def test_lumi(backend):
mc = MockConfig(
par_map={
'lumi': {
'paramset': paramset(n_parameters=1,
inits=[0],
bounds=[[-5, 5]]),
'slice': slice(0, 1),
}
},
par_order=['lumi'],
samples=['signal', 'background'],
)
mega_mods = {
'lumi/lumi': {
'signal': {
'type': 'lumi',
'name': 'lumi',
'data': {
'mask': [True, True, True]
},
},
'background': {
'type': 'lumi',
'name': 'lumi',
'data': {
'mask': [True, True, True]
},
},
},
}
hsc = lumi_combined([('lumi', 'lumi')], mc, mega_mods)
mod = hsc.apply(pyhf.tensorlib.astensor([0.5]))
shape = pyhf.tensorlib.shape(mod)
assert shape == (1, 2, 1, 3)
mod = np.asarray(pyhf.tensorlib.tolist(mod))
assert np.allclose(mod[0, 0, 0], [0.5, 0.5, 0.5])
assert np.allclose(mod[0, 1, 0], [0.5, 0.5, 0.5])
hsc = lumi_combined([('lumi', 'lumi')], mc, mega_mods, batch_size=4)
mod = hsc.apply(pyhf.tensorlib.astensor([[1.0], [2.0], [3.0], [4.0]]))
shape = pyhf.tensorlib.shape(mod)
assert shape == (1, 2, 4, 3)
mod = np.asarray(pyhf.tensorlib.tolist(mod))
assert np.allclose(mod[0, 0, 0], [1.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 1], [2.0, 2.0, 2.0])
assert np.allclose(mod[0, 0, 2], [3.0, 3.0, 3.0])
assert np.allclose(mod[0, 0, 3], [4.0, 4.0, 4.0])
def test_shapefactor(backend):
mc = MockConfig(
par_map={
'shapefac1': {
'paramset': paramset(n_parameters=1, inits=[0], bounds=[[0, 10]]),
'slice': slice(0, 1),
},
'shapefac2': {
'paramset': paramset(
n_parameters=2, inits=[0, 0], bounds=[[0, 10], [0, 10]]
),
'slice': slice(1, 3),
},
},
par_order=['shapefac1', 'shapefac2'],
samples=['signal', 'background'],
channels=['chan_one', 'chan_two'],
channel_nbins={'chan_one': 1, 'chan_two': 2},
)
mega_mods = {
'signal': {
'shapefactor/shapefac1': {
'type': 'shapefactor',
'name': 'shapefac1',
'data': {'mask': [True, False, False]},
},
'shapefactor/shapefac2': {
'type': 'shapefactor',
'name': 'shapefac2',
'data': {'mask': [False, True, True]},
},
},
'background': {
'shapefactor/shapefac1': {
'type': 'shapefactor',
'name': 'shapefac1',
'data': {'mask': [True, False, False]},
},
'shapefactor/shapefac2': {
'type': 'normfactor',
'name': 'shapefac2',
'data': {'mask': [False, True, True]},
},
},
}
hsc = shapefactor_combined(
[('shapefac1', 'shapefactor'), ('shapefac2', 'shapefactor')], mc, mega_mods
)
mod = hsc.apply(pyhf.tensorlib.astensor([2.0, 3.0, 4.0]))
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], [2.0, 1.0, 1.0])
assert np.allclose(mod[1, 0, 0], [1.0, 3.0, 4.0])
hsc = shapefactor_combined(
[('shapefac1', 'shapefactor'), ('shapefac2', 'shapefactor')],
mc,
mega_mods,
batch_size=4,
)
mod = hsc.apply(
pyhf.tensorlib.astensor(
[[2.0, 3.0, 4.0], [5.0, 6.0, 7.0], [8.0, 9.0, 10.0], [11.0, 12.0, 13.0]]
)
)
shape = pyhf.tensorlib.shape(mod)
assert shape == (2, 2, 4, 3)
mod = np.asarray(pyhf.tensorlib.tolist(mod))
assert np.allclose(mod[0, 0, 0], [2.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 1], [5.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 2], [8.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 3], [11.0, 1.0, 1.0])
assert np.allclose(mod[1, 0, 0], [1.0, 3.0, 4.0])
assert np.allclose(mod[1, 0, 1], [1.0, 6.0, 7.0])
assert np.allclose(mod[1, 0, 2], [1.0, 9.0, 10.0])
assert np.allclose(mod[1, 0, 3], [1.0, 12.0, 13.0])
def test_normfactor(backend):
mc = MockConfig(
par_map={
'mu1': {
'paramset': paramset(n_parameters=1, inits=[0], bounds=[[0, 10]]),
'slice': slice(0, 1),
},
'mu2': {
'paramset': paramset(n_parameters=1, inits=[0], bounds=[[0, 10]]),
'slice': slice(1, 2),
},
},
par_order=['mu1', 'mu2'],
samples=['signal', 'background'],
)
mega_mods = {
'signal': {
'normfactor/mu1': {
'type': 'normfactor',
'name': 'mu1',
'data': {'mask': [True, False, False]},
},
'normfactor/mu2': {
'type': 'normfactor',
'name': 'mu2',
'data': {'mask': [False, True, True]},
},
},
'background': {
'normfactor/mu1': {
'type': 'normfactor',
'name': 'mu1',
'data': {'mask': [True, False, False]},
},
'normfactor/mu2': {
'type': 'normfactor',
'name': 'mu2',
'data': {'mask': [False, True, True]},
},
},
}
hsc = normfactor_combined(
[('mu1', 'normfactor'), ('mu2', 'normfactor')], mc, mega_mods
)
mod = hsc.apply(pyhf.tensorlib.astensor([2.0, 3.0]))
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], [2.0, 1.0, 1.0])
assert np.allclose(mod[1, 0, 0], [1.0, 3.0, 3.0])
hsc = normfactor_combined(
[('mu1', 'normfactor'), ('mu2', 'normfactor')], mc, mega_mods, batch_size=4
)
mod = hsc.apply(
pyhf.tensorlib.astensor([[1.0, 5.0], [2.0, 6.0], [3.0, 7.0], [4.0, 8.0]])
)
shape = pyhf.tensorlib.shape(mod)
assert shape == (2, 2, 4, 3)
mod = np.asarray(pyhf.tensorlib.tolist(mod))
assert np.allclose(mod[0, 0, 0], [1.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 1], [2.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 2], [3.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 3], [4.0, 1.0, 1.0])
assert np.allclose(mod[1, 0, 0], [1.0, 5.0, 5.0])
assert np.allclose(mod[1, 0, 1], [1.0, 6.0, 6.0])
assert np.allclose(mod[1, 0, 2], [1.0, 7.0, 7.0])
assert np.allclose(mod[1, 0, 3], [1.0, 8.0, 8.0])
def test_histosys(backend):
mc = MockConfig(
par_map={
'hello': {
'paramset': paramset(n_parameters=1, inits=[0], bounds=[[-5, 5]]),
'slice': slice(0, 1),
},
'world': {
'paramset': paramset(n_parameters=1, inits=[0], bounds=[[-5, 5]]),
'slice': slice(1, 2),
},
},
par_order=['hello', 'world'],
samples=['signal', 'background'],
)
mega_mods = {
'signal': {
'histosys/hello': {
'type': 'histosys',
'name': 'hello',
'data': {
'hi_data': [11, 12, 13],
'lo_data': [9, 8, 7],
'nom_data': [10, 10, 10],
'mask': [True, True, True],
},
},
'histosys/world': {
'type': 'histosys',
'name': 'world',
'data': {
'hi_data': [10, 10, 10],
'lo_data': [5, 6, 7],
'nom_data': [10, 10, 10],
'mask': [True, True, True],
},
},
},
'background': {
'histosys/hello': {
'type': 'histosys',
'name': 'hello',
'data': {
'hi_data': [11, 12, 13],
'lo_data': [9, 8, 7],
'nom_data': [10, 10, 10],
'mask': [True, True, True],
},
},
'histosys/world': {
'type': 'histosys',
'name': 'world',
'data': {
'hi_data': [10, 10, 10],
'lo_data': [5, 6, 7],
'nom_data': [10, 10, 10],
'mask': [True, True, True],
},
},
},
}
hsc = histosys_combined(
[('hello', 'histosys'), ('world', 'histosys')], mc, mega_mods
)
mod = hsc.apply(pyhf.tensorlib.astensor([0.5, -1.0]))
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], [0.5, 1.0, 1.5])
hsc = histosys_combined(
[('hello', 'histosys'), ('world', 'histosys')], mc, mega_mods, batch_size=4
)
mod = hsc.apply(
pyhf.tensorlib.astensor([[-1.0, -1.0], [1.0, 1.0], [-1.0, 1.0], [1.0, 1.0]])
)
shape = pyhf.tensorlib.shape(mod)
assert shape == (2, 2, 4, 3)
mod = np.asarray(pyhf.tensorlib.tolist(mod))
assert np.allclose(mod[0, 0, 0], [-1.0, -2.0, -3.0])
assert np.allclose(mod[0, 0, 1], [1.0, 2.0, 3.0])
assert np.allclose(mod[0, 0, 2], [-1.0, -2.0, -3.0])
assert np.allclose(mod[0, 0, 3], [1.0, 2.0, 3.0])
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_normsys(backend):
mc = MockConfig(
par_map={
'hello': {
'paramset': paramset(n_parameters=1, inits=[0], bounds=[[-5, 5]]),
'slice': slice(0, 1),
},
'world': {
'paramset': paramset(n_parameters=1, inits=[0], bounds=[[-5, 5]]),
'slice': slice(1, 2),
},
},
par_order=['hello', 'world'],
samples=['signal', 'background'],
)
mega_mods = {
'signal': {
'normsys/hello': {
'type': 'normsys',
'name': 'hello',
'data': {
'hi': [1.1] * 3,
'lo': [0.9] * 3,
'nom_data': [1, 1, 1],
'mask': [True, True, True],
},
},
'normsys/world': {
'type': 'v',
'name': 'world',
'data': {
'hi': [1.3] * 3,
'lo': [0.7] * 3,
'nom_data': [1, 1, 1],
'mask': [True, True, True],
},
},
},
'background': {
'normsys/hello': {
'type': 'normsys',
'name': 'hello',
'data': {
'hi': [1.2] * 3,
'lo': [0.8] * 3,
'nom_data': [1, 1, 1],
'mask': [True, True, True],
},
},
'normsys/world': {
'type': 'normsys',
'name': 'world',
'data': {
'hi': [1.4] * 3,
'lo': [0.6] * 3,
'nom_data': [1, 1, 1],
'mask': [True, True, True],
},
},
},
}
hsc = normsys_combined([('hello', 'normsys'), ('world', 'normsys')], mc, mega_mods)
mod = hsc.apply(pyhf.tensorlib.astensor([1.0, -1.0]))
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, 1.1, 1.1])
assert np.allclose(mod[0, 1, 0], [1.2, 1.2, 1.2])
assert np.allclose(mod[1, 0, 0], [0.7, 0.7, 0.7])
assert np.allclose(mod[1, 1, 0], [0.6, 0.6, 0.6])
hsc = normsys_combined(
[('hello', 'normsys'), ('world', 'normsys')], mc, mega_mods, batch_size=4
)
mod = hsc.apply(
pyhf.tensorlib.astensor([[-1.0, -1.0], [1.0, 1.0], [-1.0, -1.0], [1.0, 1.0]])
)
shape = pyhf.tensorlib.shape(mod)
assert shape == (2, 2, 4, 3)
mod = np.asarray(pyhf.tensorlib.tolist(mod))
assert np.allclose(mod[0, 0, 0], [0.9, 0.9, 0.9])
assert np.allclose(mod[0, 0, 1], [1.1, 1.1, 1.1])
assert np.allclose(mod[0, 0, 2], [0.9, 0.9, 0.9])
assert np.allclose(mod[0, 0, 3], [1.1, 1.1, 1.1])
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_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])
