def test_fit_cost_contains_correct_numbers(self):
peak_table, refit_peak_table, fit_cost = FitGaussianPeaks(
InputWorkspace=self.data_ws,
PeakGuessTable=self.peak_guess_table,
EstimatedPeakSigma=5,
MinPeakSigma=3,
MaxPeakSigma=12,
GeneralFitTolerance=1)
chi2 = fit_cost.column(0)[0]
poisson = fit_cost.column(1)[0]
peak1 = peak_table.row(0)
peak2 = peak_table.row(1)
yvals = self.gaussian(self.x_values, peak1['centre'], peak1['height'],
peak1['sigma'])
yvals += self.gaussian(self.x_values, peak2['centre'], peak2['height'],
peak2['sigma'])
real_chi2 = self.alg_instance.function_difference(
self.y_values, yvals, np.sqrt(self.y_values))
real_poisson = self.alg_instance.poisson_cost(
self.y_values + self.background, yvals + self.background)
self.assertAlmostEqual(real_chi2, chi2, 3)
self.assertAlmostEqual(real_poisson, poisson, 3)
def test_peak_parameters_are_correct(self):
peak_table, refit_peak_table, fit_cost = FitGaussianPeaks(
InputWorkspace=self.data_ws,
PeakGuessTable=self.peak_guess_table,
EstimatedPeakSigma=5,
MinPeakSigma=3,
MaxPeakSigma=12,
GeneralFitTolerance=1)
peak1 = peak_table.row(0)
peak2 = peak_table.row(1)
centre1, height1, sigma1 = peak1['centre'], peak1['height'], peak1[
'sigma']
centre2, height2, sigma2 = peak2['centre'], peak2['height'], peak2[
'sigma']
self.assertAlmostEqual(centre1, self.centre[0])
self.assertAlmostEqual(centre2, self.centre[1])
self.assertAlmostEqual(height1, self.height[0])
self.assertAlmostEqual(height2, self.height[1])
self.assertAlmostEqual(sigma1, self.width[0])
self.assertAlmostEqual(sigma2, self.width[1])
