From 2ce14695787fc16da90c82a3bbbe58256c0791ff Mon Sep 17 00:00:00 2001
From: Brendan Sullivan <sullivanbt@ornl.gov>
Date: Tue, 31 Jul 2018 11:23:57 -0400
Subject: [PATCH] Re #22811 remove instrumentName parameter
---
.../IntegratePeaksProfileFitting.py | 2 --
scripts/SCD_Reduction/BVGFitTools.py | 28 +++++++++----------
scripts/SCD_Reduction/ICCFitTools.py | 28 ++++++++-----------
3 files changed, 26 insertions(+), 32 deletions(-)
diff --git a/Framework/PythonInterface/plugins/algorithms/IntegratePeaksProfileFitting.py b/Framework/PythonInterface/plugins/algorithms/IntegratePeaksProfileFitting.py
index a42fe352bda..111b06c4dc3 100644
--- a/Framework/PythonInterface/plugins/algorithms/IntegratePeaksProfileFitting.py
+++ b/Framework/PythonInterface/plugins/algorithms/IntegratePeaksProfileFitting.py
@@ -115,7 +115,6 @@ class IntegratePeaksProfileFitting(PythonAlgorithm):
# The default values are good for MaNDi - new instruments can be added by adding a different elif
# statement.
# If you change these values or add an instrument, documentation should also be changed.
- instrumentName = peaks_ws.getInstrument().getFullName()
try:
nTheta = peaks_ws.getInstrument().getIntParameter("numBinsTheta")[0]
nPhi = peaks_ws.getInstrument().getIntParameter("numBinsPhi")[0]
@@ -173,7 +172,6 @@ class IntegratePeaksProfileFitting(PythonAlgorithm):
maxdtBinWidth=maxdtBinWidth,
pplmin_frac=pplmin_frac, pplmax_frac=pplmax_frac,
forceCutoff=forceCutoff, edgeCutoff=edgeCutoff,
- instrumentName=instrumentName,
peakMaskSize=peakMaskSize,
iccFitDict=iccFitDict)
diff --git a/scripts/SCD_Reduction/BVGFitTools.py b/scripts/SCD_Reduction/BVGFitTools.py
index 7bceca943bd..7fbf60c1295 100644
--- a/scripts/SCD_Reduction/BVGFitTools.py
+++ b/scripts/SCD_Reduction/BVGFitTools.py
@@ -15,7 +15,7 @@ def get3DPeak(peak, peaks_ws, box, padeCoefficients, qMask, nTheta=150, nPhi=150
plotResults=False, zBG=1.96, bgPolyOrder=1, fICCParams=None, oldICCFit=None,
strongPeakParams=None, forceCutoff=250, edgeCutoff=15,
neigh_length_m=3, q_frame='sample', dtSpread=0.03, pplmin_frac=0.8, pplmax_frac=1.5, mindtBinWidth=1,
- maxdtBinWidth=50, figureNumber=2, instrumentName=None, peakMaskSize=5, iccFitDict=None):
+ maxdtBinWidth=50, figureNumber=2, peakMaskSize=5, iccFitDict=None):
n_events = box.getNumEventsArray()
if q_frame == 'lab':
@@ -31,12 +31,12 @@ def get3DPeak(peak, peaks_ws, box, padeCoefficients, qMask, nTheta=150, nPhi=150
n_events, peak=peak, box=box, qMask=qMask, calc_pp_lambda=True, padeCoefficients=padeCoefficients,
neigh_length_m=neigh_length_m, pp_lambda=None, pplmin_frac=pplmin_frac,
pplmax_frac=pplmax_frac, mindtBinWidth=mindtBinWidth, maxdtBinWidth=maxdtBinWidth,
- instrumentName=instrumentName, peakMaskSize=peakMaskSize, iccFitDict=iccFitDict)
+ peakMaskSize=peakMaskSize, iccFitDict=iccFitDict)
YTOF, fICC, x_lims = fitTOFCoordinate(
box, peak, padeCoefficients, dtSpread=dtSpread, qMask=qMask, bgPolyOrder=bgPolyOrder, zBG=zBG,
plotResults=plotResults, pp_lambda=pp_lambda, neigh_length_m=neigh_length_m, pplmin_frac=pplmin_frac,
pplmax_frac=pplmax_frac, mindtBinWidth=mindtBinWidth, maxdtBinWidth=maxdtBinWidth,
- instrumentName=instrumentName, peakMaskSize=peakMaskSize, iccFitDict=iccFitDict)
+ peakMaskSize=peakMaskSize, iccFitDict=iccFitDict)
chiSqTOF = mtd['fit_Parameters'].column(1)[-1]
else: # we already did I-C profile, so we'll just read the parameters
pp_lambda = fICCParams[-1]
@@ -50,7 +50,7 @@ def get3DPeak(peak, peaks_ws, box, padeCoefficients, qMask, nTheta=150, nPhi=150
fICC['HatWidth'] = fICCParams[10]
fICC['KConv'] = fICCParams[11]
goodIDX, _ = ICCFT.getBGRemovedIndices(
- n_events, pp_lambda=pp_lambda, qMask=qMask, instrumentName=instrumentName, peakMaskSize=peakMaskSize,
+ n_events, pp_lambda=pp_lambda, qMask=qMask, peakMaskSize=peakMaskSize,
iccFitDict=iccFitDict)
chiSqTOF = fICCParams[4] #Last entry
@@ -82,7 +82,7 @@ def get3DPeak(peak, peaks_ws, box, padeCoefficients, qMask, nTheta=150, nPhi=150
numDetCols = peaks_ws.getInstrument().getIntParameter("numDetCols")[0]
nPixels = [numDetRows, numDetCols]
except:
- UserWarning('Instrument name {} not found. Assuming a 255*255 detector!'.format(instrumentName))
+ UserWarning('Detector size not found in instrument parameters file. Assuming a 255*255 detector!')
nPixels = [255,255]
useForceParams = peak.getIntensity() < forceCutoff or peak.getRow() <= dEdge or peak.getRow(
@@ -114,11 +114,11 @@ def get3DPeak(peak, peaks_ws, box, padeCoefficients, qMask, nTheta=150, nPhi=150
phthPeak[0],
phthPeak[1]))
params, h, t, p = doBVGFit(box, nTheta=nTheta, nPhi=nPhi, fracBoxToHistogram=fracBoxToHistogram,
- goodIDX=goodIDX, forceParams=strongPeakParams[nnIDX], instrumentName=instrumentName,
+ goodIDX=goodIDX, forceParams=strongPeakParams[nnIDX],
doPeakConvolution=doPeakConvolution, sigX0Scale=sigX0Scale, sigY0Scale=sigY0Scale)
else: # Just do the fit - no nearest neighbor assumptions
params, h, t, p = doBVGFit(
- box, nTheta=nTheta, nPhi=nPhi, fracBoxToHistogram=fracBoxToHistogram, goodIDX=goodIDX, instrumentName=instrumentName,
+ box, nTheta=nTheta, nPhi=nPhi, fracBoxToHistogram=fracBoxToHistogram, goodIDX=goodIDX,
doPeakConvolution=doPeakConvolution, sigX0Scale=sigX0Scale, sigY0Scale=sigY0Scale)
if plotResults:
@@ -145,9 +145,9 @@ def get3DPeak(peak, peaks_ws, box, padeCoefficients, qMask, nTheta=150, nPhi=150
# Do scaling to the data
if doPeakConvolution: #This means peaks will have gaps, so we only use good data to scale
- Y, redChiSq, scaleFactor = fitScaling(n_events, box, YTOF, YBVG, goodIDX=goodIDX, instrumentName=instrumentName)
+ Y, redChiSq, scaleFactor = fitScaling(n_events, box, YTOF, YBVG, goodIDX=goodIDX)
else:
- Y, redChiSq, scaleFactor = fitScaling(n_events, box, YTOF, YBVG, instrumentName=instrumentName)
+ Y, redChiSq, scaleFactor = fitScaling(n_events, box, YTOF, YBVG)
YBVG2 = bvg(1.0, mu, sigma, XTHETA, XPHI, 0)
YTOF2 = getYTOF(fICC, XTOF, x_lims)
Y2 = YTOF2 * YBVG2
@@ -193,7 +193,7 @@ def boxToTOFThetaPhi(box, peak):
return X
-def fitScaling(n_events, box, YTOF, YBVG, goodIDX=None, neigh_length_m=3, instrumentName=None):
+def fitScaling(n_events, box, YTOF, YBVG, goodIDX=None, neigh_length_m=3):
YJOINT = 1.0 * YTOF * YBVG
YJOINT /= 1.0 * YJOINT.max()
@@ -252,7 +252,7 @@ def getXTOF(box, peak):
def fitTOFCoordinate(box, peak, padeCoefficients, dtSpread=0.03, minFracPixels=0.01,
neigh_length_m=3, zBG=1.96, bgPolyOrder=1, qMask=None, plotResults=False,
fracStop=0.01, pp_lambda=None, pplmin_frac=0.8, pplmax_frac=1.5, mindtBinWidth=1,
- maxdtBinWidth=50, instrumentName=None, peakMaskSize=5, iccFitDict=None):
+ maxdtBinWidth=50, peakMaskSize=5, iccFitDict=None):
# Get info from the peak
tof = peak.getTOF() # in us
@@ -271,12 +271,12 @@ def fitTOFCoordinate(box, peak, padeCoefficients, dtSpread=0.03, minFracPixels=0
neigh_length_m=neigh_length_m, zBG=zBG, pp_lambda=pp_lambda,
pplmin_frac=pplmin_frac, pplmax_frac=pplmax_frac,
mindtBinWidth=mindtBinWidth, maxdtBinWidth=maxdtBinWidth,
- instrumentName=instrumentName, peakMaskSize=peakMaskSize,
+ peakMaskSize=peakMaskSize,
iccFitDict=iccFitDict)
fitResults, fICC = ICCFT.doICCFit(tofWS, energy, flightPath,
padeCoefficients, fitOrder=bgPolyOrder, constraintScheme=1,
- instrumentName=instrumentName, iccFitDict=iccFitDict)
+ iccFitDict=iccFitDict)
for i, param in enumerate(['A', 'B', 'R', 'T0', 'Scale', 'HatWidth', 'KConv']):
fICC[param] = mtd['fit_Parameters'].row(i)['Value']
@@ -429,7 +429,7 @@ def compareBVGFitData(box, params, nTheta=200, nPhi=200, figNumber=2, fracBoxToH
def doBVGFit(box, nTheta=200, nPhi=200, zBG=1.96, fracBoxToHistogram=1.0, goodIDX=None,
- forceParams=None, forceTolerance=0.1, dth=10, dph=10, instrumentName=None,
+ forceParams=None, forceTolerance=0.1, dth=10, dph=10,
doPeakConvolution=False, sigX0Scale=1., sigY0Scale=1.):
"""
doBVGFit takes a binned MDbox and returns the fit of the peak shape along the non-TOF direction. This is done in one of two ways:
diff --git a/scripts/SCD_Reduction/ICCFitTools.py b/scripts/SCD_Reduction/ICCFitTools.py
index bc9a6eb6956..d4cc146458b 100644
--- a/scripts/SCD_Reduction/ICCFitTools.py
+++ b/scripts/SCD_Reduction/ICCFitTools.py
@@ -107,7 +107,7 @@ def getQXQYQZ(box):
def getQuickTOFWS(box, peak, padeCoefficients, goodIDX=None, dtSpread=0.03, qMask=None,
pp_lambda=None, minppl_frac=0.8, maxppl_frac=1.5, mindtBinWidth=1, maxdtBinWidth=50,
- constraintScheme=1, instrumentName=None, peakMaskSize=5, iccFitDict=None):
+ constraintScheme=1, peakMaskSize=5, iccFitDict=None):
"""
getQuickTOFWS - generates a quick-and-dirty TOFWS. Useful for determining the background.
Input:
@@ -145,11 +145,11 @@ def getQuickTOFWS(box, peak, padeCoefficients, goodIDX=None, dtSpread=0.03, qMas
tofWS, ppl = getTOFWS(box, flightPath, scatteringHalfAngle, tof, peak, qMask, dtSpread=dtSpread,
minFracPixels=0.01, neigh_length_m=3, zBG=1.96, pp_lambda=pp_lambda,
calc_pp_lambda=calc_pp_lambda, pplmin_frac=minppl_frac, pplmax_frac=minppl_frac,
- mindtBinWidth=mindtBinWidth, maxdtBinWidth=maxdtBinWidth, instrumentName=instrumentName,
+ mindtBinWidth=mindtBinWidth, maxdtBinWidth=maxdtBinWidth,
peakMaskSize=peakMaskSize, iccFitDict=iccFitDict)
fitResults, fICC = doICCFit(
tofWS, energy, flightPath, padeCoefficients, fitOrder=1, constraintScheme=constraintScheme,
- instrumentName=instrumentName, iccFitDict=iccFitDict)
+ iccFitDict=iccFitDict)
h = [tofWS.readY(0), tofWS.readX(0)]
chiSq = fitResults.OutputChi2overDoF
@@ -213,7 +213,7 @@ def getPoissionGoodIDX(n_events, zBG=1.96, neigh_length_m=3):
def getOptimizedGoodIDX(n_events, padeCoefficients, zBG=1.96, neigh_length_m=3, qMask=None,
peak=None, box=None, pp_lambda=None, peakNumber=-1, minppl_frac=0.8,
maxppl_frac=1.5, mindtBinWidth=1, maxdtBinWidth=50,
- constraintScheme=1, instrumentName=None, peakMaskSize=5, iccFitDict=None):
+ constraintScheme=1, peakMaskSize=5, iccFitDict=None):
"""
getOptimizedGoodIDX - returns a numpy arrays which is true if the voxel contains events at
the zBG z level (1.96=95%CI). Rather than using Poission statistics, this function
@@ -235,7 +235,6 @@ def getOptimizedGoodIDX(n_events, padeCoefficients, zBG=1.96, neigh_length_m=3,
mindtBinWidth - the largest dt (in us) allowed for constructing the TOF profile.
constraintScheme - sets the constraints for TOF profile fitting. Leave as 1 if you're
not sure how to modify this.
- instrumentName - string containing the instrument name
iccFitDict - a dictionary containing ICC fit constraints and possibly initial guesses
Output:
@@ -299,7 +298,7 @@ def getOptimizedGoodIDX(n_events, padeCoefficients, zBG=1.96, neigh_length_m=3,
chiSq, h, intens, sigma = getQuickTOFWS(box, peak, padeCoefficients, goodIDX=goodIDX, qMask=qMask, pp_lambda=pp_lambda,
minppl_frac=minppl_frac, maxppl_frac=maxppl_frac, mindtBinWidth=mindtBinWidth,
maxdtBinWidth=maxdtBinWidth, constraintScheme=constraintScheme,
- instrumentName=instrumentName, peakMaskSize=peakMaskSize, iccFitDict=iccFitDict)
+ peakMaskSize=peakMaskSize, iccFitDict=iccFitDict)
except:
#raise
break
@@ -323,7 +322,7 @@ def getOptimizedGoodIDX(n_events, padeCoefficients, zBG=1.96, neigh_length_m=3,
chiSq, h, intens, sigma = getQuickTOFWS(box, peak, padeCoefficients, goodIDX=goodIDX, qMask=qMask,
pp_lambda=pp_lambda, minppl_frac=minppl_frac, maxppl_frac=maxppl_frac,
mindtBinWidth=mindtBinWidth, maxdtBinWidth=maxdtBinWidth,
- instrumentName=instrumentName, peakMaskSize=peakMaskSize,
+ peakMaskSize=peakMaskSize,
iccFitDict=iccFitDict)
if qMask is not None:
return goodIDX*qMask, pp_lambda
@@ -333,7 +332,7 @@ def getOptimizedGoodIDX(n_events, padeCoefficients, zBG=1.96, neigh_length_m=3,
def getBGRemovedIndices(n_events, zBG=1.96, calc_pp_lambda=False, neigh_length_m=3, qMask=None,
peak=None, box=None, pp_lambda=None, peakNumber=-1, padeCoefficients=None,
pplmin_frac=0.8, pplmax_frac=1.5, mindtBinWidth=1, maxdtBinWidth=50,
- constraintScheme=1, instrumentName=None, peakMaskSize=5, iccFitDict=None):
+ constraintScheme=1, peakMaskSize=5, iccFitDict=None):
"""
getBGRemovedIndices - A wrapper for getOptimizedGoodIDX
Input:
@@ -391,7 +390,7 @@ def getBGRemovedIndices(n_events, zBG=1.96, calc_pp_lambda=False, neigh_length_m
minppl_frac=pplmin_frac, maxppl_frac=pplmax_frac, qMask=qMask, peak=peak,
box=box, pp_lambda=pp_lambda, peakNumber=peakNumber,
mindtBinWidth=mindtBinWidth, maxdtBinWidth=maxdtBinWidth,
- constraintScheme=constraintScheme, instrumentName=instrumentName,
+ constraintScheme=constraintScheme,
peakMaskSize=peakMaskSize, iccFitDict=iccFitDict)
except KeyboardInterrupt:
sys.exit()
@@ -553,7 +552,7 @@ def get_pp_lambda(n_events, hasEventsIDX):
def getTOFWS(box, flightPath, scatteringHalfAngle, tofPeak, peak, qMask, zBG=-1.0, dtSpread=0.02,
minFracPixels=0.005, workspaceNumber=None, neigh_length_m=0, pp_lambda=None, calc_pp_lambda=False,
padeCoefficients=None, pplmin_frac=0.8, pplmax_frac=1.5, peakMaskSize=5,
- mindtBinWidth=1, maxdtBinWidth=50, constraintScheme=1, instrumentName=None, iccFitDict=None):
+ mindtBinWidth=1, maxdtBinWidth=50, constraintScheme=1, iccFitDict=None):
"""
Builds a TOF profile from the data in box which is nominally centered around a peak.
Input:
@@ -579,7 +578,6 @@ def getTOFWS(box, flightPath, scatteringHalfAngle, tofPeak, peak, qMask, zBG=-1.
maxdtBinWidth - the largest dt (in us) allowed for constructing the TOF profile.
constraintScheme - sets the constraints for TOF profile fitting. Leave as 1 if you're
not sure how to modify this.
- instrumentName = string containing instrument name
iccFitDict - a dictionary containing ICC fit constraints and possibly initial guesses
Output:
@@ -598,7 +596,7 @@ def getTOFWS(box, flightPath, scatteringHalfAngle, tofPeak, peak, qMask, zBG=-1.
calc_pp_lambda=calc_pp_lambda, padeCoefficients=padeCoefficients,
pplmin_frac=pplmin_frac, pplmax_frac=pplmax_frac,
mindtBinWidth=mindtBinWidth, maxdtBinWidth=maxdtBinWidth,
- constraintScheme=constraintScheme, instrumentName=instrumentName,
+ constraintScheme=constraintScheme,
peakMaskSize=peakMaskSize, iccFitDict=iccFitDict)
hasEventsIDX = np.logical_and(goodIDX, qMask)
boxMeanIDX = np.where(hasEventsIDX)
@@ -828,7 +826,7 @@ def getBoxFracHKL(peak, peaks_ws, MDdata, UBMatrix, peakNumber, dQ, dQPixel=0.00
def doICCFit(tofWS, energy, flightPath, padeCoefficients, constraintScheme=None, outputWSName='fit', fitOrder=1,
- instrumentName=None, iccFitDict=None):
+ iccFitDict=None):
"""
doICCFit - Carries out the actual least squares fit for the TOF workspace.
Intput:
@@ -894,7 +892,6 @@ def doICCFit(tofWS, energy, flightPath, padeCoefficients, constraintScheme=None,
fICC.setPenalizedConstraints(A0=A0, B0=B0, R0=R0, T00=T00, KConv0=KConv0, penalty=1.0e10)
except:
fICC.setPenalizedConstraints(A0=A0, B0=B0, R0=R0, T00=T00, KConv0=KConv0, penalty=None)
- print(fICC)
if constraintScheme == 2:
try:
fICC.setPenalizedConstraints(A0=[0.0001, 1.0], B0=[0.005, 1.5], R0=[0.00, 1.], Scale0=[
@@ -964,7 +961,6 @@ def integrateSample(run, MDdata, peaks_ws, paramList, UBMatrix, dQ, qMask, padeC
fitDict - if keepFitDict is False, an empty dictionary. If keepFitDict is true, a dictionary (integer peak number as key)
containing the x, yData, yFit for each peak.
"""
- instrumentName = peaks_ws.getInstrument().getFullName()
if p is None:
p = range(peaks_ws.getNumberPeaks())
fitDict = {}
@@ -996,7 +992,7 @@ def integrateSample(run, MDdata, peaks_ws, paramList, UBMatrix, dQ, qMask, padeC
mindtBinWidth=mindtBinWidth,
maxdtBinWidth=maxdtBinWidth,
pplmin_frac=minpplfrac, pplmax_frac=maxpplfrac,
- constraintScheme=constraintScheme, instrumentName=instrumentName,
+ constraintScheme=constraintScheme,
peakMaskSize=peakMaskSize, iccFitDict=iccFitDict)
# --IN PRINCIPLE!!! WE CALCULATE THIS BEFORE GETTING HERE
tofWS = mtd['tofWS']
--
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