def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
"""Algorithmic criteria for whether the fit is good or bad.
A good fit has:
- a reduced chi-squared lower than three
- absolute amp is within [0.4, 0.6]
- base is less is within [0.4, 0.6]
- amp error is less than 0.1
- tau error is less than its value
- base error is less than 0.1
"""
amp = fit_data.fitval("amp")
tau = fit_data.fitval("tau")
base = fit_data.fitval("base")
criteria = [
fit_data.reduced_chisq < 3,
abs(amp.nominal_value - 0.5) < 0.1,
abs(base.nominal_value - 0.5) < 0.1,
curve.is_error_not_significant(amp, absolute=0.1),
curve.is_error_not_significant(tau),
curve.is_error_not_significant(base, absolute=0.1),
]
if all(criteria):
return "good"
return "bad"
def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
"""Algorithmic criteria for whether the fit is good or bad.
A good fit has:
- a reduced chi-squared lower than three
- relative error of tau is less than its value
- relative error of freq is less than its value
"""
tau = fit_data.fitval("tau")
freq = fit_data.fitval("freq")
criteria = [
fit_data.reduced_chisq < 3,
curve.is_error_not_significant(tau),
curve.is_error_not_significant(freq),
]
if all(criteria):
return "good"
return "bad"
def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
"""Algorithmic criteria for whether the fit is good or bad.
A good fit has:
- a reduced chi-squared lower than three,
- an error on the frequency smaller than the frequency.
"""
fit_freq = fit_data.fitval("freq")
criteria = [
fit_data.reduced_chisq < 3,
curve.is_error_not_significant(fit_freq),
]
if all(criteria):
return "good"
return "bad"
def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
"""Algorithmic criteria for whether the fit is good or bad.
A good fit has:
- a reduced chi-squared lower than three,
- a DRAG parameter value within the first period of the lowest number of repetitions,
- an error on the drag beta smaller than the beta.
"""
fit_beta = fit_data.fitval("beta")
fit_freq = fit_data.fitval("freq")
criteria = [
fit_data.reduced_chisq < 3,
abs(fit_beta.nominal_value) < 1 / fit_freq.nominal_value / 2,
curve.is_error_not_significant(fit_beta),
]
if all(criteria):
return "good"
return "bad"
def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
"""Algorithmic criteria for whether the fit is good or bad.
A good fit has:
- a reduced chi-squared lower than three,
- more than a quarter of a full period,
- less than 10 full periods, and
- an error on the fit frequency lower than the fit frequency.
"""
fit_freq = fit_data.fitval("freq")
criteria = [
fit_data.reduced_chisq < 3,
1.0 / 4.0 < fit_freq.nominal_value < 10.0,
curve.is_error_not_significant(fit_freq),
]
if all(criteria):
return "good"
return "bad"
def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
"""Algorithmic criteria for whether the fit is good or bad.
A good fit has:
- a reduced chi-squared less than 3,
- a peak within the scanned frequency range,
- a standard deviation that is not larger than the scanned frequency range,
- a standard deviation that is wider than the smallest frequency increment,
- a signal-to-noise ratio, defined as the amplitude of the peak divided by the
square root of the median y-value less the fit offset, greater than a
threshold of two, and
- a standard error on the kappa of the Lorentzian that is smaller than the kappa.
"""
curve_data = self._data()
max_freq = np.max(curve_data.x)
min_freq = np.min(curve_data.x)
freq_increment = np.mean(np.diff(curve_data.x))
fit_a = fit_data.fitval("a")
fit_b = fit_data.fitval("b")
fit_freq = fit_data.fitval("freq")
fit_kappa = fit_data.fitval("kappa")
snr = abs(fit_a.n) / np.sqrt(abs(np.median(curve_data.y) - fit_b.n))
fit_width_ratio = fit_kappa.n / (max_freq - min_freq)
criteria = [
min_freq <= fit_freq.n <= max_freq,
1.5 * freq_increment < fit_kappa.n,
fit_width_ratio < 0.25,
fit_data.reduced_chisq < 3,
curve.is_error_not_significant(fit_kappa),
snr > 2,
]
if all(criteria):
return "good"
return "bad"