def OptimisingFunction(self, inputargs, **kwargs):
self.optdir = 'MOGA/iteration_'
if not self.post_injector:
parameternames = self.injector_parameter_names + self.parameter_names
else:
parameternames = copy(self.parameter_names)
self.inputlist = list(map(lambda a: a[0]+[a[1]], zip(parameternames, inputargs)))
self.linac_fields = np.array([i[2] for i in self.inputlist if i[1] == 'field_amplitude'])
self.linac_phases = np.array([i[2] for i in self.inputlist if i[1] == 'phase'])
idclient = idn.zmqClient()
n = idclient.get_id()
# print('id n = ', n)
self.opt_iteration = n
dir = self.optdir+str(self.opt_iteration)
if not os.path.exists(dir):
os.makedirs(dir)
self.setup_lattice(self.inputlist, dir)
self.before_tracking()
fitvalue = self.track()
if isinstance(self.opt_iteration, int):
self.opt_iteration += 1
if e < 0.01:
print ('e too low! ', e)
l = 500
self.resultsDict.update({'e': e, 'b': b, 'ee': ee, 'be': be, 'l': l, 'g': g, 'brightness': (1e-4*e)/(1e-2*b), 'momentum': 1e-6*np.mean(g.momentum)})
return e, b, ee, be, l, g
def OptimisingFunction(self, inputargs, *args, **kwargs):
idclient = idn.zmqClient()
n = idclient.get_id()
self.opt_iteration = n
bestcli = bestclient.zmqClient()
if not self.POST_INJECTOR:
parameternames = self.injector_parameter_names + self.parameter_names
else:
parameternames = copy(self.parameter_names)
self.inputlist = [
a[0] + [a[1]] for a in zip(parameternames, inputargs)
]
self.linac_fields = np.array(
[i[2] for i in self.inputlist if i[1] == 'field_amplitude'])
self.linac_phases = np.array(
[i[2] for i in self.inputlist if i[1] == 'phase'])
if 'dir' in list(kwargs.keys()):
dir = kwargs['dir']
del kwargs['dir']
else:
dir = self.optdir + str(self.opt_iteration)
self.fit.setup_lattice(self.inputlist,
dir,
changes=self.changes,
*args,
**kwargs)
fitvalue = self.fit.calculateBeamParameters()
self.opt_iteration += 1
constraintsList = self.calculate_constraints()
fitvalue = self.cons.constraints(constraintsList)
print('fitvalue[', self.opt_iteration - 1, '] = ', fitvalue)
saveState(self.subdir, inputargs, self.opt_iteration - 1, fitvalue)
bestcli.set_best(fitvalue, self.opt_iteration)
self.bestfit = bestcli.get_best()
# print('best = ', best)
if fitvalue <= self.bestfit:
print(self.cons.constraintsList(constraintsList))
print('!!!!!! New best = ', fitvalue, inputargs)
copyfile(dir + '/changes.yaml', self.best_changes)
self.bestfit = fitvalue
return (fitvalue, )
def OptimisingFunction(self, inputargs, **kwargs):
self.optdir = 'MOGA/iteration_'
parameternames = [r[:2] for r in self.parameter_names]
self.inputlist = list(
map(lambda a: a[0] + [a[1]], zip(parameternames, inputargs)))
self.linac_fields = np.array(
[i[2] for i in self.inputlist if i[1] == 'field_amplitude'])
self.linac_phases = np.array(
[i[2] for i in self.inputlist if i[1] == 'phase'])
idclient = idn.zmqClient()
n = idclient.get_id()
self.opt_iteration = n
dir = self.optdir + str(self.opt_iteration)
if not os.path.exists(dir):
os.makedirs(dir)
self.setup_lattice(self.inputlist, dir)
self.before_tracking()
fitvalue = self.track(endfile='S07')
self.beam.read_HDF5_beam_file(self.dirname + '/CLA-S07-MARK-03.hdf5')
self.beam.slices = 50
self.beam.bin_time()
sigmat = 1e12 * np.std(self.beam.t)
sigmap = np.std(self.beam.cp)
meanp = np.mean(self.beam.cp)
emitx = 1e6 * self.beam.normalized_horizontal_emittance
emity = 1e6 * self.beam.normalized_horizontal_emittance
fitp = 100 * sigmap / meanp #percentage energy spread
peakI, peakIstd, peakIMomentumSpread, peakIEmittanceX, peakIEmittanceY, peakIMomentum, peakIDensity = self.beam.sliceAnalysis(
)
self.twiss.read_astra_emit_files([
self.dirname + '/' + n + '.Zemit.001'
for n in self.framework.fileSettings.keys()[:-1]
if self.framework.fileSettings[n]['code'].upper() == 'ASTRA'
])
max_xrms = max(1e3 * self.twiss['sigma_x'])
if isinstance(self.opt_iteration, int):
self.opt_iteration += 1
return fitp, peakI, max_xrms