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 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
