def collect_exp_data(wdir):
load_config('%s/input.py'%wdir)
conf['bootstrap']=False
istep=core.get_istep()
core.mod_conf(istep) #--set conf as specified in istep
resman=RESMAN(nworkers=1,parallel=False,datasets=True)
obsres={}
if 'idis' in conf['datasets'] : obsres['idis'] = resman.idisres
if 'pidis' in conf['datasets'] : obsres['pidis'] = resman.pidisres
if 'sidis' in conf['datasets'] : obsres['sidis'] = resman.sidisres
if 'psidis' in conf['datasets'] : obsres['psidis'] = resman.psidisres
if 'dy' in conf['datasets'] : obsres['dy'] = resman.dyres
if 'sia' in conf['datasets'] : obsres['sia'] = resman.siares
print('\ncollecting exp data using the setup as in %s\n'%wdir)
data={}
for reaction in obsres:
tabs={}
for idx in obsres[reaction].tabs:
tabs[idx]={}
tabs[idx]['value']=obsres[reaction].tabs[idx]['value']
tabs[idx]['alpha']=obsres[reaction].tabs[idx]['alpha']
data[reaction]=tabs
save(data,'%s/expdata.dat'%(wdir))
#--close resman
resman.shutdown()
def gen_samples(wdir,nsamples=100,nsize=10):
load_config('%s/input.py'%wdir)
conf['bootstrap']=False
istep=core.get_istep()
core.mod_conf(istep) #--set conf as specified in istep
resman=RESMAN(nworkers=5,parallel=True,datasets=True)
parman=resman.parman
order=parman.order
obsres={}
if 'idis' in conf['datasets'] : obsres['idis'] = resman.idisres
if 'pidis' in conf['datasets'] : obsres['pidis'] = resman.pidisres
if 'sidis' in conf['datasets'] : obsres['sidis'] = resman.sidisres
if 'psidis' in conf['datasets'] : obsres['psidis'] = resman.psidisres
if 'dy' in conf['datasets'] : obsres['dy'] = resman.dyres
if 'sia' in conf['datasets'] : obsres['sia'] = resman.siares
print('\ngen ml samples using the setup as in %s\n'%wdir)
checkdir('%s/mlsamples'%wdir)
data=gen_dataframe(parman.order,obsres,nsize)
for _ in range(nsamples):
lprint('progress: %d/%d'%(_,nsamples))
par=parman.gen_flat(setup=True)
res,rres,nres=resman.get_residuals(par)
#--fill dataframe
i=data['size']
data['params'][i]=par
for reaction in obsres:
for idx in data['reactions'][reaction]:
prediction=copy.copy(obsres[reaction].tabs[idx]['prediction'])
data['reactions'][reaction][idx][i]=prediction
data['size']+=1
if data['size']==nsize:
save(data,'%s/mlsamples/%s'%(wdir,id_generator(12)))
data=gen_dataframe(parman.order,obsres,nsize)
print
#--close resman
resman.shutdown()
class MAXLIKE:
def __init__(self,
inputfile,
nworkers=2,
verbose=False,
msrhook=None,
prior=None,
seed=None):
self.nworkers = nworkers
self.inputfile = inputfile
self.verbose = verbose
self.msrhook = msrhook
self.prior = prior
self.seed = seed
def set_counters(self):
self.chi2tot = 1e1000
self.dchi2 = 0
self.t0 = time.time()
self.cnt = 0
def print_status(self, res, rres, nres):
#--update status parameters
shifts = self.parman.shifts
etime = (time.time() - self.t0) / 60
npts = res.size
chi2 = np.sum(res**2)
rchi2 = np.sum(rres**2)
nchi2 = np.sum(nres**2)
chi2tot = chi2 + rchi2 + nchi2
dchi2 = chi2tot - self.chi2tot
if shifts > 2:
if chi2tot < self.chi2tot:
self.dchi2 = self.chi2tot - chi2tot
self.chi2tot = chi2tot
#--build header
status = []
status.append('JAM FITTER')
status.append('count = %d' % self.cnt)
status.append('elapsed time(mins)=%f' % etime)
status.append('shifts = %d' % shifts)
status.append('npts = %d' % npts)
status.append('chi2 = %f' % chi2)
status.append('rchi2 = %f' % rchi2)
status.append('nchi2 = %f' % nchi2)
status.append('chi2tot = %f' % (chi2tot))
status.append('dchi2(iter) = %f' % self.dchi2)
status.append('dchi2(local) = %f' % dchi2)
#--special output for pdfs
#if 'pdf' in conf['params']:
# for _ in conf['pdf'].sr:
# status.append('pdf %s:%f'%(_,conf['pdf'].sr[_]))
# #status.append('proton uvsr = %f'%conf['pdf'].sr['uvsr'])
# #status.append('proton dvsr = %f'%conf['pdf'].sr['dvsr'])
# #status.append('proton msr = %f'%conf['pdf'].sr['msr'])
# #if 'svsr' in conf['pdf'].sr: status.append('proton svsr = %f'%conf['pdf'].sr['svsr'])
#--report from resman
status.append('')
status.extend(self.resman.gen_report())
#--report from parman
parstatus = self.parman.gen_report()
#--print into screen
nstatus = len(status)
nparstatus = len(parstatus)
os.system('clear')
for i in range(max([nstatus, nparstatus])):
data = []
if i < nstatus: data.append(status[i])
else: data.append('')
if i < nparstatus: data.append(parstatus[i])
else: data.append('')
print '%-120s | %s' % tuple(data)
return status, parstatus
def get_residuals(self, par):
res, rres, nres = self.resman.get_residuals(par)
self.cnt += 1
if self.cnt % conf['verbose'] == 0:
self.print_status(res, rres, nres)
if len(rres) != 0: res = np.append(res, rres)
if len(nres) != 0: res = np.append(res, nres)
return res
def checklimits(self):
for k in conf['params']:
for kk in conf['params'][k]:
if conf['params'][k][kk]['fixed'] != False: continue
p = conf['params'][k][kk]['value']
pmin = conf['params'][k][kk]['min']
pmax = conf['params'][k][kk]['max']
if p < pmin or p > pmax:
print '%s-%s out of limits. ' % (k, kk)
sys.exit()
for k in conf['datasets']:
for kk in conf['datasets'][k]['norm']:
p = conf['datasets'][k]['norm'][kk]['value']
pmin = conf['datasets'][k]['norm'][kk]['min']
pmax = conf['datasets'][k]['norm'][kk]['max']
if p < pmin or p > pmax:
print '%s-%s out of limits. ' % (k, kk)
sys.exit()
def get_conf(self, _conf, step):
conf = copy.deepcopy(_conf)
#--remove pdf/ff that is not in the step
distributions = conf['params'].keys() #--pdf,ppdf,ffpion,ffkaon,...
for dist in distributions:
if dist in step['active distributions']:
continue
else:
del conf['params'][dist]
#--set fixed==True for passive distributions
if 'passive distributions' in step:
for dist in step['passive distributions']:
for par in conf['params'][dist]:
conf['params'][dist][par]['fixed'] = True
#--set prior parameters values for passive distributions
for istep in step['dep']:
prior_order = self.order[istep]
prior_params = self.params[istep]
for i in range(len(prior_order)):
_, _dist, _par = prior_order[i]
if dist == _dist and par == _par:
conf['params'][dist][par][
'value'] = prior_params[i]
#--another version for fixed parameters
if 'fix parameters' in step:
for dist in step['fix parameters']:
print dist
for par in step['fix parameters'][dist]:
print par
conf['params'][dist][par]['fixed'] = True
#--set prior parameters values for passive distributions
for istep in step['dep']:
prior_order = self.order[istep]
prior_params = self.params[istep]
for i in range(len(prior_order)):
_, _dist, _par = prior_order[i]
if dist == _dist and par == _par:
conf['params'][dist][par][
'value'] = prior_params[i]
#--remove datasets not in the step
datasets = conf['datasets'].keys() #--idis,dy,....
for dataset in datasets:
if dataset in step['datasets']:
#--remove entry from xlsx
xlsx = conf['datasets'][dataset]['xlsx'].keys()
for idx in xlsx:
if idx in step['datasets'][dataset]:
continue
else:
del conf['datasets'][dataset]['xlsx'][idx]
#--remove entry from norm
norm = conf['datasets'][dataset]['norm'].keys()
for idx in norm:
if idx in step['datasets'][dataset]:
continue
else:
del conf['datasets'][dataset]['norm'][idx]
else:
del conf['datasets'][dataset]
return conf
def get_bounds(self):
order = self.parman.order
bounds_min = []
bounds_max = []
for entry in order:
i, k, kk = entry
if i == 1:
p = conf['params'][k][kk]['value']
pmin = conf['params'][k][kk]['min']
pmax = conf['params'][k][kk]['max']
if p < pmin or p > pmax:
msg = '%s/%s outsize the limits %f %f %f' % (k, kk, p,
pmin, pmax)
raise ValueError(msg)
bounds_min.append(conf['params'][k][kk]['min'])
bounds_max.append(conf['params'][k][kk]['max'])
elif i == 2:
p = conf['datasets'][k]['norm'][kk]['value']
pmin = conf['datasets'][k]['norm'][kk]['min']
pmax = conf['datasets'][k]['norm'][kk]['max']
if p < pmin or p > pmax:
msg = '%s/%s outsize the limits %f %f %f' % (k, kk, p,
pmin, pmax)
raise ValueError(msg)
bounds_min.append(conf['datasets'][k]['norm'][kk]['min'])
bounds_max.append(conf['datasets'][k]['norm'][kk]['max'])
return (bounds_min, bounds_max)
def get_guess(self, dep=[]):
order = self.parman.order
if conf['flat par']:
if self.seed != None: np.random.seed(12345)
guess = self.parman.gen_flat()
else:
guess = self.parman.par
#--retrieve priors from previous steps according to dep
if len(dep) > 0:
for istep in dep:
prior_order = self.order[istep]
prior_params = self.params[istep]
#--compare and match prior order to current order
for i in range(len(prior_order)):
for j in range(len(order)):
if order[j] == prior_order[i]:
guess[j] = prior_params[i]
return guess
def gen_summary(self, step, par):
res, rres, nres = self.resman.get_residuals(par)
status, parstatus = self.print_status(res, rres, nres)
status.extend(parstatus)
status = [l + '\n' for l in status]
fname = 'step-%d.summary' % step
F = open(fname, 'w')
F.writelines(status)
F.close()
def get_par(self, step, dep=[]):
#--current value must be within min and max
self.checklimits()
#--initialize resman
self.resman = RESMAN(self.nworkers)
self.parman = self.resman.parman
#--setups
guess = self.get_guess(dep)
bounds = self.get_bounds()
self.set_counters()
self.parman.set_new_params(guess, initial=True)
#--run fit
fit = least_squares(self.get_residuals,
guess,
bounds=bounds,
method='trf',
ftol=conf['ftol'])
#--generate summary. It will update system with the final results
self.gen_summary(step, fit.x)
#--close resman
self.resman.shutdown()
return fit.x
def gen_output(self, istep):
"""
modification of the input is done using a dedicated scrip
at tools/inputmod.py
"""
inputmod = INPUTMOD(self.inputfile)
for kind in conf['params']:
for par in conf['params'][kind]:
value = conf['params'][kind][par]['value']
inputmod.mod_par(kind, par, 'value', value)
for reaction in conf['datasets']:
for idx in conf['datasets'][reaction]['norm']:
value = conf['datasets'][reaction]['norm'][idx]['value']
inputmod.mod_norm(reaction, idx, 'value', value)
fname = 'output-%d.py' % istep
inputmod.gen_input(fname)
def run(self):
global conf
load_config(self.inputfile)
#--modify confs
if 'hooks' not in conf:
conf['hooks'] = {}
if self.msrhook != None:
conf['hooks']['msr'] = self.msrhook
if self.verbose == False:
conf['verbose'] = 1
else:
conf['verbose'] = self.verbose
#--backup conf(after mods)
conf_bkp = copy.deepcopy(conf)
#--decide if the input should be modified
output = True
if 'bootstrap' in conf and conf['bootstrap'] == True: output = False
if 'steps' in conf:
isteps = sorted(conf['steps'])
self.order = {}
self.params = {}
chi2 = {}
if self.prior != None:
prior = load(self.prior)
self.order = prior['order']
self.params = prior['params']
chi2 = prior['chi2']
prior_steps = sorted(self.order.keys())
isteps = [i for i in isteps if i not in prior_steps]
for i in isteps:
step = conf['steps'][i]
conf.update(copy.deepcopy(conf_bkp))
conf.update(self.get_conf(conf_bkp, step))
print
msg = '--step %d: ' % i
msg += 'npQCD objects ='
for _ in conf['params'].keys():
msg += _ + ' '
msg += 'datasets ='
for _ in conf['datasets'].keys():
msg += _ + ' '
print msg
par = self.get_par(i, step['dep'])
self.order[i] = self.parman.order[:]
self.params[i] = par[:]
chi2[i] = self.resman.get_chi2()
if output: self.gen_output(i)
#--store the results from steps
data = {'order': self.order, 'params': self.params, 'chi2': chi2}
if self.prior == None:
fname = '%s.msr' % id_generator(size=12)
else:
fname = self.prior.split('/')[-1]
save(data, fname)
#--run hooks
if 'msr' in conf['hooks']: #--ms==multi steps
cmd = conf['hooks']['msr'].replace('<<fname>>', fname)
os.system(cmd)
else:
par = self.get_par(0)
if output: self.gen_output(0)
#--run remining hooks if available
for _ in conf['hooks']:
if _ == 'msr': continue
os.system(conf['hooks'][_])