doe model

class friendly_doe.doe_model.ANOVA(*, total: Total, constant: Constant, total_corrected: TotalCorrected, regression: Regression, residual: Residual, lack_of_fit: LackOfFit, pure_error: PureError)

Bases: BaseModel

constant: Constant

lack_of_fit: LackOfFit

model_config = {}

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

pure_error: PureError

regression: Regression

residual: Residual

total: Total

total_corrected: TotalCorrected

class friendly_doe.doe_model.CalculatedSetpoints(*, optimalSetpoint: OptimalSetpoint | None = <factory>, robustSetpoint: RobustSetpoint | None = <factory>, favoriteSetpoint: FavoriteSetpoint | None = <factory>)

Bases: BaseModel

favoriteSetpoint: FavoriteSetpoint | None

model_config = {}

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

optimalSetpoint: OptimalSetpoint | None

robustSetpoint: RobustSetpoint | None

class friendly_doe.doe_model.CandidateSet(*, data: list[list[int | str | float | None]])

Bases: BaseModel

data: list[list[int | str | float | None]]

model_config = {'extra': 'forbid'}

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

class friendly_doe.doe_model.Coefficients(*, scaledAndCentered: CoefficientsData | None = None, normalized: CoefficientsData | None = None, unscaled: CoefficientsData | None = None)

Bases: BaseModel

model_config = {}

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

normalized: CoefficientsData | None

scaledAndCentered: CoefficientsData | None

unscaled: CoefficientsData | None

class friendly_doe.doe_model.CoefficientsData(*, names: list[Any] | None = None, abbreviations: list[Any] | None = None, coefficients: list[Any] | None = None, confidence: list[Any] | None = None)

Bases: BaseModel

abbreviations: list[Any] | None

coefficients: list[Any] | None

confidence: list[Any] | None

model_config = {}

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

names: list[Any] | None

class friendly_doe.doe_model.Constant(*, DF: int | None, SS: float | None, MS: float | None, SD: float | None)

Bases: BaseModel

DF: int | None

MS: float | None

SD: float | None

SS: float | None

model_config = {}

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

class friendly_doe.doe_model.Constants(*, c1: float, c2: float, c3: float | None = None)

Bases: BaseModel

c1: float

c2: float

c3: float | None

model_config = {'extra': 'forbid'}

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

class friendly_doe.doe_model.Design(*, type: DesignType | None = '', settings: dict[str, Any] | None = None)

Bases: BaseModel

model_config = {}

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

settings: dict[str, Any] | None

type: DesignType | None

class friendly_doe.doe_model.DesignType(*values)

Bases: Enum

AxE = 'AxE'

AxN = 'AxN'

AxR = 'AxR'

BB = 'BB'

CCC = 'CCC'

CCF = 'CCF'

CCO = 'CCO'

Custom = 'Custom'

DOpt = 'DOpt'

Definitive_Screening = 'Definitive Screening'

Doehlert = 'Doehlert'

FF2 = 'FF2'

FF3 = 'FF3'

FFM = 'FFM'

FFR_III = 'FFR_III'

FFR_IV = 'FFR_IV'

FFR_V = 'FFR_V'

Generalized_Subset_Designs = 'Generalized Subset Designs'

J2 = 'J2'

L18 = 'L18'

L27 = 'L27'

L36 = 'L36'

L9 = 'L9'

Levels = 'Levels'

OnionDOpt = 'OnionDOpt'

PB = 'PB'

PB_SS = 'PB_SS'

RCCC = 'RCCC'

RCCF = 'RCCF'

RED_MUP = 'RED-MUP'

RSR = 'RSR'

RSS = 'RSS'

SimC = 'SimC'

SimF = 'SimF'

SimM = 'SimM'

SimSC = 'SimSC'

Stability = 'Stability'

field_ = ''

class friendly_doe.doe_model.DoeSchema(*, apiVersion: str, name: str | None = '', factors: list[Factor] | None = <factory>, responses: list[Response] | None = <factory>, settings: Settings | None = <factory>, worksheet: Worksheet | None = None, calculatedSetpoints: CalculatedSetpoints | None = None, setpoints: Any = None, objective: Objective1 | None = '', design: Design | None = None, model: Model | None = None, models: list[Model1] | None = <factory>, predictions: Predictions | None = None, candidateSet: CandidateSet | None = None)

Bases: BaseModel

apiVersion: str

calculatedSetpoints: CalculatedSetpoints | None

candidateSet: CandidateSet | None

design: Design | None

factors: list[Factor] | None

model: Model | None

model_config = {'extra': 'forbid'}

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

models: list[Model1] | None

name: str | None

objective: Objective1 | None

predictions: Predictions | None

responses: list[Response] | None

setpoints: Any

settings: Settings | None

worksheet: Worksheet | None

class friendly_doe.doe_model.Factor(*, name: str, unit: str | None = '', abbreviation: Annotated[str, _PydanticGeneralMetadata(pattern='^.{1,5}$')], type: FactorType, settings: list[float | str], precision: Precision | None = None, transform: Transform | None = None, scaling: Scaling | None = None)

Bases: BaseModel

abbreviation: str

model_config = {'extra': 'forbid'}

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

name: str

precision: Precision | None

scaling: Scaling | None

settings: list[float | str]

transform: Transform | None

type: FactorType

unit: str | None

class friendly_doe.doe_model.FactorSetting(*, name: str | None = '', value: float | str | None = 0.0, factorContribution: float | None = 0.0)

Bases: BaseModel

factorContribution: float | None

model_config = {}

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

name: str | None

value: float | str | None

class friendly_doe.doe_model.FactorSetting1(*, name: str | None = '', value: float | str | None = 0.0)

Bases: BaseModel

model_config = {}

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

name: str | None

value: float | str | None

class friendly_doe.doe_model.FactorTransformType(*values)

Bases: Enum

exp = 'exp'

linear = 'linear'

log = 'log'

logit = 'logit'

negLog = 'negLog'

power = 'power'

class friendly_doe.doe_model.FactorType(*values)

Bases: Enum

filler = 'filler'

formulation = 'formulation'

multilevel = 'multilevel'

qualitative = 'qualitative'

quantitative = 'quantitative'

class friendly_doe.doe_model.FavoriteSetpoint(*, factorSettings: list[FactorSetting1] | None = <factory>, predictedResponse: list[PredictedResponseItem1] | None = <factory>)

Bases: BaseModel

factorSettings: list[FactorSetting1] | None

model_config = {}

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

predictedResponse: list[PredictedResponseItem1] | None

class friendly_doe.doe_model.FitMethod(*values)

Bases: Enum

Auto = 'Auto'

MLR = 'MLR'

PLS = 'PLS'

Scheffé_MLR = 'Scheffé MLR'

class friendly_doe.doe_model.LackOfFit(*, DF: int | None, SS: float | None, MS: float | None, SD: float | None, p: float | None, F: float | None)

Bases: BaseModel

DF: int | None

F: float | None

MS: float | None

SD: float | None

SS: float | None

model_config = {}

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

p: float | None

class friendly_doe.doe_model.Model(*, type: ModelType | None = ModelType.interaction, fitMethod: FitMethod | None = FitMethod.Auto, settings: Settings | None = None)

Bases: BaseModel

fitMethod: FitMethod | None

model_config = {}

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

settings: Settings | None

type: ModelType | None

class friendly_doe.doe_model.Model1(*, excluded: list[float] | None = [], response: str, terms: list[str], modelStatistics: ModelStatistics | None = None)

Bases: BaseModel

excluded: list[float] | None

modelStatistics: ModelStatistics | None

model_config = {'extra': 'forbid'}

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

response: str

terms: list[str]

class friendly_doe.doe_model.ModelStatistics(*, coefficients: Coefficients, anova: ANOVA, summary: Summary, residuals: Residuals, replicates: list[float] | None = [])

Bases: BaseModel

anova: ANOVA

coefficients: Coefficients

model_config = {}

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

replicates: list[float] | None

residuals: Residuals

summary: Summary

class friendly_doe.doe_model.ModelType(*values)

Bases: Enum

cubic = 'cubic'

interaction = 'interaction'

linear = 'linear'

linear_quadratic = 'linear_quadratic'

quadratic = 'quadratic'

special_cubic = 'special cubic'

class friendly_doe.doe_model.Objective(*, type: str, min: float | None = None, target: float | None = None, max: float | None = None)

Bases: BaseModel

max: float | None

min: float | None

model_config = {'extra': 'forbid'}

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

target: float | None

type: str

class friendly_doe.doe_model.Objective1(*values)

Bases: Enum

field_ = ''

optimization = 'optimization'

robust_verification = 'robust_verification'

screening = 'screening'

system_characterization = 'system_characterization'

class friendly_doe.doe_model.OptimalSetpoint(*, factorSettings: list[FactorSetting] | None = <factory>, predictedResponse: list[PredictedResponseItem] | None = <factory>)

Bases: BaseModel

factorSettings: list[FactorSetting] | None

model_config = {}

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

predictedResponse: list[PredictedResponseItem] | None

class friendly_doe.doe_model.OptimizationSettings(*, condition: str, desirabilityWeight: float | None = 1.0, objective: Objective)

Bases: BaseModel

condition: str

desirabilityWeight: float | None

model_config = {'extra': 'forbid'}

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

objective: Objective

class friendly_doe.doe_model.Precision(*, factorPrecision: float | None = 0.99999, normalOperatingRange: float | None = None, numDecimals: float | None = 3)

Bases: BaseModel

factorPrecision: float | None

model_config = {}

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

normalOperatingRange: float | None

numDecimals: float | None

class friendly_doe.doe_model.PredictedResponseItem(*, name: str | None = '', value: float | str | None = 0.0, logD: float | None = 0.0)

Bases: BaseModel

logD: float | None

model_config = {}

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

name: str | None

value: float | str | None

class friendly_doe.doe_model.PredictedResponseItem1(*, name: str | None = '', value: float | str | None = 0.0)

Bases: BaseModel

model_config = {}

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

name: str | None

value: float | str | None

class friendly_doe.doe_model.Predictions(*, intervalType: str | None = None, confidenceLevel: float | None = 0.95, toleranceProportion: float | None = 0.95)

Bases: BaseModel

confidenceLevel: float | None

intervalType: str | None

model_config = {'extra': 'forbid'}

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

toleranceProportion: float | None

class friendly_doe.doe_model.PureError(*, DF: int | None, SS: float | None, MS: float | None, SD: float | None)

Bases: BaseModel

DF: int | None

MS: float | None

SD: float | None

SS: float | None

model_config = {}

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

class friendly_doe.doe_model.Regression(*, DF: int | None, SS: float | None, MS: float | None, SD: float | None, p: float | None, F: float | None)

Bases: BaseModel

DF: int | None

F: float | None

MS: float | None

SD: float | None

SS: float | None

model_config = {}

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

p: float | None

class friendly_doe.doe_model.Residual(*, DF: int | None, SS: float | None, MS: float | None, SD: float | None)

Bases: BaseModel

DF: int | None

MS: float | None

SD: float | None

SS: float | None

model_config = {}

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

class friendly_doe.doe_model.Residuals(*, raw: list[float | None] = [], deletedStudentized: list[float | None] = [], standardized: list[float | None] = [], normalDistributionSamples: list[float | None] = [], normalDistributionProbabilities: list[float | None] = [])

Bases: BaseModel

deletedStudentized: list[float | None]

model_config = {}

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

normalDistributionProbabilities: list[float | None]

normalDistributionSamples: list[float | None]

raw: list[float | None]

standardized: list[float | None]

class friendly_doe.doe_model.Response(*, name: str, abbreviation: str, unit: str | None = None, type: ResponseType, scaling: Scaling1 | None = None, transform: Transform1 | None = None, optimizationSettings: OptimizationSettings | None = None)

Bases: BaseModel

abbreviation: str

model_config = {'extra': 'forbid'}

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

name: str

optimizationSettings: OptimizationSettings | None

scaling: Scaling1 | None

transform: Transform1 | None

type: ResponseType

unit: str | None

class friendly_doe.doe_model.ResponseTransformType(*values)

Bases: Enum

exp = 'exp'

linear = 'linear'

log = 'log'

logit = 'logit'

negLog = 'negLog'

power = 'power'

class friendly_doe.doe_model.ResponseType(*values)

Bases: Enum

derived = 'derived'

regular = 'regular'

class friendly_doe.doe_model.RobustSetpoint(*, factorSettings: list[FactorSetting1] | None = <factory>, predictedResponse: list[PredictedResponseItem1] | None = <factory>)

Bases: BaseModel

factorSettings: list[FactorSetting1] | None

model_config = {}

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

predictedResponse: list[PredictedResponseItem1] | None

class friendly_doe.doe_model.Scaling(*, type: ScalingType)

Bases: BaseModel

model_config = {'extra': 'forbid'}

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

type: ScalingType

class friendly_doe.doe_model.Scaling1(*, type: str, autoScaleModifier: float | None = 1.0)

Bases: BaseModel

autoScaleModifier: float | None

model_config = {'extra': 'forbid'}

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

type: str

class friendly_doe.doe_model.ScalingType(*values)

Bases: Enum

midRange = 'midRange'

orthogonal = 'orthogonal'

unitVariance = 'unitVariance'

class friendly_doe.doe_model.Settings(*, alphaLevel: float | None = 0.0, coefficients: str | None = '', confidenceLevel: float | None = 0.0, toleranceProportion: float | None = 0.0, intervalType: str | None = '', power: float | None = 0.0, replicateTolerance: float | None = 0.0, residuals: str | None = '', r2: str | None = '')

Bases: BaseModel

alphaLevel: float | None

coefficients: str | None

confidenceLevel: float | None

intervalType: str | None

model_config = {'extra': 'forbid'}

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

power: float | None

r2: str | None

replicateTolerance: float | None

residuals: str | None

toleranceProportion: float | None

class friendly_doe.doe_model.Summary(*, r2: float | None, q2: float | None, modelValidity: float | None, reproducibility: float | None, conditionNumber: float | None)

Bases: BaseModel

conditionNumber: float | None

modelValidity: float | None

model_config = {}

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

q2: float | None

r2: float | None

reproducibility: float | None

class friendly_doe.doe_model.Total(*, DF: int | None, SS: float | None, MS: float | None)

Bases: BaseModel

DF: int | None

MS: float | None

SS: float | None

model_config = {}

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

class friendly_doe.doe_model.TotalCorrected(*, DF: int | None, SS: float | None, MS: float | None, SD: float | None)

Bases: BaseModel

DF: int | None

MS: float | None

SD: float | None

SS: float | None

model_config = {}

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

class friendly_doe.doe_model.Transform(*, type: FactorTransformType, constants: Constants)

Bases: BaseModel

constants: Constants

model_config = {'extra': 'forbid'}

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

type: FactorTransformType

class friendly_doe.doe_model.Transform1(*, type: ResponseTransformType, constants: Constants)

Bases: BaseModel

constants: Constants

model_config = {'extra': 'forbid'}

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

type: ResponseTransformType

class friendly_doe.doe_model.Worksheet(*, columns: list[str], data: list[list[int | str | float | None]])

Bases: BaseModel

columns: list[str]

data: list[list[int | str | float | None]]

model_config = {'extra': 'forbid'}

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