def find_good_peaks(self, xvals, peakids, acceptance, bad_peak_to_consider,
use_poisson, fit_ws, peak_width_estimate):
prog_reporter = Progress(self,
start=0.1,
end=1.0,
nreports=2 + len(peakids))
actual_peaks = []
skipped = 0
cost_idx = 1 if use_poisson else 0
prog_reporter.report('Starting fit')
logger.notice('Fitting null hypothesis')
peak_table, refit_peak_table, cost = FitGaussianPeaks(
InputWorkspace=fit_ws,
PeakGuessTable=self.generate_peak_guess_table(xvals, []),
CentreTolerance=1.0,
EstimatedPeakSigma=peak_width_estimate,
MinPeakSigma=self._min_sigma,
MaxPeakSigma=self._max_sigma,
GeneralFitTolerance=0.1,
RefitTolerance=0.001,
StoreInADS=False)
old_cost = cost.column(cost_idx)[0]
for idx, peak_idx in enumerate(peakids):
peak_table, refit_peak_table, cost = FitGaussianPeaks(
InputWorkspace=fit_ws,
PeakGuessTable=self.generate_peak_guess_table(
xvals, actual_peaks + [peak_idx]),
CentreTolerance=1.0,
EstimatedPeakSigma=peak_width_estimate,
MinPeakSigma=self._min_sigma,
MaxPeakSigma=self._max_sigma,
GeneralFitTolerance=0.1,
RefitTolerance=0.001,
StoreInADS=False)
new_cost = cost.column(cost_idx)[0]
if use_poisson:
# if p_new > p_old, but uses logs
cost_change = new_cost - old_cost
good_peak_condition = cost_change > np.log(acceptance)
else:
cost_change = abs(new_cost - old_cost) / new_cost
good_peak_condition = (new_cost <= old_cost) and (cost_change >
acceptance)
if skipped > bad_peak_to_consider:
break
msg = ''
if good_peak_condition:
msg = '** peak found, '
skipped = 0
actual_peaks.append(peak_idx)
old_cost = new_cost
else:
skipped += 1
prog_reporter.report('Iteration {}, {} peaks found'.format(
idx + 1, len(actual_peaks)))
msg += '{} peaks in total. cost={:.2}, cost change={:.5}'
logger.information(
msg.format(len(actual_peaks), new_cost, cost_change))
peak_table, refit_peak_table, cost = FitGaussianPeaks(
InputWorkspace=fit_ws,
PeakGuessTable=self.generate_peak_guess_table(xvals, actual_peaks),
CentreTolerance=1.0,
EstimatedPeakSigma=peak_width_estimate,
MinPeakSigma=self._min_sigma,
MaxPeakSigma=self._max_sigma,
GeneralFitTolerance=0.1,
RefitTolerance=0.001,
StoreInADS=False)
prog_reporter.report('Fitting done')
logger.notice(
'Fitting done, {} good peaks and {} refitted peak found'.format(
peak_table.rowCount(), refit_peak_table.rowCount()))
return actual_peaks, peak_table, refit_peak_table
def test_algorithm_does_not_need_refitting_when_given_good_data(self):
peak_table, refit_peak_table, fit_cost = FitGaussianPeaks(
InputWorkspace=self.data_ws, PeakGuessTable=self.peak_guess_table)
self.assertEqual(0, refit_peak_table.rowCount())
self.assertEqual(2, peak_table.rowCount())
