kithairon.PlateInfo

kithairon.PlateInfo#

aoeu

class kithairon.PlateInfo( *, plate_type: str, plate_format: str, usage: str, fluid: str | None = None, manufacturer: str, lot_number: str, part_number: str, rows: int, cols: int, a1_offset_y: int, center_spacing_x: int, center_spacing_y: int, plate_height: int, skirt_height: int, well_width: int, well_length: int, well_capacity: int, bottom_inset: float, center_well_pos_x: float, center_well_pos_y: float, min_well_vol: float | None = None, max_well_vol: float | None = None, max_vol_total: float | None = None, min_volume: float | None = None, drop_volume: float | None = None, )#

Bases: BaseXmlModel

Plate type information.

__init__(**data: Any) → None#

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.

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.

classmethod from_xml(

source: str | bytes,

context: Dict[str, Any] | None = None,

**kwargs: Any,

) → ModelT#

Deserializes an xml string to an object of cls type.

Parameters:

source – xml string
context – pydantic validation context
kwargs – additional xml deserialization arguments

Returns:

deserialized object

classmethod from_xml_tree( root: Element, context: Dict[str, Any] | None = None, ) → ModelT#

Deserializes an xml element tree to an object of cls type.

Parameters:

root – xml element to deserialize the object from
context – pydantic validation context

Returns:

deserialized object

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(

_fields_set: set[str] | None = None,

**values: Any,

) → Self#

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: Mapping[str, Any] | None = None, deep: bool = False, ) → Self#

!!! abstract “Usage Documentation”: [model_copy](../concepts/serialization.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.

!!! abstract “Usage Documentation”: [model_dump](../concepts/serialization.md#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].

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.

!!! abstract “Usage Documentation”: [model_dump_json](../concepts/serialization.md#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].

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.

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: 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]#

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.

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

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.

model_post_init(context: Any, /) → None#: 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(**kwargs: Any) → 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.

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. 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.

!!! 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. 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: Any, *, strict: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None, ) → Self#

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. 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_xml(

*,

skip_empty: bool = False,

exclude_none: bool = False,

exclude_unset: bool = False,

**kwargs: Any,

) → str | bytes#

Serializes the object to an xml string.

Parameters:

skip_empty – skip empty elements (elements without sub-elements, attributes and text, Nones)
exclude_none – exclude None values
exclude_unset – exclude values that haven’t been explicitly set
kwargs – additional xml serialization arguments

Returns:

object xml representation

to_xml_tree( *, skip_empty: bool = False, exclude_none: bool = False, exclude_unset: bool = False, ) → Element#

Serializes the object to an xml tree.

Parameters:

skip_empty – skip empty elements (elements without sub-elements, attributes and text, Nones)
exclude_none – exclude None values
exclude_unset – exclude values that haven’t been explicitly set

Returns:

object xml representation

kithairon.PlateInfo

Contents

kithairon.PlateInfo#