qat.backend.qblox.acquisition module

class AcqData(**data)

Bases: BaseModel

The actual acquisition data, it represents the value associated with the key “acquisition” in the acquisition blob returned by Qblox. This object contains scope data and binned data.

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.

bins: BinnedAcqData

model_config: ClassVar[ConfigDict] = {}

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

scope: ScopeAcqData

class Acquisition(**data)

Bases: BaseModel

Represents a single acquisition. In Qblox terminology, this object contains scope, integrated, and threshold data all at once. It’s up to the SW layer to pick up what it needs and adapt it to its flow.

An acquisition contains is described by a name, index, and blob data represented by AcqData

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.

static accumulate(acq1, acq2)

acq_data: AcqData

index: int

model_config: ClassVar[ConfigDict] = {}

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

name: str

class BinnedAcqData(**data)

Bases: BaseModel

Binned data is data that’s been acquired and processed via different routes such as squared acquisition, weighed integration. Processing here refers to steps like averaging, rotation, and thresholding which are executed by the hardware.

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.

avg_count: Annotated[NDArray[Shape[* x], (int8, int16, int32, int64, int16, uint8, uint16, uint32, uint64, uint16)]]

integration: IntegData

model_config: ClassVar[ConfigDict] = {}

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

threshold: Annotated[NDArray[Shape[* x], (float16, float32, float64, float32, float64)]]

class IntegData(**data)

Bases: BaseModel

Path 0 refers to I while Path 1 refers to Q

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.

i: Annotated[NDArray[Shape[* x], (float16, float32, float64, float32, float64)]]

model_config: ClassVar[ConfigDict] = {}

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

q: Annotated[NDArray[Shape[* x], (float16, float32, float64, float32, float64)]]

class PathData(**data)

Bases: BaseModel

This object wraps the actual data as a list of samples, the number of averages performed by the hardware (if any), and whether the hw observed any out-of-range samples.

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.

avg_count: int

data: Annotated[NDArray[Shape[* x], (float16, float32, float64, float32, float64)]]

model_config: ClassVar[ConfigDict] = {}

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

oor: bool

class ScopeAcqData(**data)

Bases: BaseModel

Path 0 refers to I while Path 1 refers to Q. Their lengths are statically equal to Constants.MAX_SAMPLE_SIZE_SCOPE_ACQUISITIONS

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.

i: PathData

model_config: ClassVar[ConfigDict] = {}

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

q: PathData