def test_rms_rmse(): """ Test the rms and rmse functions """ # They both give the same answer: data = np.random.randn(10) npt.assert_equal(ozu.rms(data), ozu.rmse(data, np.zeros(data.shape)))
def rRMSE(self): """ This is a measure of goodness of fit, based on a relative RMSE measure between the RMSE of the fit residuals and the actual signal and the RMS of the (mean-removed) b0 measurements. """ # Get the RMSE of the model relative to the actual data: rmse_model = np.sqrt(np.mean(self.residuals**2, -1)) # Normalize that to the variance in the b0, which is an estimate of data # reliability: rms_b0 = ozu.rms(self.data[..., self.b0_idx] - np.mean(self.S0)[..., np.newaxis]) return rmse_model / rms_b0
def rRMSE(self): """ This is a measure of goodness of fit, based on a relative RMSE measure between the RMSE of the fit residuals and the actual signal and the RMS of the (mean-removed) b0 measurements. """ # Get the RMSE of the model relative to the actual data: rmse_model = np.sqrt(np.mean(self.residuals**2, -1)) # Normalize that to the variance in the b0, which is an estimate of data # reliability: rms_b0 = ozu.rms(self.data[...,self.b0_idx]- np.mean(self.S0)[...,np.newaxis]) return rmse_model/rms_b0