IAMC Data Interface

Datapoints (ixmp4.data.iamc.datapoint)

Service

class ixmp4.data.iamc.datapoint.service.DataPointService(transport: Transport)

Bases: Service

router_prefix: ClassVar[str] = '/iamc/datapoints'

router_tags: ClassVar[Sequence[str]] = ['iamc-datapoints']

http_controller: alias of EnumerationCompatibilityController

executor: SessionExecutor

pandas: PandasRepository

versions: VersionRepository

default_filter: DataPointFilter = {'run': {'default_only': True}}

full_key = {'step_category', 'step_datetime', 'step_year', 'time_series__id', 'type'}

base_columns = {'id', 'step_category', 'step_datetime', 'step_year', 'time_series__id', 'type', 'value'}

ts_columns = {'region', 'unit', 'variable'}

run_columns = {'model', 'scenario', 'version'}

get_columns(*, join_parameters: bool, join_runs: bool, join_run_id: bool) → tuple[str, ...] | None

tabulate(join_parameters: bool = False, join_runs: bool = False, join_run_id: bool = False, **kwargs: Unpack[DataPointFilter]) → WithJsonSchema(json_schema={'properties': {'index': {'anyOf': [{'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Index'}, 'columns': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Columns'}, 'dtypes': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Dtypes'}, 'data': {'anyOf': [{'items': {'items': {'anyOf': [{'type': 'boolean'}, {'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'additionalProperties': True, 'type': 'object'}, {'items': {'type': 'number'}, 'type': 'array'}, {'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'format': 'date-time', 'type': 'string'}, {'type': 'null'}]}, 'type': 'array'}, 'type': 'array'}, {'type': 'null'}], 'title': 'Data'}}, 'required': ['data'], 'title': 'DataFrameTypeAdapter', 'type': 'object'}, mode=serialization)]

Tabulates datapoints by specified criteria.

Parameters:

join_parameters (bool, optional) – Whether to include region, unit and variable in the data frame. Default: False
join_runs (bool, optional) – Whether to include model, scenario and version in the data frame. Default: False
join_run_id (bool, optional) – Whether to include run__id in the data frame. Default: False
**kwargs (any) – Filter parameters as specified in DataPointFilter.

Returns:

A data frame with the columns:

step_year
step_category
step_datetime
type
values

if join_parameters is True:

region
unit
variable

if join_runs is True:

model
scenario
version

if join_run_id is True:

run__id

Return type:

pandas.DataFrame

tabulate_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol) → None

paginated_tabulate(pagination: Pagination, join_parameters: bool = False, join_runs: bool = False, join_run_id: bool = False, **kwargs: Unpack[DataPointFilter]) → PaginatedResult[Annotated[DataFrame, PlainValidator, PlainSerializer, WithJsonSchema]]

bulk_upsert(df: WithJsonSchema(json_schema={'properties': {'index': {'anyOf': [{'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Index'}, 'columns': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Columns'}, 'dtypes': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Dtypes'}, 'data': {'anyOf': [{'items': {'items': {'anyOf': [{'type': 'boolean'}, {'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'additionalProperties': True, 'type': 'object'}, {'items': {'type': 'number'}, 'type': 'array'}, {'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'format': 'date-time', 'type': 'string'}, {'type': 'null'}]}, 'type': 'array'}, 'type': 'array'}, {'type': 'null'}], 'title': 'Data'}}, 'required': ['data'], 'title': 'DataFrameTypeAdapter', 'type': 'object'}, mode=serialization)]) → None

Bulk inserts or updates datapoints from a supplied dataframe.

This method accepts a dataframe containing datapoint data and validates it against the upsert schema before inserting or updating records in the database. The upsert operation is keyed on the subset of full key columns present in the dataframe.

Parameters:

df (pandas.DataFrame) –

DataFrame containing rows of datapoint data to upsert. Must conform to UpsertDataPointFrameSchema structure.

Key columns include:

time_series__id
step_category and/or step_year or step_datetime
type, optional
value

Raises:

InvalidDataFrame – If the dataframe does not conform to UpsertDataPointFrameSchema.

bulk_upsert_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol, /, df: WithJsonSchema(json_schema={'properties': {'index': {'anyOf': [{'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Index'}, 'columns': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Columns'}, 'dtypes': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Dtypes'}, 'data': {'anyOf': [{'items': {'items': {'anyOf': [{'type': 'boolean'}, {'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'additionalProperties': True, 'type': 'object'}, {'items': {'type': 'number'}, 'type': 'array'}, {'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'format': 'date-time', 'type': 'string'}, {'type': 'null'}]}, 'type': 'array'}, 'type': 'array'}, {'type': 'null'}], 'title': 'Data'}}, 'required': ['data'], 'title': 'DataFrameTypeAdapter', 'type': 'object'}, mode=serialization)]) → None

bulk_delete(df: WithJsonSchema(json_schema={'properties': {'index': {'anyOf': [{'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Index'}, 'columns': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Columns'}, 'dtypes': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Dtypes'}, 'data': {'anyOf': [{'items': {'items': {'anyOf': [{'type': 'boolean'}, {'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'additionalProperties': True, 'type': 'object'}, {'items': {'type': 'number'}, 'type': 'array'}, {'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'format': 'date-time', 'type': 'string'}, {'type': 'null'}]}, 'type': 'array'}, 'type': 'array'}, {'type': 'null'}], 'title': 'Data'}}, 'required': ['data'], 'title': 'DataFrameTypeAdapter', 'type': 'object'}, mode=serialization)]) → None

Bulk deletes datapoints from a supplied dataframe.

This method accepts a dataframe containing datapoint identifiers and deletes the matching records from the database. After deletion, orphaned timeseries (those with no remaining datapoints) are also removed.

Parameters:

df (pandas.DataFrame) –

DataFrame containing rows of datapoint identifiers to delete. Must conform to DeleteDataPointFrameSchema structure.

Key columns include:

time_series__id
step_category and/or step_year or step_datetime
type, optional

Raises:

InvalidDataFrame – If the dataframe does not conform to DeleteDataPointFrameSchema.

bulk_delete_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol, df: WithJsonSchema(json_schema={'properties': {'index': {'anyOf': [{'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Index'}, 'columns': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Columns'}, 'dtypes': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Dtypes'}, 'data': {'anyOf': [{'items': {'items': {'anyOf': [{'type': 'boolean'}, {'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'additionalProperties': True, 'type': 'object'}, {'items': {'type': 'number'}, 'type': 'array'}, {'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'format': 'date-time', 'type': 'string'}, {'type': 'null'}]}, 'type': 'array'}, 'type': 'array'}, {'type': 'null'}], 'title': 'Data'}}, 'required': ['data'], 'title': 'DataFrameTypeAdapter', 'type': 'object'}, mode=serialization)]) → None

Filters

class ixmp4.data.iamc.datapoint.filter.DataPointFilter

Bases: DataPointFilter

region: InstrumentedAttribute object at 0x79d927827ba0>)]

variable: InstrumentedAttribute object at 0x79d927827ce0>)]

unit: InstrumentedAttribute object at 0x79d927827d80>)]

run: InstrumentedAttribute object at 0x79d927827b00>)]

model: InstrumentedAttribute object at 0x79d927323420>)]

scenario: InstrumentedAttribute object at 0x79d9273234c0>)]

id: int

id__in: list[int]

type: str

type__in: list[str]

step_year: int

step_year__lte: int

step_year__lt: int

step_year__gte: int

step_year__gt: int

step_year__in: list[int]

step_category: str

step_category__in: list[str]

time_series__id: int

time_series__id__in: list[int]

value: float

value__lte: float

value__lt: float

value__gte: float

value__gt: float

value__in: list[float]

class ixmp4.data.iamc.datapoint.filter.DataPointVersionFilter

Bases: DataPointFilter, VersionFilter

timeseries: InstrumentedAttribute object at 0x79d92787a8e0>]

id: int

id__in: list[int]

type: str

type__in: list[str]

step_year: int

step_year__lte: int

step_year__lt: int

step_year__gte: int

step_year__gt: int

step_year__in: list[int]

step_category: str

step_category__in: list[str]

time_series__id: int

time_series__id__in: list[int]

value: float

value__lte: float

value__lt: float

value__gte: float

value__gt: float

value__in: list[float]

valid_at_transaction: Annotated[int, <function filter_by_valid_at_transaction at 0x79d925ad8fe0>]

class ixmp4.data.iamc.datapoint.filter.FacadeStepYearFilter

Bases: TypedDict

year: int

year__lte: int

year__lt: int

year__gte: int

year__gt: int

year__in: list[int]

class ixmp4.data.iamc.datapoint.filter.FacadeStepCategoryFilter

Bases: TypedDict

category: str

category__in: list[str]

class ixmp4.data.iamc.datapoint.filter.FacadeDataPointFilter

Bases: DataPointFilter, FacadeStepYearFilter, FacadeStepCategoryFilter

region: RegionFilter | str | Iterable[str]

unit: UnitFilter | str | Iterable[str]

variable: VariableFilter | str | Iterable[str]

model: ModelFilter | str | Iterable[str]

scenario: ScenarioFilter | str | Iterable[str]

run: FacadeRunFilter

id: int

id__in: list[int]

type: str

type__in: list[str]

step_year: int

step_year__lte: int

step_year__lt: int

step_year__gte: int

step_year__gt: int

step_year__in: list[int]

step_category: str

step_category__in: list[str]

time_series__id: int

time_series__id__in: list[int]

value: float

value__lte: float

value__lt: float

value__gte: float

value__gt: float

value__in: list[float]

year: int

year__lte: int

year__lt: int

year__gte: int

year__gt: int

year__in: list[int]

category: str

category__in: list[str]

ixmp4.data.iamc.datapoint.filter.facade_to_data_filter(filter_values: Mapping[str, Any]) → DataPointFilter

Measurands (ixmp4.data.iamc.measurand)

TimeSeries (ixmp4.data.iamc.timeseries)

Service

class ixmp4.data.iamc.timeseries.service.TimeSeriesService(transport: Transport)

Bases: Service

router_prefix: ClassVar[str] = '/iamc/timeseries'

router_tags: ClassVar[Sequence[str]] = ['iamc', 'timeseries']

http_controller: alias of EnumerationCompatibilityController

executor: SessionExecutor

pandas: PandasRepository

versions: VersionRepository

measurands: PandasRepository

regions: PandasRepository

units: PandasRepository

variables: PandasRepository

runs: ItemRepository

default_filter: TimeSeriesFilter = {'run': {'default_only': True}}

tabulate_by_df(df: WithJsonSchema(json_schema={'properties': {'index': {'anyOf': [{'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Index'}, 'columns': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Columns'}, 'dtypes': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Dtypes'}, 'data': {'anyOf': [{'items': {'items': {'anyOf': [{'type': 'boolean'}, {'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'additionalProperties': True, 'type': 'object'}, {'items': {'type': 'number'}, 'type': 'array'}, {'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'format': 'date-time', 'type': 'string'}, {'type': 'null'}]}, 'type': 'array'}, 'type': 'array'}, {'type': 'null'}], 'title': 'Data'}}, 'required': ['data'], 'title': 'DataFrameTypeAdapter', 'type': 'object'}, mode=serialization)]) → WithJsonSchema(json_schema={'properties': {'index': {'anyOf': [{'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Index'}, 'columns': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Columns'}, 'dtypes': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Dtypes'}, 'data': {'anyOf': [{'items': {'items': {'anyOf': [{'type': 'boolean'}, {'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'additionalProperties': True, 'type': 'object'}, {'items': {'type': 'number'}, 'type': 'array'}, {'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'format': 'date-time', 'type': 'string'}, {'type': 'null'}]}, 'type': 'array'}, 'type': 'array'}, {'type': 'null'}], 'title': 'Data'}}, 'required': ['data'], 'title': 'DataFrameTypeAdapter', 'type': 'object'}, mode=serialization)]

Tabulates timeseries by values in a supplied dataframe.

Parameters:

df (pandas.DataFrame) –

DataFrame containing rows of timeseries keys to tabulate.

run__id
region
variable
unit

Returns:

A data frame with the columns:

id
run__id
region
variable
unit

Return type:

pandas.DataFrame

tabulate_by_df_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol) → None

tabulate(join_parameters: bool = False, **kwargs: Unpack[TimeSeriesFilter]) → WithJsonSchema(json_schema={'properties': {'index': {'anyOf': [{'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Index'}, 'columns': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Columns'}, 'dtypes': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Dtypes'}, 'data': {'anyOf': [{'items': {'items': {'anyOf': [{'type': 'boolean'}, {'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'additionalProperties': True, 'type': 'object'}, {'items': {'type': 'number'}, 'type': 'array'}, {'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'format': 'date-time', 'type': 'string'}, {'type': 'null'}]}, 'type': 'array'}, 'type': 'array'}, {'type': 'null'}], 'title': 'Data'}}, 'required': ['data'], 'title': 'DataFrameTypeAdapter', 'type': 'object'}, mode=serialization)]

Tabulates timeseries by specified criteria.

Parameters:

join_parameters (bool, optional) – Include names of related region, variable and unit rows.
**kwargs (any) – Filter parameters as specified in TimeSeriesFilter.

Returns:

A data frame with the columns:

id
run__id

if join_parameters is False (default):

region__id
measurand__id

if join_parameters is True:

region
variable
unit

Return type:

pandas.DataFrame

tabulate_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol) → None

paginated_tabulate(pagination: Pagination, join_parameters: bool = False, **kwargs: Unpack[TimeSeriesFilter]) → PaginatedResult[Annotated[DataFrame, PlainValidator, PlainSerializer, WithJsonSchema]]

merge_regions(df: DataFrame) → DataFrame

merge_units(df: DataFrame) → DataFrame

merge_variables(df: DataFrame) → DataFrame

merge_measurands(df: DataFrame) → DataFrame

bulk_upsert(df: WithJsonSchema(json_schema={'properties': {'index': {'anyOf': [{'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Index'}, 'columns': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Columns'}, 'dtypes': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Dtypes'}, 'data': {'anyOf': [{'items': {'items': {'anyOf': [{'type': 'boolean'}, {'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'additionalProperties': True, 'type': 'object'}, {'items': {'type': 'number'}, 'type': 'array'}, {'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'format': 'date-time', 'type': 'string'}, {'type': 'null'}]}, 'type': 'array'}, 'type': 'array'}, {'type': 'null'}], 'title': 'Data'}}, 'required': ['data'], 'title': 'DataFrameTypeAdapter', 'type': 'object'}, mode=serialization)]) → None

Bulk inserts or updates timeseries from a supplied dataframe.

This method accepts a dataframe containing timeseries data and automatically resolves region, unit, and variable references, upserting them as needed. Measurands (variable/unit pairs) are also created automatically. The method performs validation and merging operations before inserting or updating the timeseries records.

Parameters:

df (pandas.DataFrame) –

DataFrame containing rows of timeseries data to upsert.

Minimum required columns:

run__id

Optional columns that will be resolved automatically:

region: region name; will be resolved to region__id
unit: unit name; will be resolved to unit__id
variable: variable name; will be instantiated if missing

To skip resolving related rows, supply ids directly:

region__id
measurand__id or unit__id and variable__id

Raises:

RegionNotFound – If one or more region names in the dataframe do not exist.
UnitNotFound – If one or more unit names in the dataframe do not exist.

bulk_upsert_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol, /, df: WithJsonSchema(json_schema={'properties': {'index': {'anyOf': [{'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Index'}, 'columns': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Columns'}, 'dtypes': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Dtypes'}, 'data': {'anyOf': [{'items': {'items': {'anyOf': [{'type': 'boolean'}, {'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'additionalProperties': True, 'type': 'object'}, {'items': {'type': 'number'}, 'type': 'array'}, {'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'format': 'date-time', 'type': 'string'}, {'type': 'null'}]}, 'type': 'array'}, 'type': 'array'}, {'type': 'null'}], 'title': 'Data'}}, 'required': ['data'], 'title': 'DataFrameTypeAdapter', 'type': 'object'}, mode=serialization)]) → None

Filters

class ixmp4.data.iamc.timeseries.filter.TimeSeriesFilter

Bases: TimeSeriesFilter

region: InstrumentedAttribute object at 0x79d927827ba0>]

variable: InstrumentedAttribute object at 0x79d927827ce0>]

unit: InstrumentedAttribute object at 0x79d927827d80>]

run: InstrumentedAttribute object at 0x79d927827b00>]

id: int

id__in: list[int]

run__id: int

run__id__in: list[int]

class ixmp4.data.iamc.timeseries.filter.TimeSeriesVersionFilter

Bases: VersionFilter, TimeSeriesFilter

valid_at_transaction: Annotated[int, <function filter_by_valid_at_transaction at 0x79d925ad8fe0>]

id: int

id__in: list[int]

run__id: int

run__id__in: list[int]

class ixmp4.data.iamc.timeseries.filter.FacadeTimeSeriesFilter

Bases: TimeSeriesFilter

region: RegionFilter

variable: VariableFilter

unit: UnitFilter

run: FacadeRunFilter

id: int

id__in: list[int]

run__id: int

run__id__in: list[int]

ixmp4.data.iamc.timeseries.filter.facade_to_data_filter(filter_values: Mapping[str, Any]) → TimeSeriesFilter

Variables (ixmp4.data.iamc.variable)

Data Transfer Object

class ixmp4.data.iamc.variable.dto.Variable(*, created_at: datetime | None, created_by: str | None, id: int, name: str)

Bases: BaseModel, HasCreationInfo

IAMC variable data model.

name: str: Unique name of the variable.

model_config = {'from_attributes': True}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Service

class ixmp4.data.iamc.variable.service.VariableService(transport: Transport)

Bases: DocsService, GetByIdService

router_prefix: ClassVar[str] = '/iamc/variables'

router_tags: ClassVar[Sequence[str]] = ['iamc', 'variables']

http_controller: alias of EnumerationCompatibilityController

executor: SessionExecutor

items: ItemRepository

pandas: PandasRepository

versions: VersionRepository

default_filter: VariableFilter = {'run': {'default_only': True}}

create(name: str) → Variable

Creates a variable.

Parameters:: name (str) – The name of the model.
Raises:: VariableNotUnique – If the variable with name is not unique.
Returns:: The created variable.
Return type:: Variable

create_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol, name: str) → None

delete_by_id(id: int) → None

Deletes a variable.

Parameters:

id (int) – The unique integer id of the variable.

Raises:

VariableNotFound – If the variable with id does not exist.
VariableDeletionPrevented – If the variable with id is used in the database, preventing it’s deletion.

delete_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol) → None

get_by_name(name: str) → Variable

Retrieves a variable by its name.

Parameters:: name (str) – The unique name of the variable.
Raises:: VariableNotFound – If the variable with name does not exist.
Returns:: The retrieved variable.
Return type:: ixmp4.data.base.iamc.Variable

get_by_name_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol) → None

get_by_id(id: int) → Variable

Retrieves a variable by its id.

Parameters:: id (int) – The integer id of the variable.
Raises:: NotFound – If the variable with id does not exist.
Returns:: The retrieved variable.
Return type:: ixmp4.data.base.iamc.Variable

get_by_id_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol) → None

get_or_create(name: str) → Variable

list(**kwargs: Unpack[VariableFilter]) → List[Variable]

Lists variables by specified criteria.

Parameters:: **kwargs (any) – Filter parameters as specified in VariableFilter.
Returns:: List of variables.
Return type:: list[ixmp4.data.iamc.variable.dto.Variable]

list_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol) → None

paginated_list(pagination: Pagination, **kwargs: Unpack[VariableFilter]) → PaginatedResult[List[Variable]]

tabulate(**kwargs: Unpack[VariableFilter]) → WithJsonSchema(json_schema={'properties': {'index': {'anyOf': [{'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Index'}, 'columns': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Columns'}, 'dtypes': {'anyOf': [{'items': {'type': 'string'}, 'type': 'array'}, {'type': 'null'}], 'default': None, 'title': 'Dtypes'}, 'data': {'anyOf': [{'items': {'items': {'anyOf': [{'type': 'boolean'}, {'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'additionalProperties': True, 'type': 'object'}, {'items': {'type': 'number'}, 'type': 'array'}, {'items': {'type': 'integer'}, 'type': 'array'}, {'items': {'type': 'string'}, 'type': 'array'}, {'format': 'date-time', 'type': 'string'}, {'type': 'null'}]}, 'type': 'array'}, 'type': 'array'}, {'type': 'null'}], 'title': 'Data'}}, 'required': ['data'], 'title': 'DataFrameTypeAdapter', 'type': 'object'}, mode=serialization)]

Tabulates variables by specified criteria.

Parameters:

**kwargs (any) – Filter parameters as specified in VariableFilter.

Returns:

A data frame with the columns:

id
name

Return type:

pandas.DataFrame

tabulate_auth_check(auth_ctx: AuthorizationContext, platform: PlatformProtocol) → None

paginated_tabulate(pagination: Pagination, **kwargs: Unpack[VariableFilter]) → PaginatedResult[Annotated[DataFrame, PlainValidator, PlainSerializer, WithJsonSchema]]

Filters

ixmp4.data.iamc.variable.filter.filter_by_run(exc: Select, value: dict[str, Any] | None, *, schema: type[Any], repo: BaseRepository[Any]) → Select