aind_behavior_services.task_logic package¶

Submodules¶

aind_behavior_services.task_logic.distributions module¶

pydantic model aind_behavior_services.task_logic.distributions.BetaDistribution[source]¶

Bases: DistributionBase

A beta probability distribution.

Continuous distribution bounded between 0 and 1. Commonly used for modeling probabilities and proportions.

Show JSON schema

{
   "title": "BetaDistribution",
   "description": "A beta probability distribution.\n\nContinuous distribution bounded between 0 and 1. Commonly used\nfor modeling probabilities and proportions.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Beta",
         "default": "Beta",
         "title": "Family",
         "type": "string"
      },
      "distribution_parameters": {
         "$ref": "#/$defs/BetaDistributionParameters",
         "default": {
            "family": "Beta",
            "alpha": 5.0,
            "beta": 5.0
         },
         "description": "Parameters of the distribution"
      },
      "truncation_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/TruncationParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Truncation parameters of the distribution"
      },
      "scaling_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/ScalingParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Scaling parameters of the distribution"
      }
   },
   "$defs": {
      "BetaDistributionParameters": {
         "description": "Parameters for a beta distribution.\n\nDefined by alpha and beta shape parameters.",
         "properties": {
            "family": {
               "const": "Beta",
               "default": "Beta",
               "title": "Family",
               "type": "string"
            },
            "alpha": {
               "default": 5,
               "description": "Alpha parameter of the distribution",
               "minimum": 0,
               "title": "Alpha",
               "type": "number"
            },
            "beta": {
               "default": 5,
               "description": "Beta parameter of the distribution",
               "minimum": 0,
               "title": "Beta",
               "type": "number"
            }
         },
         "title": "BetaDistributionParameters",
         "type": "object"
      },
      "ScalingParameters": {
         "description": "Parameters for scaling and offsetting sampled distribution values.\nScaling is applied as (value * scale + offset).\n\nApplies linear transformation: result = (value * scale) + offset.",
         "properties": {
            "scale": {
               "default": 1,
               "description": "Scaling factor to apply on the sampled distribution",
               "title": "Scale",
               "type": "number"
            },
            "offset": {
               "default": 0,
               "description": "Offset factor to apply on the sampled distribution",
               "title": "Offset",
               "type": "number"
            }
         },
         "title": "ScalingParameters",
         "type": "object"
      },
      "TruncationParameters": {
         "description": "Parameters for truncating a distribution to a specified range. Truncation should\nbe applied after sampling and scaling.\n\nUsed to constrain sampled values within minimum and maximum bounds.",
         "properties": {
            "min": {
               "default": 0,
               "description": "Minimum value of the sampled distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the sampled distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "TruncationParameters",
         "type": "object"
      }
   }
}

Fields:

distribution_parameters (aind_behavior_services.task_logic.distributions.BetaDistributionParameters)
family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.BETA])

field distribution_parameters: BetaDistributionParameters = BetaDistributionParameters(family=<DistributionFamily.BETA: 'Beta'>, alpha=5, beta=5)[source]¶: Parameters of the distribution

field family: Literal[DistributionFamily.BETA] = DistributionFamily.BETA[source]¶

pydantic model aind_behavior_services.task_logic.distributions.BetaDistributionParameters[source]¶

Bases: DistributionParametersBase

Parameters for a beta distribution.

Defined by alpha and beta shape parameters.

Show JSON schema

{
   "title": "BetaDistributionParameters",
   "description": "Parameters for a beta distribution.\n\nDefined by alpha and beta shape parameters.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Beta",
         "default": "Beta",
         "title": "Family",
         "type": "string"
      },
      "alpha": {
         "default": 5,
         "description": "Alpha parameter of the distribution",
         "minimum": 0,
         "title": "Alpha",
         "type": "number"
      },
      "beta": {
         "default": 5,
         "description": "Beta parameter of the distribution",
         "minimum": 0,
         "title": "Beta",
         "type": "number"
      }
   }
}

Fields:

alpha (float)
beta (float)
family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.BETA])

field alpha: float = 5[source]¶

Alpha parameter of the distribution

Constraints:

ge = 0

field beta: float = 5[source]¶

Beta parameter of the distribution

Constraints:

ge = 0

field family: Literal[DistributionFamily.BETA] = DistributionFamily.BETA[source]¶

pydantic model aind_behavior_services.task_logic.distributions.BinomialDistribution[source]¶

Bases: DistributionBase

A binomial probability distribution.

Models the number of successes in a fixed number of independent Bernoulli trials with constant success probability.

Show JSON schema

{
   "title": "BinomialDistribution",
   "description": "A binomial probability distribution.\n\nModels the number of successes in a fixed number of independent\nBernoulli trials with constant success probability.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Binomial",
         "default": "Binomial",
         "title": "Family",
         "type": "string"
      },
      "distribution_parameters": {
         "$ref": "#/$defs/BinomialDistributionParameters",
         "default": {
            "family": "Binomial",
            "n": 1,
            "p": 0.5
         },
         "description": "Parameters of the distribution"
      },
      "truncation_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/TruncationParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Truncation parameters of the distribution"
      },
      "scaling_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/ScalingParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Scaling parameters of the distribution"
      }
   },
   "$defs": {
      "BinomialDistributionParameters": {
         "description": "Parameters for a binomial distribution.\n\nDefined by number of trials (n) and success probability (p).",
         "properties": {
            "family": {
               "const": "Binomial",
               "default": "Binomial",
               "title": "Family",
               "type": "string"
            },
            "n": {
               "default": 1,
               "description": "Number of trials",
               "minimum": 0,
               "title": "N",
               "type": "integer"
            },
            "p": {
               "default": 0.5,
               "description": "Probability of success",
               "maximum": 1,
               "minimum": 0,
               "title": "P",
               "type": "number"
            }
         },
         "title": "BinomialDistributionParameters",
         "type": "object"
      },
      "ScalingParameters": {
         "description": "Parameters for scaling and offsetting sampled distribution values.\nScaling is applied as (value * scale + offset).\n\nApplies linear transformation: result = (value * scale) + offset.",
         "properties": {
            "scale": {
               "default": 1,
               "description": "Scaling factor to apply on the sampled distribution",
               "title": "Scale",
               "type": "number"
            },
            "offset": {
               "default": 0,
               "description": "Offset factor to apply on the sampled distribution",
               "title": "Offset",
               "type": "number"
            }
         },
         "title": "ScalingParameters",
         "type": "object"
      },
      "TruncationParameters": {
         "description": "Parameters for truncating a distribution to a specified range. Truncation should\nbe applied after sampling and scaling.\n\nUsed to constrain sampled values within minimum and maximum bounds.",
         "properties": {
            "min": {
               "default": 0,
               "description": "Minimum value of the sampled distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the sampled distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "TruncationParameters",
         "type": "object"
      }
   }
}

Fields:

distribution_parameters (aind_behavior_services.task_logic.distributions.BinomialDistributionParameters)
family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.BINOMIAL])

field distribution_parameters: BinomialDistributionParameters = BinomialDistributionParameters(family=<DistributionFamily.BINOMIAL: 'Binomial'>, n=1, p=0.5)[source]¶: Parameters of the distribution

field family: Literal[DistributionFamily.BINOMIAL] = DistributionFamily.BINOMIAL[source]¶

pydantic model aind_behavior_services.task_logic.distributions.BinomialDistributionParameters[source]¶

Bases: DistributionParametersBase

Parameters for a binomial distribution.

Defined by number of trials (n) and success probability (p).

Show JSON schema

{
   "title": "BinomialDistributionParameters",
   "description": "Parameters for a binomial distribution.\n\nDefined by number of trials (n) and success probability (p).",
   "type": "object",
   "properties": {
      "family": {
         "const": "Binomial",
         "default": "Binomial",
         "title": "Family",
         "type": "string"
      },
      "n": {
         "default": 1,
         "description": "Number of trials",
         "minimum": 0,
         "title": "N",
         "type": "integer"
      },
      "p": {
         "default": 0.5,
         "description": "Probability of success",
         "maximum": 1,
         "minimum": 0,
         "title": "P",
         "type": "number"
      }
   }
}

Fields:

family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.BINOMIAL])
n (int)
p (float)

field family: Literal[DistributionFamily.BINOMIAL] = DistributionFamily.BINOMIAL[source]¶

field n: int = 1[source]¶

Number of trials

Constraints:

ge = 0

field p: float = 0.5[source]¶

Probability of success

Constraints:

ge = 0
le = 1

pydantic model aind_behavior_services.task_logic.distributions.DistributionBase[source]¶

Bases: BaseModel

Base class for all distribution models. This class should not be instantiated directly.

Combines distribution parameters with optional truncation and scaling transformations for flexible probability distributions.

Show JSON schema

{
   "title": "DistributionBase",
   "description": "Base class for all distribution models. This class should not be instantiated directly.\n\nCombines distribution parameters with optional truncation and scaling\ntransformations for flexible probability distributions.",
   "type": "object",
   "properties": {
      "family": {
         "$ref": "#/$defs/DistributionFamily",
         "description": "Family of the distribution"
      },
      "distribution_parameters": {
         "$ref": "#/$defs/DistributionParameters"
      },
      "truncation_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/TruncationParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Truncation parameters of the distribution"
      },
      "scaling_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/ScalingParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Scaling parameters of the distribution"
      }
   },
   "$defs": {
      "BetaDistributionParameters": {
         "description": "Parameters for a beta distribution.\n\nDefined by alpha and beta shape parameters.",
         "properties": {
            "family": {
               "const": "Beta",
               "default": "Beta",
               "title": "Family",
               "type": "string"
            },
            "alpha": {
               "default": 5,
               "description": "Alpha parameter of the distribution",
               "minimum": 0,
               "title": "Alpha",
               "type": "number"
            },
            "beta": {
               "default": 5,
               "description": "Beta parameter of the distribution",
               "minimum": 0,
               "title": "Beta",
               "type": "number"
            }
         },
         "title": "BetaDistributionParameters",
         "type": "object"
      },
      "BinomialDistributionParameters": {
         "description": "Parameters for a binomial distribution.\n\nDefined by number of trials (n) and success probability (p).",
         "properties": {
            "family": {
               "const": "Binomial",
               "default": "Binomial",
               "title": "Family",
               "type": "string"
            },
            "n": {
               "default": 1,
               "description": "Number of trials",
               "minimum": 0,
               "title": "N",
               "type": "integer"
            },
            "p": {
               "default": 0.5,
               "description": "Probability of success",
               "maximum": 1,
               "minimum": 0,
               "title": "P",
               "type": "number"
            }
         },
         "title": "BinomialDistributionParameters",
         "type": "object"
      },
      "DistributionFamily": {
         "description": "Enumeration of supported probability distribution families.\n\nDefines all available statistical distributions that can be used\nfor sampling random values in the task logic system.",
         "enum": [
            "Scalar",
            "Normal",
            "LogNormal",
            "Uniform",
            "Exponential",
            "Gamma",
            "Binomial",
            "Beta",
            "Poisson",
            "Pdf"
         ],
         "title": "DistributionFamily",
         "type": "string"
      },
      "DistributionParameters": {
         "description": "Parameters of the distribution",
         "discriminator": {
            "mapping": {
               "Beta": "#/$defs/BetaDistributionParameters",
               "Binomial": "#/$defs/BinomialDistributionParameters",
               "Exponential": "#/$defs/ExponentialDistributionParameters",
               "Gamma": "#/$defs/GammaDistributionParameters",
               "LogNormal": "#/$defs/LogNormalDistributionParameters",
               "Normal": "#/$defs/NormalDistributionParameters",
               "Pdf": "#/$defs/PdfDistributionParameters",
               "Poisson": "#/$defs/PoissonDistributionParameters",
               "Scalar": "#/$defs/ScalarDistributionParameter",
               "Uniform": "#/$defs/UniformDistributionParameters"
            },
            "propertyName": "family"
         },
         "oneOf": [
            {
               "$ref": "#/$defs/ScalarDistributionParameter"
            },
            {
               "$ref": "#/$defs/NormalDistributionParameters"
            },
            {
               "$ref": "#/$defs/LogNormalDistributionParameters"
            },
            {
               "$ref": "#/$defs/ExponentialDistributionParameters"
            },
            {
               "$ref": "#/$defs/UniformDistributionParameters"
            },
            {
               "$ref": "#/$defs/PoissonDistributionParameters"
            },
            {
               "$ref": "#/$defs/BinomialDistributionParameters"
            },
            {
               "$ref": "#/$defs/BetaDistributionParameters"
            },
            {
               "$ref": "#/$defs/GammaDistributionParameters"
            },
            {
               "$ref": "#/$defs/PdfDistributionParameters"
            }
         ],
         "title": "DistributionParameters"
      },
      "ExponentialDistributionParameters": {
         "description": "Parameters for an exponential distribution.\n\nDefined by the rate parameter (inverse of mean).",
         "properties": {
            "family": {
               "const": "Exponential",
               "default": "Exponential",
               "title": "Family",
               "type": "string"
            },
            "rate": {
               "default": 0,
               "description": "Rate parameter of the distribution",
               "minimum": 0,
               "title": "Rate",
               "type": "number"
            }
         },
         "title": "ExponentialDistributionParameters",
         "type": "object"
      },
      "GammaDistributionParameters": {
         "description": "Parameters for a gamma distribution.\n\nDefined by shape (k) and rate (\u03b8\u207b\u00b9) parameters.",
         "properties": {
            "family": {
               "const": "Gamma",
               "default": "Gamma",
               "title": "Family",
               "type": "string"
            },
            "shape": {
               "default": 1,
               "description": "Shape parameter of the distribution",
               "minimum": 0,
               "title": "Shape",
               "type": "number"
            },
            "rate": {
               "default": 1,
               "description": "Rate parameter of the distribution",
               "minimum": 0,
               "title": "Rate",
               "type": "number"
            }
         },
         "title": "GammaDistributionParameters",
         "type": "object"
      },
      "LogNormalDistributionParameters": {
         "description": "Parameters for a log-normal distribution.\n\nDefined by the mean and standard deviation of the underlying normal distribution.",
         "properties": {
            "family": {
               "const": "LogNormal",
               "default": "LogNormal",
               "title": "Family",
               "type": "string"
            },
            "mean": {
               "default": 0,
               "description": "Mean of the distribution",
               "title": "Mean",
               "type": "number"
            },
            "std": {
               "default": 0,
               "description": "Standard deviation of the distribution",
               "title": "Std",
               "type": "number"
            }
         },
         "title": "LogNormalDistributionParameters",
         "type": "object"
      },
      "NormalDistributionParameters": {
         "description": "Parameters for a normal (Gaussian) distribution.\n\nDefined by mean (center) and standard deviation (spread).",
         "properties": {
            "family": {
               "const": "Normal",
               "default": "Normal",
               "title": "Family",
               "type": "string"
            },
            "mean": {
               "default": 0,
               "description": "Mean of the distribution",
               "title": "Mean",
               "type": "number"
            },
            "std": {
               "default": 0,
               "description": "Standard deviation of the distribution",
               "title": "Std",
               "type": "number"
            }
         },
         "title": "NormalDistributionParameters",
         "type": "object"
      },
      "PdfDistributionParameters": {
         "description": "Parameters for a custom probability density function distribution.\n\nDefined by explicit probability values and their corresponding indices.\nProbabilities are automatically normalized to sum to 1.",
         "properties": {
            "family": {
               "const": "Pdf",
               "default": "Pdf",
               "title": "Family",
               "type": "string"
            },
            "pdf": {
               "default": [
                  1
               ],
               "description": "The probability density function",
               "items": {
                  "minimum": 0,
                  "type": "number"
               },
               "title": "Pdf",
               "type": "array"
            },
            "index": {
               "default": [
                  0
               ],
               "description": "The index of the probability density function",
               "items": {
                  "type": "number"
               },
               "title": "Index",
               "type": "array"
            }
         },
         "title": "PdfDistributionParameters",
         "type": "object"
      },
      "PoissonDistributionParameters": {
         "description": "Parameters for a Poisson distribution.\n\nDefined by the rate parameter (average number of events).",
         "properties": {
            "family": {
               "const": "Poisson",
               "default": "Poisson",
               "title": "Family",
               "type": "string"
            },
            "rate": {
               "default": 1,
               "description": "Rate parameter of the Poisson process that generates the distribution",
               "minimum": 0,
               "title": "Rate",
               "type": "number"
            }
         },
         "title": "PoissonDistributionParameters",
         "type": "object"
      },
      "ScalarDistributionParameter": {
         "description": "Parameters for a scalar (constant) distribution.\n\nRepresents a deterministic value that always returns the same number.",
         "properties": {
            "family": {
               "const": "Scalar",
               "default": "Scalar",
               "title": "Family",
               "type": "string"
            },
            "value": {
               "default": 0,
               "description": "The static value of the distribution",
               "title": "Value",
               "type": "number"
            }
         },
         "title": "ScalarDistributionParameter",
         "type": "object"
      },
      "ScalingParameters": {
         "description": "Parameters for scaling and offsetting sampled distribution values.\nScaling is applied as (value * scale + offset).\n\nApplies linear transformation: result = (value * scale) + offset.",
         "properties": {
            "scale": {
               "default": 1,
               "description": "Scaling factor to apply on the sampled distribution",
               "title": "Scale",
               "type": "number"
            },
            "offset": {
               "default": 0,
               "description": "Offset factor to apply on the sampled distribution",
               "title": "Offset",
               "type": "number"
            }
         },
         "title": "ScalingParameters",
         "type": "object"
      },
      "TruncationParameters": {
         "description": "Parameters for truncating a distribution to a specified range. Truncation should\nbe applied after sampling and scaling.\n\nUsed to constrain sampled values within minimum and maximum bounds.",
         "properties": {
            "min": {
               "default": 0,
               "description": "Minimum value of the sampled distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the sampled distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "TruncationParameters",
         "type": "object"
      },
      "UniformDistributionParameters": {
         "description": "Parameters for a uniform distribution.\n\nDefined by minimum and maximum bounds of the distribution.",
         "properties": {
            "family": {
               "const": "Uniform",
               "default": "Uniform",
               "title": "Family",
               "type": "string"
            },
            "min": {
               "default": 0,
               "description": "Minimum value of the distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "UniformDistributionParameters",
         "type": "object"
      }
   },
   "required": [
      "family",
      "distribution_parameters"
   ]
}

Fields:

distribution_parameters (DistributionParameters)
family (aind_behavior_services.task_logic.distributions.DistributionFamily)
scaling_parameters (aind_behavior_services.task_logic.distributions.ScalingParameters | None)
truncation_parameters (aind_behavior_services.task_logic.distributions.TruncationParameters | None)

field distribution_parameters: DistributionParameters [Required][source]¶: Parameters of the distribution

field family: DistributionFamily [Required][source]¶: Family of the distribution

field scaling_parameters: ScalingParameters | None = None[source]¶: Scaling parameters of the distribution

field truncation_parameters: TruncationParameters | None = None[source]¶: Truncation parameters of the distribution

class aind_behavior_services.task_logic.distributions.DistributionFamily(*values)[source]¶

Bases: str, Enum

Enumeration of supported probability distribution families.

Defines all available statistical distributions that can be used for sampling random values in the task logic system.

BETA = 'Beta'[source]¶

BINOMIAL = 'Binomial'[source]¶

EXPONENTIAL = 'Exponential'[source]¶

GAMMA = 'Gamma'[source]¶

LOGNORMAL = 'LogNormal'[source]¶

NORMAL = 'Normal'[source]¶

PDF = 'Pdf'[source]¶

POISSON = 'Poisson'[source]¶

SCALAR = 'Scalar'[source]¶

UNIFORM = 'Uniform'[source]¶

pydantic model aind_behavior_services.task_logic.distributions.DistributionParametersBase[source]¶

Bases: BaseModel

Base class for all distribution parameter models. This class should not be instantiated directly.

Provides common family field for discriminated union validation.

Show JSON schema

{
   "title": "DistributionParametersBase",
   "description": "Base class for all distribution parameter models. This class should not be instantiated directly.\n\nProvides common family field for discriminated union validation.",
   "type": "object",
   "properties": {
      "family": {
         "$ref": "#/$defs/DistributionFamily",
         "description": "Family of the distribution"
      }
   },
   "$defs": {
      "DistributionFamily": {
         "description": "Enumeration of supported probability distribution families.\n\nDefines all available statistical distributions that can be used\nfor sampling random values in the task logic system.",
         "enum": [
            "Scalar",
            "Normal",
            "LogNormal",
            "Uniform",
            "Exponential",
            "Gamma",
            "Binomial",
            "Beta",
            "Poisson",
            "Pdf"
         ],
         "title": "DistributionFamily",
         "type": "string"
      }
   },
   "required": [
      "family"
   ]
}

Fields:

family (aind_behavior_services.task_logic.distributions.DistributionFamily)

field family: DistributionFamily [Required][source]¶: Family of the distribution

pydantic model aind_behavior_services.task_logic.distributions.ExponentialDistribution[source]¶

Bases: DistributionBase

An exponential probability distribution.

Models time between events in a Poisson process. Commonly used for wait times and inter-event intervals.

Show JSON schema

{
   "title": "ExponentialDistribution",
   "description": "An exponential probability distribution.\n\nModels time between events in a Poisson process. Commonly used\nfor wait times and inter-event intervals.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Exponential",
         "default": "Exponential",
         "title": "Family",
         "type": "string"
      },
      "distribution_parameters": {
         "$ref": "#/$defs/ExponentialDistributionParameters",
         "default": {
            "family": "Exponential",
            "rate": 0.0
         },
         "description": "Parameters of the distribution"
      },
      "truncation_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/TruncationParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Truncation parameters of the distribution"
      },
      "scaling_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/ScalingParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Scaling parameters of the distribution"
      }
   },
   "$defs": {
      "ExponentialDistributionParameters": {
         "description": "Parameters for an exponential distribution.\n\nDefined by the rate parameter (inverse of mean).",
         "properties": {
            "family": {
               "const": "Exponential",
               "default": "Exponential",
               "title": "Family",
               "type": "string"
            },
            "rate": {
               "default": 0,
               "description": "Rate parameter of the distribution",
               "minimum": 0,
               "title": "Rate",
               "type": "number"
            }
         },
         "title": "ExponentialDistributionParameters",
         "type": "object"
      },
      "ScalingParameters": {
         "description": "Parameters for scaling and offsetting sampled distribution values.\nScaling is applied as (value * scale + offset).\n\nApplies linear transformation: result = (value * scale) + offset.",
         "properties": {
            "scale": {
               "default": 1,
               "description": "Scaling factor to apply on the sampled distribution",
               "title": "Scale",
               "type": "number"
            },
            "offset": {
               "default": 0,
               "description": "Offset factor to apply on the sampled distribution",
               "title": "Offset",
               "type": "number"
            }
         },
         "title": "ScalingParameters",
         "type": "object"
      },
      "TruncationParameters": {
         "description": "Parameters for truncating a distribution to a specified range. Truncation should\nbe applied after sampling and scaling.\n\nUsed to constrain sampled values within minimum and maximum bounds.",
         "properties": {
            "min": {
               "default": 0,
               "description": "Minimum value of the sampled distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the sampled distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "TruncationParameters",
         "type": "object"
      }
   }
}

Fields:

distribution_parameters (aind_behavior_services.task_logic.distributions.ExponentialDistributionParameters)
family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.EXPONENTIAL])

field distribution_parameters: ExponentialDistributionParameters = ExponentialDistributionParameters(family=<DistributionFamily.EXPONENTIAL: 'Exponential'>, rate=0)[source]¶: Parameters of the distribution

field family: Literal[DistributionFamily.EXPONENTIAL] = DistributionFamily.EXPONENTIAL[source]¶

pydantic model aind_behavior_services.task_logic.distributions.ExponentialDistributionParameters[source]¶

Bases: DistributionParametersBase

Parameters for an exponential distribution.

Defined by the rate parameter (inverse of mean).

Show JSON schema

{
   "title": "ExponentialDistributionParameters",
   "description": "Parameters for an exponential distribution.\n\nDefined by the rate parameter (inverse of mean).",
   "type": "object",
   "properties": {
      "family": {
         "const": "Exponential",
         "default": "Exponential",
         "title": "Family",
         "type": "string"
      },
      "rate": {
         "default": 0,
         "description": "Rate parameter of the distribution",
         "minimum": 0,
         "title": "Rate",
         "type": "number"
      }
   }
}

Fields:

family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.EXPONENTIAL])
rate (float)

field family: Literal[DistributionFamily.EXPONENTIAL] = DistributionFamily.EXPONENTIAL[source]¶

field rate: float = 0[source]¶

Rate parameter of the distribution

Constraints:

ge = 0

pydantic model aind_behavior_services.task_logic.distributions.GammaDistribution[source]¶

Bases: DistributionBase

A gamma probability distribution.

Generalizes the exponential distribution. Used for modeling positive continuous variables with right-skewed distributions.

Show JSON schema

{
   "title": "GammaDistribution",
   "description": "A gamma probability distribution.\n\nGeneralizes the exponential distribution. Used for modeling\npositive continuous variables with right-skewed distributions.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Gamma",
         "default": "Gamma",
         "title": "Family",
         "type": "string"
      },
      "distribution_parameters": {
         "$ref": "#/$defs/GammaDistributionParameters",
         "default": {
            "family": "Gamma",
            "shape": 1.0,
            "rate": 1.0
         },
         "description": "Parameters of the distribution"
      },
      "truncation_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/TruncationParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Truncation parameters of the distribution"
      },
      "scaling_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/ScalingParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Scaling parameters of the distribution"
      }
   },
   "$defs": {
      "GammaDistributionParameters": {
         "description": "Parameters for a gamma distribution.\n\nDefined by shape (k) and rate (\u03b8\u207b\u00b9) parameters.",
         "properties": {
            "family": {
               "const": "Gamma",
               "default": "Gamma",
               "title": "Family",
               "type": "string"
            },
            "shape": {
               "default": 1,
               "description": "Shape parameter of the distribution",
               "minimum": 0,
               "title": "Shape",
               "type": "number"
            },
            "rate": {
               "default": 1,
               "description": "Rate parameter of the distribution",
               "minimum": 0,
               "title": "Rate",
               "type": "number"
            }
         },
         "title": "GammaDistributionParameters",
         "type": "object"
      },
      "ScalingParameters": {
         "description": "Parameters for scaling and offsetting sampled distribution values.\nScaling is applied as (value * scale + offset).\n\nApplies linear transformation: result = (value * scale) + offset.",
         "properties": {
            "scale": {
               "default": 1,
               "description": "Scaling factor to apply on the sampled distribution",
               "title": "Scale",
               "type": "number"
            },
            "offset": {
               "default": 0,
               "description": "Offset factor to apply on the sampled distribution",
               "title": "Offset",
               "type": "number"
            }
         },
         "title": "ScalingParameters",
         "type": "object"
      },
      "TruncationParameters": {
         "description": "Parameters for truncating a distribution to a specified range. Truncation should\nbe applied after sampling and scaling.\n\nUsed to constrain sampled values within minimum and maximum bounds.",
         "properties": {
            "min": {
               "default": 0,
               "description": "Minimum value of the sampled distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the sampled distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "TruncationParameters",
         "type": "object"
      }
   }
}

Fields:

distribution_parameters (aind_behavior_services.task_logic.distributions.GammaDistributionParameters)
family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.GAMMA])

field distribution_parameters: GammaDistributionParameters = GammaDistributionParameters(family=<DistributionFamily.GAMMA: 'Gamma'>, shape=1, rate=1)[source]¶: Parameters of the distribution

field family: Literal[DistributionFamily.GAMMA] = DistributionFamily.GAMMA[source]¶

pydantic model aind_behavior_services.task_logic.distributions.GammaDistributionParameters[source]¶

Bases: DistributionParametersBase

Parameters for a gamma distribution.

Defined by shape (k) and rate (θ⁻¹) parameters.

Show JSON schema

{
   "title": "GammaDistributionParameters",
   "description": "Parameters for a gamma distribution.\n\nDefined by shape (k) and rate (\u03b8\u207b\u00b9) parameters.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Gamma",
         "default": "Gamma",
         "title": "Family",
         "type": "string"
      },
      "shape": {
         "default": 1,
         "description": "Shape parameter of the distribution",
         "minimum": 0,
         "title": "Shape",
         "type": "number"
      },
      "rate": {
         "default": 1,
         "description": "Rate parameter of the distribution",
         "minimum": 0,
         "title": "Rate",
         "type": "number"
      }
   }
}

Fields:

family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.GAMMA])
rate (float)
shape (float)

field family: Literal[DistributionFamily.GAMMA] = DistributionFamily.GAMMA[source]¶

field rate: float = 1[source]¶

Rate parameter of the distribution

Constraints:

ge = 0

field shape: float = 1[source]¶

Shape parameter of the distribution

Constraints:

ge = 0

pydantic model aind_behavior_services.task_logic.distributions.LogNormalDistribution[source]¶

Bases: DistributionBase

A log-normal probability distribution.

Distribution where the logarithm of the variable is normally distributed. Always produces positive values and is right-skewed.

Show JSON schema

{
   "title": "LogNormalDistribution",
   "description": "A log-normal probability distribution.\n\nDistribution where the logarithm of the variable is normally distributed.\nAlways produces positive values and is right-skewed.",
   "type": "object",
   "properties": {
      "family": {
         "const": "LogNormal",
         "default": "LogNormal",
         "title": "Family",
         "type": "string"
      },
      "distribution_parameters": {
         "$ref": "#/$defs/LogNormalDistributionParameters",
         "default": {
            "family": "LogNormal",
            "mean": 0.0,
            "std": 0.0
         },
         "description": "Parameters of the distribution"
      },
      "truncation_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/TruncationParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Truncation parameters of the distribution"
      },
      "scaling_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/ScalingParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Scaling parameters of the distribution"
      }
   },
   "$defs": {
      "LogNormalDistributionParameters": {
         "description": "Parameters for a log-normal distribution.\n\nDefined by the mean and standard deviation of the underlying normal distribution.",
         "properties": {
            "family": {
               "const": "LogNormal",
               "default": "LogNormal",
               "title": "Family",
               "type": "string"
            },
            "mean": {
               "default": 0,
               "description": "Mean of the distribution",
               "title": "Mean",
               "type": "number"
            },
            "std": {
               "default": 0,
               "description": "Standard deviation of the distribution",
               "title": "Std",
               "type": "number"
            }
         },
         "title": "LogNormalDistributionParameters",
         "type": "object"
      },
      "ScalingParameters": {
         "description": "Parameters for scaling and offsetting sampled distribution values.\nScaling is applied as (value * scale + offset).\n\nApplies linear transformation: result = (value * scale) + offset.",
         "properties": {
            "scale": {
               "default": 1,
               "description": "Scaling factor to apply on the sampled distribution",
               "title": "Scale",
               "type": "number"
            },
            "offset": {
               "default": 0,
               "description": "Offset factor to apply on the sampled distribution",
               "title": "Offset",
               "type": "number"
            }
         },
         "title": "ScalingParameters",
         "type": "object"
      },
      "TruncationParameters": {
         "description": "Parameters for truncating a distribution to a specified range. Truncation should\nbe applied after sampling and scaling.\n\nUsed to constrain sampled values within minimum and maximum bounds.",
         "properties": {
            "min": {
               "default": 0,
               "description": "Minimum value of the sampled distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the sampled distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "TruncationParameters",
         "type": "object"
      }
   }
}

Fields:

distribution_parameters (aind_behavior_services.task_logic.distributions.LogNormalDistributionParameters)
family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.LOGNORMAL])

field distribution_parameters: LogNormalDistributionParameters = LogNormalDistributionParameters(family=<DistributionFamily.LOGNORMAL: 'LogNormal'>, mean=0, std=0)[source]¶: Parameters of the distribution

field family: Literal[DistributionFamily.LOGNORMAL] = DistributionFamily.LOGNORMAL[source]¶

pydantic model aind_behavior_services.task_logic.distributions.LogNormalDistributionParameters[source]¶

Bases: DistributionParametersBase

Parameters for a log-normal distribution.

Defined by the mean and standard deviation of the underlying normal distribution.

Show JSON schema

{
   "title": "LogNormalDistributionParameters",
   "description": "Parameters for a log-normal distribution.\n\nDefined by the mean and standard deviation of the underlying normal distribution.",
   "type": "object",
   "properties": {
      "family": {
         "const": "LogNormal",
         "default": "LogNormal",
         "title": "Family",
         "type": "string"
      },
      "mean": {
         "default": 0,
         "description": "Mean of the distribution",
         "title": "Mean",
         "type": "number"
      },
      "std": {
         "default": 0,
         "description": "Standard deviation of the distribution",
         "title": "Std",
         "type": "number"
      }
   }
}

Fields:

family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.LOGNORMAL])
mean (float)
std (float)

field family: Literal[DistributionFamily.LOGNORMAL] = DistributionFamily.LOGNORMAL[source]¶

field mean: float = 0[source]¶: Mean of the distribution

field std: float = 0[source]¶: Standard deviation of the distribution

pydantic model aind_behavior_services.task_logic.distributions.NormalDistribution[source]¶

Bases: DistributionBase

A normal (Gaussian) probability distribution.

Bell-shaped distribution symmetric around the mean, commonly used for modeling naturally occurring random variables.

Show JSON schema

{
   "title": "NormalDistribution",
   "description": "A normal (Gaussian) probability distribution.\n\nBell-shaped distribution symmetric around the mean, commonly used\nfor modeling naturally occurring random variables.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Normal",
         "default": "Normal",
         "title": "Family",
         "type": "string"
      },
      "distribution_parameters": {
         "$ref": "#/$defs/NormalDistributionParameters",
         "default": {
            "family": "Normal",
            "mean": 0.0,
            "std": 0.0
         },
         "description": "Parameters of the distribution"
      },
      "truncation_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/TruncationParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Truncation parameters of the distribution"
      },
      "scaling_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/ScalingParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Scaling parameters of the distribution"
      }
   },
   "$defs": {
      "NormalDistributionParameters": {
         "description": "Parameters for a normal (Gaussian) distribution.\n\nDefined by mean (center) and standard deviation (spread).",
         "properties": {
            "family": {
               "const": "Normal",
               "default": "Normal",
               "title": "Family",
               "type": "string"
            },
            "mean": {
               "default": 0,
               "description": "Mean of the distribution",
               "title": "Mean",
               "type": "number"
            },
            "std": {
               "default": 0,
               "description": "Standard deviation of the distribution",
               "title": "Std",
               "type": "number"
            }
         },
         "title": "NormalDistributionParameters",
         "type": "object"
      },
      "ScalingParameters": {
         "description": "Parameters for scaling and offsetting sampled distribution values.\nScaling is applied as (value * scale + offset).\n\nApplies linear transformation: result = (value * scale) + offset.",
         "properties": {
            "scale": {
               "default": 1,
               "description": "Scaling factor to apply on the sampled distribution",
               "title": "Scale",
               "type": "number"
            },
            "offset": {
               "default": 0,
               "description": "Offset factor to apply on the sampled distribution",
               "title": "Offset",
               "type": "number"
            }
         },
         "title": "ScalingParameters",
         "type": "object"
      },
      "TruncationParameters": {
         "description": "Parameters for truncating a distribution to a specified range. Truncation should\nbe applied after sampling and scaling.\n\nUsed to constrain sampled values within minimum and maximum bounds.",
         "properties": {
            "min": {
               "default": 0,
               "description": "Minimum value of the sampled distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the sampled distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "TruncationParameters",
         "type": "object"
      }
   }
}

Fields:

distribution_parameters (aind_behavior_services.task_logic.distributions.NormalDistributionParameters)
family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.NORMAL])

field distribution_parameters: NormalDistributionParameters = NormalDistributionParameters(family=<DistributionFamily.NORMAL: 'Normal'>, mean=0, std=0)[source]¶: Parameters of the distribution

field family: Literal[DistributionFamily.NORMAL] = DistributionFamily.NORMAL[source]¶

pydantic model aind_behavior_services.task_logic.distributions.NormalDistributionParameters[source]¶

Bases: DistributionParametersBase

Parameters for a normal (Gaussian) distribution.

Defined by mean (center) and standard deviation (spread).

Show JSON schema

{
   "title": "NormalDistributionParameters",
   "description": "Parameters for a normal (Gaussian) distribution.\n\nDefined by mean (center) and standard deviation (spread).",
   "type": "object",
   "properties": {
      "family": {
         "const": "Normal",
         "default": "Normal",
         "title": "Family",
         "type": "string"
      },
      "mean": {
         "default": 0,
         "description": "Mean of the distribution",
         "title": "Mean",
         "type": "number"
      },
      "std": {
         "default": 0,
         "description": "Standard deviation of the distribution",
         "title": "Std",
         "type": "number"
      }
   }
}

Fields:

family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.NORMAL])
mean (float)
std (float)

field family: Literal[DistributionFamily.NORMAL] = DistributionFamily.NORMAL[source]¶

field mean: float = 0[source]¶: Mean of the distribution

field std: float = 0[source]¶: Standard deviation of the distribution

pydantic model aind_behavior_services.task_logic.distributions.PdfDistribution[source]¶

Bases: DistributionBase

A custom probability density function distribution.

Allows defining arbitrary discrete distributions by specifying probability values and their corresponding indices.

Show JSON schema

{
   "title": "PdfDistribution",
   "description": "A custom probability density function distribution.\n\nAllows defining arbitrary discrete distributions by specifying\nprobability values and their corresponding indices.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Pdf",
         "default": "Pdf",
         "title": "Family",
         "type": "string"
      },
      "distribution_parameters": {
         "$ref": "#/$defs/PdfDistributionParameters",
         "default": {
            "family": "Pdf",
            "pdf": [
               1.0
            ],
            "index": [
               0.0
            ]
         },
         "description": "Parameters of the distribution"
      },
      "truncation_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/TruncationParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Truncation parameters of the distribution"
      },
      "scaling_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/ScalingParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Scaling parameters of the distribution"
      }
   },
   "$defs": {
      "PdfDistributionParameters": {
         "description": "Parameters for a custom probability density function distribution.\n\nDefined by explicit probability values and their corresponding indices.\nProbabilities are automatically normalized to sum to 1.",
         "properties": {
            "family": {
               "const": "Pdf",
               "default": "Pdf",
               "title": "Family",
               "type": "string"
            },
            "pdf": {
               "default": [
                  1
               ],
               "description": "The probability density function",
               "items": {
                  "minimum": 0,
                  "type": "number"
               },
               "title": "Pdf",
               "type": "array"
            },
            "index": {
               "default": [
                  0
               ],
               "description": "The index of the probability density function",
               "items": {
                  "type": "number"
               },
               "title": "Index",
               "type": "array"
            }
         },
         "title": "PdfDistributionParameters",
         "type": "object"
      },
      "ScalingParameters": {
         "description": "Parameters for scaling and offsetting sampled distribution values.\nScaling is applied as (value * scale + offset).\n\nApplies linear transformation: result = (value * scale) + offset.",
         "properties": {
            "scale": {
               "default": 1,
               "description": "Scaling factor to apply on the sampled distribution",
               "title": "Scale",
               "type": "number"
            },
            "offset": {
               "default": 0,
               "description": "Offset factor to apply on the sampled distribution",
               "title": "Offset",
               "type": "number"
            }
         },
         "title": "ScalingParameters",
         "type": "object"
      },
      "TruncationParameters": {
         "description": "Parameters for truncating a distribution to a specified range. Truncation should\nbe applied after sampling and scaling.\n\nUsed to constrain sampled values within minimum and maximum bounds.",
         "properties": {
            "min": {
               "default": 0,
               "description": "Minimum value of the sampled distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the sampled distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "TruncationParameters",
         "type": "object"
      }
   }
}

Fields:

distribution_parameters (aind_behavior_services.task_logic.distributions.PdfDistributionParameters)
family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.PDF])

field distribution_parameters: PdfDistributionParameters = PdfDistributionParameters(family=<DistributionFamily.PDF: 'Pdf'>, pdf=[1], index=[0])[source]¶: Parameters of the distribution

field family: Literal[DistributionFamily.PDF] = DistributionFamily.PDF[source]¶

pydantic model aind_behavior_services.task_logic.distributions.PdfDistributionParameters[source]¶

Bases: DistributionParametersBase

Parameters for a custom probability density function distribution.

Defined by explicit probability values and their corresponding indices. Probabilities are automatically normalized to sum to 1.

Show JSON schema

{
   "title": "PdfDistributionParameters",
   "description": "Parameters for a custom probability density function distribution.\n\nDefined by explicit probability values and their corresponding indices.\nProbabilities are automatically normalized to sum to 1.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Pdf",
         "default": "Pdf",
         "title": "Family",
         "type": "string"
      },
      "pdf": {
         "default": [
            1
         ],
         "description": "The probability density function",
         "items": {
            "minimum": 0,
            "type": "number"
         },
         "title": "Pdf",
         "type": "array"
      },
      "index": {
         "default": [
            0
         ],
         "description": "The index of the probability density function",
         "items": {
            "type": "number"
         },
         "title": "Index",
         "type": "array"
      }
   }
}

Fields:

family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.PDF])
index (List[float])
pdf (List[Annotated[float, annotated_types.Ge(ge=0)]])

Validators:

normalize_pdf » pdf
validate_matching_length » all fields

field family: Literal[DistributionFamily.PDF] = DistributionFamily.PDF[source]¶

Validated by:

validate_matching_length

field index: List[float] = [0][source]¶

The index of the probability density function

Validated by:

validate_matching_length

field pdf: List[Annotated[float, Ge(ge=0)]] = [1][source]¶

The probability density function

Validated by:

normalize_pdf
validate_matching_length

validator normalize_pdf » pdf[source]¶

Normalizes the PDF values to sum to 1

Parameters:: v (List[Annotated[float, Ge(ge=0)]])
Return type:: List[Annotated[float, Ge(ge=0)]]

validator validate_matching_length » all fields[source]¶

Ensures that pdf and index arrays have matching lengths

Return type:: Self

pydantic model aind_behavior_services.task_logic.distributions.PoissonDistribution[source]¶

Bases: DistributionBase

A Poisson probability distribution.

Models the number of events occurring in a fixed interval of time or space when events occur independently at a constant rate.

Show JSON schema

{
   "title": "PoissonDistribution",
   "description": "A Poisson probability distribution.\n\nModels the number of events occurring in a fixed interval of time or space\nwhen events occur independently at a constant rate.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Poisson",
         "default": "Poisson",
         "title": "Family",
         "type": "string"
      },
      "distribution_parameters": {
         "$ref": "#/$defs/PoissonDistributionParameters",
         "default": {
            "family": "Poisson",
            "rate": 1.0
         },
         "description": "Parameters of the distribution"
      },
      "truncation_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/TruncationParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Truncation parameters of the distribution"
      },
      "scaling_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/ScalingParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Scaling parameters of the distribution"
      }
   },
   "$defs": {
      "PoissonDistributionParameters": {
         "description": "Parameters for a Poisson distribution.\n\nDefined by the rate parameter (average number of events).",
         "properties": {
            "family": {
               "const": "Poisson",
               "default": "Poisson",
               "title": "Family",
               "type": "string"
            },
            "rate": {
               "default": 1,
               "description": "Rate parameter of the Poisson process that generates the distribution",
               "minimum": 0,
               "title": "Rate",
               "type": "number"
            }
         },
         "title": "PoissonDistributionParameters",
         "type": "object"
      },
      "ScalingParameters": {
         "description": "Parameters for scaling and offsetting sampled distribution values.\nScaling is applied as (value * scale + offset).\n\nApplies linear transformation: result = (value * scale) + offset.",
         "properties": {
            "scale": {
               "default": 1,
               "description": "Scaling factor to apply on the sampled distribution",
               "title": "Scale",
               "type": "number"
            },
            "offset": {
               "default": 0,
               "description": "Offset factor to apply on the sampled distribution",
               "title": "Offset",
               "type": "number"
            }
         },
         "title": "ScalingParameters",
         "type": "object"
      },
      "TruncationParameters": {
         "description": "Parameters for truncating a distribution to a specified range. Truncation should\nbe applied after sampling and scaling.\n\nUsed to constrain sampled values within minimum and maximum bounds.",
         "properties": {
            "min": {
               "default": 0,
               "description": "Minimum value of the sampled distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the sampled distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "TruncationParameters",
         "type": "object"
      }
   }
}

Fields:

distribution_parameters (aind_behavior_services.task_logic.distributions.PoissonDistributionParameters)
family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.POISSON])

field distribution_parameters: PoissonDistributionParameters = PoissonDistributionParameters(family=<DistributionFamily.POISSON: 'Poisson'>, rate=1)[source]¶: Parameters of the distribution

field family: Literal[DistributionFamily.POISSON] = DistributionFamily.POISSON[source]¶

pydantic model aind_behavior_services.task_logic.distributions.PoissonDistributionParameters[source]¶

Bases: DistributionParametersBase

Parameters for a Poisson distribution.

Defined by the rate parameter (average number of events).

Show JSON schema

{
   "title": "PoissonDistributionParameters",
   "description": "Parameters for a Poisson distribution.\n\nDefined by the rate parameter (average number of events).",
   "type": "object",
   "properties": {
      "family": {
         "const": "Poisson",
         "default": "Poisson",
         "title": "Family",
         "type": "string"
      },
      "rate": {
         "default": 1,
         "description": "Rate parameter of the Poisson process that generates the distribution",
         "minimum": 0,
         "title": "Rate",
         "type": "number"
      }
   }
}

Fields:

family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.POISSON])
rate (float)

field family: Literal[DistributionFamily.POISSON] = DistributionFamily.POISSON[source]¶

field rate: float = 1[source]¶

Rate parameter of the Poisson process that generates the distribution

Constraints:

ge = 0

pydantic model aind_behavior_services.task_logic.distributions.Scalar[source]¶

Bases: DistributionBase

A scalar distribution that returns a constant value.

Useful for fixed parameters that don’t vary across trials or samples.

Show JSON schema

{
   "title": "Scalar",
   "description": "A scalar distribution that returns a constant value.\n\nUseful for fixed parameters that don't vary across trials or samples.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Scalar",
         "default": "Scalar",
         "title": "Family",
         "type": "string"
      },
      "distribution_parameters": {
         "$ref": "#/$defs/ScalarDistributionParameter",
         "default": {
            "family": "Scalar",
            "value": 0.0
         },
         "description": "Parameters of the distribution"
      },
      "truncation_parameters": {
         "const": null,
         "default": null,
         "title": "Truncation Parameters",
         "type": "null"
      },
      "scaling_parameters": {
         "const": null,
         "default": null,
         "title": "Scaling Parameters",
         "type": "null"
      }
   },
   "$defs": {
      "ScalarDistributionParameter": {
         "description": "Parameters for a scalar (constant) distribution.\n\nRepresents a deterministic value that always returns the same number.",
         "properties": {
            "family": {
               "const": "Scalar",
               "default": "Scalar",
               "title": "Family",
               "type": "string"
            },
            "value": {
               "default": 0,
               "description": "The static value of the distribution",
               "title": "Value",
               "type": "number"
            }
         },
         "title": "ScalarDistributionParameter",
         "type": "object"
      }
   }
}

Fields:

distribution_parameters (aind_behavior_services.task_logic.distributions.ScalarDistributionParameter)
family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.SCALAR])
scaling_parameters (Literal[None])
truncation_parameters (Literal[None])

field distribution_parameters: ScalarDistributionParameter = ScalarDistributionParameter(family=<DistributionFamily.SCALAR: 'Scalar'>, value=0)[source]¶: Parameters of the distribution

field family: Literal[DistributionFamily.SCALAR] = DistributionFamily.SCALAR[source]¶

field scaling_parameters: Literal[None] = None[source]¶

field truncation_parameters: Literal[None] = None[source]¶

pydantic model aind_behavior_services.task_logic.distributions.ScalarDistributionParameter[source]¶

Bases: DistributionParametersBase

Parameters for a scalar (constant) distribution.

Represents a deterministic value that always returns the same number.

Show JSON schema

{
   "title": "ScalarDistributionParameter",
   "description": "Parameters for a scalar (constant) distribution.\n\nRepresents a deterministic value that always returns the same number.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Scalar",
         "default": "Scalar",
         "title": "Family",
         "type": "string"
      },
      "value": {
         "default": 0,
         "description": "The static value of the distribution",
         "title": "Value",
         "type": "number"
      }
   }
}

Fields:

family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.SCALAR])
value (float)

field family: Literal[DistributionFamily.SCALAR] = DistributionFamily.SCALAR[source]¶

field value: float = 0[source]¶: The static value of the distribution

pydantic model aind_behavior_services.task_logic.distributions.ScalingParameters[source]¶

Bases: BaseModel

Parameters for scaling and offsetting sampled distribution values. Scaling is applied as (value * scale + offset).

Applies linear transformation: result = (value * scale) + offset.

Show JSON schema

{
   "title": "ScalingParameters",
   "description": "Parameters for scaling and offsetting sampled distribution values.\nScaling is applied as (value * scale + offset).\n\nApplies linear transformation: result = (value * scale) + offset.",
   "type": "object",
   "properties": {
      "scale": {
         "default": 1,
         "description": "Scaling factor to apply on the sampled distribution",
         "title": "Scale",
         "type": "number"
      },
      "offset": {
         "default": 0,
         "description": "Offset factor to apply on the sampled distribution",
         "title": "Offset",
         "type": "number"
      }
   }
}

Fields:

offset (float)
scale (float)

field offset: float = 0[source]¶: Offset factor to apply on the sampled distribution

field scale: float = 1[source]¶: Scaling factor to apply on the sampled distribution

pydantic model aind_behavior_services.task_logic.distributions.TruncationParameters[source]¶

Bases: BaseModel

Parameters for truncating a distribution to a specified range. Truncation should be applied after sampling and scaling.

Used to constrain sampled values within minimum and maximum bounds.

Show JSON schema

{
   "title": "TruncationParameters",
   "description": "Parameters for truncating a distribution to a specified range. Truncation should\nbe applied after sampling and scaling.\n\nUsed to constrain sampled values within minimum and maximum bounds.",
   "type": "object",
   "properties": {
      "min": {
         "default": 0,
         "description": "Minimum value of the sampled distribution",
         "title": "Min",
         "type": "number"
      },
      "max": {
         "default": 0,
         "description": "Maximum value of the sampled distribution",
         "title": "Max",
         "type": "number"
      }
   }
}

Fields:

max (float)
min (float)

Validators:

validate_min_less_than_max » all fields

field max: float = 0[source]¶

Maximum value of the sampled distribution

Validated by:

validate_min_less_than_max

field min: float = 0[source]¶

Minimum value of the sampled distribution

Validated by:

validate_min_less_than_max

validator validate_min_less_than_max » all fields[source]¶

Ensures that min is less than max when truncation is enabled

Return type:: Self

pydantic model aind_behavior_services.task_logic.distributions.UniformDistribution[source]¶

Bases: DistributionBase

A uniform probability distribution.

All values between min and max have equal probability of being sampled.

Show JSON schema

{
   "title": "UniformDistribution",
   "description": "A uniform probability distribution.\n\nAll values between min and max have equal probability of being sampled.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Uniform",
         "default": "Uniform",
         "title": "Family",
         "type": "string"
      },
      "distribution_parameters": {
         "$ref": "#/$defs/UniformDistributionParameters",
         "default": {
            "family": "Uniform",
            "min": 0.0,
            "max": 0.0
         },
         "description": "Parameters of the distribution"
      },
      "truncation_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/TruncationParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Truncation parameters of the distribution"
      },
      "scaling_parameters": {
         "anyOf": [
            {
               "$ref": "#/$defs/ScalingParameters"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Scaling parameters of the distribution"
      }
   },
   "$defs": {
      "ScalingParameters": {
         "description": "Parameters for scaling and offsetting sampled distribution values.\nScaling is applied as (value * scale + offset).\n\nApplies linear transformation: result = (value * scale) + offset.",
         "properties": {
            "scale": {
               "default": 1,
               "description": "Scaling factor to apply on the sampled distribution",
               "title": "Scale",
               "type": "number"
            },
            "offset": {
               "default": 0,
               "description": "Offset factor to apply on the sampled distribution",
               "title": "Offset",
               "type": "number"
            }
         },
         "title": "ScalingParameters",
         "type": "object"
      },
      "TruncationParameters": {
         "description": "Parameters for truncating a distribution to a specified range. Truncation should\nbe applied after sampling and scaling.\n\nUsed to constrain sampled values within minimum and maximum bounds.",
         "properties": {
            "min": {
               "default": 0,
               "description": "Minimum value of the sampled distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the sampled distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "TruncationParameters",
         "type": "object"
      },
      "UniformDistributionParameters": {
         "description": "Parameters for a uniform distribution.\n\nDefined by minimum and maximum bounds of the distribution.",
         "properties": {
            "family": {
               "const": "Uniform",
               "default": "Uniform",
               "title": "Family",
               "type": "string"
            },
            "min": {
               "default": 0,
               "description": "Minimum value of the distribution",
               "title": "Min",
               "type": "number"
            },
            "max": {
               "default": 0,
               "description": "Maximum value of the distribution",
               "title": "Max",
               "type": "number"
            }
         },
         "title": "UniformDistributionParameters",
         "type": "object"
      }
   }
}

Fields:

distribution_parameters (aind_behavior_services.task_logic.distributions.UniformDistributionParameters)
family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.UNIFORM])

field distribution_parameters: UniformDistributionParameters = UniformDistributionParameters(family=<DistributionFamily.UNIFORM: 'Uniform'>, min=0, max=0)[source]¶: Parameters of the distribution

field family: Literal[DistributionFamily.UNIFORM] = DistributionFamily.UNIFORM[source]¶

pydantic model aind_behavior_services.task_logic.distributions.UniformDistributionParameters[source]¶

Bases: DistributionParametersBase

Parameters for a uniform distribution.

Defined by minimum and maximum bounds of the distribution.

Show JSON schema

{
   "title": "UniformDistributionParameters",
   "description": "Parameters for a uniform distribution.\n\nDefined by minimum and maximum bounds of the distribution.",
   "type": "object",
   "properties": {
      "family": {
         "const": "Uniform",
         "default": "Uniform",
         "title": "Family",
         "type": "string"
      },
      "min": {
         "default": 0,
         "description": "Minimum value of the distribution",
         "title": "Min",
         "type": "number"
      },
      "max": {
         "default": 0,
         "description": "Maximum value of the distribution",
         "title": "Max",
         "type": "number"
      }
   }
}

Fields:

family (Literal[aind_behavior_services.task_logic.distributions.DistributionFamily.UNIFORM])
max (float)
min (float)

field family: Literal[DistributionFamily.UNIFORM] = DistributionFamily.UNIFORM[source]¶

field max: float = 0[source]¶: Maximum value of the distribution

field min: float = 0[source]¶: Minimum value of the distribution

Module contents¶

pydantic model aind_behavior_services.task_logic.AindBehaviorTaskLogicModel[source]¶

Bases: Task

Show JSON schema

{
   "title": "AindBehaviorTaskLogicModel",
   "type": "object",
   "properties": {
      "name": {
         "description": "Name of the task.",
         "title": "Name",
         "type": "string"
      },
      "description": {
         "default": "",
         "description": "Description of the task.",
         "title": "Description",
         "type": "string"
      },
      "task_parameters": {
         "$ref": "#/$defs/TaskParameters",
         "description": "Parameters of the task logic"
      },
      "version": {
         "description": "task schema version",
         "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-]+)*))?$",
         "title": "Version",
         "type": "string"
      },
      "stage_name": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Optional stage name the `Task` object instance represents.",
         "title": "Stage Name"
      }
   },
   "$defs": {
      "TaskParameters": {
         "additionalProperties": true,
         "properties": {
            "rng_seed": {
               "anyOf": [
                  {
                     "type": "number"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Seed of the random number generator",
               "title": "Rng Seed"
            },
            "aind_behavior_services_pkg_version": {
               "const": "0.12.2-rc1",
               "default": "0.12.2-rc1",
               "title": "aind_behavior_services package version",
               "type": "string"
            }
         },
         "title": "TaskParameters",
         "type": "object"
      }
   },
   "additionalProperties": false,
   "required": [
      "name",
      "task_parameters",
      "version"
   ]
}

Config:

extra: str = forbid
validate_assignment: bool = True
validate_default: bool = True
strict: bool = True
str_strip_whitespace: bool = True

Fields:

description (str)
name (str)
stage_name (Optional[str])
task_parameters (aind_behavior_services.task_logic.TaskParameters)
version (str)

Validators:

coerce_version » version

field description: str = ''[source]¶: Description of the task.

field name: str [Required][source]¶: Name of the task.

field stage_name: str | None = None[source]¶: Optional stage name the Task object instance represents.

field task_parameters: TaskParameters [Required][source]¶: Parameters of the task logic

field version: str [Required][source]¶

task schema version

Constraints:

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-]+)*))?$

Validated by:

coerce_version

validator coerce_version » version[source]¶

Parameters:: v (str)
Return type:: str

pydantic model aind_behavior_services.task_logic.TaskParameters[source]¶

Bases: AindBehaviorModelExtra

Show JSON schema

{
   "title": "TaskParameters",
   "type": "object",
   "properties": {
      "rng_seed": {
         "anyOf": [
            {
               "type": "number"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Seed of the random number generator",
         "title": "Rng Seed"
      },
      "aind_behavior_services_pkg_version": {
         "const": "0.12.2-rc1",
         "default": "0.12.2-rc1",
         "title": "aind_behavior_services package version",
         "type": "string"
      }
   },
   "additionalProperties": true
}

Config:

extra: str = allow
validate_assignment: bool = True
validate_default: bool = True
strict: bool = True
str_strip_whitespace: bool = True

Fields:

aind_behavior_services_pkg_version (Literal['0.12.2-rc1'])
rng_seed (float | None)

Validators:

coerce_version » aind_behavior_services_pkg_version

field aind_behavior_services_pkg_version: Literal['0.12.2-rc1'] = '0.12.2-rc1'[source]¶

Constraints:

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-]+)*))?$

Validated by:

coerce_version

field rng_seed: float | None = None[source]¶: Seed of the random number generator

validator coerce_version » aind_behavior_services_pkg_version[source]¶

Parameters:: v (str)
Return type:: str