Base Model and Manager#

Synchronous#

class patent_client.util.manager.Manager(config=None)[source]#

Manager Class (Synchronous)

This class is essentially a configurable generator. It is intended to be initialized as an empty object at Model.objects. Users can then call methods to modify the manager. All methods should return a brand-new manager with the appropriate parameters re-set. The manager’s attributes are stored in a dictionary at Manager.config.

first() → ModelType[source]#

Get the first object in the manager

get(*args, **kwargs) → ModelType[source]#

If the critera results in a single record, return it, else raise an exception

all() → Manager[ModelType]#

Return self. Does nothing

async ato_json(*args, **kwargs) → str#

Convert objects to JSON format

async ato_list() → List[T]#

Return a list of item objects from the Collection

async ato_pandas(annotate=[]) → pandas.DataFrame#

Convert Collection into a Pandas DataFrame

count() → int#

Returns number of records in the QuerySet. Alias for len(self)

explode(attribute, unpack=False, connector='.', prefix=True) → Union[UnpackedCollection, ExplodedCollection]#

Implement an “explode” function for nested listed objects.

filter(*args, **kwargs) → Self#

Apply a new filtering condition

limit(limit) → Self#

Limit the number of records that are returned

offset(offset) → Self#

Specify the number of records from the beginning from which to apply an offset

option(**kwargs) → Self#

Set a key:value option on the manager

order_by(*args) → Self#

Specify the order that argument should be returned in

to_json(*args, **kwargs) → str#

Convert objects to JSON format

to_list() → List[T]#

Return a list of item objects from the Collection

to_mongo() → List[dict]#

Return a list of dictionaries containing MongoDB compatible datatypes

to_pandas(annotate=[]) → pandas.DataFrame#

Convert Collection into a Pandas DataFrame

to_records(item_class=<class 'dict'>, collection_class=<class 'list'>) → List[dict]#

Return a list of dictionaries containing item data in ordinary Python types Useful for ingesting into NoSQL databases

unpack(attribute, connector='.', prefix=True) → UnpackedCollection#

Implement an “unpack” function for nested single objects

values(*fields, **kw_fields) → ValuesCollection#

Return a Collection that will return a Row object for each item with a subset of attributes positional arguments will result in Row objects where the fields match the field names on the item, keyword arguments can be used to rename attributes. When passed as key=field, the resulting dictionary will have key: item[field]

values_list(*fields, flat=False, **kw_fields) → ValuesListCollection#

Return a Collection that will return tuples for each item with a subset of attributes. If only a single field is passed, the keyword argument “flat” can be passed to return a simple list

Asynchronous#

class patent_client.util.manager.AsyncManager(config=None)[source]#

Manager Class (Asynchronous)

This class is essentially a configurable generator. It is intended to be initialized as an empty object at Model.objects. Users can then call methods to modify the manager. All methods should return a brand-new manager with the appropriate parameters re-set. The manager’s attributes are stored in a dictionary at Manager.config.

async count() → int[source]#

Returns number of records in the QuerySet. Alias for len(self)

async len() → int[source]#

Returns number of records in the QuerySet. Alias for self.count()

async first() → ModelType[source]#

Get the first object in the manager

async get(*args, **kwargs) → ModelType[source]#

If the critera results in a single record, return it, else raise an exception

async to_list() → list[ModelType][source]#

Return a list of all objects in the manager

all() → Manager[ModelType]#

Return self. Does nothing

async ato_json(*args, **kwargs) → str#

Convert objects to JSON format

async ato_list() → List[T]#

Return a list of item objects from the Collection

async ato_pandas(annotate=[]) → pandas.DataFrame#

Convert Collection into a Pandas DataFrame

explode(attribute, unpack=False, connector='.', prefix=True) → Union[UnpackedCollection, ExplodedCollection]#

Implement an “explode” function for nested listed objects.

filter(*args, **kwargs) → Self#

Apply a new filtering condition

limit(limit) → Self#

Limit the number of records that are returned

offset(offset) → Self#

Specify the number of records from the beginning from which to apply an offset

option(**kwargs) → Self#

Set a key:value option on the manager

order_by(*args) → Self#

Specify the order that argument should be returned in

to_json(*args, **kwargs) → str#

Convert objects to JSON format

to_mongo() → List[dict]#

Return a list of dictionaries containing MongoDB compatible datatypes

to_pandas(annotate=[]) → pandas.DataFrame#

Convert Collection into a Pandas DataFrame

to_records(item_class=<class 'dict'>, collection_class=<class 'list'>) → List[dict]#

Return a list of dictionaries containing item data in ordinary Python types Useful for ingesting into NoSQL databases

unpack(attribute, connector='.', prefix=True) → UnpackedCollection#

Implement an “unpack” function for nested single objects

values(*fields, **kw_fields) → ValuesCollection#

Return a Collection that will return a Row object for each item with a subset of attributes positional arguments will result in Row objects where the fields match the field names on the item, keyword arguments can be used to rename attributes. When passed as key=field, the resulting dictionary will have key: item[field]

values_list(*fields, flat=False, **kw_fields) → ValuesListCollection#

Return a Collection that will return tuples for each item with a subset of attributes. If only a single field is passed, the keyword argument “flat” can be passed to return a simple list

class patent_client.util.pydantic_util.BaseModel[source]#
model_config: ClassVar[ConfigDict] = {'ignored_types': (<class 'patent_client.util.pydantic_util.ClassProperty'>, <class 'async_property.base.AsyncPropertyDescriptor'>)}#

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

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Model[source]#

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) `

Parameters:

  • 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.

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}#

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

classmethod model_construct(_fields_set: Optional[set[str]] = None, **values: Any) → Model[source]#

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 – 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: Optional[dict[str, Any]] = None, deep: bool = False) → Model[source]#

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

Returns a copy of the model.

Parameters:

  • 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: Union[Literal['json', 'python'], str] = 'python', include: Optional[Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any]]] = None, exclude: Optional[Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any]]] = None, context: Optional[dict[str, Any]] = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: Union[bool, Literal['none', 'warn', 'error']] = True, serialize_as_any: bool = False) → dict[str, Any][source]#

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.

Parameters:

  • 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: Optional[int] = None, include: Optional[Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any]]] = None, exclude: Optional[Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any]]] = None, context: Optional[dict[str, Any]] = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: Union[bool, Literal['none', 'warn', 'error']] = True, serialize_as_any: bool = False) → str[source]#

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.

Parameters:

  • 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: 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”.

model_fields: ClassVar[dict[str, FieldInfo]] = {}#

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: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') → dict[str, Any][source]#

Generates a JSON schema for a model class.

Parameters:

  • 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.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str[source]#

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 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(_BaseModel__context: Any) → None[source]#

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.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: Optional[dict[str, Any]] = None) → bool | None[source]#

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 – 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: Any, *, strict: Optional[bool] = None, from_attributes: Optional[bool] = None, context: Optional[dict[str, Any]] = None) → Model[source]#

Validate a pydantic model instance.

Parameters:

  • 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: str | bytes | bytearray, *, strict: Optional[bool] = None, context: Optional[dict[str, Any]] = None) → Model[source]#

Usage docs: https://docs.pydantic.dev/2.7/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

  • 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: Any, *, strict: Optional[bool] = None, context: Optional[dict[str, Any]] = None) → Model[source]#

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

Parameters:

  • 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.