Source code for quantify_scheduler.helpers.validators

# Repository:
# Licensed according to the LICENCE file on the main branch

import numpy as np
from qcodes.utils import validators
from qcodes.utils.validators import numbertypes

# this is a custom qcodes Numbers validator that allows for nan values.
[docs]class Numbers(validators.Numbers): def __init__( self, min_value: numbertypes = -float("inf"), max_value: numbertypes = float("inf"), allow_nan: bool = False, ) -> None: """ Requires a number of type int, float, numpy.integer or numpy.floating. Parameters ---------- min_value: Minimal value allowed, default -inf. max_value: Maximal value allowed, default inf. allow_nan: if nan values are allowed, default False. Raises ------ TypeError: If min or max value not a number. Or if min_value is larger than the max_value. """ super().__init__(min_value, max_value) self._allow_nan = allow_nan
[docs] def validate(self, value: numbertypes, context: str = "") -> None: """ Validate if number else raises error. Parameters ---------- value: A number. context: Context for validation. Raises ------ TypeError: If not int or float. ValueError: If number is not between the min and the max value. """ if not isinstance(value, self.validtypes): raise TypeError(f"{repr(value)} is not an int or float; {context}") if self._allow_nan and np.isnan(value): # return early as the next statement will otherwise trigger return # pylint: disable=superfluous-parens if not (self._min_value <= value <= self._max_value): raise ValueError( "{} is invalid: must be between " "{} and {} inclusive; {}".format( repr(value), self._min_value, self._max_value, context ) )