold(left=None, right=None, push=None, pull=None)

Threshold for the force sensors to be considered quiescent.

pydantic model aind_behavior_iso_force.task_logic.ResponsePeriod

Bases: BaseModel

Defines a response period

Fields:

• duration (Distribution)

• force_duration (Distribution)

• force_threshold (aind_behavior_iso_force.task_logic.ForceThreshold)

• rewarded_action (aind_behavior_iso_force.task_logic.Action)

Validators:

• _validate_rewarded_action_vs_threshold » all fields

field duration: Distribution = Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=0.5), truncation_parameters=None, scaling_parameters=None)

Duration of the response period. I.e. the time the animal has to make a choice.

Validated by:

• _validate_rewarded_action_vs_threshold

field force_duration: Distribution = Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=0.05), truncation_parameters=None, scaling_parameters=None)

Duration the force must stay above threshold.

Validated by:

• _validate_rewarded_action_vs_threshold

field force_threshold: ForceThreshold = ForceThreshold(left=None, right=None, push=None, pull=None)

Threshold for the force sensors to be considered active. If None, the crossings will be ignored.

Validated by:

• _validate_rewarded_action_vs_threshold

field rewarded_action: Action = <Action.NONE: 0>

Validated by:

• _validate_rewarded_action_vs_threshold

pydantic model aind_behavior_iso_force.task_logic.Reward

Bases: BaseModel

Fields:

• amount (Distribution)

• delay (Distribution)

• reward_type (Literal['Pavlovian'])

field amount: Distribution = Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=1.0), truncation_parameters=None, scaling_parameters=None)

Amount of reward to dispense

field delay: Distribution = Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=0.0), truncation_parameters=None, scaling_parameters=None)

Delay before dispensing the reward

field reward_type: Literal['Pavlovian'] = 'Pavlovian'

pydantic model aind_behavior_iso_force.task_logic.ThresholdedJoystickForce

Bases: BaseModel

Fields:

• joystick_force (aind_behavior_iso_force.task_logic.JoystickForce)

• left (bool)

• pull (bool)

• push (bool)

• right (bool)

field joystick_force: JoystickForce [Required]

Joystick force values that triggered the event

field left: bool [Required]

Whether the left force is above the threshold

field pull: bool [Required]

Whether the pull force is above the threshold

field push: bool [Required]

Whether the push force is above the threshold

field right: bool [Required]

Whether the right force is above the threshold

pydantic model aind_behavior_iso_force.task_logic.Timestamped

Bases: BaseModel, Generic[T]

Fields:

• seconds (float)

• value (aind_behavior_iso_force.task_logic.T)

field seconds: float [Required]

field value: T [Required]

pydantic model aind_behavior_iso_force.task_logic.Trial

Bases: BaseModel

Defines a trial

Fields:

• inter_trial_interval (Distribution)

• quiescence_period (aind_behavior_iso_force.task_logic.QuiescencePeriod | None)

• response_period (aind_behavior_iso_force.task_logic.ResponsePeriod)

• reward_period (RewardPeriod)

field inter_trial_interval: Distribution = Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=0.5), truncation_parameters=None, scaling_parameters=None)

Time between trials

field quiescence_period: QuiescencePeriod | None = None

Quiescence settings

field response_period: ResponsePeriod = ResponsePeriod(duration=Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=0.5), truncation_parameters=None, scaling_parameters=None), force_threshold=ForceThreshold(left=None, right=None, push=None, pull=None), rewarded_action=<Action.NONE: 0>, force_duration=Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=0.05), truncation_parameters=None, scaling_parameters=None))

Response settings

field reward_period: RewardPeriod = Reward(reward_type='Pavlovian', amount=Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=1.0), truncation_parameters=None, scaling_parameters=None), delay=Scalar(family=<DistributionFamily.SCALAR: 'Scalar'>, distribution_parameters=ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=0.0), truncation_parameters=None, scaling_parameters=None))

Reward settings

aind_behavior_iso_force.task_logic.normal_distribution_value(mean: float, std: float) → NormalDistribution

Helper function to create a normal distribution for a given range.

Parameters:

• mean (float) – The mean value of the normal distribution.

• std (float) – The standard deviation of the normal distribution.

Returns:

The normal distribution type.

Return type:

distributions.Normal

aind_behavior_iso_force.task_logic.scalar_value(value: float) → Scalar

Helper function to create a scalar value distribution for a given value.

Parameters:

value (float) – The value of the scalar distribution.

Returns:

The scalar distribution type.

Return type:

distributions.Scalar

aind_behavior_iso_force.task_logic.uniform_distribution_value(min: float, max: float) → UniformDistribution

Helper function to create a uniform distribution for a given range.

Parameters:

• min (float) – The minimum value of the uniform distribution.

• max (float) – The maximum value of the uniform distribution.

Returns:

The uniform distribution type.

Return type:

distributions.Uniform