def analyzeData(self,gd):
"""
Analysis of the BasicGraphData() instance with data.
Returns the error value.
"""
if(gd.getNumbOfPoints() == 0): return 0.
y_max = gd.getMaxY()
y_min = gd.getMinY()
amp0 = (y_max - y_min)/2.0
amp_step = 0.05*amp0
phase_step = 3.0
self.param_arr[0] = (y_max + y_min)/2.0
for i_h in range(self.n_harm+1):
param_local_arr = self.param_arr[0:2*i_h+1]
#print "debug i_h=",i_h," =====start ==== param_arr=",param_local_arr
variables = ArrayList()
delta_hint = InitialDelta()
var = Variable("base",param_local_arr[0], - Double.MAX_VALUE, Double.MAX_VALUE)
variables.add(var)
delta_hint.addInitialDelta(var,amp_step)
for i_fit in range(i_h):
var = Variable("amp"+str(i_fit),param_local_arr[2*i_fit+1], - Double.MAX_VALUE, Double.MAX_VALUE)
variables.add(var)
delta_hint.addInitialDelta(var,amp_step)
var = Variable("phase"+str(i_fit),param_local_arr[2*i_fit+2], - Double.MAX_VALUE, Double.MAX_VALUE)
variables.add(var)
delta_hint.addInitialDelta(var,phase_step)
scorer = HarmScorer(gd,variables)
self.harm_func = scorer.getHarmFunc()
if(i_h == 0):
scorer.copyFromExternalParams(self.param_arr)
(amp,shift) = scorer.getNextHarm_Amp_Shift()
#print "debug zero harm (amp,shift)=",(amp,shift)
self.param_arr[2*i_h+1] = amp
self.param_arr[2*i_h+2] = shift
#print "debug i_h=",i_h," =====final ==== param_arr=", self.param_arr
continue
maxSolutionStopper = SolveStopperFactory.maxEvaluationsStopper((i_h+1)*self.n_iter)
solver = Solver(SimplexSearchAlgorithm(),maxSolutionStopper)
problem = ProblemFactory.getInverseSquareMinimizerProblem(variables,scorer,0.0001)
problem.addHint(delta_hint)
solver.solve(problem)
#------- get results
trial = solver.getScoreBoard().getBestSolution()
err2 = scorer.score(trial,variables)
scorer.copyToExternalParams(self.param_arr)
#print "debug i_h=",i_h," =====final err2=",math.sqrt(err2),"==== param_arr=", self.param_arr
if(i_h != self.n_harm):
(amp,shift) = scorer.getNextHarm_Amp_Shift()
#print "debug last step (amp,shift)=",(amp,shift)
self.param_arr[2*i_h+1] = amp
self.param_arr[2*i_h+2] = shift
#print "debug i_h=",i_h," ===== last step==== param_arr=", self.param_arr
return math.sqrt(err2)
def analyzeData(self,gd):
"""
Analysis of the BasicGraphData() instance with data.
Returns the error value.
"""
if(gd.getNumbOfPoints() == 0): return 0.
y_max = gd.getMaxY()
y_min = gd.getMinY()
amp0 = (y_max - y_min)/2.0
amp_step = 0.05*amp0
phase_step = 3.0
self.param_arr[0] = (y_max + y_min)/2.0
for i_h in range(self.n_harm+1):
param_local_arr = self.param_arr[0:2*i_h+1]
#print "debug i_h=",i_h," =====start ==== param_arr=",param_local_arr
variables = ArrayList()
delta_hint = InitialDelta()
var = Variable("base",param_local_arr[0], - Double.MAX_VALUE, Double.MAX_VALUE)
variables.add(var)
delta_hint.addInitialDelta(var,amp_step)
for i_fit in range(i_h):
var = Variable("amp"+str(i_fit),param_local_arr[2*i_fit+1], - Double.MAX_VALUE, Double.MAX_VALUE)
variables.add(var)
delta_hint.addInitialDelta(var,amp_step)
var = Variable("phase"+str(i_fit),param_local_arr[2*i_fit+2], - Double.MAX_VALUE, Double.MAX_VALUE)
variables.add(var)
delta_hint.addInitialDelta(var,phase_step)
scorer = HarmScorer(gd,variables)
self.harm_func = scorer.getHarmFunc()
if(i_h == 0):
scorer.copyFromExternalParams(self.param_arr)
(amp,shift) = scorer.getNextHarm_Amp_Shift()
#print "debug zero harm (amp,shift)=",(amp,shift)
self.param_arr[2*i_h+1] = amp
self.param_arr[2*i_h+2] = shift
#print "debug i_h=",i_h," =====final ==== param_arr=", self.param_arr
continue
maxSolutionStopper = SolveStopperFactory.maxEvaluationsStopper((i_h+1)*self.n_iter)
solver = Solver(SimplexSearchAlgorithm(),maxSolutionStopper)
problem = ProblemFactory.getInverseSquareMinimizerProblem(variables,scorer,0.0001)
problem.addHint(delta_hint)
solver.solve(problem)
#------- get results
trial = solver.getScoreBoard().getBestSolution()
err2 = scorer.score(trial,variables)
scorer.copyToExternalParams(self.param_arr)
#print "debug i_h=",i_h," =====final err2=",math.sqrt(err2),"==== param_arr=", self.param_arr
if(i_h != self.n_harm):
(amp,shift) = scorer.getNextHarm_Amp_Shift()
#print "debug last step (amp,shift)=",(amp,shift)
self.param_arr[2*i_h+1] = amp
self.param_arr[2*i_h+2] = shift
#print "debug i_h=",i_h," ===== last step==== param_arr=", self.param_arr
return math.sqrt(err2)
def fit(self):
if(self.active_cav_wrapper == null): return
variables = ArrayList()
delta_hint = InitialDelta()
#----- variable eKin_in
var = Variable("eKin_in",self.eKin_in, - Double.MAX_VALUE, Double.MAX_VALUE)
variables.add(var)
delta_hint.addInitialDelta(var,0.3)
#----- variable cavity amplitude
var = Variable("cav_amp",self.cav_amp, - Double.MAX_VALUE, Double.MAX_VALUE)
variables.add(var)
delta_hint.addInitialDelta(var,self.cav_amp*0.01)
#----- variable cavity phase offset
var = Variable("phase_offset",self.cav_phase_shift, - Double.MAX_VALUE, Double.MAX_VALUE)
variables.add(var)
delta_hint.addInitialDelta(var,1.0)
#-------- solve the fitting problem
scorer = CavAmpPhaseScorer(self,variables)
maxSolutionStopper = SolveStopperFactory.maxEvaluationsStopper(120)
self.solver = Solver(SimplexSearchAlgorithm(),maxSolutionStopper)
problem = ProblemFactory.getInverseSquareMinimizerProblem(variables,scorer,0.0001)
problem.addHint(delta_hint)
self.solver.solve(problem)
#------- get results
trial = self.solver.getScoreBoard().getBestSolution()
err2 = scorer.score(trial,variables)
[self.eKin_in,self.cav_amp,self.cav_phase_shift] = scorer .getTrialParams(trial)
self.active_cav_wrapper.eKin_in = self.eKin_in
self.active_cav_wrapper.designPhase = makePhaseNear(self.active_cav_wrapper.livePhase - self.cav_phase_shift,0.)
self.active_cav_wrapper.eKin_err = math.sqrt(err2)
cav_phase = self.active_cav_wrapper.livePhase
self.active_cav_wrapper.eKin_out = self.getModelEnergyOut(self.eKin_in,self.cav_amp,cav_phase,self.cav_phase_shift)
#print "debug cav=",self.active_cav_wrapper.alias," shift=",self.cav_phase_shift," amp=",self.cav_amp," err2=", math.sqrt(err2)," ekinOut=", self.active_cav_wrapper.eKin_out
#----- this defenition of the avg. gap phase will be replaced by another with self.model_eKin_in
self.active_cav_wrapper.avg_gap_phase = self.getAvgGapPhase()
self.active_cav_wrapper.designAmp = self.cav_amp
self.solver = null
#----make theory graph plot
x_arr = []
y_arr = []
for i in range(self.scan_gd.getNumbOfPoints()):
phase = self.scan_gd.getX(i)
y = self.scan_gd.getY(i)
x_arr.append(phase)
y_arr.append(y)
self.active_cav_wrapper.eKinOutPlotTh.addPoint(x_arr,y_arr)
field_step = (field_max - field_min) / 30
for dch_ind in range(len(dchs)):
dch = dchs[dch_ind]
field = dch.getField()
var = Variable(dch.getId(), field, field_min, field_max)
variables.add(var)
delta_hint.addInitialDelta(var, field_step)
scorer = OrbitScorer(bpms, dchs, variables)
n_iterations = 200
maxSolutionStopper = SolveStopperFactory.maxEvaluationsStopper(n_iterations)
#solver = Solver(SimplexSearchAlgorithm(),maxSolutionStopper)
solver = Solver(RandomShrinkSearch(), maxSolutionStopper)
problem = ProblemFactory.getInverseSquareMinimizerProblem(
variables, scorer, 0.00000001)
problem.addHint(delta_hint)
solver.solve(problem)
#------- get optimization results
trial = solver.getScoreBoard().getBestSolution()
dch_field_arr = scorer.getDCH_Field_Arr_for_Trial(trial)
print "========== put new dch fields into VA ======"
#---- send all results to EPICS
for dch_ind in range(len(dchs)):
dch = dchs[dch_ind]
field = dch_field_arr[dch_ind]
dch.setField(field)
print "dch=", dch.getId(), " field= %+8.6f " % field
def run(self):
tr_twiss_analysis_controller = self.linac_wizard_document.tr_twiss_analysis_controller
transverse_twiss_fitting_controller = tr_twiss_analysis_controller.transverse_twiss_fitting_controller
init_and_fit_params_controller = transverse_twiss_fitting_controller.init_and_fit_params_controller
initial_twiss_params_holder = transverse_twiss_fitting_controller.initial_twiss_params_holder
final_twiss_params_holder = init_and_fit_params_controller.final_twiss_params_holder
nIterations = int(init_and_fit_params_controller.fit_iter_text.getValue())
#print "debug Twiss_Fitter start to run! Iter=",nIterations
(alphaX, betaX, emittX) = initial_twiss_params_holder.getParams(0)
(alphaY, betaY, emittY) = initial_twiss_params_holder.getParams(1)
(alphaZ, betaZ, emittZ) = initial_twiss_params_holder.getParams(2)
twiss_arr = []
twiss_arr.append(Twiss(alphaX, betaX, emittX))
twiss_arr.append(Twiss(alphaY, betaY, emittY))
twiss_arr.append(Twiss(alphaZ, betaZ, emittZ))
(alphaXStep, betaXStep, emittXStep) = initial_twiss_params_holder.getParamsStep(0)
(alphaYStep, betaYStep, emittYStep) = initial_twiss_params_holder.getParamsStep(1)
(alphaZStep, betaZStep, emittZStep) = initial_twiss_params_holder.getParamsStep(2)
variables = ArrayList()
variables.add(Variable("alphaX", alphaX, - Double.MAX_VALUE, Double.MAX_VALUE))
variables.add(Variable("betaX", betaX, - Double.MAX_VALUE, Double.MAX_VALUE))
variables.add(Variable("emittX", emittX, - Double.MAX_VALUE, Double.MAX_VALUE))
variables.add(Variable("alphaY", alphaY, - Double.MAX_VALUE, Double.MAX_VALUE))
variables.add(Variable("betaY", betaY, - Double.MAX_VALUE, Double.MAX_VALUE))
variables.add(Variable("emittY", emittY, - Double.MAX_VALUE, Double.MAX_VALUE))
variables.add(Variable("alphaZ", alphaZ, - Double.MAX_VALUE, Double.MAX_VALUE))
variables.add(Variable("betaZ", betaZ, - Double.MAX_VALUE, Double.MAX_VALUE))
variables.add(Variable("emittZ", emittZ, - Double.MAX_VALUE, Double.MAX_VALUE))
delta_hint = InitialDelta()
variables_fit = ArrayList()
#---------- X
if(alphaXStep != 0.):
variables_fit.add(variables.get(0))
delta_hint.addInitialDelta(variables.get(0), alphaXStep)
if(betaXStep != 0.):
variables_fit.add(variables.get(1))
delta_hint.addInitialDelta(variables.get(1), betaXStep)
if(emittXStep != 0.):
variables_fit.add(variables.get(2))
delta_hint.addInitialDelta(variables.get(2), emittXStep)
#---------- Y
if(alphaYStep != 0.):
variables_fit.add(variables.get(3))
delta_hint.addInitialDelta(variables.get(3), alphaYStep)
if(betaYStep != 0.):
variables_fit.add(variables.get(4))
delta_hint.addInitialDelta(variables.get(4), betaYStep)
if(emittYStep != 0.):
variables_fit.add(variables.get(5))
delta_hint.addInitialDelta(variables.get(5), emittYStep)
#---------- Z
if(alphaZStep != 0.):
variables_fit.add(variables.get(6))
delta_hint.addInitialDelta(variables.get(6), alphaZStep)
if(betaZStep != 0.):
variables_fit.add(variables.get(7))
delta_hint.addInitialDelta(variables.get(7), betaZStep)
if(emittZStep != 0.):
variables_fit.add(variables.get(8))
delta_hint.addInitialDelta(variables.get(8), emittZStep)
#------- fitting process with solver
if(variables_fit.isEmpty()):
return
scorer = AccScoreCalculator(self.linac_wizard_document,variables,twiss_arr)
maxSolutionStopper = SolveStopperFactory.maxEvaluationsStopper(nIterations)
solver = Solver(SimplexSearchAlgorithm(),maxSolutionStopper)
self.solver = solver
problem = ProblemFactory.getInverseSquareMinimizerProblem(variables_fit,scorer,0.001)
problem.addHint(delta_hint)
solver.solve(problem)
#------- get results
trial = solver.getScoreBoard().getBestSolution()
scorer.trialToTwiss(trial)
twiss_arr = scorer.getTwissArr()
(alphaX, betaX, emittX) = (twiss_arr[0].getAlpha(),twiss_arr[0].getBeta(),twiss_arr[0].getEmittance())
(alphaY, betaY, emittY) = (twiss_arr[1].getAlpha(),twiss_arr[1].getBeta(),twiss_arr[1].getEmittance())
(alphaZ, betaZ, emittZ) = (twiss_arr[2].getAlpha(),twiss_arr[2].getBeta(),twiss_arr[2].getEmittance())
final_twiss_params_holder.setParams(0,alphaX, betaX, emittX)
final_twiss_params_holder.setParams(1,alphaY, betaY, emittY)
final_twiss_params_holder.setParams(2,alphaZ, betaZ, emittZ)
init_and_fit_params_controller.finalTwiss_table.getModel().fireTableDataChanged()
init_and_fit_params_controller.fit_iter_left_text.setValue(0.)
scorer.setUpLastNode(false)
scorer.calculateDiff2()
