alt.hst.api.dataset Module#

enum ConstraintCondition(value)#

Bases: Enum

  • FEASIBLE The results are within the relaxed constraint bounds.

  • ACCEPTABLE The results are within constraint bounds.

  • VIOLATED The results are outside the strict constraint bounds.

  • UNKNOWN There is no known constraint condition.

Valid values are as follows:

UNKNOWN = <ConstraintCondition.UNKNOWN: 'Unknown'>#
FEASIBLE = <ConstraintCondition.FEASIBLE: 'Feasible'>#
ACCEPTABLE = <ConstraintCondition.ACCEPTABLE: 'Acceptable'>#
VIOLATED = <ConstraintCondition.VIOLATED: 'Violated'>#

The Enum and its members also have the following methods:

classmethod fromString(value: str) → ConstraintCondition#
enum DataFrameID(value)#

Bases: Enum

Valid values are as follows:

PRIMARY = <DataFrameID.PRIMARY: '$primary'>#
START_POINT = <DataFrameID.START_POINT: '$start_point'>#
VERIFICATION = <DataFrameID.VERIFICATION: '$verification'>#
CROSS_VALIDATION = <DataFrameID.CROSS_VALIDATION: '$cross_validation'>#
VALIDATION = <DataFrameID.VALIDATION: '$validation'>#
INITIALIZATION = <DataFrameID.INITIALIZATION: '$initialization'>#
RECONCILE_FIT = <DataFrameID.RECONCILE_FIT: '$reconcile_fit'>#
ITERATIVE_USER_REQUESTS = <DataFrameID.ITERATIVE_USER_REQUESTS: '$iterative_user_requests'>#
EDIT_TEMPORARY = <DataFrameID.EDIT_TEMPORARY: '$edit_temp'>#
class DataSet(approach: Approach)#

Bases: object

getCategory(varname: str) → VariableCategory#

Get the category for the item in the dataset with the corresponding varname.

Parameters:

varname (str) – The varname of the item in the dataset.

Returns:

The category of the item.

Return type:

VariableCategory

Raises:

RuntimeError – If the variable does not exist in the dataset.

getCategoryDescription(varname: str) → str#

Get the description of the category for the given varname.

Parameters:

varname (str) – The varname of the item in the dataset.

Returns:

The description of the category of the item.

Return type:

str

Raises:

RuntimeError – If the variable does not exist in the dataset.

getCategoryInternal(varname: str) → int#

Get the internal category for the item in the dataset with the corresponding varname.

Parameters:

varname (str) – The varname of the item in the dataset.

Returns:

The internal category of the item.

Return type:

int

Raises:

RuntimeError – If the variable does not exist in the dataset.

getCellMetadata(evaluationIndex: int, key: MetadataKey, varname: str) → int#

Get cell metadata specified by the key and variable name for the evaluation at the given index.

Note

The metadata for MetadataKey.OPT_CONSTRAINT_VIOLATION_VALUE is returned as an integer that needs to be bitwise cast to a float.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • key (MetadataKey) – The key for the desired metadata.

  • varname (str) – The varname for the column to get metadata for.

Returns:

The integer values for the cell metadata.

Return type:

int

Raises:

  • IndexError – If the index is out of bounds for the number of evaluations in the dataset.

  • ValueError – If the variable does not exist in the dataset.

See also

intToFloatBitwise
getConversionStrings() → List[str]#

Get the list of strings that map to the the equivalent index given in stored data.

Returns:

The list of string values that can be mapped to evaluation values for string data types.

Return type:

List[str]

getDataSourceData(evaluationIndex: int, varname: str, frameIdentifier: DataFrameID = DataFrameID.PRIMARY) → Union[npt.NDArray[np.str_], npt.NDArray[np.float64]]#

Get data source data for the given varname and evaluation index.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • varname (str) – The varname of the data source in the dataset.

  • frameIdentifier (DataFrameID) – The name of the data frame to get values from.

Returns:

The data source data as a NumPy array.

Return type:

typing.Union[npt.NDArray[np.str_], npt.NDArray[np.float64]]

Raises:

  • ValueError – If the variable does not exist in the dataset.

  • IndexError – If the index is out of bounds for the number of evaluations in the dataset.

  • RuntimeError – If the variable is not an array variable.

getDescription(varname: str) → str#

Get the description for the item in the dataset with the corresponding varname.

Parameters:

varname (str) – The varname of the item in the dataset.

Returns:

The description of the item.

Return type:

str

Raises:

RuntimeError – If the variable does not exist in the dataset.

static getDisplayIndexFromEvaluationIndex(index: int) → int#

Convert an internal evaluation or iteration index to an index that is suitable for display.

Parameters:

index (int) – The original evaluation index (0-based).

Returns:

The display index (1-based).

Return type:

int

Raises:

ValueError – If the index is less than 0.

getEvaluationComment(evaluationIndex: int) → str#

Get the comment for the evaluation at the given index.

Parameters:

evaluationIndex (int) – The evaluation index (0-based).

Returns:

The comment for the evaluation.

Return type:

str

Raises:

IndexError – If the index is out of bounds for the number of evaluations in the dataset.

getEvaluationCondition(evaluationIndex: int) → ConstraintCondition#

Get the constraint condition for the evaluation at the given index.

Parameters:

evaluationIndex (int) – The evaluation index (0-based).

Returns:

The constraint condition for the evaluation.

Return type:

ConstraintCondition

Raises:

IndexError – If the index is out of bounds for the number of evaluations in the dataset.

getEvaluationCount() → int#

Get the number of evaluations in the dataset.

Returns:

The number of evaluations.

Return type:

int

getEvaluationData(frameIdentifier: DataFrameID = DataFrameID.PRIMARY) → Dict[str, List[float] | List[str]]#

Get the evaluation values for scalar variables in the dataset.

Parameters:

frameIdentifier (DataFrameID) – The name of the data frame to get values from.

Returns:

The evaluation data with its string data converted to its string representation.

Return type:

Dict[str, List[float, int, str]]

static getEvaluationIndexFromDisplayIndex(index: int) → int#

Convert an index that has been adjusted for display purposes back into the original index used for evaluation or iteration.

Parameters:

index (int) – The display index (1-based).

Returns:

The original evaluation index (0-based).

Return type:

int

Raises:

ValueError – If the index is less than 1.

getEvaluationIndexes() → List[int]#

Get the list of indexes for all evaluations in the dataset.

Returns:

A list of evaluation indexes (0-based).

getEvaluationMetadata(evaluationIndex: int, key: MetadataKey) → int#

Get the metadata for the evaluation at the given index.

Note

The metadata for MetadataKey.OPT_CONSTRAINT_VIOLATION_VALUE is returned as an integer that needs to be bitwise cast to a float.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • key (MetadataKey) – The key for the desired metadata.

Returns:

The integer values for the metadata.

Return type:

int

Raises:

IndexError – If the index is out of bounds for the number of evaluations in the dataset.

getEvaluationMetadataString(evaluationIndex: int, key: MetadataKey) → str#

Get the metadata for the evaluation at the given index as a string

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • key (MetadataKey) – The key for the desired metadata.

Returns:

The string values for the metadata.

Return type:

str

Raises:

IndexError – If the index is out of bounds for the number of evaluations in the dataset.

getEvaluationOrigin(evaluationIndex: int) → EvaluationOriginInfo#

Get the origin information for the evaluation at the given index.

Parameters:

evaluationIndex (int) – The evaluation index (0-based).

Returns:

The origin information for the evaluation.

Return type:

EvaluationOriginInfo

Raises:

IndexError – If the index is out of bounds for the number of evaluations in the dataset.

getEvaluationPostProcessState(evaluationIndex: int) → bool#

Check if a given evaluation is included in post-processing.

Parameters:

evaluationIndex (int) – The evaluation index (0-based).

Returns:

True if the evaluation is included in post-processing, False otherwise.

Return type:

bool

Raises:

IndexError – If the index is out of bounds for the number of evaluations in the dataset.

getEvaluationValues(varname: str, frameIdentifier: DataFrameID = DataFrameID.PRIMARY) → List[float] | List[str]#

Get the values for all evaluations for the scalar variable with the given varname.

Parameters:

  • varname (str) – The varname of the scalar variable in the dataset.

  • frameIdentifier (DataFrameID) – The name of the data frame to get values from.

Returns:

The evaluations values, converting string data to its string representation.

Return type:

Union[List[float], List[str]]

Raises:

ValueError – If the variable does not exist in the dataset.

getEvaluationsForIteration(iterationIndex: int) → List[int]#

Get the evaluations that were run during the given iteration.

Parameters:

evaluationIndex (int) – The evaluation index (0-based).

Returns:

A list of evaluation indexes run during the given iteration.

Return type:

List[int]

Raises:

IndexError – If the index is out of bounds for the number of iteration in the dataset.

getExtendedCellMetadata(evaluationIndex: int, group: ExtendedMetadataGroup, field: ExtendedMetadataField, index: int, varname: str) → int#

Get extended cell metadata specified by the group, field, and index for the evaluation at the given index.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • group (ExtendedMetadataGroup) – The group of the extended metadata.

  • field (ExtendedMetadataField) – The field of the extended metadata.

  • index (int) – The index of the extended metadata within the group and field.

  • varname (str) – The varname for the column to get metadata for.

Returns:

The integer values for the extended cell metadata.

Return type:

int

Raises:

  • IndexError – If the index is out of bounds for the number of evaluations in the dataset.

  • ValueError – If the variable does not exist in the dataset.

getExtendedEvaluationMetadata(evaluationIndex: int, group: ExtendedMetadataGroup, field: ExtendedMetadataField, index: int) → int#

Get extended metadata specified by the group, field, and index for the evaluation at the given index.

Example - Get the execution time for the first evaluation of the first model#
executionTime = ds.getExtendedEvaluationMetadata(
    0, dataset.ExtendedMetadataGroup.MODEL,
    dataset.ExtendedMetadataField.TIME_EXECUTE, 0)
Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • group (ExtendedMetadataGroup) – The group of the extended metadata.

  • field (ExtendedMetadataField) – The field of the extended metadata.

  • index (int) – The index of the extended metadata within the group and field.

Returns:

The integer values for the extended metadata.

Return type:

int

Raises:

IndexError – If the index is out of bounds for the number of evaluations in the dataset.

getExtendedEvaluationMetadataString(evaluationIndex: int, group: ExtendedMetadataGroup, field: ExtendedMetadataField, index: int) → str#

Get extended string metadata specified by the group, field, and index for the evaluation at the given index.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • group (ExtendedMetadataGroup) – The group of the extended metadata.

  • field (ExtendedMetadataField) – The field of the extended metadata.

  • index (int) – The index of the extended metadata within the group and field.

Returns:

The string values for the extended metadata.

Return type:

str

Raises:

IndexError – If the index is out of bounds for the number of evaluations in the dataset.

getFlattenedDataSourceData(evaluationIndex: int, varname: str, frameIdentifier: DataFrameID = DataFrameID.PRIMARY) → Tuple[Tuple[int, ...], List[float] | List[str]]#

Get data source data as a 1-dimensional array. The output is a tuple of two lists, where the first list specifies the resulting dimensions of the data source data and the second list is the data.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • varname (str) – The varname of the data source in the dataset.

  • frameIdentifier (DataFrameID) – The name of the data frame to get values from.

Returns:

A tuple containing the shape of the data source data as a tuple of integers and the flattened data as a list.

Return type:

Tuple[Tuple[int, …], Union[List[float], List[str]]]

Raises:

  • ValueError – If the variable does not exist in the dataset.

  • IndexError – If the index is out of bounds for the number of evaluations in the dataset.

  • RuntimeError – If the variable is not an array variable.

getItem(varname: str) → DefinitionItem#

Get the DefinitionItem instance for the given varname.

Parameters:

varname (str) – The varname of the scalar variable or data source.

Returns:

The DefinitionItem instance for the given varname.

Return type:

api_objects.DefinitionItem

Raises:

  • ValueError – If the variable does not exist in the dataset or if the varname does not correspond to a valid DefinitionItem.

  • RuntimeError – If the varname does not correspond to a valid DefinitionItem in the dataset.

getItemOptional(varname: str) → DefinitionItem | None#

Get the DefinitionItem instance for the given varname if it exists, otherwise return None.

Parameters:

varname (str) – The varname of the scalar variable or data source.

Returns:

The DefinitionItem instance for the given varname or None if it does not exist.

Return type:

Optional[api_objects.DefinitionItem]

getIterationCount() → int | None#

Get the number of iterations in the dataset.

Returns:

The number of iterations.

Return type:

int

getIterationForEvaluation(evaluationIndex: int) → int#

Get index of the iteration that the given evaluation belongs to.

Parameters:

evaluationIndex (int) – The evaluation index (0-based).

Returns:

The iteration index for the evaluation.

Return type:

int

Raises:

IndexError – If the index is out of bounds for the number of evaluations in the dataset.

getIterationIndexes() → List[int]#

Get the list of indexes for all iterations in the dataset.

Returns:

A list of iteration indexes (0-based).

getLabel(varname: str) → str#

Get the label for the item in the dataset with the corresponding varname.

Parameters:

varname (str) – The varname of the item in the dataset.

Returns:

The label of the item.

Return type:

str

Raises:

RuntimeError – If the variable does not exist in the dataset.

getOptimalEvaluationIndexes(iterationIndex: int | None = None) → List[int]#

” Get the indexes of the optimal evaluations after the given iteration. These are the non-dominated points after an iteration, and some or all may end up dominated after subsequent iterations. If no iteration index is provided, it defaults to the last iteration.

Parameters:

iterationIndex (Optional[int]) – The iteration index (0-based). If None, defaults to the last iteration.

Returns:

A list of evaluation indexes that are considered optimal for the specified iteration.

Return type:

List[int]

Raises:

IndexError – If the iteration index is out of bounds for the number of iterations.

getOptimalIterationIndexes(iterationIndex: int | None = None) → List[int]#

Get the indexes of the optimal iterations used by the given iteration.

Parameters:

iterationIndex (Optional[int]) – The iteration index (0-based). If None, defaults to the last iteration.

Returns:

A list of iteration indexes that are considered optimal for the specified iteration.

Return type:

List[int]

Raises:

IndexError – If the iteration index is out of bounds for the number of iterations

getScalarVarnames() → List[str]#

Get the varnames for all the scalar variables in the dataset.

Note

This includes inputs and outputs.

Returns:

A list of varnames.

Return type:

List[str]

getSingleConstraintConditionForEvaluationIndex(evaluationIndex: int, constraintVarname: str) → ConstraintCondition#

Get the constraint condition for a specific evaluation index and constraint variable.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • constraintVarname (str) – The varname of the constraint variable.

Returns:

The constraint condition for the evaluation.

Return type:

ConstraintCondition

Raises:

  • IndexError – If the index is out of bounds for the number of evaluations in the dataset.

  • ValueError – If the variable does not exist in the dataset.

  • RuntimeError – If the variable is not a constraint variable.

getStoredDataSourceData(evaluationIndex: int, varname: str, frameIdentifier: DataFrameID = DataFrameID.PRIMARY) → Union[npt.NDArray[np.str_], npt.NDArray[np.float64]]#

Get datasource data for the given varname, exactly as it is stored.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • varname (str) – The varname of the data source in the dataset.

  • frameIdentifier (DataFrameID) – The name of the data frame to get values from.

Returns:

The data source data as a NumPy array, without converting string data to its string representation.

Return type:

typing.Union[npt.NDArray[np.str_], npt.NDArray[np.float64]]

Raises:

  • ValueError – If the variable does not exist in the dataset.

  • IndexError – If the index is out of bounds for the number of evaluations in the dataset.

  • RuntimeError – If the variable is not an array variable.

getStoredEvaluationData(frameIdentifier: DataFrameID = DataFrameID.PRIMARY) → Dict[str, List[float]]#

Get the stored evaluation values for all scalar variables in the dataset.

Parameters:

frameIdentifier (DataFrameID) – The name of the data frame to get values from.

Returns:

The evaluation data without converting string data to it’s string representation.

Return type:

Dict[str, List[float]]

getStoredEvaluationValues(varname: str, frameIdentifier: DataFrameID = DataFrameID.PRIMARY) → List[float]#

Get the stored evaluation values for a scalar variable in the dataset.

Parameters:

  • varname (str) – The varname of the scalar variable in the dataset.

  • frameIdentifier (DataFrameID) – The name of the data frame to get values from.

Returns:

The evaluations values as they are stored, without converting string data to it’s string representation.

Return type:

List[float]

Raises:

ValueError – If the variable does not exist in the dataset.

getStoredFlattenedDataSourceData(evaluationIndex: int, varname: str, frameIdentifier: DataFrameID = DataFrameID.PRIMARY) → Tuple[Tuple[int, ...], List[float]]#

Get data source data exactly as it is stored as a 1-dimensional array. The output is a tuple of two lists, where the first list specifies the resulting dimensions of the data source data and the second list is the data.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • varname (str) – The varname of the data source in the dataset.

  • frameIdentifier (DataFrameID) – The name of the data frame to get values from.

Returns:

A tuple containing the shape of the data source data as a tuple of integers and the flattened data as a list, without converting string data to its string representation.

Return type:

Tuple[Tuple[int, …], List[float]]

Raises:

  • ValueError – If the variable does not exist in the dataset.

  • IndexError – If the index is out of bounds for the number of evaluations in the dataset.

  • RuntimeError – If the variable is not an array variable.

getVarnames() → List[str]#

Get the varnames for all the items in the dataset.

Note

This includes inputs and outputs.

Returns:

A list of varnames.

Return type:

List[str]

static intToFloatBitwise(value: int) → float#

Convenience method to perform a bitwise conversion of an integer to a float.

Parameters:

value (int) – The integer value to convert.

Returns:

The float value obtained by interpreting the bits of the integer.

Return type:

float

Raises:

RuntimeError – If the conversion fails.

isArrayVariable(varname: str) → bool#

Check if the given varname corresponds to an array variable in the dataset.

Parameters:

varname (str) – The varname of the variable to check.

Returns:

True if the varname corresponds to an array variable, False otherwise.

Return type:

bool

isScalarVariable(varname: str) → bool#

Check if the given varname corresponds to a scalar variable in the dataset.

Parameters:

varname (str) – The varname of the variable to check.

Returns:

True if the varname corresponds to a scalar variable, False otherwise.

Return type:

bool

setDataSourceData(evaluationIndex: int, varname: str, data: Union[npt.NDArray[np.str_], npt.NDArray[np.float64]], frameIdentifier: DataFrameID = DataFrameID.PRIMARY) → None#

Set datasource data for the given varname and evaluation index.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • varname (str) – The varname of the data source in the dataset.

  • data (typing.Union[npt.NDArray[np.str_], npt.NDArray[np.float64]]) – The data to set for the given evaluation, as a NumPy array

Raises:

  • ValueError – If the variable does not exist in the dataset.

  • IndexError – If the index is out of bounds for the number of evaluations in the dataset.

  • RuntimeError – If the variable is not an array variable.

setEvaluationValue(evaluationIndex: int, varname: str, value: float | int | str) → None#

Set the evaluation value for a scalar variable at the given evaluation index.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • varname (str) – The varname of the scalar variable in the dataset.

  • value (Union[float, int, str]) – The value to set for the given evaluation.

Raises:

  • ValueError – If the variable does not exist in the dataset.

  • IndexError – If the index is out of bounds for the number of evaluations in the dataset.

setFlattenedDataSourceData(evaluationIndex: int, varname: str, data: Tuple[List[int], List[float] | List[str]], frameIdentifier: DataFrameID = DataFrameID.PRIMARY) → None#

Set data source data as a 1-dimensional array. The input should be a tuple of two lists, where the first list specifies the resulting dimensions of the data source data and the second list is the data.

Parameters:

  • evaluationIndex (int) – The evaluation index (0-based).

  • varname (str) – The varname of the data source in the dataset.

  • data (Tuple[Tuple[int, ...], Union[List[float], List[str]]]) – A list containing the shape of the data source data as a tuple of integers and the flattened data as a list.

Raises:

  • ValueError – If the variable does not exist in the dataset.

  • IndexError – If the index is out of bounds for the number of evaluations in the dataset.

  • RuntimeError – If the variable is not an array variable.

class EvaluationOriginInfo(originIndex: int, originName: str)#

Bases: object

getOriginApproach() → Approach#

Get the origin approach of the evaluation.

Returns:

The origin approach.

Return type:

api_objects.Approach

getOriginApproachVarname() → str#

Get the origin approach varname of the evaluation.

Returns:

The origin approach varname.

Return type:

str

getOriginIndex() → int#

Get the origin index of the evaluation.

Returns:

The origin index.

Return type:

int

getOriginType() → EvaluationOriginType#

Get the origin type of the evaluation.

Returns:

The origin type.

Return type:

EvaluationOriginType

enum EvaluationOriginType(value)#

Bases: Enum

Valid values are as follows:

MANUAL = <EvaluationOriginType.MANUAL: '@manual'>#
DESIGN = <EvaluationOriginType.DESIGN: '@design'>#
EVALUATION = <EvaluationOriginType.EVALUATION: '@eval'>#

The Enum and its members also have the following methods:

getLabel() → str#
enum ExtendedMetadataField(value)#

Bases: IntEnum

Member Type:

int

Valid values are as follows:

WRITE = <ExtendedMetadataField.WRITE: 1>#
EXECUTE = <ExtendedMetadataField.EXECUTE: 2>#
EXTRACT = <ExtendedMetadataField.EXTRACT: 3>#
TIME_WRITE = <ExtendedMetadataField.TIME_WRITE: 4>#
TIME_EXECUTE = <ExtendedMetadataField.TIME_EXECUTE: 5>#
TIME_EXTRACT = <ExtendedMetadataField.TIME_EXTRACT: 6>#
TIME_PURGE = <ExtendedMetadataField.TIME_PURGE: 7>#
enum ExtendedMetadataGroup(value)#

Bases: IntEnum

Member Type:

int

Valid values are as follows:

MODEL = <ExtendedMetadataGroup.MODEL: 1>#
enum MetadataKey(value)#

Bases: IntEnum

Member Type:

int

Valid values are as follows:

STATE = <MetadataKey.STATE: 1>#
INVALID = <MetadataKey.INVALID: 0>#
WRITE = <MetadataKey.WRITE: 2>#
EXECUTE = <MetadataKey.EXECUTE: 3>#
EXTRACT = <MetadataKey.EXTRACT: 4>#
OPT_OPTIMAL = <MetadataKey.OPT_OPTIMAL: 5>#
OPT_ITERATION = <MetadataKey.OPT_ITERATION: 6>#
STEP_MAJOR = <MetadataKey.STEP_MAJOR: 7>#
OPT_GLOBAL_CONTRAINT_VIOLATION = <MetadataKey.OPT_GLOBAL_CONTRAINT_VIOLATION: 8>#
OPT_CONSTRAINT_VIOLATION_CODE = <MetadataKey.OPT_CONSTRAINT_VIOLATION_CODE: 9>#
OPT_CONSTRAINT_VIOLATION_VALUE = <MetadataKey.OPT_CONSTRAINT_VIOLATION_VALUE: 10>#
OPT_MATRIX_VALIDATION = <MetadataKey.OPT_MATRIX_VALIDATION: 11>#
RUN_TYPE = <MetadataKey.RUN_TYPE: 12>#
POST_PROCESS_STATE = <MetadataKey.POST_PROCESS_STATE: 13>#
RUN_USER_COMMENT = <MetadataKey.RUN_USER_COMMENT: 14>#
TIME = <MetadataKey.TIME: 15>#
VERIFY_REFERENCE_INDEX = <MetadataKey.VERIFY_REFERENCE_INDEX: 16>#
ADAPTIVE_ITERATION = <MetadataKey.ADAPTIVE_ITERATION: 17>#
ADAPTIVE_LAST_INCLUSION = <MetadataKey.ADAPTIVE_LAST_INCLUSION: 18>#
ADAPTIVE_PRIMARY_METRIC = <MetadataKey.ADAPTIVE_PRIMARY_METRIC: 19>#
QUEUE_STATE = <MetadataKey.QUEUE_STATE: 20>#
QUEUE_EVALUATION_INDEX = <MetadataKey.QUEUE_EVALUATION_INDEX: 21>#
USER_REQUEST = <MetadataKey.USER_REQUEST: 22>#
RUN_SYSTEM_COMMENT = <MetadataKey.RUN_SYSTEM_COMMENT: 23>#
DUPLICATE_TYPE = <MetadataKey.DUPLICATE_TYPE: 24>#
DUPLICATE_INDEX = <MetadataKey.DUPLICATE_INDEX: 25>#
PERIODIC_REPORT = <MetadataKey.PERIODIC_REPORT: 26>#
ORIGIN_INDEX = <MetadataKey.ORIGIN_INDEX: 27>#
ORIGIN_STRING = <MetadataKey.ORIGIN_STRING: 28>#
enum VariableCategory(value)#

Bases: IntEnum

Member Type:

int

Valid values are as follows:

INVALID = <VariableCategory.INVALID: 0>#
DESIGN_VARIABLE = <VariableCategory.DESIGN_VARIABLE: 202>#
RESPONSE = <VariableCategory.RESPONSE: 2202>#
GRADIENT = <VariableCategory.GRADIENT: 2302>#
CONSTRAINT = <VariableCategory.CONSTRAINT: 2402>#
OBJECTIVE = <VariableCategory.OBJECTIVE: 2502>#
PERCENTILE = <VariableCategory.PERCENTILE: 2602>#
FIT_RESPONSE = <VariableCategory.FIT_RESPONSE: 2702>#
OBJECTIVE_FUNCTION = <VariableCategory.OBJECTIVE_FUNCTION: 2802>#
TARGET_VALUE = <VariableCategory.TARGET_VALUE: 2902>#
NORMALIZED_TARGET = <VariableCategory.NORMALIZED_TARGET: 3002>#
SYSTEM_RELIABILITY = <VariableCategory.SYSTEM_RELIABILITY: 3102>#
STANDARD_DEVIATION_OF_OBJECTIVE = <VariableCategory.STANDARD_DEVIATION_OF_OBJECTIVE: 3202>#
INTERNAL = <VariableCategory.INTERNAL: 9900>#
DATA_SOURCE = <VariableCategory.DATA_SOURCE: 10002>#
FIT_DATA_SOURCE = <VariableCategory.FIT_DATA_SOURCE: 10102>#

The Enum and its members also have the following methods:

classmethod fromInternalCategory(internalCategory: int) → VariableCategory#
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