def test_lumi(backend):
mc = MockConfig(
par_map={
'lumi': {
'paramset': constrained_by_normal(
n_parameters=1,
inits=[0],
bounds=[[-5, 5]],
fixed=False,
auxdata=[None],
sigmas=[None],
),
'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_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 = {
'signal': {
'lumi/lumi': {
'type': 'lumi',
'name': 'lumi',
'data': {
'mask': [True, True, True]
},
}
},
'background': {
'lumi/lumi': {
'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])