How to use the pyhf.interpolators.get function in pyhf

def test_interpolator_alphaset_change(
    backend, interpcode, random_histosets_alphasets_pair
):
    histogramssets, alphasets = random_histosets_alphasets_pair
    interpolator = pyhf.interpolators.get(interpcode)(
        histogramssets.tolist(), subscribe=False
    )
    # set to None to force recomputation
    interpolator.alphasets_shape = None
    # expect recomputation to not fail
    interpolator._precompute_alphasets(alphasets.shape)
    # make sure it sets the right shape
    assert interpolator.alphasets_shape == alphasets.shape

def test_interpolator(backend, interpcode, random_histosets_alphasets_pair):
    histogramssets, alphasets = random_histosets_alphasets_pair

    interpolator = pyhf.interpolators.get(interpcode)(
        histogramssets.tolist(), subscribe=False
    )
    assert interpolator.alphasets_shape == (histogramssets.shape[0], 1)
    interpolator.alphasets_shape = None
    interpolator(pyhf.tensorlib.astensor(alphasets.tolist()))
    assert interpolator.alphasets_shape == alphasets.shape

def test_invalid_interpcode():
    with pytest.raises(pyhf.exceptions.InvalidInterpCode):
        pyhf.interpolators.get('fake')

    with pytest.raises(pyhf.exceptions.InvalidInterpCode):
        pyhf.interpolators.get(1.2)

    with pytest.raises(pyhf.exceptions.InvalidInterpCode):
        pyhf.interpolators.get(-1)

def test_interpolator_subscription(interpcode, random_histosets_alphasets_pair):
    histogramssets, alphasets = random_histosets_alphasets_pair
    ename = 'tensorlib_changed'

    # inject into our interpolator class
    interpolator_cls = pyhf.interpolators.get(interpcode)
    with mock.patch('{0:s}._precompute'.format(interpolator_cls.__module__)) as m:
        interpolator = interpolator_cls(histogramssets.tolist(), subscribe=False)
        assert m.call_count == 1
        assert m not in pyhf.events.__events.get(ename, [])
        pyhf.events.trigger(ename)()
        assert m.call_count == 1

    with mock.patch('{0:s}._precompute'.format(interpolator_cls.__module__)) as m:
        interpolator = interpolator_cls(histogramssets.tolist(), subscribe=True)
        assert m.call_count == 1
        assert m in pyhf.events.__events.get(ename, [])
        pyhf.events.trigger(ename)()
        assert m.call_count == 2

def test_validate_implementation(backend, interpcode, random_histosets_alphasets_pair):
    histogramssets, alphasets = random_histosets_alphasets_pair

    # single-float precision backends, calculate using single-floats
    if pyhf.tensorlib.name in ['tensorflow', 'pytorch']:
        abs_tolerance = 1e-6
        histogramssets = np.asarray(histogramssets, dtype=np.float32)
        alphasets = np.asarray(alphasets, dtype=np.float32)
    else:
        abs_tolerance = 1e-12

    histogramssets = histogramssets.tolist()
    alphasets = pyhf.tensorlib.astensor(alphasets.tolist())

    slow_interpolator = pyhf.interpolators.get(interpcode, do_tensorized_calc=False)(
        histogramssets, subscribe=False
    )
    fast_interpolator = pyhf.interpolators.get(interpcode, do_tensorized_calc=True)(
        histogramssets, subscribe=False
    )
    slow_result = np.asarray(pyhf.tensorlib.tolist(slow_interpolator(alphasets)))
    fast_result = np.asarray(pyhf.tensorlib.tolist(fast_interpolator(alphasets)))

    assert slow_result.shape == fast_result.shape

    assert (
        pytest.approx(
            slow_result[~np.isnan(slow_result)].ravel().tolist(), abs=abs_tolerance
        )
        == fast_result[~np.isnan(fast_result)].ravel().tolist()
    )

def test_interpolator_structure(interpcode, random_histosets_alphasets_pair):
    histogramssets, alphasets = random_histosets_alphasets_pair

    interpolator = pyhf.interpolators.get(interpcode)(
        histogramssets.tolist(), subscribe=False
    )
    assert callable(interpolator)
    assert hasattr(interpolator, 'alphasets_shape')
    assert hasattr(interpolator, '_precompute') and callable(interpolator._precompute)
    assert hasattr(interpolator, '_precompute_alphasets') and callable(
        interpolator._precompute_alphasets
    )

def test_invalid_interpcode():
    with pytest.raises(pyhf.exceptions.InvalidInterpCode):
        pyhf.interpolators.get('fake')

    with pytest.raises(pyhf.exceptions.InvalidInterpCode):
        pyhf.interpolators.get(1.2)

    with pytest.raises(pyhf.exceptions.InvalidInterpCode):
        pyhf.interpolators.get(-1)

def test_code1_validation(backend, do_tensorized_calc):
    histogramssets = [[[[0.9], [1.0], [1.1]]]]
    alphasets = pyhf.tensorlib.astensor([[-2, -1, 0, 1, 2]])
    expected = pyhf.tensorlib.astensor(
        [[[[0.9 ** 2], [0.9], [1.0], [1.1], [1.1 ** 2]]]]
    )

    interpolator = pyhf.interpolators.get(1, do_tensorized_calc=do_tensorized_calc)(
        histogramssets, subscribe=False
    )
    result_deltas = interpolator(alphasets)

    # calculate the actual change
    histogramssets = pyhf.tensorlib.astensor(histogramssets)
    allsets_allhistos_noms_repeated = pyhf.tensorlib.einsum(
        'sa,shb->shab',
        pyhf.tensorlib.ones(pyhf.tensorlib.shape(alphasets)),
        histogramssets[:, :, 1],
    )
    results = allsets_allhistos_noms_repeated * result_deltas

    assert (
        pytest.approx(np.asarray(pyhf.tensorlib.tolist(results)).ravel().tolist())
        == np.asarray(pyhf.tensorlib.tolist(expected)).ravel().tolist()

def test_code0_validation(backend, do_tensorized_calc):
    histogramssets = [[[[0.5], [1.0], [2.0]]]]
    alphasets = pyhf.tensorlib.astensor([[-2, -1, 0, 1, 2]])
    expected = pyhf.tensorlib.astensor([[[[0], [0.5], [1.0], [2.0], [3.0]]]])

    interpolator = pyhf.interpolators.get(0, do_tensorized_calc=do_tensorized_calc)(
        histogramssets, subscribe=False
    )
    result_deltas = pyhf.tensorlib.astensor(interpolator(alphasets))

    # calculate the actual change
    histogramssets = pyhf.tensorlib.astensor(histogramssets)
    allsets_allhistos_noms_repeated = pyhf.tensorlib.einsum(
        'sa,shb->shab',
        pyhf.tensorlib.ones(pyhf.tensorlib.shape(alphasets)),
        histogramssets[:, :, 1],
    )
    results = allsets_allhistos_noms_repeated + result_deltas

    assert (
        pytest.approx(np.asarray(pyhf.tensorlib.tolist(results)).ravel().tolist())
        == np.asarray(pyhf.tensorlib.tolist(expected)).ravel().tolist()

def test_invalid_interpcode():
    with pytest.raises(pyhf.exceptions.InvalidInterpCode):
        pyhf.interpolators.get('fake')

    with pytest.raises(pyhf.exceptions.InvalidInterpCode):
        pyhf.interpolators.get(1.2)

    with pytest.raises(pyhf.exceptions.InvalidInterpCode):
        pyhf.interpolators.get(-1)