def test_shapesys_zero(backend):
mc = MockConfig(
par_map={
'SigXsecOverSM': {
'paramset':
paramset(
name='SigXsecOverSM',
is_scalar=True,
n_parameters=1,
inits=[0],
bounds=[[0, 10]],
fixed=False,
),
'slice':
slice(0, 1),
},
'syst': {
'paramset':
constrained_by_poisson(
name='syst',
is_scalar=False,
n_parameters=5,
inits=[0] * 5,
bounds=[[0, 10]] * 5,
fixed=False,
auxdata=[None] * 5,
factors=[None] * 5,
),
'slice':
slice(1, 6),
},
},
channels=['channel1'],
channel_nbins={'channel1': 6},
par_order=['SigXsecOverSM', 'syst'],
samples=['signal', 'background'],
)
mega_mods = {
'shapesys/syst': {
'background': {
'type': 'shapesys',
'name': 'syst',
'data': {
'mask': [True, True, False, True, True, True],
'nom_data': [100.0, 90.0, 0.0, 70, 0.1, 50],
'uncrt': [10, 9, 1, 0.0, 0.1, 5],
},
},
'signal': {
'type': 'shapesys',
'name': 'syst',
'data': {
'mask': [False, False, False, False, False, False],
'nom_data': [20.0, 10.0, 5.0, 3.0, 2.0, 1.0],
'uncrt': [10, 9, 1, 0.0, 0.1, 5],
},
},
}
}
hsc = shapesys_combined([('syst', 'shapesys')], mc, mega_mods)
mod = hsc.apply(pyhf.tensorlib.astensor([-10, 1.1, 1.2, 1.3, -20, -30]))
shape = pyhf.tensorlib.shape(mod)
assert shape == (1, 2, 1, 6)
# expect the 'background' sample to have a single masked bin for 'syst'
assert mod[0, 1, 0, 2] == 1.0
def test_shapesys(backend):
mc = MockConfig(
par_map={
'dummy1': {
'paramset': paramset(n_parameters=1, inits=[0], bounds=[[0, 10]]),
'slice': slice(0, 1),
},
'shapesys1': {
'paramset': paramset(n_parameters=1, inits=[0], bounds=[[0, 10]]),
'slice': slice(1, 2),
},
'shapesys2': {
'paramset': paramset(
n_parameters=2, inits=[0, 0], bounds=[[0, 10], [0, 10]]
),
'slice': slice(2, 4),
},
'dummy2': {
'paramset': paramset(n_parameters=1, inits=[0], bounds=[[0, 10]]),
'slice': slice(4, 5),
},
},
channels=['chan1', 'chan2'],
channel_nbins={'chan1': 1, 'chan2': 2},
par_order=['dummy1', 'shapesys1', 'shapesys2', 'dummy2'],
samples=['signal', 'background'],
)
mega_mods = {
'signal': {
'shapesys/shapesys1': {
'type': 'shapesys',
'name': 'shapesys1',
'data': {
'mask': [True, False, False],
'nom_data': [10, 10, 10],
'uncrt': [1, 0, 0],
},
},
'shapesys/shapesys2': {
'type': 'shapesys',
'name': 'shapesys1',
'data': {
'mask': [False, True, True],
'nom_data': [10, 10, 10],
'uncrt': [0, 1, 1],
},
},
},
'background': {
'shapesys/shapesys1': {
'type': 'shapesys',
'name': 'shapesys1',
'data': {
'mask': [True, False, False],
'nom_data': [10, 10, 10],
'uncrt': [1, 0, 0],
},
},
'shapesys/shapesys2': {
'type': 'shapesys',
'name': 'shapesys1',
'data': {
'mask': [False, True, True],
'nom_data': [10, 10, 10],
'uncrt': [0, 1, 1],
},
},
},
}
hsc = shapesys_combined(
[('shapesys1', 'shapesys'), ('shapesys2', 'shapesys')], mc, mega_mods
)
mod = hsc.apply(pyhf.tensorlib.astensor([-10, 1.1, 1.2, 1.3, -20]))
shape = pyhf.tensorlib.shape(mod)
assert shape == (2, 2, 1, 3)
mod = np.asarray(pyhf.tensorlib.tolist(mod))
assert np.allclose(mod[0, 0, 0], [1.1, 1.0, 1.0])
assert np.allclose(mod[1, 0, 0], [1, 1.2, 1.3])
def test_shapesys_zero(backend):
mc = MockConfig(
par_map={
'SigXsecOverSM': {
'paramset': paramset(n_parameters=1, inits=[0], bounds=[[0, 10]]),
'slice': slice(0, 1),
},
'syst': {
'paramset': paramset(
n_parameters=5, inits=[0] * 5, bounds=[[0, 10]] * 5
),
'slice': slice(1, 6),
},
'syst_lowstats': {
'paramset': paramset(
n_parameters=0, inits=[0] * 0, bounds=[[0, 10]] * 0
),
'slice': slice(6, 6),
},
},
channels=['channel1'],
channel_nbins={'channel1': 6},
par_order=['SigXsecOverSM', 'syst', 'syst_lowstats'],
samples=['signal', 'background'],
)
mega_mods = {
'shapesys/syst': {
'background': {
'type': 'shapesys',
'name': 'syst',
'data': {
'mask': [True, True, False, True, True, True],
'nom_data': [100.0, 90.0, 0.0, 70, 0.1, 50],
'uncrt': [10, 9, 1, 0.0, 0.1, 5],
},
},
'signal': {
'type': 'shapesys',
'name': 'syst',
'data': {
'mask': [False, False, False, False, False, False],
'nom_data': [20.0, 10.0, 5.0, 3.0, 2.0, 1.0],
'uncrt': [10, 9, 1, 0.0, 0.1, 5],
},
},
},
'shapesys/syst_lowstats': {
'background': {
'type': 'shapesys',
'name': 'syst_lowstats',
'data': {
'mask': [False, False, False, False, False, False],
'nom_data': [100.0, 90.0, 0.0, 70, 0.1, 50],
'uncrt': [0, 0, 0, 0, 0, 0],
},
},
'signal': {
'type': 'shapesys',
'name': 'syst',
'data': {
'mask': [False, False, False, False, False, False],
'nom_data': [20.0, 10.0, 5.0, 3.0, 2.0, 1.0],
'uncrt': [10, 9, 1, 0.0, 0.1, 5],
},
},
},
}
hsc = shapesys_combined(
[('syst', 'shapesys'), ('syst_lowstats', 'shapesys')], mc, mega_mods
)
mod = hsc.apply(pyhf.tensorlib.astensor([-10, 1.1, 1.2, 1.3, -20, -30]))
shape = pyhf.tensorlib.shape(mod)
assert shape == (2, 2, 1, 6)
# expect the 'background' sample to have a single masked bin for 'syst'
assert mod[0, 1, 0, 2] == 1.0
# expect the 'background' sample to have all bins masked for 'syst_lowstats'
assert np.all(kappa == 1 for kappa in mod[1, 1, 0])