def __init__(self):
super(Optimise_Genesis_Elegant, self).__init__()
self.cons = constraintsClass()
self.changes = None
self.lattice = None
self.resultsDict = {}
self.opt_iteration = 0
self.bestfit = 1e26
# ******************************************************************************
CLARA_dir = os.path.relpath(__file__ + '/../../')
self.POST_INJECTOR = True
CREATE_BASE_FILES = False
scaling = 5
if self.POST_INJECTOR and CREATE_BASE_FILES:
self.optfunc = partial(self.OptimisingFunction,
scaling=scaling,
post_injector=self.POST_INJECTOR,
verbose=False,
basefiles='../basefiles_' + str(scaling) +
'/',
CLARA_dir=CLARA_dir)
else:
self.optfunc = partial(self.OptimisingFunction,
scaling=scaling,
post_injector=self.POST_INJECTOR,
verbose=False,
CLARA_dir=CLARA_dir)
# ******************************************************************************
if not self.POST_INJECTOR:
best = injector_startingvalues + best
elif CREATE_BASE_FILES:
for i in [scaling]:
pass
def __init__(self):
super(Optimise_Genesis_Elegant, self).__init__()
self.cons = constraintsClass()
self.changes = None
self.lattice = None
self.resultsDict = {}
self.opt_iteration = 0
self.bestfit = 1e26
self.beam_file = 'CLA-S07-APER-01.hdf5'
# ******************************************************************************
CLARA_dir = os.path.relpath(__file__ + '/../../')
def optfunc(inputargs, verbose=False, *args, **kwargs):
global startingvalues, global_best
process = multiprocessing.current_process()
runno = process.pid
# inputargs = [a*b for a,b in zip(startingvalues,inputargsmult)]
# print 'inputargs = ', inputargs
with runEle.TemporaryDirectory(dir=os.getcwd()) as tmpdir:
try:
fit = runEle.fitnessFunc(inputargs, tmpdir, *args, **kwargs)
fitvalue = fit.calculateBeamParameters()
sys.stdout = open(tmpdir + '/' + 'std.out', 'w')
sys.stderr = open(tmpdir + '/' + 'std.err', 'w')
e, b = evalBeamWithGenesis(tmpdir, dirno=runno)
sys.stdout = sys.__stdout__
sys.stderr = sys.__stderr__
if e > global_best:
global_best = e
print 'New Best: ', global_best, 1e4 * e, 1e2 * b, inputargs
if verbose:
print 'bandwidth = ', 1e2 * b
print 'pulse energy =', 1e4 * e
constraintsList = {
'energy': {
'type': 'greaterthan',
'value': abs(1e4 * e),
'limit': 3,
'weight': 1
},
'bandwidth': {
'type': 'lessthan',
'value': abs(1e2 * b),
'limit': 1,
'weight': 1
},
}
cons = constraintsClass()
fitness = cons.constraints(constraintsList)
saveState(inputargs, fitness, e, b)
# print cons.constraintsList(constraintsList)
return 1e4 * e, 1e2 * b
except Exception as e:
print 'Error! ', e
return 0, 10
def __init__(self):
super(Optimise_Elegant, self).__init__()
self.cons = constraintsClass()
self.changes = None
self.lattice = None
self.resultsDict = {}
# ******************************************************************************
self.CLARA_dir = os.path.relpath(__file__ + '/../../CLARA')
self.POST_INJECTOR = True
CREATE_BASE_FILES = False
self.scaling = 5
self.verbose = False
self.basefiles = '../../CLARA/basefiles_' + str(self.scaling) + '/'
# ******************************************************************************
if not self.POST_INJECTOR:
best = injector_startingvalues + best
elif CREATE_BASE_FILES:
for i in [self.scaling]:
pass
def optfunc(inputargs, *args, **kwargs):
idclient = idn.zmqClient()
n = idclient.get_id() #str(uuid.uuid4())
# kwargs = args[0]
# print 'optfunc kwargs = ', kwargs
cons = constraintsClass()
if 'dir' in kwargs.keys():
dir = kwargs['dir']
del kwargs['dir']
e, b, ee, be, l, g = genesisBeamFile.optfunc(inputargs,
dir=dir,
**kwargs)
else:
e, b, ee, be, l, g = genesisBeamFile.optfunc(inputargs,
dir='de/de_iteration_' +
str(n),
**kwargs)
e = 1e2 * e
ee = 1e2 * ee
pos12m = list(g.z).index(10.)
pos20m = list(g.z).index(20.)
momentum = 1e-6 * np.mean(g.momentum)
print 'Momentum = ', momentum
if e < 1:
l = 500
constraintsList = {
'bandwidth': {
'type': 'lessthan',
'value': b,
'limit': 0.25,
'weight': 3
},
'pulse_energy': {
'type': 'greaterthan',
'value': e,
'limit': 120,
'weight': 4
},
'bandwidth_end': {
'type': 'lessthan',
'value': 1e2 * abs(g.spectrum_lamdwidth_std[pos20m]),
'limit': 0.4,
'weight': 1
},
'pulse_energy_end': {
'type': 'greaterthan',
'value': 1e6 * abs(g.energy[pos20m]),
'limit': 250,
'weight': 2
},
'max_brightness_position': {
'type': 'lessthan',
'value': abs(l),
'limit': 10,
'weight': 2
},
'energy': {
'type': 'greaterthan',
'value': abs(momentum),
'limit': 240,
'weight': 2
},
}
fitvalue = cons.constraints(constraintsList)
print cons.constraintsList(constraintsList)
print 'fitvalue[', n, '] = ', fitvalue
try:
saveState(inputargs, n, fitvalue)
except Exception as e:
print 'Error! ', e
return fitvalue