def test_data_MC(
mock_asimov,
mock_unc,
mock_stdev,
mock_table_bin,
mock_table_channel,
mock_dict,
mock_bins,
mock_draw,
example_spec,
):
config = {"config": "abc"}
figure_folder = "tmp"
model, data = model_utils.model_and_data(example_spec)
# pre-fit plot
visualize.data_MC(model, data, config=config, figure_folder=figure_folder)
# Asimov parameter calculation and pre-fit uncertainties
assert mock_stdev.call_count == 1
assert mock_asimov.call_args_list[0][0][0] == model
assert mock_unc.call_count == 1
assert mock_unc.call_args_list[0][0][0] == model
# call to stdev calculation
assert mock_stdev.call_count == 1
assert mock_stdev.call_args_list[0][0][0] == model
assert np.allclose(mock_stdev.call_args_list[0][0][1], [1.0, 1.0])
assert np.allclose(mock_stdev.call_args_list[0][0][2], [0.04956657, 0.0])
assert np.allclose(mock_stdev.call_args_list[0][0][3],
np.asarray([[1.0, 0.0], [0.0, 1.0]]))
assert mock_stdev.call_args_list[0][1] == {}
# yield table per bin
assert mock_table_bin.call_count == 1
assert mock_table_bin.call_args_list[0][0][0] == model
assert mock_table_bin.call_args_list[0][0][1] == [[[51.839756]]]
assert mock_table_bin.call_args_list[0][0][2] == [[0.3]]
assert mock_table_bin.call_args_list[0][0][3] == [[data[0]]]
assert mock_table_bin.call_args_list[0][1] == {}
# yield table per channel
assert mock_table_channel.call_count == 1
assert mock_table_channel.call_args_list[0][0][0] == model
assert mock_table_channel.call_args_list[0][0][1] == [[51.839756]]
assert mock_table_channel.call_args_list[0][0][2] == [0.3]
assert mock_table_channel.call_args_list[0][0][3] == [data[0]]
assert mock_table_channel.call_args_list[0][1] == {}
assert mock_dict.call_args_list == [[(config, "Signal Region"), {}]]
assert mock_bins.call_args_list == [[({
"Name": "region",
"Variable": "x"
}, ), {}]]
expected_histograms = [
{
"label": "Signal",
"isData": False,
"yields": np.asarray([51.839756]),
"variable": "x",
},
{
"label": "Data",
"isData": True,
"yields": np.asarray(data[:1]),
"variable": "x",
},
]
assert mock_draw.call_count == 1
assert mock_draw.call_args_list[0][0][0] == expected_histograms
assert np.allclose(mock_draw.call_args_list[0][0][1], np.asarray([0.3]))
np.testing.assert_equal(mock_draw.call_args_list[0][0][2],
np.asarray([1, 2]))
assert mock_draw.call_args_list[0][0][3] == pathlib.Path(
"tmp/Signal-Region_prefit.pdf")
assert mock_draw.call_args_list[0][1] == {
"log_scale": None,
"log_scale_x": False
}
# post-fit plot and custom scale
fit_results = fit.FitResults(
np.asarray([1.01, 1.1]),
np.asarray([0.03, 0.1]),
[],
np.asarray([[1.0, 0.2], [0.2, 1.0]]),
0.0,
)
visualize.data_MC(
model,
data,
config=config,
figure_folder=figure_folder,
fit_results=fit_results,
log_scale=False,
)
assert mock_asimov.call_count == 1 # no new call
# call to stdev calculation
assert mock_stdev.call_count == 2
assert mock_stdev.call_args_list[1][0][0] == model
assert np.allclose(mock_stdev.call_args_list[1][0][1], [1.01, 1.1])
assert np.allclose(mock_stdev.call_args_list[1][0][2], [0.03, 0.1])
assert np.allclose(mock_stdev.call_args_list[1][0][3],
np.asarray([[1.0, 0.2], [0.2, 1.0]]))
assert mock_stdev.call_args_list[1][1] == {}
assert mock_draw.call_count == 2
# yield at best-fit point is different from pre-fit
assert np.allclose(mock_draw.call_args_list[1][0][0][0]["yields"],
57.59396892)
assert np.allclose(mock_draw.call_args_list[1][0][1], np.asarray([0.3]))
np.testing.assert_equal(mock_draw.call_args_list[1][0][2],
np.asarray([1, 2]))
assert mock_draw.call_args_list[1][0][3] == pathlib.Path(
"tmp/Signal-Region_postfit.pdf")
assert mock_draw.call_args_list[1][1] == {
"log_scale": False,
"log_scale_x": False
}
# no yield table
visualize.data_MC(model, data, config=config, include_table=False)
assert mock_table_bin.call_count == 2 # 2 calls from before
assert mock_table_channel.call_count == 2
# no config specified, default variable name and bin edges, data without auxdata
visualize.data_MC(model, data[:1])
assert mock_draw.call_args[0][0][0]["variable"] == "bin"
assert mock_draw.call_args[0][0][1]["variable"] == "bin"
assert mock_draw.call_args[0][0][1]["yields"] == np.asarray(data[:1])
np.testing.assert_equal(mock_draw.call_args[0][2], np.asarray([0, 1]))
# unknown plotting method
with pytest.raises(NotImplementedError, match="unknown backend: unknown"):
visualize.data_MC(model,
data,
config=config,
figure_folder=figure_folder,
method="unknown")
def test_data_MC(mock_load, mock_draw, tmp_path):
"""contrib.histogram_drawing is only imported depending on the keyword argument,
so the patch works differently from the patch of load_from_config
Generally it seems like following the path to the module is preferred, but that
does not work for the `data_MC_matplotlib` case. For some information see also
https://docs.python.org/3/library/unittest.mock.html#where-to-patch
"""
config = {
"Regions": [{
"Name": "reg_1",
"Variable": "x"
}],
"Samples": [{
"Name": "sample_1"
}],
}
visualize.data_MC(config,
tmp_path,
tmp_path,
prefit=True,
method="matplotlib")
# the call_args_list contains calls (outer round brackets), first filled with
# arguments (inner round brackets) and then keyword arguments
assert mock_load.call_args_list == [(
(
tmp_path,
{
"Name": "sample_1"
},
{
"Name": "reg_1",
"Variable": "x"
},
{
"Name": "nominal"
},
),
{
"modified": True
},
)]
assert mock_draw.call_args_list == [((
[{
"label": "sample_1",
"isData": False,
"hist": {},
"variable": "x"
}],
tmp_path / "reg_1_prefit.pdf",
), )]
# other plotting method
with pytest.raises(NotImplementedError, match="unknown backend") as e_info:
visualize.data_MC(config,
tmp_path,
tmp_path,
prefit=True,
method="unknown")
# postfit
with pytest.raises(NotImplementedError,
match="only prefit implemented so far") as e_info:
visualize.data_MC(config,
tmp_path,
tmp_path,
prefit=False,
method="matplotlib")
def test_data_MC(
mock_asimov,
mock_unc,
mock_stdev,
mock_table,
mock_dict,
mock_bins,
mock_draw,
example_spec,
):
config = {}
figure_folder = "tmp"
model_spec = pyhf.Workspace(example_spec).model().spec
# pre-fit plot
visualize.data_MC(config, example_spec, figure_folder=figure_folder)
# Asimov parameter calculation and pre-fit uncertainties
assert mock_stdev.call_count == 1
assert mock_asimov.call_args_list[0][0][0].spec == model_spec
assert mock_unc.call_count == 1
assert mock_unc.call_args_list[0][0][0].spec == model_spec
# call to stdev calculation
assert mock_stdev.call_count == 1
assert mock_stdev.call_args_list[0][0][0].spec == model_spec
assert np.allclose(mock_stdev.call_args_list[0][0][1], [1.0, 1.0])
assert np.allclose(mock_stdev.call_args_list[0][0][2], [0.04956657, 0.0])
assert np.allclose(
mock_stdev.call_args_list[0][0][3], np.asarray([[1.0, 0.0], [0.0, 1.0]])
)
assert mock_stdev.call_args_list[0][1] == {}
# yield table
assert mock_table.call_count == 1
assert mock_table.call_args_list[0][0][0].spec == model_spec
assert mock_table.call_args_list[0][0][1] == [np.asarray([[51.839756]])]
assert mock_table.call_args_list[0][0][2] == [[0.3]]
assert mock_table.call_args_list[0][0][3] == [np.asarray([475])]
assert mock_table.call_args_list[0][1] == {}
assert mock_dict.call_args_list == [[(config, "Signal Region"), {}]]
assert mock_bins.call_args_list == [[({"Name": "region", "Variable": "x"},), {}]]
expected_histograms = [
{
"label": "Signal",
"isData": False,
"yields": np.asarray([51.839756]),
"variable": "x",
},
{
"label": "Data",
"isData": True,
"yields": np.asarray([475]),
"variable": "x",
},
]
assert mock_draw.call_count == 1
assert mock_draw.call_args_list[0][0][0] == expected_histograms
assert np.allclose(mock_draw.call_args_list[0][0][1], np.asarray([0.3]))
assert np.allclose(mock_draw.call_args_list[0][0][2], np.asarray([1, 2]))
assert mock_draw.call_args_list[0][0][3] == pathlib.Path(
"tmp/Signal-Region_prefit.pdf"
)
assert mock_draw.call_args_list[0][1] == {"log_scale": None}
# post-fit plot and custom scale
fit_results = fit.FitResults(
np.asarray([1.01, 1.1]),
np.asarray([0.03, 0.1]),
[],
np.asarray([[1.0, 0.2], [0.2, 1.0]]),
0.0,
)
visualize.data_MC(
config,
example_spec,
figure_folder=figure_folder,
fit_results=fit_results,
log_scale=False,
)
assert mock_asimov.call_count == 1 # no new call
# call to stdev calculation
assert mock_stdev.call_count == 2
assert mock_stdev.call_args_list[1][0][0].spec == model_spec
assert np.allclose(mock_stdev.call_args_list[1][0][1], [1.01, 1.1])
assert np.allclose(mock_stdev.call_args_list[1][0][2], [0.03, 0.1])
assert np.allclose(
mock_stdev.call_args_list[1][0][3], np.asarray([[1.0, 0.2], [0.2, 1.0]])
)
assert mock_stdev.call_args_list[1][1] == {}
assert mock_draw.call_count == 2
# yield at best-fit point is different from pre-fit
assert np.allclose(mock_draw.call_args_list[1][0][0][0]["yields"], 57.59396892)
assert np.allclose(mock_draw.call_args_list[1][0][1], np.asarray([0.3]))
assert np.allclose(mock_draw.call_args_list[1][0][2], np.asarray([1, 2]))
assert mock_draw.call_args_list[1][0][3] == pathlib.Path(
"tmp/Signal-Region_postfit.pdf"
)
assert mock_draw.call_args_list[1][1] == {"log_scale": False}
# no yield table
visualize.data_MC(config, example_spec, include_table=False)
assert mock_table.call_count == 2 # 2 calls from before
# unknown plotting method
with pytest.raises(NotImplementedError, match="unknown backend: unknown"):
visualize.data_MC(
config, example_spec, figure_folder=figure_folder, method="unknown"
)