def test_limit(mock_draw):
folder_path = "tmp"
figure_path = pathlib.Path(folder_path) / "limit.pdf"
observed_CLs = np.asarray([0.75, 0.32, 0.02])
expected_CLs = np.asarray([[0.1, 0.2, 0.3, 0.4, 0.5] for _ in range(3)])
poi_values = np.asarray([0, 1, 2])
limit_results = fit.LimitResults(3.0, np.empty(5), observed_CLs,
expected_CLs, poi_values)
visualize.limit(limit_results, figure_folder=folder_path)
assert mock_draw.call_count == 1
assert np.allclose(mock_draw.call_args[0][0], limit_results.observed_CLs)
assert np.allclose(mock_draw.call_args[0][1], limit_results.expected_CLs)
assert np.allclose(mock_draw.call_args[0][2], limit_results.poi_values)
assert mock_draw.call_args[0][3] == figure_path
assert mock_draw.call_args[1] == {"close_figure": False}
# close figure
visualize.limit(limit_results,
figure_folder=folder_path,
close_figure=True)
assert mock_draw.call_args[1] == {"close_figure": True}
# unknown plotting method
with pytest.raises(NotImplementedError, match="unknown backend: unknown"):
visualize.limit(limit_results,
figure_folder=folder_path,
method="unknown")
def test_limit(mock_limit, mock_vis, tmp_path):
workspace = {"workspace": "mock"}
workspace_path = str(tmp_path / "workspace.json")
# need to save workspace to file since click looks for it
with open(workspace_path, "w") as f:
f.write('{"workspace": "mock"}')
limit_results = fit.LimitResults(
3.0,
np.asarray([1.0, 2.0, 3.0, 4.0, 5.0]),
np.asarray([0.05]),
np.asarray([0.01, 0.02, 0.05, 0.07, 0.10]),
np.asarray([3.0]),
)
runner = CliRunner()
# default
result = runner.invoke(cli.limit, [workspace_path])
assert result.exit_code == 0
assert mock_limit.call_args_list == [((workspace, ), {
"asimov": False,
"tolerance": 0.01
})]
assert mock_vis.call_count == 1
assert np.allclose(mock_vis.call_args[0][0].observed_limit,
limit_results.observed_limit)
assert np.allclose(mock_vis.call_args[0][0].expected_limit,
limit_results.expected_limit)
assert np.allclose(mock_vis.call_args[0][0].observed_CLs,
limit_results.observed_CLs)
assert np.allclose(mock_vis.call_args[0][0].expected_CLs,
limit_results.expected_CLs)
assert np.allclose(mock_vis.call_args[0][0].poi_values,
limit_results.poi_values)
assert mock_vis.call_args[1] == {"figure_folder": "figures"}
# Asimov, tolerance, custom folder
result = runner.invoke(
cli.limit,
[
"--asimov", "--tolerance", "0.1", "--figfolder", "folder",
workspace_path
],
)
assert result.exit_code == 0
assert mock_limit.call_args_list[-1] == (
(workspace, ),
{
"asimov": True,
"tolerance": 0.1
},
)
assert mock_vis.call_args_list[-1][1] == {"figure_folder": "folder"}
def test_LimitResults():
observed_limit = 3.0
expected_limit = np.asarray([1.0, 2.0, 3.0, 4.0, 5.0])
observed_CLs = np.asarray([0.05])
expected_CLs = np.asarray([0.01, 0.02, 0.05, 0.07, 0.10])
poi_values = np.asarray([3.0])
limit_results = fit.LimitResults(observed_limit, expected_limit,
observed_CLs, expected_CLs, poi_values)
assert limit_results.observed_limit == observed_limit
assert np.allclose(limit_results.expected_limit, expected_limit)
assert np.allclose(limit_results.observed_CLs, observed_CLs)
assert np.allclose(limit_results.expected_CLs, expected_CLs)
assert np.allclose(limit_results.poi_values, poi_values)
("model", "data", par_name),
{
"par_range": (0.0, 2.0),
"n_steps": 21
},
)
assert mock_vis.call_args[1] == {"figure_folder": "folder"}
@mock.patch("cabinetry.visualize.limit", autospec=True)
@mock.patch(
"cabinetry.fit.limit",
return_value=fit.LimitResults(
3.0,
np.asarray([1.0, 2.0, 3.0, 4.0, 5.0]),
np.asarray([0.05]),
np.asarray([0.01, 0.02, 0.05, 0.07, 0.10]),
np.asarray([3.0]),
),
autospec=True,
)
@mock.patch(
"cabinetry.model_utils.model_and_data",
return_value=("model", "data"),
autospec=True,
)
def test_limit(mock_util, mock_limit, mock_vis, tmp_path):
workspace = {"workspace": "mock"}
workspace_path = str(tmp_path / "workspace.json")
# need to save workspace to file since click looks for it