qblox_scheduler.structure.model#

Root models for data structures used within the package.

Attributes#

_Unset

Classes#

`Numbers`	Dict used to emulate the behaviour of the `Numbers` qcodes validator.
`_SerializableBaseModel`	Mixin class that enables dict, JSON and YAML serialization and deserialization
`SchedulerBaseModel`	Pydantic base model to support qcodes-style instrument and parameter definitions.
`SchedulerSubmodule`	Compatibility class emulating the behaviour of `InstrumentModule`/`InstrumentChannel`.
`DataStructure`	A parent for all data structures.

Functions#

`Parameter`(→ Any)	Wrapper function around :func:~pydantic.Field that tries to emulate qcodes parameters
`is_identifier`(→ str)	Pydantic validator for names that are valid identifiers.
`deserialize_function`(...)	Import a python function from a dotted import string (e.g.,
`deserialize_class`(→ type)	Import a python class from a dotted import string (e.g.,

Module Contents#

_Unset: Any[source]#

class Numbers[source]#

Bases: TypedDict

Dict used to emulate the behaviour of the Numbers qcodes validator.

min_value: float[source]#

max_value: float[source]#

allow_nan: bool[source]#

Parameter(*, initial_value: Any = _Unset, label: str | None = _Unset, docstring: str | None = _Unset, unit: str | None = _Unset, vals: Numbers | None = _Unset, **kwargs: Any) → Any[source]#

Wrapper function around :func:~pydantic.Field that tries to emulate qcodes parameters as closely as possible, to facilitate migration and reduce diff lines.

Parameters:

initial_value – Maps to default or default_factory (if callable).
label – Maps to title, displayed on the JSON schema.
docstring – Maps to description, displayed on the JSON schema.
unit – Stored internally, retrievable using get_unit().
vals – Maps to allow_inf_nan, ge and le.
kwargs – Other arguments passed on to the original Field function.

Returns:

Any: To appease the linters; actually a :class:~pydantic.fields.FieldInfo instance.

is_identifier(value: str) → str[source]#: Pydantic validator for names that are valid identifiers.

class _SerializableBaseModel(/, **data: Any)[source]#

Bases: pydantic.BaseModel

Mixin class that enables dict, JSON and YAML serialization and deserialization by attaching to_ and from_ helper methods.

to_dict() → dict[str, Any][source]#: Alias for BaseModel.model_dump.

classmethod from_dict(data: dict[str, Any]) → typing_extensions.Self[source]#: Alias for BaseModel.model_validate.

to_json(indent: int | None = None) → str[source]#: Alias for BaseModel.model_dump_json.

classmethod from_json(json_data: str) → typing_extensions.Self[source]#: Alias for BaseModel.model_validate_json.

_generate_file_name(path: str | None, add_timestamp: bool, extension: str) → str[source]#: Generate a file name to be used by to_*_file methods.

to_json_file(path: str | None = None, add_timestamp: bool = True) → str[source]#: Convert the object’s data structure to a JSON string and store it in a file.

classmethod from_json_file(filename: str | pathlib.Path) → typing_extensions.Self[source]#: Read JSON data from a file and convert it to an instance of the attached class.

to_yaml() → str[source]#

Convert the object’s data structure to a YAML string.

For performance reasons, to save to file use to_yaml_file() instead.

classmethod from_yaml(yaml_data: str) → typing_extensions.Self[source]#

Convert YAML data to an instance of the attached class.

For performance reasons, to load from file use from_yaml_file() instead.

to_yaml_file(path: str | None = None, add_timestamp: bool = True) → str[source]#: Convert the object’s data structure to a YAML string and store it in a file.

classmethod from_yaml_file(filename: str | pathlib.Path) → typing_extensions.Self[source]#: Read YAML data from a file and convert it to an instance of the attached class.

class SchedulerBaseModel(/, name, **data: Any)[source]#

Bases: _SerializableBaseModel

Pydantic base model to support qcodes-style instrument and parameter definitions.

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

name: Annotated[str, AfterValidator(is_identifier)] = None[source]#

property parameters: dict[str, Any][source]#: Mapping of parameters of this element.

property submodules: dict[str, Any][source]#: Mapping of submodules of this element.

classmethod get_unit(field_name: str) → str | None[source]#: Get the unit declared for a certain field/parameter.

classmethod create_submodule_instances(data: Any) → Any[source]#: During model instantiation, create an empty/default instance of all submodules.

fill_submodule_parent_defaults() → typing_extensions.Self[source]#: After module creation, for each submodule, fill in the default values for fields that read from an attribute of the parent model.

close() → None[source]#: Does nothing.

classmethod close_all() → None[source]#: Does nothing.

class SchedulerSubmodule(/, name, *, parent: SchedulerBaseModel | None = None, **data: Any)[source]#

Bases: SchedulerBaseModel

Compatibility class emulating the behaviour of InstrumentModule/InstrumentChannel.

_parent: SchedulerBaseModel | SchedulerSubmodule | None = None[source]#

property parent: SchedulerBaseModel | None[source]#

class DataStructure(/, **data: Any)[source]#

Bases: _SerializableBaseModel

A parent for all data structures.

Data attributes are generated from the class’ type annotations, similarly to dataclasses. If data attributes are JSON-serializable, data structure can be serialized using json() method. This string can be deserialized using parse_raw() classmethod of a correspondent child class.

If required, data fields can be validated, see examples for more information. It is also possible to define custom field types with advanced validation.

This class is a pre-configured pydantic model. See its documentation for details of usage information.

Examples

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

deserialize_function(fun: str) → collections.abc.Callable[Ellipsis, Any][source]#

Import a python function from a dotted import string (e.g., “qblox_scheduler.structure.model.deserialize_function”).

Parameters:: fun (str) – A dotted import path to a function (e.g., “qblox_scheduler.waveforms.square”), or a function pointer.
Returns:: Callable[[Any], Any]
Raises:: ValueError – Raised if the function cannot be imported from path in the string.

deserialize_class(cls: str) → type[source]#

Import a python class from a dotted import string (e.g., “qblox_scheduler.structure.model.DataStructure”).

Parameters:

cls (str) – A dotted import path to a class (e.g., “qblox_scheduler.structure.model.DataStructure”), or a class pointer.

Returns:

: The type you are trying to import.

Raises:

ValueError – Raised if the class cannot be imported from path in the string.