Dimension Config

pydantic model dsgrid.config.dimensions.DimensionModel

Defines a non-time dimension

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:

• file_hash (str | None)

• filename (str | None)

• records (List)

Validators:

• add_records » records

• check_file » filename

• compute_file_hash » file_hash

field file_hash: str | None = None

Hash of the contents of the file

Validated by:

• compute_file_hash

field filename: str | None = None (alias 'file')

Filename containing dimension records. Only assigned for user input and output purposes. The registry database stores records in the dimension JSON document.

Validated by:

• check_file

field records: List = []

Dimension records in filename that get loaded at runtime

Validated by:

• add_records

validator add_records  »  records

Add records from the file.

validator check_file  »  filename

Validate that dimension file exists and has no errors

validator compute_file_hash  »  file_hash

serialize_cls(val, _)

DateTime Dimension Config

pydantic model dsgrid.config.dimensions.DateTimeDimensionModel

Defines a time dimension where timestamps translate to datetime objects.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:

• frequency (datetime.timedelta)

• leap_day_adjustment (dsgrid.dimension.time.LeapDayAdjustmentType | None)

• measurement_type (dsgrid.dimension.time.MeasurementType)

• ranges (List[dsgrid.config.dimensions.TimeRangeModel])

• str_format (str | None)

• time_interval_type (dsgrid.dimension.time.TimeIntervalType)

• timezone (dsgrid.dimension.time.TimeZone)

Validators:

• check_frequency » all fields

• check_time_type_and_class_consistency » all fields

• check_times » ranges

field frequency: timedelta [Required]

Resolution of the timestamps

Validated by:

• check_frequency

• check_time_type_and_class_consistency

field leap_day_adjustment: LeapDayAdjustmentType | None = LeapDayAdjustmentType.NONE

Leap day adjustment method applied to time data

Validated by:

• check_frequency

• check_time_type_and_class_consistency

field measurement_type: MeasurementType = MeasurementType.TOTAL

The type of measurement represented by a value associated with a timestamp:

mean, min, max, measured, total

Validated by:

• check_frequency

• check_time_type_and_class_consistency

field ranges: List[TimeRangeModel] [Required]

Defines the continuous ranges of time in the data, inclusive of start and end time.

Validated by:

• check_frequency

• check_time_type_and_class_consistency

• check_times

field str_format: str | None = '%Y-%m-%d %H:%M:%s'

Timestamp string format

Validated by:

• check_frequency

• check_time_type_and_class_consistency

field time_interval_type: TimeIntervalType [Required]

The range of time that the value associated with a timestamp represents, e.g., period-beginning

Validated by:

• check_frequency

• check_time_type_and_class_consistency

field timezone: TimeZone [Required]

Time zone of data:

UTC, HawaiiAleutianStandard, AlaskaStandard, AlaskaPrevailing, PacificStandard, PacificPrevailing, MountainStandard, MountainPrevailing, CentralStandard, CentralPrevailing, EasternStandard, EasternPrevailing,

Validated by:

• check_frequency

• check_time_type_and_class_consistency

validator check_frequency  »  all fields

validator check_time_type_and_class_consistency  »  all fields

validator check_times  »  ranges

is_time_zone_required_in_geography()

Returns True if the geography dimension records must contain a time_zone column.

Annual Time Dimension Config

pydantic model dsgrid.config.dimensions.AnnualTimeDimensionModel

Defines an annual time dimension where timestamps are years.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:

• include_leap_day (bool)

• measurement_type (dsgrid.dimension.time.MeasurementType)

• ranges (List[dsgrid.config.dimensions.TimeRangeModel])

• str_format (str | None)

• time_type (dsgrid.dimension.time.TimeDimensionType)

Validators:

• check_time_type_and_class_consistency » all fields

• check_times » ranges

field include_leap_day: bool = False

Whether annual time includes leap day.

Validated by:

• check_time_type_and_class_consistency

field measurement_type: MeasurementType = MeasurementType.TOTAL

The type of measurement represented by a value associated with a timestamp:

mean, min, max, measured, total

Validated by:

• check_time_type_and_class_consistency

field ranges: List[TimeRangeModel] [Required]

Defines the contiguous ranges of time in the data, inclusive of start and end time.

Validated by:

• check_time_type_and_class_consistency

• check_times

field str_format: str | None = '%Y'

Timestamp string format

Validated by:

• check_time_type_and_class_consistency

field time_type: TimeDimensionType = TimeDimensionType.ANNUAL

Validated by:

• check_time_type_and_class_consistency

validator check_time_type_and_class_consistency  »  all fields

validator check_times  »  ranges

is_time_zone_required_in_geography()

Returns True if the geography dimension records must contain a time_zone column.

Representative Period Time Dimension Config

pydantic model dsgrid.config.dimensions.RepresentativePeriodTimeDimensionModel

Defines a representative time dimension.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:

• format (dsgrid.dimension.time.RepresentativePeriodFormat)

• measurement_type (dsgrid.dimension.time.MeasurementType)

• ranges (List[dsgrid.config.dimensions.MonthRangeModel])

• time_interval_type (dsgrid.dimension.time.TimeIntervalType)

Validators:

field format: RepresentativePeriodFormat [Required]

Format of the timestamps in the load data

field measurement_type: MeasurementType = MeasurementType.TOTAL

The type of measurement represented by a value associated with a timestamp:

mean, min, max, measured, total

field ranges: List[MonthRangeModel] [Required]

Defines the continuous ranges of time in the data, inclusive of start and end time.

field time_interval_type: TimeIntervalType [Required]

The range of time that the value associated with a timestamp represents

is_time_zone_required_in_geography()

Returns True if the geography dimension records must contain a time_zone column.

No-op Time Dimension Config

pydantic model dsgrid.config.dimensions.NoOpTimeDimensionModel

Defines a NoOp time dimension.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:

• time_type (dsgrid.dimension.time.TimeDimensionType)

Validators:

• check_time_type_and_class_consistency » all fields

field time_type: TimeDimensionType = TimeDimensionType.NOOP

Validated by:

• check_time_type_and_class_consistency

validator check_time_type_and_class_consistency  »  all fields

is_time_zone_required_in_geography()

Returns True if the geography dimension records must contain a time_zone column.

pydantic model dsgrid.config.dimensions.TimeRangeModel

Defines a continuous range of time.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:

• end (str)

• start (str)

field end: str [Required]

Last timestamp in the data (inclusive)

field start: str [Required]

First timestamp in the data

pydantic model dsgrid.config.dimensions.MonthRangeModel

Defines a continuous range of time.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:

• end (int)

• start (int)

field end: int [Required]

Last month in the data (inclusive)

field start: int [Required]

First month in the data (January is 1, December is 12)

Dimension Reference

pydantic model dsgrid.config.dimensions.DimensionReferenceModel

Reference to a dimension stored in the registry

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:

• dimension_id (str)

• dimension_type (dsgrid.dimension.base_models.DimensionType)

• version (str)

field dimension_id: str [Required]

Unique ID of the dimension in the registry

field dimension_type: DimensionType [Required] (alias 'type')

Type of the dimension

field version: str [Required]

Version of the dimension