Ctrl+K

ewoksserver.app.routes.execution.models.EwoksEventList

ewoksserver.app.routes.execution.models.EwoksEventList#

class ewoksserver.app.routes.execution.models.EwoksEventList(**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:

`py 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:

  • self (Model) –

  • include (AbstractSetIntStr | MappingIntStrAny | None) –

  • exclude (AbstractSetIntStr | MappingIntStrAny | None) –

  • update (Dict[str, Any] | None) –

  • deep (bool) –

Return type:

Model

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)#
Parameters:

  • include (Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any], None]) –

  • exclude (Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any], None]) –

  • by_alias (bool) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

Return type:

Dict[str, Any]

classmethod from_orm(obj)#
Parameters:

obj (Any) –

Return type:

TypeVar(Model, bound= BaseModel)

jobs: List[List[EwoksEvent]]#
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: ClassVar[dict[str, ComputedFieldInfo]] = {}#

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}#

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: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)#

Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#model_copy

Returns a copy of the model.

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=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)#

Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump

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. 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].

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

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)#

Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json

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. 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. 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].

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

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: ClassVar[dict[str, FieldInfo]] = {'jobs': FieldInfo(annotation=List[List[ewoksserver.app.routes.execution.models.EwoksEvent]], required=True, title='Workflow execution jobs grouped per job ID')}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

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')#

Generates a JSON schema for a model class.

Args:

by_alias: Whether to use attribute aliases or not. ref_template: The reference template. 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']) –

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(_BaseModel__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:

_BaseModel__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, from_attributes=None, context=None)#

Validate a pydantic model instance.

Args:

obj: The object to validate. strict: Whether to enforce types strictly. from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator.

Raises:

ValidationError: If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)#

Usage docs: https://docs.pydantic.dev/2.7/concepts/json/#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. context: Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

Raises:

ValueError: If json_data is not a JSON string.

classmethod model_validate_strings(obj, *, strict=None, context=None)#

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

Args:

obj: The object contains string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)#
classmethod parse_obj(obj)#
Parameters:

obj (Any) –

Return type:

TypeVar(Model, bound= BaseModel)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)#
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

classmethod update_forward_refs(**localns)#
Parameters:

localns (Any) –

Return type:

None

classmethod validate(value)#
Parameters:

value (Any) –

Return type:

TypeVar(Model, bound= BaseModel)

Contents