soprano.nmr.site

soprano.nmr.site#

The Site class is a pydantic model representing a single nuclear spin site in a SpinSystem. It (can) contain: - the isotope of the nucleus at this site - a label for this site - the magnetic shielding tensor at this site, in ppm - the electric field gradient tensor at this site, in atomic units

In addition, you can set the following options: - the convention used for the magnetic shielding tensor - the convention used for the electric field gradient tensor - the Euler angle conventions - the Euler angle passive or active - the Euler angle degrees or radians

This is modelled roughly on the MRSimulator code: deepanshs/mrsimulator

Functions

check_efg_tensor(func)

check_magnetic_shielding_tensor(func)

check_tensor_present(tensor_name)

Classes

Site(*, isotope, label, index[, ms, efg])

Represents a single nuclear spin site in a SpinSystem.
class soprano.nmr.site.Site(*, isotope, label, index, ms=None, efg=None)[source]#

Bases: BaseModel

Represents a single nuclear spin site in a SpinSystem.

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:
__copy__()#

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)#

Returns a deep copy of the model.

Parameters:

memo (dict[int, Any] | None)

Return type:

Self

__eq__(other)[source]#

Check if two Site objects are equal.

Sites are considered equal if they have: - The same isotope - The same label - Equivalent magnetic shielding tensor (if present) - Equivalent electric field gradient tensor (if present)

Parameters: other (Site): Another Site object to compare against

Returns: bool: True if sites are equivalent, False otherwise

Parameters:

other (Site)

Return type:

bool

classmethod __get_pydantic_json_schema__(core_schema, handler, /)#

Hook into generating the model’s JSON schema.

Parameters:

  • core_schema (CoreSchema) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

  • handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Returns:

A JSON schema, as a Python object.

Return type:

JsonSchemaValue

__hash__()[source]#

Generate a hash for the Site object.

This allows Site objects to be used in sets and as dictionary keys.

Returns: int: A hash value for the Site object

Return type:

int

__iter__()#

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)#

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Parameters:

  • fmt (Callable[[Any], Any])

  • kwargs (Any)

Return type:

Generator[Any]

classmethod __pydantic_init_subclass__(**kwargs)#

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after basic class initialization is complete. In particular, attributes like model_fields will be present when this is called, but forward annotations are not guaranteed to be resolved yet, meaning that creating an instance of the class may fail.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by Pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by Pydantic.

Return type:

None

Note

You may want to override [__pydantic_on_complete__()][pydantic.main.BaseModel.__pydantic_on_complete__] instead, which is called once the class and its fields are fully initialized and ready for validation.

classmethod __pydantic_on_complete__()#

This is called once the class and its fields are fully initialized and ready to be used.

This typically happens when the class is created (just before [__pydantic_init_subclass__()][pydantic.main.BaseModel.__pydantic_init_subclass__] is called on the superclass), except when forward annotations are used that could not immediately be resolved. In that case, it will be called later, when the model is rebuilt automatically or explicitly using [model_rebuild()][pydantic.main.BaseModel.model_rebuild].

Return type:

None

__repr_name__()#

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)#

Returns the string representation of a recursive object.

Parameters:

object (Any)

Return type:

str

__rich_repr__()#

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

RichReprResult

_setattr_handler(name, value)#

Get a handler for setting an attribute on the model instance.

Returns:

A handler for setting an attribute on the model instance. Used for memoization of the handler. Memoizing the handlers leads to a dramatic performance improvement in __setattr__ Returns None when memoization is not safe, then the attribute is set directly.

Parameters:

  • name (str)

  • value (Any)

Return type:

Callable[[BaseModel, str, Any], None] | None

copy(deep=True, **kwargs)[source]#

Create a copy of the Site object.

This method overrides the deprecated copy method with model_copy.

Parameters:#

deepbool, optional

If True, performs a deep copy of nested objects. Default is True to ensure tensor objects are properly copied.

**kwargsdict

Additional keyword arguments to pass to model_copy

Returns:#

Site

A new Site object that is a copy of the current object

Notes:#

Deprecation warning for the original copy method is automatically handled by Pydantic.

Parameters:

deep (bool)

Return type:

Site

property element: str#

Return the element symbol of the isotope.

Converts e.g. 1H to H, 13C to C.

Returns: str: The element symbol, e.g., ‘H’, ‘C’.

classmethod from_dict(data)[source]#

Create a Site instance from a dictionary representation.

Parameters: data (dict[str, Any]): A dictionary containing Site configuration.

Can include nested dictionaries for ms and efg.

Returns: Site: A new Site instance created from the input dictionary.

Parameters:

data (dict[str, Any])

Return type:

Site

make_isotropic()[source]#

Create a copy of the Site object with the magnetic shielding tensor replaced by its isotropic version.

Returns:#

Site

A new Site object with the isotropic magnetic shielding tensor.

Return type:

Site

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

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

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.

!!! 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.

Parameters:

  • _fields_set (set[str] | None) – 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 (Any) – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

Return type:

Self

model_copy(*, update=None, deep=False)#
!!! 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]).

Parameters:

  • update (Mapping[str, Any] | None) – 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 (bool) – Set to True to make a deep copy of the model.

Returns:

New model instance.

Return type:

Self

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, polymorphic_serialization=None)#
!!! 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.

Parameters:

  • mode (Literal['json', 'python'] | str) – 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 (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – A set of fields to include in the output.

  • exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – A set of fields to exclude from the output.

  • context (Any | None) – Additional context to pass to the serializer.

  • by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

  • exclude_none (bool) – Whether to exclude fields that have a value of None.

  • exclude_computed_fields (bool) – 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 (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

  • warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

  • fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

  • polymorphic_serialization (bool | None) – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

Return type:

dict[str, Any]

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, polymorphic_serialization=None)#
!!! abstract “Usage Documentation”

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

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

Parameters:

  • indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

  • ensure_ascii (bool) – 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 (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – Field(s) to include in the JSON output.

  • exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – Field(s) to exclude from the JSON output.

  • context (Any | None) – Additional context to pass to the serializer.

  • by_alias (bool | None) – Whether to serialize using field aliases.

  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

  • exclude_none (bool) – Whether to exclude fields that have a value of None.

  • exclude_computed_fields (bool) – 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 (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

  • warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

  • fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

  • polymorphic_serialization (bool | None) – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A JSON string representation of the model.

Return type:

str

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.

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.

Parameters:

  • by_alias (bool) – Whether to use attribute aliases or not.

  • ref_template (str) – The reference template.

  • union_format (Literal['any_of', 'primitive_type_array']) –

    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 (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

  • mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

Return type:

dict[str, Any]

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.

Parameters:

params (tuple[type[Any], ...]) – 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.

Return type:

str

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.

Parameters:

context (Any)

Return type:

None

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.

Parameters:

  • force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

  • raise_errors (bool) – Whether to raise errors, defaults to True.

  • _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

  • _types_namespace (MappingNamespace | None) – 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.

Return type:

bool | None

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

Validate a pydantic model instance.

Parameters:

  • obj (Any) – The object to validate.

  • strict (bool | None) – Whether to enforce types strictly.

  • extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

  • from_attributes (bool | None) – Whether to extract data from object attributes.

  • context (Any | None) – Additional context to pass to the validator.

  • by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

  • by_name (bool | None) – 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.

Return type:

Self

classmethod model_validate_json(json_data, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)#
!!! abstract “Usage Documentation”

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

Validate the given JSON data against the Pydantic model.

Parameters:

  • json_data (str | bytes | bytearray) – The JSON data to validate.

  • strict (bool | None) – Whether to enforce types strictly.

  • extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

  • context (Any | None) – Extra variables to pass to the validator.

  • by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

  • by_name (bool | None) – 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.

Return type:

Self

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.

Parameters:

  • obj (Any) – The object containing string data to validate.

  • strict (bool | None) – Whether to enforce types strictly.

  • extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

  • context (Any | None) – Extra variables to pass to the validator.

  • by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

  • by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Return type:

Self

to_mrsimulator(include_ms=True, ms_isotropic=False, include_efg=True, include_angles=True, include_ms_angles=None, include_efg_angles=None)[source]#

Convert the Site object to a dictionary representation compatible with MRSimulator.

Parameters: include_ms (bool): If True, include the magnetic shielding tensor in the output. ms_isotropic (bool): If True, only output the isotropic magnetic shielding (no orientation or anisotropy information). include_efg (bool): If True, include the electric field gradient tensor in the output. include_angles (bool): If True, include the Euler angles in the output. include_ms_angles (bool): If True, include the Euler angles for the magnetic shielding tensor.

Note this overrides include_angles.

include_efg_angles (bool): If True, include the Euler angles for the electric field gradient tensor.

Note this overrides include_angles.

Returns: dict: A dictionary representation of the Site object.

Parameters:

  • include_ms (bool)

  • ms_isotropic (bool)

  • include_efg (bool)

  • include_angles (bool)

  • include_ms_angles (bool | None)

  • include_efg_angles (bool | None)

Return type:

dict[str, Any]

to_simpson(q_order=None, include_ms=True, ms_isotropic=False, include_efg=True, include_angles=True, include_ms_angles=None, include_efg_angles=None)[source]#

Convert the Site object to a dictionary representation compatible with Simpson.

Parameters: q_order (int, optional): The order of the quadrupole interaction. If

None, and the site is quadrupole active, the order is set to 2.

include_ms (bool): If True, include the magnetic shielding tensor in the output. ms_isotropic (bool): If True, only output the isotropic magnetic shielding (no orientation or anisotropy information). include_efg (bool): If True, include the electric field gradient tensor in the output. include_angles (bool): If True, include the Euler angles in the output. If False, the angles are set to 0. include_ms_angles (bool): If True, include the Euler angles for the magnetic shielding tensor.

Note this overrides include_angles. If False, the angles are set to 0.

include_efg_angles (bool): If True, include the Euler angles for the electric field gradient tensor.

Note this overrides include_angles. If False, the angles are set to 0.

Returns: dict: A dictionary representation of the Site object.

Parameters:

  • q_order (int | None)

  • include_ms (bool)

  • ms_isotropic (bool)

  • include_efg (bool)

  • include_angles (bool)

  • include_ms_angles (bool | None)

  • include_efg_angles (bool | None)

Return type:

tuple[str, str]

static validate_species(species)[source]#

Validate the given species string.

Parameters: species (str): The isotope string to validate, e.g., ‘1H’, ‘13C’.

Returns: str: The validated species string.

Raises: ValueError: If the species string is not valid.

Parameters:

species (str)

Return type:

str