def test_apply_range_set(self, logging_mixin, min_val, max_val,
min_val_func, max_val_func, expected_func,
test_sparse):
""" Test apply a range set to an axis via a HistAxisRange object.
This is intentionally tested against SetRangeUser, so we can be certain that it reproduces
that selection as expected.
Note:
It doens't matter whether we operate on TH1 or THn, since they both set ranges on TAxis.
Note:
This implicity tests apply_func_to_find_bin, which is fine given how often the two are used
together (almost always).
"""
import ROOT
# Setup functions
function_map = {
None:
lambda x: projectors.HistAxisRange.apply_func_to_find_bin(None, x),
"find_bin_min":
lambda x: projectors.HistAxisRange.apply_func_to_find_bin(
ROOT.TAxis.FindBin, x + utils.epsilon),
"find_bin_max":
lambda x: projectors.HistAxisRange.apply_func_to_find_bin(
ROOT.TAxis.FindBin, x - utils.epsilon),
"n_bins":
lambda x: projectors.HistAxisRange.apply_func_to_find_bin(
ROOT.TAxis.GetNbins),
}
min_val_func = function_map[min_val_func]
max_val_func = function_map[max_val_func]
selected_axis = SparseAxisLabels.axis_two
sparse, _ = test_sparse
expected_axis = sparse.GetAxis(selected_axis.value).Clone("axis2")
expected_func(expected_axis, min_val, max_val)
obj = projectors.HistAxisRange(axis_range_name="axis_two_test",
axis_type=selected_axis,
min_val=min_val_func(min_val),
max_val=max_val_func(max_val))
# Applys the restriction to the sparse.
obj.apply_range_set(sparse)
ax = sparse.GetAxis(selected_axis.value)
# Unfortunately, equality comparison doesn't work for TAxis...
# GetXmin() and GetXmax() aren't restircted by SetRange(), so instead use GetFirst() and GetLast()
assert ax.GetFirst() == expected_axis.GetFirst()
assert ax.GetLast() == expected_axis.GetLast()
# Sanity check that the overall axis still agrees
assert ax.GetNbins() == expected_axis.GetNbins()
assert ax.GetName() == expected_axis.GetName()
def create_hist_axis_range():
""" Create a HistAxisRange object to use for testing. """
object_args = {
"axis_range_name": "z_axis_test_projector",
"axis_type": projectors.TH1AxisType.y_axis,
"min_val": lambda x: x,
"max_val": lambda y: y
}
obj = projectors.HistAxisRange(**object_args)
# axis_range_name is referred to as name internally, so we rename to that
object_args["name"] = object_args.pop("axis_range_name")
return (obj, object_args)
def _project_to_part_level(hist: Hist,
outliers_removal_axis: OutliersRemovalAxis) -> Hist:
"""Project the input histogram to the particle level axis.
Args:
hist: Histogram to check for outliers.
outliers_removal_axis: Axis along which outliers removal will be performed. Usually
the particle level aixs.
Returns:
The histogram to check for outliers.
"""
# Setup the projector
import ROOT
if isinstance(hist, (ROOT.TH2, ROOT.TH3)):
projection_information: Dict[str, Any] = {}
output_object = _OutputObject(None)
projector = projectors.HistProjector(
observable_to_project_from=hist,
output_observable=output_object,
output_attribute_name="output",
projection_name_format="outliers_removal_hist",
projection_information=projection_information,
)
# No additional_axis_cuts or projection_dependent_cut_axes
# Projection axis
projector.projection_axes.append(
projectors.HistAxisRange(
axis_type=outliers_removal_axis,
axis_range_name="outliers_removal_axis",
min_val=projectors.HistAxisRange.apply_func_to_find_bin(
None, 1),
max_val=projectors.HistAxisRange.apply_func_to_find_bin(
ROOT.TAxis.GetNbins),
))
# Perform the actual projection and return the output.
projector.project()
return output_object.output
# If we already have a 1D hist, just return that existing hist.
return hist
def test_disagreement_with_set_range_user(self, logging_mixin,
test_sparse):
""" Test the disagreement between SetRange and SetRangeUser when the epsilon shift is not included. """
import ROOT
# Setup values
selected_axis = SparseAxisLabels.axis_two
min_val = 2
max_val = 8
sparse, _ = test_sparse
# Detemine expected value (must be first to avoid interfering with applying the axis range)
expected_axis = sparse.GetAxis(selected_axis.value).Clone("axis2")
expected_axis.SetRangeUser(min_val, max_val)
obj = projectors.HistAxisRange(
axis_range_name="axis_two_test",
axis_type=selected_axis,
min_val=projectors.HistAxisRange.apply_func_to_find_bin(
ROOT.TAxis.FindBin, min_val),
max_val=projectors.HistAxisRange.apply_func_to_find_bin(
ROOT.TAxis.FindBin, max_val))
# Applys the restriction to the sparse.
obj.apply_range_set(sparse)
ax = sparse.GetAxis(selected_axis.value)
# Unfortunately, equality comparison doesn't work for TAxis...
# GetXmin() and GetXmax() aren't restircted by SetRange(), so instead use GetFirst() and GetLast()
# The lower bin will still agree.
assert ax.GetFirst() == expected_axis.GetFirst()
# The upper bin will not.
assert ax.GetLast() != expected_axis.GetLast()
# If we subtract a bin (equivalent to including - epsilon), it will agree.
assert ax.GetLast() - 1 == expected_axis.GetLast()
# Sanity check that the overall axis still agrees
assert ax.GetNbins() == expected_axis.GetNbins()
assert ax.GetName() == expected_axis.GetName()
ROOT.TAxis.FindBin,
hist_range.max_val - utils.epsilon # type: ignore
)
return hist_range
# Convenient access to hist axis ranges.
HistAxisRanges = dataclasses.make_dataclass("HistAxisRanges",
["x_axis", "y_axis", "z_axis"])
# Hist axis ranges
# NOTE: We don't define these in the class because we won't be able to access these variables when
# defining the tests restricted ranges, but with entries.
hist_axis_ranges = HistAxisRanges(
projectors.HistAxisRange(axis_type=projectors.TH1AxisType.x_axis,
axis_range_name="an_x_axis",
min_val=0.1,
max_val=0.8),
projectors.HistAxisRange(axis_type=projectors.TH1AxisType.y_axis,
axis_range_name="an_y_axis",
min_val=0,
max_val=12),
projectors.HistAxisRange(axis_type=projectors.TH1AxisType.z_axis,
axis_range_name="an_z_axis",
min_val=10,
max_val=60))
# Restricted ranges with no counts
hist_axis_ranges_without_entries = HistAxisRanges(
projectors.HistAxisRange(axis_type=projectors.TH1AxisType.x_axis,
axis_range_name="an_x_axis_no_entries",
min_val=0.2,
max_val=0.8),