def benchmark_sv(self, pto):
"""Scale Variation"""
th = self.vfns_theory.copy()
th.update({"PTO": [pto], "XIR": [0.7071067811865475, 1.4142135623730951]})
self.run(
cartesian_product(th), operators.build(operators.apfel_config), ["ToyLH"]
)
def benchmark_plain(self, pto):
"""Plain configuration"""
th = self.vfns_theory.copy()
th.update({"PTO": [pto]})
self.run(
cartesian_product(th), operators.build(operators.apfel_config), ["ToyLH"]
)
def benchmark_sv(self, pto):
"""Scale Variation"""
th = self.zm_theory.copy()
th.update({
"PTO": [pto],
"fact_to_ren_scale_ratio": [np.sqrt(0.5),
np.sqrt(2.0)],
})
self.run(cartesian_product(th),
operators.build(operators.pegasus_config), ["ToyLH"])
def benchmark_kthr(self, pto):
"""Threshold scale different from heavy quark mass"""
th = self.vfns_theory.copy()
th.update(
{
"PTO": [pto],
"kcThr": [1.23],
"kbThr": [1.45],
}
)
self.run(
cartesian_product(th), operators.build(operators.apfel_config), ["ToyLH"]
)
def benchmark_sv(self, pto):
"""Scale Variation"""
ts = []
th = self.ffns_theory.copy()
th.update(
{
"PTO": [pto],
"XIR": [np.sqrt(0.5)],
"fact_to_ren_scale_ratio": [np.sqrt(2.0)],
}
)
ts.extend(cartesian_product(th))
th = self.ffns_theory.copy()
th.update(
{
"PTO": [pto],
"XIR": [np.sqrt(2.0)],
"fact_to_ren_scale_ratio": [np.sqrt(0.5)],
}
)
ts.extend(cartesian_product(th))
self.run(ts, operators.build(operators.apfel_config), ["ToyLH"])
def build(update=None):
"""
Generate all operator card updates
Parameters
----------
update : dict
base modifiers
Returns
-------
cards : list(dict)
list of update
"""
cards = []
if update is None:
update = {}
for c in cartesian_product(update):
card = dict()
card.update(c)
cards.append(card)
return cards