blissoda.tomo.tomo_model.TomoProcessorModel#
- class blissoda.tomo.tomo_model.TomoProcessorModel(**data)[source]#
Bases:
BaseModel- Parameters:
data (
Any)
- classmethod construct(_fields_set=None, **values)#
- copy(*, include=None, exclude=None, update=None, deep=False)#
Returns a copy of the model.
- !!! 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.
- Parameters:
include (AbstractSetIntStr | MappingIntStrAny | None)
exclude (AbstractSetIntStr | MappingIntStrAny | None)
update (Dict[str, Any] | None)
deep (bool)
- Return type:
Self
- cor_algorithm: Union[int, float, Literal['centered', 'sliding-window', 'growing-window', 'composite-coarse-to-fine', 'octave-accurate']]#
- delta_beta: Union[str, float, int]#
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)#
- classmethod from_orm(obj)#
- Parameters:
obj (
Any)- Return type:
Self
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)#
- model_computed_fields = {}#
- model_config: ClassVar[ConfigDict] = {'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.
- 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)#
- !!! 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, 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.
- 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. polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
- 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, 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.
- 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. polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
- Returns:
A JSON string representation of the model.
- property model_extra#
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'cor_algorithm': FieldInfo(annotation=Union[int, float, Literal['centered', 'sliding-window', 'growing-window', 'composite-coarse-to-fine', 'octave-accurate']], required=False, default='sliding-window'), 'delta_beta': FieldInfo(annotation=Union[str, float, int], required=False, default='100'), 'nabu_config_file': FieldInfo(annotation=str, required=False, default='/home/docs/checkouts/readthedocs.org/user_builds/blissoda/envs/latest/lib/python3.9/site-packages/blissoda/resources/tomo/nabu.conf'), 'offset_mm': FieldInfo(annotation=Union[str, float, int], required=False, default='0'), 'output_format': FieldInfo(annotation=Literal['tiff', 'hdf5', 'jp2', 'edf', 'vol'], required=False, default='hdf5'), 'phase_retrieval_method': FieldInfo(annotation=Union[Literal['CTF', 'Paganin', 'None'], NoneType], required=False, default='None'), 'queue': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Ewoks queue to submit the workflow to', examples=['tomo_queue']), 'show_last_slice': FieldInfo(annotation=bool, required=False, default=False, description='Display last reconstructed slice in Flint'), 'slice_index': FieldInfo(annotation=Union[Literal['first', 'middle', 'last'], int], required=False, default='middle'), 'slice_index_range': FieldInfo(annotation=Union[Literal['all'], Tuple[int, int], List[int]], required=False, default='all'), 'slice_reconstruction_workflow': FieldInfo(annotation=str, required=False, default='slice_reconstruction.json', description='Workflow file for slice reconstruction', examples=['slice_reconstruction.json']), 'volume_reconstruction': FieldInfo(annotation=bool, required=False, default=False), 'volume_reconstruction_workflow': FieldInfo(annotation=str, required=False, default='volume_reconstruction.json', description='Workflow file for volume reconstruction', examples=['volume_reconstruction.json'])}#
- 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.
- 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:
‘any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
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.
- Parameters:
by_alias (
bool)ref_template (
str)schema_generator (
type[GenerateJsonSchema])mode (
Literal['validation','serialization'])union_format (
Literal['any_of','primitive_type_array'])
- 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.
- 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.
- Parameters:
params (
tuple[type[Any],...])- 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.
- 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.
- 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)#
- !!! 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.
- 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.
- nabu_config_file: str#
- offset_mm: Union[str, float, int]#
- output_format: Literal['tiff', 'hdf5', 'jp2', 'edf', 'vol']#
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)#
- classmethod parse_obj(obj)#
- Parameters:
obj (
Any)- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)#
- phase_retrieval_method: Union[Literal['CTF', 'Paganin', 'None'], None]#
- queue: Optional[str]#
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')#
- Parameters:
by_alias (
bool)ref_template (
str)
- Return type:
Dict[str,Any]
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)#
- Parameters:
by_alias (
bool)ref_template (
str)dumps_kwargs (
Any)
- Return type:
str
- show_last_slice: bool#
- slice_index: Union[Literal['first', 'middle', 'last'], int]#
- slice_index_range: Union[Literal['all'], Tuple[int, int], List[int]]#
- slice_reconstruction_workflow: str#
- classmethod update_forward_refs(**localns)#
- Parameters:
localns (
Any)- Return type:
None
- classmethod validate(value)#
- Parameters:
value (
Any)- Return type:
Self
- volume_reconstruction: bool#
- volume_reconstruction_workflow: str#