def createSampleWorkspace(self): """ Create a dummy workspace that looks like a sample run""" #create a dummy workspace function = "name=Lorentzian,Amplitude=1,PeakCentre=5,FWHM=1" ws = CreateSampleWorkspace("Histogram", Function="User Defined", UserDefinedFunction=function, XMin=0, XMax=10, BinWidth=0.01, XUnit="DeltaE") ws = ScaleX(ws, -5, "Add") #shift to center on 0 ws = ScaleX(ws, 0.1) #scale to size LoadInstrument(ws, InstrumentName='IRIS', RewriteSpectraMap=True) return ws
def createSampleWorkspace(self): function = "name=Lorentzian,Amplitude=1,PeakCentre=5,FWHM=1" workspace = CreateSampleWorkspace(WorkspaceType="Histogram", Function="User Defined", UserDefinedFunction=function, XMin=0, XMax=10, BinWidth=0.01, XUnit="DeltaE") # Shift to center on 0 and then scale to size workspace = ScaleX(workspace, -5, "Add") workspace = ScaleX(workspace, 0.1) LoadInstrument(Workspace=workspace, InstrumentName='IRIS', RewriteSpectraMap=True) return workspace
def _generate_big_sample_ws(ws_name, n_spec): sample_data_x = np.arange(0, 10, 0.01) sample_data_y = _rayleigh(sample_data_x, 1) data_x = np.empty(0) data_y = np.empty(0) v_axis = list() for idx in range(0, n_spec): data_x = np.append(data_x, sample_data_x) data_y = np.append(data_y, sample_data_y) v_axis.append(str(0.1 * idx)) # Create the workspace and give it some units CreateWorkspace(OutputWorkspace=ws_name, DataX=data_x, DataY=data_y, NSpec=n_spec, UnitX='MomentumTransfer', VerticalAxisUnit='QSquared', VerticalAxisValues=','.join(v_axis)) # Centre the peak over 0 ScaleX(InputWorkspace=ws_name, Factor=-1, Operation="Add", OutputWorkspace=ws_name) return mtd[ws_name]
def create_test_ws_and_group(): myFunc = "name=Gaussian, PeakCentre=2, Height=100, Sigma=0.01;" + \ "name=Gaussian, PeakCentre=1, Height=100, Sigma=0.01;" + \ "name=Gaussian, PeakCentre=4, Height=100, Sigma=0.01" ws = CreateSampleWorkspace("Event", "User Defined", myFunc, BankPixelWidth=1, XUnit='dSpacing', XMax=5, BinWidth=0.001, NumEvents=100000, NumBanks=8) for n in range(1, 5): MoveInstrumentComponent(ws, ComponentName=f'bank{n}', X=1 + n / 10, Y=0, Z=1 + n / 10, RelativePosition=False) MoveInstrumentComponent(ws, ComponentName=f'bank{n+4}', X=2 + n / 10, Y=0, Z=2 + n / 10, RelativePosition=False) MaskDetectors(ws, WorkspaceIndexList=[3, 7]) ws = ScaleX(ws, Factor=1.05, IndexMin=1, IndexMax=1) ws = ScaleX(ws, Factor=0.95, IndexMin=2, IndexMax=2) ws = ScaleX(ws, Factor=1.05, IndexMin=4, IndexMax=6) ws = ScaleX(ws, Factor=1.02, IndexMin=5, IndexMax=5) ws = ScaleX(ws, Factor=0.98, IndexMin=6, IndexMax=6) ws = Rebin(ws, '0,0.001,5') ws = ConvertUnits(ws, Target='TOF') groups, _, _ = CreateGroupingWorkspace(InputWorkspace=ws, ComponentName='basic_rect', CustomGroupingString='1-4,5-8') return ws, groups
def modelB_EC_C(model, resolution, convolved, qvalues, assembled, expdata=None, costfile=None): """Assemble the Background, Elastic line and Convolution of the resolution with the simulated S(Q,E) This is a hard-coded model consisting of a linear background, and elastic line, and a convolution: b0+b1*E + +EC(Q)*e0*exp(-e1*Q^2)*Elastic(E) + c0*Resolution(E)xSimulated(Q,E) We load Resolution(E)xSimulated(Q,E) as Convolved(Q,E) EC(Q) is a fit to the Q-dependence of the integrated intensity of the empty can EC(Q) = 2.174495971 - 2.065826056*Q + 0.845367259*Q^2 Arguments: model: beamline model file is a single line, e.g, b0=1.3211; b1=0.00 e0=0.99; e1=1.9; c0=2.3 resolution: Nexus file containing the resolution. This will be used to produce a elastic line. convolved: Nexus file containing the convolution of the simulated S(Q,E) with the resolution. qvalues: single-column file containing list of Q-values assembled: output Nexus file containing the assembled S(Q,E) of the beamline model and the simulated S(Q,E) expdata: Optional, experimental nexus file. If passed, output convolved will be binned as expdata. costfile: Optional, file to store cost. If passed, the cost of comparing convolved and expdata will be saved. Returns: workspace containing the assembled S(Q,E) """ import numpy from mantid.simpleapi import (LoadNexus, ScaleX, ConvertToPointData, SaveNexus, DakotaChiSquared) EC = lambda Q: 2.174495971 - 2.065826056*Q + 0.845367259*Q*Q Q=[float(q) for q in open(qvalues,'r').read().split('\n')] p={} for pair in open(model,'r').readline().split(';'): key,val=pair.split('=') p[key.strip()]=float(val.strip()) wsr=LoadNexus(Filename=resolution,OutputWorkspace='resolution') wsr=ConvertToPointData(wsr) E=wsr.readX(0) wse=ScaleX(InputWorkspace=wsr, OutputWorkspace='elastic',factor=-1) # elastic line wsc=LoadNexus(Filename=convolved,OutputWorkspace='convolved') for i in range(wsc.getNumberHistograms()): elastic=wse.readY(i) # elastic spectrum at a given Q convolved=wsc.readY(i) # convolved spectrum at a given Q wsc.setY(i, (p['b0']+p['b1']*E) + (EC(Q[i])*p['e0']*numpy.exp(-p['e1']*Q[i])*elastic) + (p['c0']*convolved) ) # overwrite spectrum SaveNexus(InputWorkspace=wsc, Filename=assembled) if expdata and costfile: DakotaChiSquared(DataFile=assembled,CalculatedFile=expdata,OutputFile=costfile) return wsc
def _generate_sample_ws(ws_name): data_x = np.arange(0, 10, 0.01) data_y = _rayleigh(data_x, 1) # Create the workspace and give it some units CreateWorkspace(OutputWorkspace=ws_name, DataX=data_x, DataY=data_y, \ UnitX='MomentumTransfer', VerticalAxisUnit='QSquared', VerticalAxisValues='0.2') # Centre the peak over 0 ScaleX(InputWorkspace=ws_name, Factor=-1, Operation="Add", OutputWorkspace=ws_name) return mtd[ws_name]
def convertToWavenumber(self, ws): mev2cm = (constants.elementary_charge / 1000) / (constants.h * constants.c * 100) u0 = ws.getAxis(0).getUnit().unitID() u1 = ws.getAxis(1).getUnit().unitID() if u0 == 'DeltaE' or u1 == 'DeltaE': if u0 == 'MomentumTransfer': ws = Transpose(ws) ws.getAxis(0).setUnit('DeltaE_inWavenumber') ws = ScaleX(ws, mev2cm) ws = Scale(ws, 1/mev2cm) if u0 == 'MomentumTransfer': ws = Transpose(ws) return ws
def add_previous_pulse(w): """ Duplicate the events but shift them by one pulse, then add to input workspace Parameters ---------- w: Mantid.EventsWorkspace Returns ------- Mantid.EventsWorkspace """ pulse_width = 1.e6 / 60 # in micro-seconds _t_w = ScaleX(w, Factor=-pulse_width, Operation='Add') _t_w = Plus(w, _t_w, OutputWorkspace=w.name()) return _t_w
def _shift_workspace(self, workspace, shift_factor): return ScaleX(InputWorkspace=workspace, Factor=shift_factor, OutputWorkspace="__shifted", Operation="Add", StoreInADS=False)
def modelB_freeE_C(model, resolution, convolved, assembled, expdata=None, costfile=None, derivdata=None, derivexclude=[], doshift=None): """Assemble the Background, Elastic line and Convolution of the resolution with the simulated S(Q,E) This is a hard-coded model consisting of a linear background, and elastic line, and a convolution: b0+b1*E + +e0(Q)*Elastic(E) + c0*Resolution(E)xSimulated(Q,E) We load Resolution(E)xSimulated(Q,E) as Convolved(Q,E) e0(Q) are a set of fitting parameters, one for each Q Arguments: model: beamline model file is a single line, e.g, b0=1.3211; b1=0.00; e0.0=0.99; e0.1=0.99; e0.2=0.99;...e0.N=0.99; e1=1.9; c0=2.3 resolution: Nexus file containing the resolution. This will be used to produce a elastic line. convolved: Nexus file containing the convolution of the simulated S(Q,E) with the resolution. assembled: output Nexus file containing the assembled S(Q,E) of the beamline model and the simulated S(Q,E) expdata: Optional, experimental nexus file. If passed, output convolved will be binned as expdata. costfile: Optional, file to store cost. If passed, residuals and (optionally) partial derivatives will be stored derivdata: Optional, perform analytic derivatives (store in costfile if provided) derivexclude: list of fitting parameters for which partial derivatives will not be computed doshift: Optional, perform the shift of the model function Returns: wsm: workspace containing the assembled S(Q,E) gradients: dictionary of partial derivatives with respect to model parameters """ import numpy from copy import copy,deepcopy from mantid.simpleapi import (LoadNexus, ScaleX, ConvertToPointData, SaveNexus, DakotaChiSquared, AddSampleLog) from math import sqrt def shiftalongX(*kargs,**kwargs): """ Function to do the shift along the E-axis. By default, does nothing """ pass import interpX if doshift: # replace the dummy function with the real thing if doshift in dir(interpX): shiftalongX=getattr(__import__('interpX'), doshift) else: shiftalongX = getattr(__import__('interpX'), 'itp_simple') def computemodel(p,wse,wsc): """Assemble the model Arguments p: dictionary with parameter values wse: Mantid workspace holding the elastic line wsc: Mantid workspace holding the convolution of the resolution and the simulation Returns: wsm: Mantid workspace holding the resulting model """ from mantid.simpleapi import CloneWorkspace wsm=CloneWorkspace(wsc) E=wse.readX(0) # energy values, bins boundary values Eshifted=(E[1:]+E[:-1])/2 # energy values, center bin values for i in range(wsc.getNumberHistograms()): elastic=wse.readY(i) # elastic spectrum at a given Q convolved=wsc.readY(i) # convolved spectrum at a given Q wsm.setY(i, p['b0']+p['b1']*Eshifted + p['e0.'+str(i)]*elastic + p['c0']*convolved) # overwrite spectrum return wsm # init list of parameters names for which analytical derivative exists, same order as in the input model file derivparnames=[] # filled only if derivdata different than None p={} for pair in open(model,'r').readline().split(';'): key,val=[x.strip() for x in pair.split('=')] if derivdata and key not in derivexclude: derivparnames.append(key) p[key]=float(val) # read various inputs wsr=LoadNexus(Filename=resolution,OutputWorkspace='resolution') wse=ScaleX(InputWorkspace=wsr, OutputWorkspace='elastic',factor=-1) # elastic line wsc=LoadNexus(Filename=convolved,OutputWorkspace='convolved') E=wsr.readX(0) # energy values, bins boundary values de=E[1]-E[0] # assume all bins have same bin width Eshifted=(E[1:]+E[:-1])/2 # energy values, center bin values nhist=wsc.getNumberHistograms() nrsl=len(Eshifted)*nhist # number of residuals # calculate partial numerical derivative with respect to eshift gradients={} if 'eshift' in derivparnames: wsm=computemodel(p,wse,wsc) eshiftderiv=(shiftalongX(wsm,p['eshift']+0.5*de,newWorkspace='wsplus') - shiftalongX(wsm,p['eshift']-0.5*de,newWorkspace='wsminus')) / de # forward-backward difference with a 0.5*de step gradients['eshift']=numpy.zeros(0) for i in range(eshiftderiv.getNumberHistograms()): gradients['eshift'] = numpy.concatenate([gradients['eshift'], eshiftderiv.readY(i)]) # do eshift of component workspaces if doshift: Eshifted-=p['eshift'] wse=shiftalongX(wse,p['eshift']) # shift the spectrum, does nothing if shiftalongX is the dummy function wsc=shiftalongX(wsc,p['eshift']) # shift the spectrum, does nothing if shiftalongX is the dummy function # find difference in convolutions with FF1 changed if 'FF1' not in derivexclude: derivparnames.append('FF1') # difference in FF1 workspaces convolvedf=convolved.replace('.nxs','_1.nxs') wscf=LoadNexus(Filename=convolvedf,OutputWorkspace='convolvedf') convolvedb=convolved.replace('.nxs','_0.nxs') wscb=LoadNexus(Filename=convolvedb,OutputWorkspace='convolvedb') wksp_diff=wscf-wscb # calculate analytic partial derivatives with respect to the fit parameters if derivparnames: gradients['b0']=numpy.ones(nrsl) gradients['b1']=numpy.zeros(0) gradients['c0']=numpy.zeros(0) gradients['FF1']=numpy.zeros(0) for i in range(nhist): gradients['b1'] = numpy.concatenate([gradients['b1'], Eshifted]) gradients['c0'] = numpy.concatenate([gradients['c0'], wsc.readY(i)]) if 'FF1' not in derivexclude: gradients['FF1'] = numpy.concatenate([gradients['FF1'], wksp_diff.readY(i)]) gradients['e0.'+str(i)]=numpy.zeros(0) for j in range(nhist): if i==j: gradients['e0.'+str(i)] = numpy.concatenate([gradients['e0.'+str(i)], wse.readY(i)]) else: gradients['e0.'+str(i)] = numpy.concatenate([gradients['e0.'+str(i)], numpy.zeros(len(Eshifted))]) if 'FF1' not in derivexclude: FF1_f=wscf.getRun().getLogData('FF1').value FF1_b=wscb.getRun().getLogData('FF1').value gradients['FF1'] *= p['c0']/(FF1_f-FF1_b) # save model to file wsm=computemodel(p,wse,wsc) # add all parameters to assembled file for pair in open(model,'r').readline().split(';'): key,val=[x.strip() for x in pair.split('=')] AddSampleLog(Workspace=wsm,LogName=key,LogText=str(p[key]),LogType='Number') print key, "=", p[key] print "FF1 =", wsm.getRun().getLogData('FF1').value SaveNexus(InputWorkspace=wsm, Filename=assembled) # save residuals and partial derivatives buf='' if expdata and costfile: wex=LoadNexus(Filename=expdata,OutputWorkspace='experiment') chisq,wR=DakotaChiSquared(DataFile=expdata,CalculatedFile=assembled,OutputFile=costfile,ResidualsWorkspace='wR') Xe=numpy.zeros(0) # list of errors for each residual for i in range(nhist): Xe = numpy.concatenate([Xe, wex.readE(i)]) Ry=wR.readY(i) for j in range(len(Ry)): buf+=str(Ry[j])+" least_sq_term_"+str(i*len(Ry)+j+1)+"\n" for parname in derivparnames: gradients[parname]/=numpy.where(Xe>0,Xe,1) # divide by experimental error (with non-positive elements replaced by one) if derivparnames: for i in range(nrsl): buf+="[" for parname in derivparnames: buf+=" %.10e"%(-gradients[parname][i]) buf+=" ]\n" open(costfile,'w').write(buf) AddSampleLog(Workspace=wsm,LogName="chisq",LogText=str(chisq),LogType='Number') norm_chisq=chisq/(len(Ry)-len(derivparnames)) print costfile, " R = ", sqrt(norm_chisq) AddSampleLog(Workspace=wsm,LogName="norm_chisq",LogText=str(norm_chisq),LogType='Number') AddSampleLog(Workspace=wsm,LogName="norm_chi",LogText=str(sqrt(norm_chisq)),LogType='Number') SaveNexus(InputWorkspace=wsm, Filename=assembled) return {'model':wsm, 'gradients':gradients}