Model Usage¶

measurement_config.core provides a Pydantic-based Model class that supports custom serialization for numpy and tunits objects.

In [ ]:

from typing import Any

import numpy as np
import numpy.typing as npt
from qxcore import Model
from qxcore.quantities import Frequency
from qxcore.units import GHz

from typing import Any import numpy as np import numpy.typing as npt from qxcore import Model from qxcore.quantities import Frequency from qxcore.units import GHz

Defining a Model¶

You can define a model by inheriting from Model. Fields can include standard Python types, NumPy arrays, and tunits values.

In [ ]:

from pydantic import Field


class ExperimentConfig(Model):
    """Configuration model for experiment parameters."""

    name: str
    frequency: Frequency
    pulse_shape: npt.NDArray[np.complexfloating]
    metadata: dict[str, Any] = Field(default_factory=dict)

from pydantic import Field class ExperimentConfig(Model): """Configuration model for experiment parameters.""" name: str frequency: Frequency pulse_shape: npt.NDArray[np.complexfloating] metadata: dict[str, Any] = Field(default_factory=dict)

In [ ]:

config = ExperimentConfig(
    name="Rabi Oscillation",
    frequency=5.0 * GHz,
    pulse_shape=np.array([0.0 + 0.0j, 1.0 + 1.0j, 0.0 + 0.0j]),
    metadata={"some_value": 0.01},
)
print(config)

config = ExperimentConfig( name="Rabi Oscillation", frequency=5.0 * GHz, pulse_shape=np.array([0.0 + 0.0j, 1.0 + 1.0j, 0.0 + 0.0j]), metadata={"some_value": 0.01}, ) print(config)

Serialization and Deserialization¶

The Model class handles serialization of special types automatically to a dictionary or JSON string.

Serialized output includes a top-level __meta__ object with a version field for future format changes.

In [ ]:

# To Dictionary
data_dict = config.to_dict()
print("Serialized dict:", data_dict)

# To JSON
json_str = config.to_json(indent=2)
print("JSON string:\n", json_str)

# To Dictionary data_dict = config.to_dict() print("Serialized dict:", data_dict) # To JSON json_str = config.to_json(indent=2) print("JSON string:\n", json_str)

In [ ]:

# From Dictionary/JSON
restored_config = ExperimentConfig.from_json(json_str)
print("Restored config:", restored_config)

# Verify equality
np.testing.assert_array_equal(config.pulse_shape, restored_config.pulse_shape)
assert config.frequency == restored_config.frequency

# From Dictionary/JSON restored_config = ExperimentConfig.from_json(json_str) print("Restored config:", restored_config) # Verify equality np.testing.assert_array_equal(config.pulse_shape, restored_config.pulse_shape) assert config.frequency == restored_config.frequency