Parametrizations: BaseModel

Parametrizations: BaseModel#

Parametrization: Base#

Definition of the full parametrization data for the extended tight-binding methods.

The dataclass can represent a complete parametrization file produced by the tblite library, however it only stores the raw data rather than the full representation, i.e., the transformation to the corresponding atom-resolved quantities must be carried out separately.

class dxtb._src.param.base.Param(**data)[source]

Bases: BaseModel

Complete self-contained representation of an extended tight-binding model.

The parametrization of a calculator with the model data must account for missing transformations, like extracting the principal quantum numbers from the shells. The respective checks are therefore deferred to the instantiation of the calculator, while a deserialized model in tblite is already verified at this stage.

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.

Parameters:
clean_model_dump()[source]

Clean the model from any None values.

Return type:

dict[str, Any]

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

Return type:

Self

Parameters:
!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Args:

include: Optional set or mapping specifying which fields to include in the copied model. exclude: Optional set or mapping specifying which fields to exclude in the copied model. update: Optional dictionary of field-value pairs to override field values in the copied model. deep: If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

classmethod from_file(filepath)[source]

Load a parametrization from a file. The file format is determined by the file extension. Supported formats are JSON, TOML, and YAML.

Return type:

Self

Parameters:

filepath (PathLike) – The file path to the parametrization file.

Returns:

The loaded parametrization data.

Return type:

Param

Raises:

ValueError – If the file format is not supported.

Parameters:

filepath (str | Path)

classmethod from_json_file(filepath)[source]

Load a parametrization from a JSON file.

Return type:

Self

Parameters:

filepath (PathLike) – The file path to the parametrization file.

Returns:

The loaded parametrization data.

Return type:

Param

Parameters:

filepath (str | Path)

classmethod from_toml_file(filepath)[source]

Load a parametrization from a TOML file.

Return type:

Self

Parameters:

filepath (PathLike) – The file path to the parametrization file.

Returns:

The loaded parametrization data.

Return type:

Param

Parameters:

filepath (str | Path)

classmethod from_yaml_file(filepath)[source]

Load a parametrization from a YAML file.

Return type:

Self

Parameters:

filepath (Path)

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

Return type:

Self

Parameters:
!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,

this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

values: Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)
Return type:

Self

Parameters:
!!! abstract “Usage Documentation”

[model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Args:
update: Values to change/add in the new model. Note: the data is not validated

before creating the new model. You should trust this data.

deep: Set to True to make a deep copy of the model.

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
Return type:

dict[str, Any]

Parameters:
!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#python-mode)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args:
mode: The mode in which to_python should run.

If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

include: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field’s alias in the dictionary key if defined. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. exclude_computed_fields: Whether to exclude computed fields.

While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors,

“error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

fallback: A function to call when an unknown value is encountered. If not provided,

a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, ensure_ascii=False, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
Return type:

str

Parameters:
!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#json-mode)

Generates a JSON representation of the model using Pydantic’s to_json method.

Args:

indent: Indentation to use in the JSON output. If None is passed, the output will be compact. ensure_ascii: If True, the output is guaranteed to have all incoming non-ASCII characters escaped.

If False (the default), these characters will be output as-is.

include: Field(s) to include in the JSON output. exclude: Field(s) to exclude from the JSON output. context: Additional context to pass to the serializer. by_alias: Whether to serialize using field aliases. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. exclude_computed_fields: Whether to exclude computed fields.

While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors,

“error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

fallback: A function to call when an unknown value is encountered. If not provided,

a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation', *, union_format='any_of')

Generates a JSON schema for a model class.

Return type:

dict[str, Any]

Parameters:
Args:

by_alias: Whether to use attribute aliases or not. ref_template: The reference template. union_format: The format to use when combining schemas from unions together. Can be one of:

keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

schema_generator: To override the logic used to generate the JSON schema, as a subclass of

GenerateJsonSchema with your desired modifications

mode: The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Return type:

str

Parameters:

params (tuple[type[Any], ...])

Args:
params: Tuple of types of the class. Given a generic class

Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError: Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Return type:

bool | None

Parameters:
Args:

force: Whether to force the rebuilding of the model schema, defaults to False. raise_errors: Whether to raise errors, defaults to True. _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. _types_namespace: The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, extra=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Return type:

Self

Parameters:

  • obj (Any)

  • strict (bool | None)

  • extra (Literal['allow', 'ignore', 'forbid'] | None)

  • from_attributes (bool | None)

  • context (Any | None)

  • by_alias (bool | None)

  • by_name (bool | None)

Args:

obj: The object to validate. strict: Whether to enforce types strictly. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError: If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)
Return type:

Self

Parameters:
!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Args:

json_data: The JSON data to validate. strict: Whether to enforce types strictly. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError: If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Return type:

Self

Parameters:

  • obj (Any)

  • strict (bool | None)

  • extra (Literal['allow', 'ignore', 'forbid'] | None)

  • context (Any | None)

  • by_alias (bool | None)

  • by_name (bool | None)

Args:

obj: The object containing string data to validate. strict: Whether to enforce types strictly. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

to_file(filepath, **kwargs)[source]

Save the parametrization to a file. The file format is determined by the file extension. Supported formats are JSON, TOML, and YAML.

Return type:

None

Parameters:

filepath (PathLike) – The file path to save the parametrization data.

Raises:

ValueError – If the file format is not supported.

Parameters:

filepath (str | Path)

to_json_file(filepath, **kwargs)[source]

Save the parametrization to a JSON file.

Return type:

None

Parameters:

  • filepath (PathLike) – The file path to save the parametrization data.

  • kwargs (dict) – Additional keyword arguments for the dump function of the JSON writer.

Parameters:

filepath (str | Path)

to_toml_file(filepath, **kwargs)[source]

Save the parametrization to a TOML file.

Return type:

None

Parameters:

  • filepath (PathLike) – The file path to save the parametrization data.

  • kwargs (dict) – Additional keyword arguments for the dump function of the TOML writer.

Raises:

ImportError – If the TOML writer package is not installed.

Parameters:
to_yaml_file(filepath, **kwargs)[source]

Save the parametrization to a YAML file.

Return type:

None

Parameters:

filepath (PathLike) – The file path to save the parametrization data.

Raises:

ImportError – If the PyYAML package is not installed.

Parameters:
charge: Optional[Charge]

Definition of the isotropic second-order charge interactions.

dispersion: Optional[Dispersion]

Definition of the dispersion correction.

element: Dict[str, Element]

Element specific parameter records.

halogen: Optional[Halogen]

Definition of the halogen bonding correction.

hamiltonian: Optional[Hamiltonian]

Definition of the Hamiltonian, always required.

meta: Optional[Meta]

Descriptive data on the model.

model_config: ClassVar[ConfigDict] = {}

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

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:
A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

multipole: Optional[Multipole]

Definition of the anisotropic second-order multipolar interactions.

repulsion: Optional[Repulsion]

Definition of the repulsion contribution.

solvation: Optional[Solvation]

Definition of the solvation model.

thirdorder: Optional[ThirdOrder]

Definition of the isotropic third-order charge interactions.

property xtb_version: str

Return the version of the xtb package.

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