"""Various helper objects."""
from dataclasses import dataclass
from enum import Enum, IntEnum, auto
from typing import Iterable, List, Optional, overload
import numpy as np
import numpy.typing as npt
[docs]@dataclass
class Config:
"""General RFSoC Configuration."""
repetition_duration: int = 100
"""Time to wait between shots (us)."""
adc_trig_offset: int = 200
"""Time to wait between readout pulse and acquisition (ADC clock ticks)."""
reps: int = 1000
"""Number of shots."""
soft_avgs: int = 1
"""Number of software averages."""
average: bool = True
"""Returns integrated results if true."""
[docs]class OperationCode(IntEnum):
"""Available operations."""
EXECUTE_PULSE_SEQUENCE = auto()
EXECUTE_PULSE_SEQUENCE_RAW = auto()
EXECUTE_SWEEPS = auto()
[docs]@dataclass
class Qubit:
"""Qubit object, storing flux information."""
bias: Optional[float] = None
"""Amplitude factor, for sweetspot."""
dac: Optional[int] = None
"""DAC responsible for flux control."""
[docs]class Parameter(str, Enum):
"""Available parameters for sweepers."""
FREQUENCY = "freq"
AMPLITUDE = "gain"
RELATIVE_PHASE = "phase"
DELAY = "t"
BIAS = "bias"
DURATION = "duration"
@overload
@classmethod
def variants(cls, parameters: str) -> "Parameter": # type: ignore
"""Convert a string to a Parameter."""
@overload
@classmethod
def variants(cls, parameters: Iterable[str]) -> Iterable["Parameter"]:
"""Convert a iterable of str to an iterable of Parameters."""
[docs] @classmethod
def variants(cls, parameters):
"""Convert from strings to Parameters."""
if isinstance(parameters, str):
return cls[parameters.upper()]
return type(parameters)(cls[par.upper()] for par in parameters)
[docs]@dataclass
class Sweeper:
"""Sweeper object."""
expts: int
"""Number of points of the sweeper."""
parameters: List[Parameter]
"""List of parameter to update."""
indexes: List[int]
"""Index of the parameter to sweep relative to list of pulses or list of qubits."""
starts: npt.NDArray[np.float64]
"""Start value for each parameter to sweep."""
stops: npt.NDArray[np.float64]
"""Stop value for each parameter to sweep."""
def __post_init__(self):
"""Convert starts and stops in np.arrays if needed."""
if isinstance(self.starts, list):
self.starts = np.array(self.starts, dtype=np.float64)
if isinstance(self.stops, list):
self.stops = np.array(self.stops, dtype=np.float64)
@property
def serialized(self) -> dict:
"""Convert a Sweeper object into a dictionary.
In particular, takes care of the convertion arrays -> lists.
"""
return {
"expts": self.expts,
"parameters": self.parameters,
"indexes": self.indexes,
"starts": self.starts.tolist(),
"stops": self.stops.tolist(),
}