def test_scan(mock_draw):
folder_path = "tmp"
figure_path = pathlib.Path(folder_path) / "scan_a_0.pdf"
par_name = "a[0]"
par_mle = 1.5
par_unc = 0.2
par_vals = np.asarray([1.3, 1.5, 1.7])
par_nlls = np.asarray([0.9, 0.0, 1.1])
scan_results = fit.ScanResults(par_name, par_mle, par_unc, par_vals,
par_nlls)
visualize.scan(scan_results, figure_folder=folder_path)
assert mock_draw.call_count == 1
assert mock_draw.call_args[0][0] == par_name
assert mock_draw.call_args[0][1] == par_mle
assert mock_draw.call_args[0][2] == par_unc
assert np.allclose(mock_draw.call_args[0][3], par_vals)
assert np.allclose(mock_draw.call_args[0][4], par_nlls)
assert mock_draw.call_args[0][5] == figure_path
assert mock_draw.call_args[1] == {}
# unknown plotting method
with pytest.raises(NotImplementedError, match="unknown backend: unknown"):
visualize.scan(scan_results,
figure_folder=folder_path,
method="unknown")
def test_ScanResults():
name = "par_a"
bestfit = 1.2
uncertainty = 0.3
parameter_values = np.asarray([0.9, 1.2, 1.5])
delta_nlls = np.asarray([1.0, 0.0, 1.0])
scan_results = fit.ScanResults(name, bestfit, uncertainty,
parameter_values, delta_nlls)
assert scan_results.name == name
assert scan_results.bestfit == bestfit
assert scan_results.uncertainty == uncertainty
assert np.allclose(scan_results.parameter_values, parameter_values)
assert np.allclose(scan_results.delta_nlls, delta_nlls)
def test_scan(mock_util, mock_scan, 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"}')
par_name = "par"
scan_results = fit.ScanResults(par_name, 1.0, 0.1, np.asarray([1.5]),
np.asarray([3.5]))
runner = CliRunner()
# default
result = runner.invoke(cli.scan, [workspace_path, par_name])
assert result.exit_code == 0
assert mock_util.call_args_list == [((workspace, ), {"asimov": False})]
assert mock_scan.call_args_list == [(("model", "data", par_name), {
"par_range": None,
"n_steps": 11
})]
assert mock_vis.call_count == 1
assert mock_vis.call_args[0][0].name == scan_results.name
assert mock_vis.call_args[0][0].bestfit == scan_results.bestfit
assert mock_vis.call_args[0][0].uncertainty == scan_results.uncertainty
assert np.allclose(mock_vis.call_args[0][0].parameter_values,
scan_results.parameter_values)
assert np.allclose(mock_vis.call_args[0][0].delta_nlls,
scan_results.delta_nlls)
assert mock_vis.call_args[1] == {"figure_folder": "figures"}
# only one bound
with pytest.raises(
ValueError,
match=
"Need to either specify both lower_bound and upper_bound, or neither.",
):
runner.invoke(
cli.scan,
["--lower_bound", 1.0, workspace_path, par_name],
catch_exceptions=False,
)
with pytest.raises(
ValueError,
match=
"Need to either specify both lower_bound and upper_bound, or neither.",
):
runner.invoke(
cli.scan,
["--upper_bound", 1.0, workspace_path, par_name],
catch_exceptions=False,
)
# custom bounds, number of steps and Asimov
result = runner.invoke(
cli.scan,
[
"--lower_bound",
0.0,
"--upper_bound",
2.0,
"--n_steps",
21,
"--asimov",
"--figfolder",
"folder",
workspace_path,
par_name,
],
)
assert result.exit_code == 0
assert mock_util.call_args_list[-1] == ((workspace, ), {"asimov": True})
assert mock_scan.call_args_list[-1] == (
("model", "data", par_name),
{
"par_range": (0.0, 2.0),
"n_steps": 21
},
)
assert mock_vis.call_args[1] == {"figure_folder": "folder"}
assert mock_rank.call_args_list[-1] == (
("model", "data"),
{
"fit_results": fit_results
},
)
assert mock_vis.call_args_list[-1][1] == {
"figure_folder": "folder",
"max_pars": 3
}
@mock.patch("cabinetry.visualize.scan", autospec=True)
@mock.patch(
"cabinetry.fit.scan",
return_value=fit.ScanResults("par", 1.0, 0.1, np.asarray([1.5]),
np.asarray([3.5])),
autospec=True,
)
@mock.patch(
"cabinetry.model_utils.model_and_data",
return_value=("model", "data"),
autospec=True,
)
def test_scan(mock_util, mock_scan, 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"}')
