nudb_config.pydantic package

nudb_config.pydantic.datasets module

class Dataset(**data)

Bases: DotMapBaseModel

Dataset configuration entry under [datasets].

Parameters:

  • team (str)

  • bucket (str)

  • path_glob (str)

  • variables (list[str] | None)

  • thresholds_empty (DotMapDict[float] | None)

  • min_values (DotMapDict[str] | None)

  • max_values (DotMapDict[str] | None)

  • dataset_specific_renames (DotMapDict[str] | None)

team

The name of the dapla-team who owns the dataset.

bucket

The bucket name, under the dapla-team, where the dataset is located.

path_glob

A glob that can be used in the bucket to pinpoint all available periods and versions of the dataset.

variables

Ordered list of variable names included in the dataset.

thresholds_empty

Allowable proportions of emptiness per variable.

min_values

Minimum inclusive filter values per variable.

max_values

Maximum inclusive filter values per variable.

dataset_specific_renames

Custom renames to apply only for this dataset.

bucket: str
dataset_specific_renames: DotMapDict[str] | None
max_values: DotMapDict[str] | None
min_values: DotMapDict[str] | None
model_config: ClassVar[ConfigDict] = {}

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

path_glob: str
team: str
thresholds_empty: DotMapDict[float] | None
variables: list[str] | None
class DatasetsFile(**data)

Bases: DotMapBaseModel

Root schema of datasets.toml.

Parameters:

datasets (DotMapDict[Dataset])

datasets

Mapping of dataset name to its configuration.

datasets: DotMapDict[Dataset]
model_config: ClassVar[ConfigDict] = {}

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

nudb_config.pydantic.dotmap module

class DotMap(*args, **kwargs)

Bases: object

Provide dot- and item-access for wrappers and Pydantic models.

Usage: - Wrapper: DotMap({"a": 1}).a and DotMap({"a": 1})["a"]. - Base class: class M(DotMap, BaseModel): ... so m.a and

m["a"] both work, without conflicting with Pydantic’s iterator.

  • When mixed into Pydantic models, initialization defers to Pydantic; otherwise a mapping positional argument or data= keyword seeds the internal store.

Parameters:

  • args (Any)

  • kwargs (Any)

get(key, default=None)

Return the value for key if present; otherwise default.

Tries attribute access first and then falls back to the mapping view.

Parameters:

  • key (str) – The key to look up.

  • default (Any | None) – Value to return if key is not found.

Returns:

The found value or default if absent.

Return type:

Any

items()

Return a dynamic view of key/value pairs across model or mapping.

Return type:

ItemsView[str, Any]

keys()

Return a dynamic view of keys across model or mapping.

Return type:

KeysView[str]

values()

Return a dynamic view of values across model or mapping.

Return type:

ValuesView[Any]

class DotMapBaseModel(**data)

Bases: BaseModel, DotMap

Combines the Dotmap class with Basemodel, placing BaseModel first.

model_config: ClassVar[ConfigDict] = {}

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

class DotMapDict(data=None, *, value_type=None)

Bases: Generic[T]

Dictionary wrapper that supports both attribute and item access.

Parameters:

  • data (Mapping[str, T] | None)

  • value_type (type[T] | None)

get(key, default=None)

Return the value for key if present; otherwise default.

Return type:

Optional[TypeVar(T)]

Parameters:

  • key (str)

  • default (T | None)

items()

Return a dynamic view of key/value pairs.

Return type:

ItemsView[str, TypeVar(T)]

keys()

Return a dynamic view of keys.

Return type:

KeysView[str]

values()

Return a dynamic view of values.

Return type:

ValuesView[TypeVar(T)]

nudb_config.pydantic.load module

class NudbConfig(**data)

Bases: DotMapBaseModel

Unified configuration built from the TOML files.

This model aggregates values from settings.toml ,``options.toml``, variables.toml, variables_outdated.toml (if present), datasets.toml, and paths.toml. It mirrors the top-level structure exposed by the Dynaconf-based loader to preserve compatibility. Any codelist augmentations performed by the Dynaconf variant are also applied during loading so that downstream behavior matches.

Parameters:
model_config

Pydantic configuration allowing arbitrary DotMap content.

dapla_team

Team identifier from settings.toml.

short_name

Short project name from settings.toml.

variables_sort_unit

Unit sort order from variables.toml.

variables

Mapping of variable name to definition.

datasets

Mapping of dataset name to configuration.

paths

Mapping of environment name to paths configuration.

options

Options from options.toml.

dapla_team: str
datasets: DotMapDict[Dataset]
merge_tomls(toml_dir)

Merge values from external TOML files into this config and return it.

Return type:

NudbConfig

Parameters:

toml_dir (str | Path)

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

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

options: Options
paths: DotMapDict[PathEntry]
short_name: str
variables: DotMapDict[Variable]
variables_sort_unit: list[str] | None
load_pydantic_settings()

Load and assemble configuration using Pydantic models.

Reads the package-embedded TOML files under nudb_config/config_tomls (settings.toml, variables.toml, optional variables_outdated.toml, datasets.toml, paths.toml), parses them into their respective Pydantic models, applies the same codelist augmentations as the Dynaconf-based loader, and returns a unified NudbConfig object mirroring the Dynaconf structure.

Returns:

Aggregated configuration ready for downstream use.

Return type:

NudbConfig

nudb_config.pydantic.options module

class Options(**data)

Bases: DotMapBaseModel

Typed options configuration under [options].

Parameters:

warn_unsafe_derive (bool)

model_config: ClassVar[ConfigDict] = {}

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

warn_unsafe_derive: bool
class OptionsFile(**data)

Bases: DotMapBaseModel

Root schema of options.toml.

Parameters:

options (Options)

options

Options configuration block.

model_config: ClassVar[ConfigDict] = {}

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

options: Options

nudb_config.pydantic.paths module

class PathEntry(**data)

Bases: DotMapBaseModel

Path configuration for a named environment under [paths].

Parameters:

  • katalog (str | None)

  • delt_utdanning (str)

katalog

Base catalog path for configuration artifacts.

delt_utdanning

Location of shared education data.

delt_utdanning: str
katalog: str | None
model_config: ClassVar[ConfigDict] = {}

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

class PathsFile(**data)

Bases: DotMapBaseModel

Root schema of paths.toml.

Parameters:

paths (DotMapDict[PathEntry])

paths

Mapping of environment name to its paths configuration.

model_config: ClassVar[ConfigDict] = {}

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

paths: DotMapDict[PathEntry]

nudb_config.pydantic.settings module

class SettingsFile(**data)

Bases: DotMapBaseModel

Root schema of settings.toml.

Parameters:

  • dapla_team (str)

  • short_name (str)

dapla_team

Name of the Dapla team that owns the data.

short_name

Short identifier for the project or dataset.

dapla_team: str
model_config: ClassVar[ConfigDict] = {}

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

short_name: str

nudb_config.pydantic.variables module

class Variable(**data)

Bases: DotMapBaseModel

Definition of a single variable from variables.toml.

Parameters:

  • name (str)

  • unit (str)

  • dtype (Literal['INTEGER', 'FLOAT', 'STRING', 'DATETIME', 'BOOLEAN'])

  • description_short (str | None)

  • length (list[int] | None)

  • klass_codelist (int | None)

  • klass_codelist_from_date (str | None)

  • klass_variant (int | None)

  • klass_variant_search_term (str | None)

  • klass_correspondence_to (int | None)

  • klass_codelist_metadata (KlassClassification | None)

  • klass_variant_metadata (KlassVariant | None)

  • renamed_from (list[str] | None)

  • derived_from (list[str] | None)

  • derived_uses_datasets (list[str] | None)

  • derived_join_keys (list[str] | None)

  • codelist_extras (dict[str, str] | None)

  • outdated_comment (str | None)

name

Variable name injected from its TOML key.

unit

Logical grouping the variable belongs to.

dtype

Storage or semantic type of the variable.

description_short

Optional short description.

length

Allowed lengths for string fields, if constrained.

klass_codelist

KLASS codelist identifier, if applicable.

klass_codelist_from_date

Earliest date for the codelist.

klass_variant

KLASS variant identifier, if applicable.

klass_variant_search_term

Search term used to find a variant.

klass_correspondence_to

The Classification ID for what we are mapping from the klass_codelist to.

klass_codelist_metadata

Metadata fetched from KLASS for the codelist.

klass_variant_metadata

Metadata fetched from KLASS for the variant.

renamed_from

Previous column name(s) that map to this variable.

derived_from

Source variables used to derive this value.

derived_uses_datasets

Names of datasets a derived variable should be derived from (in the cases it is “all-data dependent”).

derived_join_keys

The keys the derived variable can be joined back on a dataset with, if its a variable with one value per person, this could be just “snr” for example.

codelist_extras

Additional code mappings injected at load time.

outdated_comment

Explanation when the unit is outdated.

model_config

Pydantic configuration allowing arbitrary KLASS types.

codelist_extras: dict[str, str] | None
derived_from: list[str] | None
derived_join_keys: list[str] | None
derived_uses_datasets: list[str] | None
description_short: str | None
dtype: Literal['INTEGER', 'FLOAT', 'STRING', 'DATETIME', 'BOOLEAN']
klass_codelist: int | None
klass_codelist_from_date: str | None
klass_codelist_metadata: KlassClassification | None
klass_correspondence_to: int | None
klass_variant: int | None
klass_variant_metadata: KlassVariant | None
klass_variant_search_term: str | None
length: list[int] | None
model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

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

name: str
outdated_comment: str | None
renamed_from: list[str] | None
unit: str
class VariablesFile(**data)

Bases: DotMapBaseModel

Root schema of variables.toml.

Parameters:
variables_sort_unit

Preferred order of variable units.

variables

Mapping of variable name to its definition.

model_config: ClassVar[ConfigDict] = {}

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

variables: DotMapDict[Variable]
variables_sort_unit: list[str] | None