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more info in orso header, fixed typos

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stahn
2024-09-13 08:06:30 +02:00
parent e651b64b9c
commit e0d08b7af2
2 changed files with 8 additions and 4 deletions
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4
libeos/file_reader.py
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@@ -163,6 +163,10 @@ class AmorData:
)
self.header.measurement_instrument_settings.mu = fileio.Value(round(self.mu, 3), 'deg', comment='sample angle to horizon')
self.header.measurement_instrument_settings.nu = fileio.Value(round(self.nu, 3), 'deg', comment='detector angle to horizon')
self.header.measurement_instrument_settings.div = fileio.Value(round(self.div, 3), 'deg', comment='incoming beam divergence')
self.header.measurement_instrument_settings.kap = fileio.Value(round(self.kap, 3), 'deg', comment='incoming beam inclination')
if abs(self.kad)>0.02:
self.header.measurement_instrument_settings.kad = fileio.Value(round(self.kad, 3), 'deg', comment='incoming beam angular offset')
if norm:
self.header.measurement_additional_files.append(fileio.File(file=fileName.split('/')[-1], timestamp=self.fileDate))
else:

8
libeos/reduction.py
View File

@@ -408,18 +408,18 @@ class AmorReduction:
int_lz = np.where(mask_lz, int_lz, np.nan)
thetaF_lz = np.where(mask_lz, alphaF_lz, np.nan)
print(self.reduction_config.normalisationMethod)
if self.reduction_config.normalisationMethod == 'o':
logging.debug(' assuming an overilluminated sample and correcting for the angle of incidence')
ref_lz = (int_lz * np.absolute(thetaN_lz)) / (norm_lz * np.absolute(thetaF_lz)) * self.normMonitor
elif self.reduction_config.normalisationMethod == 'u':
logging.debug(' assuming an underilluminated sample and ignoring the angle of incidence')
ref_lz = (int_lz / norm_lz) * self.normMonitor
elif self.reduction_config.normalisationMethod == 'u':
logging.debug(' assuming direct beam for normalisation an underilluminated sample and ignoring the angle of incidence')
elif self.reduction_config.normalisationMethod == 'd':
logging.debug(' assuming direct beam for normalisation and ignoring the angle of incidence')
norm_lz = np.flip(norm_lz,1)
ref_lz = (int_lz / norm_lz) * self.normMonitor
else:
logging.debug('unknown normalisation method! Use [u], [o] or [d]')
logging.error('unknown normalisation method! Use [u], [o] or [d]')
ref_lz = (int_lz * np.absolute(thetaN_lz)) / (norm_lz * np.absolute(thetaF_lz)) * self.normMonitor
err_lz = ref_lz * np.sqrt( 1/(int_lz+.1) + 1/norm_lz )