def _fitCurvesPerBank(self, vanWS, banks, spline_breaks):
"""
Fits one curve to every bank (where for every bank the data fitted is the result of
summing up all the spectra of the bank). The fitting is done in d-spacing.
@param vanWS :: Vanadium run workspace to fit, expected in TOF units as they are archived
@param banks :: list of banks to consider which is normally all the banks of the instrument
@param spline_breaks :: number of break points when fitting spline functions
@returns a workspace with fitting results for all banks (3 spectra per bank). The spectra
are in dSpacing units.
"""
curves = {}
for b in banks:
indices = EnggUtils.getWsIndicesForBank(vanWS, b)
if not indices:
# no indices at all for this bank, not interested in it, don't add it to the dictionary
# (as when doing Calibrate (not-full)) which does CropData() the original workspace
continue
wsToFit = EnggUtils.cropData(self, vanWS, indices)
wsToFit = EnggUtils.convertToDSpacing(self, wsToFit)
wsToFit = EnggUtils.sumSpectra(self, wsToFit)
fitWS = self._fitBankCurve(wsToFit, b, spline_breaks)
curves.update({b: fitWS})
curvesWS = self._prepareCurvesWS(curves)
return curvesWS
def _fitCurvesPerBank(self, vanWS, banks, spline_breaks):
"""
Fits one curve to every bank (where for every bank the data fitted is the result of
summing up all the spectra of the bank). The fitting is done in d-spacing.
@param vanWS :: Vanadium run workspace to fit, expected in TOF units as they are archived
@param banks :: list of banks to consider which is normally all the banks of the instrument
@param spline_breaks :: number of break points when fitting spline functions
@returns a workspace with fitting results for all banks (3 spectra per bank). The spectra
are in dSpacing units.
"""
curves = {}
for b in banks:
indices = EnggUtils.getWsIndicesForBank(vanWS, b)
if not indices:
# no indices at all for this bank, not interested in it, don't add it to the dictionary
# (as when doing Calibrate (not-full)) which does CropData() the original workspace
continue
wsToFit = EnggUtils.cropData(self, vanWS, indices)
wsToFit = EnggUtils.convertToDSpacing(self, wsToFit)
wsToFit = EnggUtils.sumSpectra(self, wsToFit)
fitWS = self._fitBankCurve(wsToFit, b, spline_breaks)
curves.update({b: fitWS})
curvesWS = self._prepareCurvesWS(curves)
return curvesWS
def _divideByCurves(self, ws, curves):
"""
Expects a workspace in ToF units. All operations are done in-place (the workspace is
input/output). For every bank-curve pair, divides the corresponding spectra in the
workspace by the (simulated) fitted curve. The division is done in d-spacing (the
input workspace is converted to d-spacing inside this method, but results are converted
back to ToF before returning from this method). The curves workspace is expected in
d-spacing units (since it comes from fitting a sum of spectra for a bank or group of
detectors).
This method is capable of dealing with workspaces with range and bin size different from
the range and bin size of the curves. It will rebin the curves workspace to match the
input 'ws' workspace (using the algorithm RebinToWorkspace).
@param ws :: workspace with (sample) spectra to divide by curves fitted to Vanadium spectra
@param curves :: dictionary of fitting workspaces (in d-spacing), one per bank. The keys are
the bank identifier and the values are their fitting workspaces. The fitting workspaces are
expected as returned by the algorithm 'Fit': 3 spectra: original data, simulated data with fit,
difference between original and simulated data.
"""
# Note that this division could use the algorithm 'Divide'
# This is simple and more efficient than using divide workspace, which requires
# cropping separate workspaces, dividing them separately, then appending them
# with AppendSpectra, etc.
ws = EnggUtils.convertToDSpacing(self, ws)
for b in curves:
# process all the spectra (indices) in one bank
fittedCurve = curves[b]
idxs = EnggUtils.getWsIndicesForBank(ws, b)
if not idxs:
pass
# This RebinToWorkspace is required here: normal runs will have narrower range of X values,
# and possibly different bin size, as compared to (long) Vanadium runs. Same applies to short
# Ceria runs (for Calibrate -non-full) and even long Ceria runs (for Calibrate-Full).
rebinnedFitCurve = self._rebinToMatchWS(fittedCurve, ws)
for i in idxs:
# take values of the second spectrum of the workspace (fit simulation - fitted curve)
ws.setY(i, np.divide(ws.dataY(i), rebinnedFitCurve.readY(1)))
# finally, convert back to ToF
EnggUtils.convertToToF(self, ws)
def _divideByCurves(self, ws, curves):
"""
Expects a workspace in ToF units. All operations are done in-place (the workspace is
input/output). For every bank-curve pair, divides the corresponding spectra in the
workspace by the (simulated) fitted curve. The division is done in d-spacing (the
input workspace is converted to d-spacing inside this method, but results are converted
back to ToF before returning from this method). The curves workspace is expected in
d-spacing units (since it comes from fitting a sum of spectra for a bank or group of
detectors).
This method is capable of dealing with workspaces with range and bin size different from
the range and bin size of the curves. It will rebin the curves workspace to match the
input 'ws' workspace (using the algorithm RebinToWorkspace).
@param ws :: workspace with (sample) spectra to divide by curves fitted to Vanadium spectra
@param curves :: dictionary of fitting workspaces (in d-spacing), one per bank. The keys are
the bank identifier and the values are their fitting workspaces. The fitting workspaces are
expected as returned by the algorithm 'Fit': 3 spectra: original data, simulated data with fit,
difference between original and simulated data.
"""
# Note that this division could use the algorithm 'Divide'
# This is simple and more efficient than using divide workspace, which requires
# cropping separate workspaces, dividing them separately, then appending them
# with AppendSpectra, etc.
ws = EnggUtils.convertToDSpacing(self, ws)
for b in curves:
# process all the spectra (indices) in one bank
fittedCurve = curves[b]
idxs = EnggUtils.getWsIndicesForBank(ws, b)
if not idxs:
pass
# This RebinToWorkspace is required here: normal runs will have narrower range of X values,
# and possibly different bin size, as compared to (long) Vanadium runs. Same applies to short
# Ceria runs (for Calibrate -non-full) and even long Ceria runs (for Calibrate-Full).
rebinnedFitCurve = self._rebinToMatchWS(fittedCurve, ws)
for i in idxs:
# take values of the second spectrum of the workspace (fit simulation - fitted curve)
ws.setY(i, np.divide(ws.dataY(i), rebinnedFitCurve.readY(1)))
# finally, convert back to ToF
EnggUtils.convertToToF(self, ws)
