def test_get_channels(tmp_path):
example_config = {
"Regions": [{"Name": "region_1"}],
"Samples": [{"Name": "signal"}],
"NormFactors": [],
}
# create a histogram for testing
histo_path = tmp_path / "signal_region_1_nominal.npz"
histogram = histo.to_dict(
np.asarray([1.0, 2.0]), np.asarray([1.0, 1.0]), np.asarray([0.0, 1.0, 2.0])
)
histo.save(histogram, histo_path)
channels = workspace.get_channels(example_config, tmp_path)
expected_channels = [
{
"name": "region_1",
"samples": [
{
"name": "signal",
"data": [1.0, 2.0],
"modifiers": [
{
"name": "staterror_region_1",
"type": "staterror",
"data": [1.0, 1.0],
}
],
}
],
}
]
assert channels == expected_channels
def test_to_dict():
yields = np.asarray([1.0, 2.0])
sumw2 = np.asarray([0.1, 0.2])
bins = np.asarray([1, 2, 3])
assert histo.to_dict(yields, sumw2, bins) == {
"yields": yields,
"sumw2": sumw2,
"bins": bins,
}
def test_data_MC_matplotlib(tmp_path):
fname = tmp_path / "subdir" / "fig.pdf"
bg_hist = histo.to_dict(
np.asarray([12.5, 14]), np.asarray([0.4, 0.5]), np.asarray([1, 2, 3])
)
sig_hist = histo.to_dict(
np.asarray([2, 5]), np.asarray([0.1, 0.2]), np.asarray([1, 2, 3])
)
data_hist = histo.to_dict(
np.asarray([13, 15]), np.asarray([3.61, 3.87]), np.asarray([1, 2, 3])
)
histo_dict_list = [
{"label": "Background", "isData": False, "hist": bg_hist, "variable": "x"},
{"label": "Signal", "isData": False, "hist": sig_hist, "variable": "x"},
{"label": "Data", "isData": True, "hist": data_hist, "variable": "x"},
]
histogram_drawing.data_MC_matplotlib(histo_dict_list, fname)
assert (
compare_images("tests/contrib/reference/ref_data_MC.pdf", str(fname), 0) is None
)
def test_get_observations(tmp_path):
histo_path = tmp_path / "Data_test_region_nominal.npz"
# build a test histogram and save it
histogram = histo.to_dict(
np.asarray([1.0, 2.0]), np.asarray([1.0, 1.0]), np.asarray([0.0, 1.0, 2.0])
)
histo.save(histogram, histo_path)
# create observations list from config
config = {
"Samples": [{"Name": "Data", "Tree": "tree", "Path": tmp_path, "Data": True}],
"Regions": [{"Name": "test_region"}],
}
obs = workspace.get_observations(config, tmp_path)
expected_obs = [{"name": "test_region", "data": [1.0, 2.0]}]
assert obs == expected_obs
def test_run(tmp_path):
config = {
"Samples": [{
"Name": "signal"
}],
"Regions": [{
"Name": "region_1"
}]
}
# create an input histogram
histo_path = tmp_path / "signal_region_1_nominal.npz"
histogram = histo.to_dict(np.asarray([1.0, 2.0]), np.asarray([1.0, 1.0]),
np.asarray([0.0, 1.0, 2.0]))
histo.save(histogram, histo_path)
template_postprocessor.run(config, tmp_path)
modified_histo = histo._load(histo_path, modified=True)
assert np.allclose(modified_histo["yields"], histogram["yields"])
assert np.allclose(modified_histo["sumw2"], histogram["sumw2"])
assert np.allclose(modified_histo["bins"], histogram["bins"])
def nan_sumw2_nonempty_bin():
yields = np.asarray([1.0, 2.0])
sumw2 = np.asarray([0.1, float("NaN")])
bins = np.asarray([1, 2, 3])
return histo.to_dict(yields, sumw2, bins)
def empty_bin():
yields = np.asarray([1.0, 0.0])
sumw2 = np.asarray([0.1, 0.2])
bins = np.asarray([1, 2, 3])
return histo.to_dict(yields, sumw2, bins)
def single_bin():
yields = np.asarray([1.0])
sumw2 = np.asarray([0.1])
bins = np.asarray([1, 2])
return histo.to_dict(yields, sumw2, bins)
def normal():
yields = np.asarray([1.0, 2.0])
sumw2 = np.asarray([0.1, 0.2])
bins = np.asarray([1, 2, 3])
return histo.to_dict(yields, sumw2, bins)
