def test_invalid_modifier_name_resuse():
spec = {
'channels': [{
'name':
'singlechannel',
'samples': [{
'name':
'signal',
'data': [5.],
'modifiers': [{
'name': 'reused_name',
'type': 'normfactor',
'data': None
}]
}, {
'name':
'background',
'data': [50.],
'modifiers': [{
'name': 'reused_name',
'type': 'normsys',
'data': {
'lo': 0.9,
'hi': 1.1
}
}]
}]
}]
}
with pytest.raises(pyhf.exceptions.InvalidNameReuse):
pdf = pyhf.Model(spec, poiname='reused_name')
pdf = pyhf.Model(spec, poiname='reused_name', qualify_names=True)
def test_invalid_bin_wise_modifier(datadir, patch_file):
"""
Test that bin-wise modifiers will raise an exception if their data shape
differs from their sample's.
"""
spec = json.load(open(datadir.join("spec.json")))
assert pyhf.Model(spec)
patch = JsonPatch.from_string(open(datadir.join(patch_file)).read())
bad_spec = patch.apply(spec)
with pytest.raises(pyhf.exceptions.InvalidModifier):
pyhf.Model(bad_spec)
def test_parameters_fixed():
spec = {
'channels': [{
'name':
'channel',
'samples': [
{
'name':
'sample',
'data': [10.0],
'modifiers': [{
'name': 'unfixed',
'type': 'normfactor',
'data': None
}],
},
{
'name':
'another_sample',
'data': [5.0],
'modifiers': [{
'name': 'mypoi',
'type': 'normfactor',
'data': None
}],
},
],
}],
'parameters': [{
'name': 'mypoi',
'inits': [1],
'fixed': True
}],
}
pyhf.Model(spec, poi_name='mypoi')
def test_shapefactor_build():
spec = {
'channels': [{
'name':
'channel',
'samples': [
{
'name':
'sample',
'data': [10.0] * 3,
'modifiers': [
{
'name': 'mu',
'type': 'normfactor',
'data': None
},
],
},
{
'name':
'another_sample',
'data': [5.0] * 3,
'modifiers': [{
'name': 'freeshape',
'type': 'shapefactor',
'data': None
}],
},
],
}],
}
model = pyhf.Model(spec)
assert model
def test_optim_correlations(backend, source, spec, mu):
pdf = pyhf.Model(spec)
data = source['bindata']['data'] + pdf.config.auxdata
init_pars = pdf.config.suggested_init()
par_bounds = pdf.config.suggested_bounds()
optim = pyhf.optimizer
result = optim.minimize(pyhf.infer.mle.twice_nll, data, pdf, init_pars,
par_bounds)
assert pyhf.tensorlib.tolist(result)
result, correlations = optim.minimize(
pyhf.infer.mle.twice_nll,
data,
pdf,
init_pars,
par_bounds,
[(pdf.config.poi_index, mu)],
return_correlations=True,
)
assert result.shape == (2, )
assert correlations.shape == (2, 2)
assert pyhf.tensorlib.tolist(result)
assert pyhf.tensorlib.tolist(correlations)
assert np.allclose([[0.0, 0.0], [0.0, 1.0]],
pyhf.tensorlib.tolist(correlations))
def test_validation(setup):
source = setup['source']
pdf = pyhf.Model(setup['spec'],
modifier_settings={'normsys': {
'interpcode': 'code1'
}})
if 'channels' in source:
data = []
for c in pdf.config.channels:
data += source['channels'][c]['bindata']['data']
data = data + pdf.config.auxdata
else:
data = source['bindata']['data'] + pdf.config.auxdata
if 'auxdata' in setup['expected']['config']:
assert len(
pdf.config.auxdata) == setup['expected']['config']['auxdata']
assert len(pdf.config.suggested_init()
) == setup['expected']['config']['init_pars']
assert (len(pdf.config.suggested_bounds()) == setup['expected']['config']
['par_bounds'])
validate_hypotest(
pdf,
data,
setup['mu'],
setup['expected']['result'],
test_stat=setup['test_stat'],
tolerance=setup['tolerance'],
calctype=setup['calctype'],
)
def test_optim_with_value(backend, source, spec, mu):
pdf = pyhf.Model(spec)
data = source['bindata']['data'] + pdf.config.auxdata
init_pars = pdf.config.suggested_init()
par_bounds = pdf.config.suggested_bounds()
optim = pyhf.optimizer
result = optim.minimize(pyhf.infer.mle.twice_nll, data, pdf, init_pars,
par_bounds)
assert pyhf.tensorlib.tolist(result)
result, fitted_val = optim.minimize(
pyhf.infer.mle.twice_nll,
data,
pdf,
init_pars,
par_bounds,
fixed_vals=[(pdf.config.poi_index, mu)],
return_fitted_val=True,
)
assert pyhf.tensorlib.tolist(result)
assert pyhf.tensorlib.shape(fitted_val) == ()
assert pytest.approx(17.52954975,
rel=1e-5) == pyhf.tensorlib.tolist(fitted_val)
def test_model_integration_fixed_parameters_shapesys():
spec = {
'channels': [
{
'name': 'channel',
'samples': [
{
'name': 'sample',
'data': [10.0] * 3,
'modifiers': [
{'name': 'unfixed', 'type': 'normfactor', 'data': None},
{'name': 'uncorr', 'type': 'shapesys', 'data': [1.5] * 3},
],
},
{
'name': 'another_sample',
'data': [5.0] * 3,
'modifiers': [
{'name': 'mypoi', 'type': 'normfactor', 'data': None}
],
},
],
}
],
'parameters': [{'name': 'uncorr', 'inits': [1.0, 2.0, 3.0], 'fixed': True}],
}
model = pyhf.Model(spec, poi_name='mypoi')
assert len(model.config.suggested_fixed()) == 5
assert model.config.suggested_fixed() == [False, True, True, True, False]
assert model.config.poi_index == 4
def test_override_paramsets_incorrect_num_parameters():
source = json.load(open('validation/data/2bin_histosys_example2.json'))
spec = {
'channels': [
{
'name': 'singlechannel',
'samples': [
{
'name': 'signal',
'data': source['bindata']['sig'],
'modifiers': [
{'name': 'mu', 'type': 'normfactor', 'data': None}
],
},
{
'name': 'background',
'data': source['bindata']['bkg'],
'modifiers': [
{'name': 'bkg_norm', 'type': 'shapesys', 'data': [10, 10]}
],
},
],
}
],
'parameters': [{'name': 'bkg_norm', 'inits': [99, 99], 'bounds': [[95, 95]]}],
}
with pytest.raises(pyhf.exceptions.InvalidModel):
pyhf.Model(spec)
def test_validation(setup_and_tolerance):
setup, tolerance = setup_and_tolerance
source = setup['source']
pdf = pyhf.Model(setup['spec'])
if 'channels' in source:
data = []
for c in pdf.config.channels:
data += source['channels'][c]['bindata']['data']
data = data + pdf.config.auxdata
else:
data = source['bindata']['data'] + pdf.config.auxdata
if 'auxdata' in setup['expected']['config']:
assert len(
pdf.config.auxdata) == setup['expected']['config']['auxdata']
assert len(pdf.config.suggested_init()
) == setup['expected']['config']['init_pars']
assert (len(pdf.config.suggested_bounds()) == setup['expected']['config']
['par_bounds'])
validate_hypotest(pdf,
data,
setup['mu'],
setup['expected']['result'],
tolerance=tolerance)
def test_import_shapesys():
parsed_xml = pyhf.readxml.parse(
'validation/xmlimport_input3/config/examples/example_ShapeSys.xml',
'validation/xmlimport_input3',
)
# build the spec, strictly checks properties included
spec = {
'channels': parsed_xml['channels'],
'parameters': parsed_xml['measurements'][0]['config']['parameters'],
}
pdf = pyhf.Model(spec, poiname='SigXsecOverSM')
data = [
binvalue for k in pdf.spec['channels']
for binvalue in next(obs for obs in parsed_xml['observations']
if obs['name'] == k['name'])['data']
] + pdf.config.auxdata
channels = {channel['name']: channel for channel in pdf.spec['channels']}
samples = {
channel['name']: [sample['name'] for sample in channel['samples']]
for channel in pdf.spec['channels']
}
assert channels['channel1']['samples'][1]['modifiers'][0]['type'] == 'lumi'
assert channels['channel1']['samples'][1]['modifiers'][1][
'type'] == 'shapesys'
# NB: assert that relative uncertainty is converted to absolute uncertainty for shapesys
assert channels['channel1']['samples'][1]['data'] == pytest.approx(
[100.0, 1.0e-4])
assert channels['channel1']['samples'][1]['modifiers'][1][
'data'] == pytest.approx([10.0, 1.5e-5])
def test_override_paramset_defaults():
source = json.load(open('validation/data/2bin_histosys_example2.json'))
spec = {
'channels': [
{
'name': 'singlechannel',
'samples': [
{
'name': 'signal',
'data': source['bindata']['sig'],
'modifiers': [
{'name': 'mu', 'type': 'normfactor', 'data': None}
],
},
{
'name': 'background',
'data': source['bindata']['bkg'],
'modifiers': [
{'name': 'bkg_norm', 'type': 'shapesys', 'data': [10, 10]}
],
},
],
}
],
'parameters': [
{'name': 'bkg_norm', 'inits': [99, 99], 'bounds': [[95, 95], [95, 95]]}
],
}
pdf = pyhf.Model(spec)
assert pdf.config.param_set('bkg_norm').suggested_bounds == [[95, 95], [95, 95]]
assert pdf.config.param_set('bkg_norm').suggested_init == [99, 99]
def test_parameters_incorrect_format():
spec = {
'channels': [{
'name':
'channel',
'samples': [
{
'name': 'sample',
'data': [10.0],
'modifiers': []
},
{
'name':
'another_sample',
'data': [5.0],
'modifiers': [{
'name': 'mypoi',
'type': 'normfactor',
'data': None
}],
},
],
}],
'parameters': {
'a': 'fake',
'object': 2
},
}
with pytest.raises(pyhf.exceptions.InvalidSpecification):
pyhf.Model(spec, poiname='mypoi')
def test_import_histosys():
parsed_xml = pyhf.readxml.parse(
'validation/xmlimport_input2/config/example.xml',
'validation/xmlimport_input2')
# build the spec, strictly checks properties included
spec = {
'channels': parsed_xml['channels'],
'parameters': parsed_xml['measurements'][0]['config']['parameters'],
}
pdf = pyhf.Model(spec, poiname='SigXsecOverSM')
data = [
binvalue for k in pdf.spec['channels']
for binvalue in next(obs for obs in parsed_xml['observations']
if obs['name'] == k['name'])['data']
] + pdf.config.auxdata
channels = {channel['name']: channel for channel in pdf.spec['channels']}
samples = {
channel['name']: [sample['name'] for sample in channel['samples']]
for channel in pdf.spec['channels']
}
assert channels['channel2']['samples'][0]['modifiers'][0]['type'] == 'lumi'
assert channels['channel2']['samples'][0]['modifiers'][1][
'type'] == 'histosys'
def test_poiless_model(backend):
spec = {
'channels': [{
'name':
'channel',
'samples': [
{
'name':
'goodsample',
'data': [10.0],
'modifiers': [{
'type': 'normsys',
'name': 'shape',
'data': {
"hi": 0.5,
"lo": 1.5
},
}],
},
],
}]
}
model = pyhf.Model(spec, poi_name=None)
data = [12] + model.config.auxdata
pyhf.infer.mle.fit(data, model)
with pytest.raises(pyhf.exceptions.UnspecifiedPOI):
pyhf.infer.mle.fixed_poi_fit(1.0, data, model)
with pytest.raises(pyhf.exceptions.UnspecifiedPOI):
pyhf.infer.hypotest(1.0, data, model)
def test_parameters_normfactor_bad_attribute(bad_parameter):
spec = {
'channels': [{
'name':
'channel',
'samples': [
{
'name': 'sample',
'data': [10.0],
'modifiers': []
},
{
'name':
'another_sample',
'data': [5.0],
'modifiers': [{
'name': 'mypoi',
'type': 'normfactor',
'data': None
}],
},
],
}],
'parameters': [bad_parameter],
}
with pytest.raises(pyhf.exceptions.InvalidModel):
pyhf.Model(spec, poiname='mypoi')
def test_one_sample_missing_modifiers():
spec = {
'channels': [{
'name':
'channel',
'samples': [
{
'name': 'sample',
'data': [10.0],
'modifiers': []
},
{
'name':
'another_sample',
'data': [5.0],
'modifiers': [{
'name': 'mypoi',
'type': 'normfactor',
'data': None
}],
},
],
}]
}
pyhf.Model(spec, poiname='mypoi')
def test_parameters_all_props():
spec = {
'channels': [{
'name':
'channel',
'samples': [
{
'name': 'sample',
'data': [10.0],
'modifiers': []
},
{
'name':
'another_sample',
'data': [5.0],
'modifiers': [{
'name': 'mypoi',
'type': 'normfactor',
'data': None
}],
},
],
}],
'parameters': [{
'name': 'mypoi',
'inits': [1],
'bounds': [[0, 1]]
}],
}
pyhf.Model(spec, poiname='mypoi')
def test_parameters_definition():
spec = {
'channels': [{
'name':
'channel',
'samples': [
{
'name': 'sample',
'data': [10.0],
'modifiers': []
},
{
'name':
'another_sample',
'data': [5.0],
'modifiers': [{
'name': 'mypoi',
'type': 'normfactor',
'data': None
}],
},
],
}],
'parameters': [{
'name': 'mypoi'
}],
}
pyhf.Model(spec, poiname='mypoi')
def test_additional_properties():
spec = {
'channels': [{
'name':
'channel',
'samples': [
{
'name': 'sample',
'data': [10.0],
'modifiers': []
},
{
'name':
'another_sample',
'data': [5.0],
'modifiers': [{
'name': 'mypoi',
'type': 'normfactor',
'data': None
}],
},
],
}],
'fake_additional_property':
2,
}
with pytest.raises(pyhf.exceptions.InvalidSpecification):
pyhf.Model(spec)
def test_optim_uncerts(backend, source, spec, mu):
pdf = pyhf.Model(spec)
data = source['bindata']['data'] + pdf.config.auxdata
init_pars = pdf.config.suggested_init()
par_bounds = pdf.config.suggested_bounds()
optim = pyhf.optimizer
result = optim.minimize(pyhf.infer.mle.twice_nll, data, pdf, init_pars,
par_bounds)
assert pyhf.tensorlib.tolist(result)
result = optim.minimize(
pyhf.infer.mle.twice_nll,
data,
pdf,
init_pars,
par_bounds,
fixed_vals=[(pdf.config.poi_index, mu)],
return_uncertainties=True,
)
assert result.shape[1] == 2
assert pytest.approx([0.0, 0.26418431]) == pyhf.tensorlib.tolist(result[:,
1])
def test_pdf_integration_normsys(backend):
source = json.load(open('validation/data/2bin_histosys_example2.json'))
spec = {
'channels': [
{
'name': 'singlechannel',
'samples': [
{
'name': 'signal',
'data': source['bindata']['sig'],
'modifiers': [
{'name': 'mu', 'type': 'normfactor', 'data': None}
],
},
{
'name': 'background',
'data': source['bindata']['bkg'],
'modifiers': [
{
'name': 'bkg_norm',
'type': 'normsys',
'data': {'lo': 0.9, 'hi': 1.1},
}
],
},
],
}
]
}
pdf = pyhf.Model(spec)
pars = [None, None]
pars[pdf.config.par_slice('mu')], pars[pdf.config.par_slice('bkg_norm')] = [
[0.0],
[0.0],
]
assert np.allclose(
pyhf.tensorlib.tolist(pdf.expected_data(pars, include_auxdata=False)),
[100, 150],
)
pars[pdf.config.par_slice('mu')], pars[pdf.config.par_slice('bkg_norm')] = [
[0.0],
[1.0],
]
assert np.allclose(
pyhf.tensorlib.tolist(pdf.expected_data(pars, include_auxdata=False)),
[100 * 1.1, 150 * 1.1],
)
pars[pdf.config.par_slice('mu')], pars[pdf.config.par_slice('bkg_norm')] = [
[0.0],
[-1.0],
]
assert np.allclose(
pyhf.tensorlib.tolist(pdf.expected_data(pars, include_auxdata=False)),
[100 * 0.9, 150 * 0.9],
)
def test_no_poi_test_stats():
spec = {
"channels": [{
"name":
"channel",
"samples": [
{
"name":
"sample",
"data": [10.0],
"modifiers": [{
"type": "normsys",
"name": "shape",
"data": {
"hi": 0.5,
"lo": 1.5
},
}],
},
],
}]
}
model = pyhf.Model(spec, poi_name=None)
test_poi = 1.0
data = [12] + model.config.auxdata
init_pars = model.config.suggested_init()
par_bounds = model.config.suggested_bounds()
fixed_params = model.config.suggested_fixed()
with pytest.raises(pyhf.exceptions.UnspecifiedPOI) as excinfo:
pyhf.infer.test_statistics.qmu(test_poi, data, model, init_pars,
par_bounds, fixed_params)
assert (
"No POI is defined. A POI is required for profile likelihood based test statistics."
in str(excinfo.value))
with pytest.raises(pyhf.exceptions.UnspecifiedPOI) as excinfo:
pyhf.infer.test_statistics.qmu_tilde(test_poi, data, model, init_pars,
par_bounds, fixed_params)
assert (
"No POI is defined. A POI is required for profile likelihood based test statistics."
in str(excinfo.value))
with pytest.raises(pyhf.exceptions.UnspecifiedPOI) as excinfo:
pyhf.infer.test_statistics.tmu(test_poi, data, model, init_pars,
par_bounds, fixed_params)
assert (
"No POI is defined. A POI is required for profile likelihood based test statistics."
in str(excinfo.value))
with pytest.raises(pyhf.exceptions.UnspecifiedPOI) as excinfo:
pyhf.infer.test_statistics.tmu_tilde(test_poi, data, model, init_pars,
par_bounds, fixed_params)
assert (
"No POI is defined. A POI is required for profile likelihood based test statistics."
in str(excinfo.value))
def test_import_prepHistFactory():
parsed_xml = pyhf.readxml.parse(
'validation/xmlimport_input/config/example.xml',
'validation/xmlimport_input/')
# build the spec, strictly checks properties included
spec = {'channels': parsed_xml['channels']}
pdf = pyhf.Model(spec, poiname='SigXsecOverSM')
data = [
binvalue for k in pdf.spec['channels']
for binvalue in parsed_xml['data'][k['name']]
] + pdf.config.auxdata
channels = {channel['name'] for channel in pdf.spec['channels']}
samples = {
channel['name']: [sample['name'] for sample in channel['samples']]
for channel in pdf.spec['channels']
}
###
### signal overallsys
### bkg1 overallsys (stat ignored)
### bkg2 stateror (2 bins)
### bkg2 overallsys
assert 'channel1' in channels
assert 'signal' in samples['channel1']
assert 'background1' in samples['channel1']
assert 'background2' in samples['channel1']
assert pdf.spec['channels'][0]['samples'][2]['modifiers'][0][
'type'] == 'staterror'
assert pdf.spec['channels'][0]['samples'][2]['modifiers'][0]['data'] == [
0, 10.
]
assert pdf.spec['channels'][0]['samples'][1]['modifiers'][0][
'type'] == 'staterror'
assert all(
np.isclose(
pdf.spec['channels'][0]['samples'][1]['modifiers'][0]['data'],
[5.0, 0.0]))
assert pdf.expected_actualdata(
pdf.config.suggested_init()).tolist() == [120.0, 110.0]
assert pdf.config.auxdata_order == [
'syst1', 'staterror_channel1', 'syst2', 'syst3'
]
assert data == [122.0, 112.0, 0.0, 1.0, 1.0, 0.0, 0.0]
pars = pdf.config.suggested_init()
pars[pdf.config.par_slice('SigXsecOverSM')] = [2.0]
assert pdf.expected_data(pars,
include_auxdata=False).tolist() == [140, 120]
def test_pdf_integration_shapesys():
source = json.load(open('validation/data/2bin_histosys_example2.json'))
spec = {
'channels': [{
'name':
'singlechannel',
'samples': [{
'name':
'signal',
'data':
source['bindata']['sig'],
'modifiers': [{
'name': 'mu',
'type': 'normfactor',
'data': None
}]
}, {
'name':
'background',
'data':
source['bindata']['bkg'],
'modifiers': [{
'name': 'bkg_norm',
'type': 'shapesys',
'data': [10, 10]
}]
}]
}]
}
pdf = pyhf.Model(spec)
pars = [None, None]
pars[pdf.config.par_slice('mu')], pars[pdf.config.par_slice(
'bkg_norm')] = [[0.0], [1.0, 1.0]]
assert pdf.expected_data(pars,
include_auxdata=False).tolist() == [100, 150]
pars[pdf.config.par_slice('mu')], pars[pdf.config.par_slice(
'bkg_norm')] = [[0.0], [1.1, 1.0]]
assert pdf.expected_data(
pars, include_auxdata=False).tolist() == [100 * 1.1, 150]
pars[pdf.config.par_slice('mu')], pars[pdf.config.par_slice(
'bkg_norm')] = [[0.0], [1.0, 1.1]]
assert pdf.expected_data(
pars, include_auxdata=False).tolist() == [100, 150 * 1.1]
pars[pdf.config.par_slice('mu')], pars[pdf.config.par_slice(
'bkg_norm')] = [[0.0], [1.1, 0.9]]
assert pdf.expected_data(
pars, include_auxdata=False).tolist() == [100 * 1.1, 150 * 0.9]
pars[pdf.config.par_slice('mu')], pars[pdf.config.par_slice(
'bkg_norm')] = [[0.0], [0.9, 1.1]]
assert pdf.expected_data(
pars, include_auxdata=False).tolist() == [100 * 0.9, 150 * 1.1]
def test_no_samples():
spec = {
'channels': [
{
'name': 'channel',
'samples': []
},
]
}
with pytest.raises(pyhf.exceptions.InvalidSpecification):
pyhf.Model(spec)
def test_pdf_integration_staterror(backend):
spec = {
'channels': [{
'name':
'firstchannel',
'samples': [
{
'name':
'mu',
'data': [10.0, 10.0],
'modifiers': [{
'name': 'mu',
'type': 'normfactor',
'data': None
}],
},
{
'name':
'bkg1',
'data': [50.0, 70.0],
'modifiers': [{
'name': 'stat_firstchannel',
'type': 'staterror',
'data': [12.0, 12.0],
}],
},
{
'name':
'bkg2',
'data': [30.0, 20.0],
'modifiers': [{
'name': 'stat_firstchannel',
'type': 'staterror',
'data': [5.0, 5.0],
}],
},
{
'name': 'bkg3',
'data': [20.0, 15.0],
'modifiers': []
},
],
}]
}
pdf = pyhf.Model(spec)
par = pdf.config.par_slice('stat_firstchannel')
par_set = pdf.config.param_set('stat_firstchannel')
tensorlib, _ = backend
uncerts = tensorlib.astensor([[12.0, 12.0], [5.0, 5.0]])
nominal = tensorlib.astensor([[50.0, 70.0], [30.0, 20.0]])
quad = tensorlib.sqrt(tensorlib.sum(tensorlib.power(uncerts, 2), axis=0))
totals = tensorlib.sum(nominal, axis=0)
assert pytest.approx(tensorlib.tolist(par_set.sigmas)) == tensorlib.tolist(
tensorlib.divide(quad, totals))
def test_sample_missing_data():
spec = {
'channels': [
{
'name': 'channel',
'samples': [{'name': 'sample', 'data': [], 'modifiers': []}],
}
]
}
with pytest.raises(pyhf.exceptions.InvalidSpecification):
pyhf.Model(spec)
def test_pdf_integration_staterror():
spec = {
'channels': [
{
'name':
'firstchannel',
'samples': [{
'name':
'mu',
'data': [10., 10.],
'modifiers': [{
'name': 'mu',
'type': 'normfactor',
'data': None
}]
}, {
'name':
'bkg1',
'data': [50.0, 70.0],
'modifiers': [{
'name': 'stat_firstchannel',
'type': 'staterror',
'data': [12., 12.]
}]
}, {
'name':
'bkg2',
'data': [30.0, 20.],
'modifiers': [{
'name': 'stat_firstchannel',
'type': 'staterror',
'data': [5., 5.]
}]
}, {
'name': 'bkg3',
'data': [20.0, 15.0],
'modifiers': []
}]
},
]
}
pdf = pyhf.Model(spec)
par = pdf.config.par_slice('stat_firstchannel')
mod = pdf.config.modifier('stat_firstchannel')
assert mod.uncertainties == [[12., 12.], [5., 5.]]
assert mod.nominal_counts == [[50., 70.], [30., 20.]]
computed = pyhf.tensorlib.tolist(mod.pdf([1.0, 1.0], [1.0, 1.0]))
expected = pyhf.tensorlib.tolist(
pyhf.tensorlib.normal([1.0, 1.0],
mu=[1.0, 1.0],
sigma=[13. / 80., 13. / 90.]))
for c, e in zip(computed, expected):
assert c == e
def test_sample_missing_all_modifiers():
spec = {
'channels': [
{
'name': 'channel',
'samples': [{'name': 'sample', 'data': [10.0], 'modifiers': []}],
}
]
}
with pytest.raises(pyhf.exceptions.InvalidModel):
pyhf.Model(spec)
