def _vana_correct(self):
"""
creates the corrected workspace
"""
wslist = []
# 1. normalize Vanadium and Background
vana_normws = api.mtd[self.vanaws.getName() + '_NORM']
bkg_normws = api.mtd[self.bkgws.getName() + '_NORM']
_vana_norm_ = api.Divide(self.vanaws, vana_normws)
wslist.append(_vana_norm_.getName())
_bkg_norm_ = api.Divide(self.bkgws, bkg_normws)
wslist.append(_bkg_norm_.getName())
# 2. substract background from Vanadium
_vana_bg_ = _vana_norm_ - _bkg_norm_
wslist.append(_vana_bg_.getName())
# check negative values, throw exception
arr = np.array(_vana_bg_.extractY()).flatten()
neg_values = np.where(arr < 0)[0]
if len(neg_values):
mlzutils.cleanup(wslist)
message = "Background " + self.bkgws.getName() + " is higher than Vanadium " + \
self.vanaws.getName() + " signal!"
self.log().error(message)
raise RuntimeError(message)
# 3. calculate correction coefficients
_vana_mean_ws_ = self._vana_mean(_vana_bg_)
if not _vana_mean_ws_:
mlzutils.cleanup(wslist)
return None
if not self.vana_mean_name:
wslist.append(_vana_mean_ws_.getName())
_coef_ws_ = api.Divide(LHSWorkspace=_vana_bg_, RHSWorkspace=_vana_mean_ws_, WarnOnZeroDivide=True)
wslist.append(_coef_ws_.getName())
# 4. correct raw data (not normalized!)
api.Divide(LHSWorkspace=self.dataws, RHSWorkspace=_coef_ws_, WarnOnZeroDivide=True,
OutputWorkspace=self.outws_name)
outws = api.mtd[self.outws_name]
# cleanup
mlzutils.cleanup(wslist)
return outws
def _fr_correction(self):
"""
applies flipping ratio correction
creates the corrected workspaces
"""
wslist = []
# 1. normalize NiCr and Background
sf_nicr_normws = api.AnalysisDataService.retrieve(self.input_workspaces['SF_NiCr'] + '_NORM')
sf_nicr = api.AnalysisDataService.retrieve(self.input_workspaces['SF_NiCr'])
_sf_nicr_norm_ = api.Divide(sf_nicr, sf_nicr_normws)
wslist.append(_sf_nicr_norm_.getName())
nsf_nicr_normws = api.AnalysisDataService.retrieve(self.input_workspaces['NSF_NiCr'] + '_NORM')
nsf_nicr = api.AnalysisDataService.retrieve(self.input_workspaces['NSF_NiCr'])
_nsf_nicr_norm_ = api.Divide(nsf_nicr, nsf_nicr_normws)
wslist.append(_nsf_nicr_norm_.getName())
sf_bkgr_normws = api.AnalysisDataService.retrieve(self.input_workspaces['SF_Background'] + '_NORM')
sf_bkgr = api.AnalysisDataService.retrieve(self.input_workspaces['SF_Background'])
_sf_bkgr_norm_ = api.Divide(sf_bkgr, sf_bkgr_normws)
wslist.append(_sf_bkgr_norm_.getName())
nsf_bkgr_normws = api.AnalysisDataService.retrieve(self.input_workspaces['NSF_Background'] + '_NORM')
nsf_bkgr = api.AnalysisDataService.retrieve(self.input_workspaces['NSF_Background'])
_nsf_bkgr_norm_ = api.Divide(nsf_bkgr, nsf_bkgr_normws)
wslist.append(_nsf_bkgr_norm_.getName())
# 2. subtract background from NiCr
_sf_nicr_bg_ = _sf_nicr_norm_ - _sf_bkgr_norm_
wslist.append(_sf_nicr_bg_.getName())
_nsf_nicr_bg_ = _nsf_nicr_norm_ - _nsf_bkgr_norm_
wslist.append(_nsf_nicr_bg_.getName())
# check negative values, throw exception
sf_arr = np.array(_sf_nicr_bg_.extractY()).flatten()
nsf_arr = np.array(_nsf_nicr_bg_.extractY()).flatten()
sf_neg_values = np.where(sf_arr < 0)[0]
nsf_neg_values = np.where(nsf_arr < 0)[0]
if len(sf_neg_values) or len(nsf_neg_values):
mlzutils.cleanup(wslist)
message = "Background is higher than NiCr signal!"
self.log().error(message)
raise RuntimeError(message)
# 3. calculate correction coefficients
_coef_ws_ = api.Divide(LHSWorkspace=_nsf_nicr_bg_, RHSWorkspace=_sf_nicr_bg_, WarnOnZeroDivide=True)
wslist.append(_coef_ws_.getName())
# 4. apply correction raw data (not normalized!)
sf_data_ws = api.AnalysisDataService.retrieve(self.input_workspaces['SF_Data'])
nsf_data_ws = api.AnalysisDataService.retrieve(self.input_workspaces['NSF_Data'])
# NSF_corr[i] = NSF[i] - SF[i]/c[i]
_tmp_ws_ = api.Divide(LHSWorkspace=sf_data_ws, RHSWorkspace=_coef_ws_, WarnOnZeroDivide=True)
_tmp_ws_.setYUnit(nsf_data_ws.YUnit())
api.Minus(LHSWorkspace=nsf_data_ws, RHSWorkspace=_tmp_ws_, OutputWorkspace=self.nsf_outws_name)
nsf_outws = api.AnalysisDataService.retrieve(self.nsf_outws_name)
api.DeleteWorkspace(_tmp_ws_)
# SF_corr[i] = SF[i] - NSF[i]/c[i]
_tmp_ws_ = api.Divide(LHSWorkspace=nsf_data_ws, RHSWorkspace=_coef_ws_, WarnOnZeroDivide=True)
_tmp_ws_.setYUnit(sf_data_ws.YUnit())
api.Minus(LHSWorkspace=sf_data_ws, RHSWorkspace=_tmp_ws_, OutputWorkspace=self.sf_outws_name)
sf_outws = api.AnalysisDataService.retrieve(self.sf_outws_name)
api.DeleteWorkspace(_tmp_ws_)
# 5. Apply correction for a double spin-flip scattering
if self.dfr > 1e-7:
_tmp_ws_ = sf_outws * self.dfr
_tmp_ws_.setYUnit(nsf_outws.YUnit())
wslist.append(_tmp_ws_.getName())
# NSF_corr[i] = NSF_prev_corr[i] - SF_prev_corr*dfr, SF_corr = SF_prev_corr
api.Minus(LHSWorkspace=nsf_outws, RHSWorkspace=_tmp_ws_, OutputWorkspace=self.nsf_outws_name)
# cleanup
mlzutils.cleanup(wslist)
return
