How to use the pyhf.tensorlib function in pyhf

),
                    axis=0,
                )

                constraint_term = tensorlib.poisson_logpdf(thisauxdata, paralphas)
            summands = (
                constraint_term
                if summands is None
                else tensorlib.concatenate([summands, constraint_term])
            )
        return tensorlib.sum(summands) if summands is not None else 0

    def fast(self, auxdata, pars):
        return self.constraint_logpdf(auxdata, pars)

    auxd = pyhf.tensorlib.astensor(m.config.auxdata)
    pars = pyhf.tensorlib.astensor(m.config.suggested_init())
    slow_result = pyhf.tensorlib.tolist(slow(m, auxd, pars))
    fast_result = pyhf.tensorlib.tolist(fast(m, auxd, pars))
    assert pytest.approx(slow_result) == fast_result

assert pdf.spec['channels'][0]['samples'][1]['modifiers'][0]['type'] == 'lumi'
    assert pdf.spec['channels'][0]['samples'][2]['modifiers'][0]['type'] == 'lumi'

    assert pdf.spec['channels'][0]['samples'][2]['modifiers'][1]['type'] == 'staterror'
    assert pdf.spec['channels'][0]['samples'][2]['modifiers'][1]['data'] == [0, 10.0]

    assert pdf.spec['channels'][0]['samples'][1]['modifiers'][1]['type'] == 'staterror'
    assert all(
        np.isclose(
            pdf.spec['channels'][0]['samples'][1]['modifiers'][1]['data'], [5.0, 0.0]
        )
    )

    assert pdf.expected_actualdata(
        pyhf.tensorlib.astensor(pdf.config.suggested_init())
    ).tolist() == [120.0, 110.0]

    assert pdf.config.auxdata_order == sorted(
        ['lumi', 'syst1', 'staterror_channel1', 'syst2', 'syst3']
    )

    assert data == [122.0, 112.0, 1.0, 1.0, 1.0, 0.0, 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_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,
        [(pdf.config.poi_index, mu)],
        return_uncertainties=True,
    )
    assert result.shape[1] == 2
    assert pyhf.tensorlib.tolist(result)

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,
        [(pdf.config.poi_index, mu)],
        return_uncertainties=True,
    )
    assert result.shape[1] == 2
    assert pyhf.tensorlib.tolist(result)

def test_paramviewer_simple_nonbatched(backend):
    pars = pyhf.tensorlib.astensor([1, 2, 3, 4, 5, 6, 7])

    parshape = pyhf.tensorlib.shape(pars)

    view = ParamViewer(
        parshape,
        {'hello': {'slice': slice(0, 2)}, 'world': {'slice': slice(5, 7)}},
        ['hello', 'world'],
    )
    par_slice = view.get(pars)
    assert pyhf.tensorlib.tolist(par_slice[view.slices[0]]) == [1, 2]

    assert pyhf.tensorlib.tolist(par_slice[view.slices[1]]) == [6, 7]

    assert pyhf.tensorlib.tolist(par_slice) == [1, 2, 6, 7]

def test_custom_backend_name_notsupported():
    class custom_backend(object):
        def __init__(self, **kwargs):
            self.name = "notsupported"

    backend = custom_backend()
    assert pyhf.tensorlib.name != backend.name
    pyhf.set_backend(backend)
    assert pyhf.tensorlib.name == backend.name

def test_paramviewer_simple_nonbatched(backend):
    pars = pyhf.tensorlib.astensor([1, 2, 3, 4, 5, 6, 7])

    parshape = pyhf.tensorlib.shape(pars)

    view = ParamViewer(
        parshape,
        {'hello': {'slice': slice(0, 2)}, 'world': {'slice': slice(5, 7)}},
        ['hello', 'world'],
    )
    par_slice = view.get(pars)
    assert pyhf.tensorlib.tolist(par_slice[view.slices[0]]) == [1, 2]

    assert pyhf.tensorlib.tolist(par_slice[view.slices[1]]) == [6, 7]

    assert pyhf.tensorlib.tolist(par_slice) == [1, 2, 6, 7]

'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])

def test_custom_backend_name_notsupported():
    class custom_backend(object):
        def __init__(self, **kwargs):
            self.name = "notsupported"

    backend = custom_backend()
    assert pyhf.tensorlib.name != backend.name
    pyhf.set_backend(backend)
    assert pyhf.tensorlib.name == backend.name

def test_shape(backend):
    tb = pyhf.tensorlib
    assert tb.shape(tb.ones((1, 2, 3, 4, 5))) == (1, 2, 3, 4, 5)
    assert tb.shape(tb.ones((0, 0))) == (0, 0)
    assert tb.shape(tb.astensor([])) == (0,)
    assert tb.shape(tb.astensor([1.0])) == (1,)
    assert tb.shape(tb.astensor(1.0)) == tb.shape(tb.astensor([1.0]))
    assert tb.shape(tb.astensor(0.0)) == tb.shape(tb.astensor([0.0]))
    assert tb.shape(tb.astensor((1.0, 1.0))) == tb.shape(tb.astensor([1.0, 1.0]))
    assert tb.shape(tb.astensor((0.0, 0.0))) == tb.shape(tb.astensor([0.0, 0.0]))
    with pytest.raises((ValueError, RuntimeError)):
        _ = tb.astensor([1, 2]) + tb.astensor([3, 4, 5])
    with pytest.raises((ValueError, RuntimeError)):
        _ = tb.astensor([1, 2]) - tb.astensor([3, 4, 5])
    with pytest.raises((ValueError, RuntimeError)):
        _ = tb.astensor([1, 2]) < tb.astensor([3, 4, 5])
    with pytest.raises((ValueError, RuntimeError)):
        _ = tb.astensor([1, 2]) > tb.astensor([3, 4, 5])