def parse_observables(self, obslitst):
observables = []
allnames = []
for obs in obslitst:
if isinstance(obs, str):
obsdict = {'name':obs}
elif isinstance(obs, dict):
obsdict = obs.copy()
else:
raise ConfigError("Observable format not understood: %s" % obs)
namestr = obsdict.pop('name')
names = glob.glob(namestr)
if not names:
raise ConfigError("No observables found for %s" %
namestr)
allnames += names
for name in allnames:
try:
obsobj = make_observable(name, **obsdict)
except ValueError as e:
raise ConfigError("Could not parse the observable %s: %s"
% (name, e))
except TypeError as e:
raise ConfigError("Incorrect arguments passed to process "
"observable %s: %s" % (name, e))
observables.append(obsobj)
s = set(observables)
if len(s) != len(observables):
c = Counter(observables)
dups = [obs.name for obs in observables if c[obs]>1]
obs = c.keys()
logging.warn("Duplicate observables: %s" % dups)
return observables
def test_const_results(self):
pdf = PDF('NNPDF30_nlo_as_0118')
obs = make_observable('data/higgs/ggh_13tev.root', order=1)
res = produce_results(pdf, obs)[0]._data.as_matrix()
previous = [ 31.22150023, 28.9888971 , 31.3305855 , 31.35700134,
30.66074282, 28.2795592 , 31.50932389, 31.43558106,
31.28601552, 31.36406676, 30.69818304, 30.86453794,
31.62708945, 31.6582997 , 31.11008626, 31.59319744,
31.52866286, 31.28639456, 31.07427256, 31.41568155,
31.60044526, 31.36966168, 30.93032191, 33.05392731,
30.96536474, 31.4317893 , 30.72436163, 31.22605137,
31.24243901, 31.21880536, 31.68798426, 31.07646885,
30.59035729, 31.34800315, 31.21256586, 32.19365014,
31.58647388, 31.74649083, 31.04692704, 30.2313781 ,
31.4132261 , 31.01493873, 31.36481456, 31.26872855,
31.09018502, 31.51168931, 31.28105268, 30.91102961,
30.88952926, 31.31389055, 31.22196011, 31.19094082,
31.03705732, 31.54684624, 31.25938782, 31.10403155,
30.87467055, 31.83239232, 31.05799506, 31.68930494,
31.0387522 , 31.27327964, 31.30919955, 31.51614659,
31.06029472, 31.43211917, 31.35963882, 32.68355944,
30.96018759, 30.60001451, 31.25165147, 31.37222175,
31.19241142, 29.4805518 , 31.66853784, 30.96975209,
30.61955437, 31.34032813, 32.0724701 , 30.9346566 ,
31.27130234, 31.38000397, 30.83068422, 31.50593514,
31.22489125, 30.74042979, 31.21191289, 31.4358684 ,
31.27231758, 31.81871636, 31.10093208, 30.64649533,
31.40680264, 31.12985161, 31.8378625 , 31.18650963,
31.51129514, 31.18025208, 31.87366192, 31.78494394, 31.45846653]
self.assertTrue(np.allclose(res, previous))
import contextlib
from smpdflib.initialization import init_app
if __name__ == '__main__':
with contextlib.suppress(RuntimeError):
init_app()
import matplotlib.pyplot as plt
from smpdflib.core import (PDF, make_observable, produce_results,
)
from smpdflib.reducedset import get_smpdf_params
pdf = PDF("MC900_nlo")
obs = [make_observable(path, order='NLO') for path in itertools.chain(
sorted(glob.glob("data/z/*.root")),
sorted(glob.glob("data/w/*.root")),
sorted(glob.glob("data/higgs/*.root")),
sorted(glob.glob("data/ttbar/*.root")),
)]
tolerance = 0.05
thresholds = [0.0, 0.25, 0.5, 0.75, 0.9, 0.99]
if __name__ == '__main__':
neig = []
logging.basicConfig(level=logging.INFO)
from smpdflib.initialization import init_app
if __name__ == '__main__':
init_app()
import matplotlib.pyplot as plt
from smpdflib.core import make_observable, PDF, produce_results, results_table
from smpdflib.plots import plot_bindist
#Db folder should exist...
db = shelve.open('db/db')
to_plot = {make_observable('data/applgrid/CMSWCHARM-WpCb-eta4.root', order=1) : 4,
make_observable('data/applgrid/APPLgrid-LHCb-Z0-ee_arXiv1212.4260-eta34.root', order=1) : 8,
}
pdfs = [PDF('MC900_nnlo', label="legendtext"),
PDF('CMC100_nnlo', label="other"),
PDF('MCH_nnlo_100', label="another")
]
if __name__ == '__main__':
for obs, bin in to_plot.items():
results = produce_results(pdfs, [obs], db=db)
obs_table = results_table(results)
for (obs,b), fig in plot_bindist(obs_table, bin, base_pdf = pdfs[0]):
ax = fig.axes[0]
obs_name = 'data/z/z_13tev.root'
prior_name = "MC900_nnlo"
prefixes = ['z2tr' + x for x in '0 10 25 50 75 90 99'.split()]
smpdf_names = [prefix + 'smpdf_' + prior_name for prefix in prefixes]
lincoef_paths = ['output/%s_lincomb.csv' % name for name in smpdf_names]
if __name__ == '__main__':
prior = PDF(prior_name)
smpdfs = [PDF(name) for name in smpdf_names]
pdfs = [prior] + smpdfs
obs = make_observable(obs_name, order='NLO')
with shelve.open('db/db') as db:
res_prior, *res_smpdfs = produce_results(pdfs, [obs], db=db)
coefs = [pd.DataFrame.from_csv(path, sep='\t') for path in lincoef_paths]
prior_std = res_prior.std_error()
real_tols = [1 - res.std_error()/prior_std for res in res_smpdfs]
prior_diffs = (res_prior._all_vals.T - res_prior.central_value).as_matrix().ravel()
neig = [len(pdf) - 1 for pdf in smpdfs]
rotated_tols = []
for path in lincoef_paths:
coefs = pd.DataFrame.from_csv(path, sep='\t')
