Time Series

pydantic model infrasys.time_series_models.TimeSeriesData

Base class for all time series models

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.

self is explicitly positional-only to allow self as a field name.

Show JSON schema
{
   "title": "TimeSeriesData",
   "description": "Base class for all time series models",
   "type": "object",
   "properties": {
      "uuid": {
         "format": "uuid",
         "title": "Uuid",
         "type": "string"
      },
      "variable_name": {
         "title": "Variable Name",
         "type": "string"
      },
      "normalization": {
         "anyOf": [
            {
               "discriminator": {
                  "mapping": {
                     "by_value": "#/$defs/NormalizationByValue",
                     "max": "#/$defs/NormalizationMax"
                  },
                  "propertyName": "normalization_type"
               },
               "oneOf": [
                  {
                     "$ref": "#/$defs/NormalizationMax"
                  },
                  {
                     "$ref": "#/$defs/NormalizationByValue"
                  }
               ]
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Defines the type of normalization performed on the data, if any.",
         "title": "Normalization"
      }
   },
   "$defs": {
      "NormalizationByValue": {
         "additionalProperties": false,
         "description": "Perform normalization by a user-defined value.",
         "properties": {
            "value": {
               "title": "Value",
               "type": "number"
            },
            "normalization_type": {
               "const": "by_value",
               "default": "by_value",
               "enum": [
                  "by_value"
               ],
               "title": "Normalization Type",
               "type": "string"
            }
         },
         "required": [
            "value"
         ],
         "title": "NormalizationByValue",
         "type": "object"
      },
      "NormalizationMax": {
         "additionalProperties": false,
         "description": "Perform normalization by the max value in an array.",
         "properties": {
            "max_value": {
               "anyOf": [
                  {
                     "type": "number"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "title": "Max Value"
            },
            "normalization_type": {
               "const": "max",
               "default": "max",
               "enum": [
                  "max"
               ],
               "title": "Normalization Type",
               "type": "string"
            }
         },
         "title": "NormalizationMax",
         "type": "object"
      }
   },
   "additionalProperties": false,
   "required": [
      "variable_name"
   ]
}

Config:

  • str_strip_whitespace: bool = True

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • use_enum_values: bool = False

  • arbitrary_types_allowed: bool = True

  • populate_by_name: bool = True

Fields:
field normalization: Annotated[None | NormalizationMax | NormalizationByValue, FieldInfo(annotation=NoneType, required=True, description='Defines the type of normalization performed on the data, if any.', discriminator='normalization_type')] = None

Defines the type of normalization performed on the data, if any.

field variable_name: str [Required]
abstract static get_time_series_metadata_type() → Type

Return the metadata type associated with this time series type.

property summary: str

Return the variable_name of the time series array with its type.

pydantic model infrasys.time_series_models.SingleTimeSeries

Defines a time array with a single dimension of floats.

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.

self is explicitly positional-only to allow self as a field name.

Show JSON schema
{
   "title": "SingleTimeSeries",
   "description": "Defines a time array with a single dimension of floats.",
   "type": "object",
   "properties": {
      "uuid": {
         "format": "uuid",
         "title": "Uuid",
         "type": "string"
      },
      "variable_name": {
         "title": "Variable Name",
         "type": "string"
      },
      "normalization": {
         "anyOf": [
            {
               "discriminator": {
                  "mapping": {
                     "by_value": "#/$defs/NormalizationByValue",
                     "max": "#/$defs/NormalizationMax"
                  },
                  "propertyName": "normalization_type"
               },
               "oneOf": [
                  {
                     "$ref": "#/$defs/NormalizationMax"
                  },
                  {
                     "$ref": "#/$defs/NormalizationByValue"
                  }
               ]
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Defines the type of normalization performed on the data, if any.",
         "title": "Normalization"
      },
      "data": {
         "anyOf": [],
         "title": "Data"
      },
      "resolution": {
         "format": "duration",
         "title": "Resolution",
         "type": "string"
      },
      "initial_time": {
         "format": "date-time",
         "title": "Initial Time",
         "type": "string"
      }
   },
   "$defs": {
      "NormalizationByValue": {
         "additionalProperties": false,
         "description": "Perform normalization by a user-defined value.",
         "properties": {
            "value": {
               "title": "Value",
               "type": "number"
            },
            "normalization_type": {
               "const": "by_value",
               "default": "by_value",
               "enum": [
                  "by_value"
               ],
               "title": "Normalization Type",
               "type": "string"
            }
         },
         "required": [
            "value"
         ],
         "title": "NormalizationByValue",
         "type": "object"
      },
      "NormalizationMax": {
         "additionalProperties": false,
         "description": "Perform normalization by the max value in an array.",
         "properties": {
            "max_value": {
               "anyOf": [
                  {
                     "type": "number"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "title": "Max Value"
            },
            "normalization_type": {
               "const": "max",
               "default": "max",
               "enum": [
                  "max"
               ],
               "title": "Normalization Type",
               "type": "string"
            }
         },
         "title": "NormalizationMax",
         "type": "object"
      }
   },
   "additionalProperties": false,
   "required": [
      "variable_name",
      "data",
      "resolution",
      "initial_time"
   ]
}

Config:

  • str_strip_whitespace: bool = True

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • use_enum_values: bool = False

  • arbitrary_types_allowed: bool = True

  • populate_by_name: bool = True

Fields:
Validators:
field data: ndarray[Any, dtype[_ScalarType_co]] | Quantity [Required]
Validated by:
field initial_time: datetime [Required]
field resolution: timedelta [Required]
validator check_data  »  data

Check time series data.

classmethod from_array(data: Sequence | ndarray[Any, dtype[_ScalarType_co]] | Quantity, variable_name: str, initial_time: datetime, resolution: timedelta, normalization: Annotated[None | NormalizationMax | NormalizationByValue, FieldInfo(annotation=NoneType, required=True, description='Defines the type of normalization performed on the data, if any.', discriminator='normalization_type')] = None) → SingleTimeSeries

Method of SingleTimeSeries that creates an instance from a sequence.

Parameters:

  • data – Sequence that contains the values of the time series

  • initial_time – Start time for the time series (e.g., datetime(2020,1,1))

  • resolution – Resolution of the time series (e.g., 30min, 1hr)

  • variable_name – Name assigned to the values of the time series (e.g., active_power)

Return type:

SingleTimeSeries

See also

from_time_array

Time index implementation

Note

  • Length of the sequence is inferred from the data.

classmethod from_time_array(data: Sequence | ndarray[Any, dtype[_ScalarType_co]] | Quantity, variable_name: str, time_index: Sequence[datetime], normalization: Annotated[None | NormalizationMax | NormalizationByValue, FieldInfo(annotation=NoneType, required=True, description='Defines the type of normalization performed on the data, if any.', discriminator='normalization_type')] = None) → SingleTimeSeries

Create SingleTimeSeries using time_index provided.

Parameters:

  • data – Sequence that contains the values of the time series

  • variable_name – Name assigned to the values of the time series (e.g., active_power)

  • time_index – Sequence that contains the index of the time series

Return type:

SingleTimeSeries

See also

from_array

Base implementation

Note

The current implementation only uses the time_index to infer the initial time and resolution.

static get_time_series_metadata_type() → Type

Return the metadata type associated with this time series type.

property length: int

Return the length of the data.