def test_recursive_getattr_fail(logging_mixin: Any, mocker: Any) -> None:
""" Test for failure of recursive getattr.
It will fail the same was as the standard getattr.
"""
obj = mocker.MagicMock(spec=["sole_attr"])
with pytest.raises(AttributeError) as exception_info:
utils.recursive_getattr(obj, "nonexistent_attr")
assert "nonexistent_attr" in exception_info.value.args[0]
def merge_pt_hard_binned_analyses(analyses: Iterator[Tuple[Any, analysis_objects.JetHBase]],
hist_attribute_name: str,
output_analysis_object: Any) -> None:
""" Merge together all scaled histograms.
Args:
analyses: Pt hard dependent analyses which should be merged together.
hist_attribute_name: Name of the attribute where the hist is stored.
output_analysis_object: Object where the histogram will be stored under ``hist_attribute_name``.
Returns:
None
"""
output_hist: Hist = None
for _, analysis in analyses:
input_hist = utils.recursive_getattr(analysis, hist_attribute_name)
if output_hist is None:
# NOTE: We don't need to set a new name - it will be fine with the same name.
output_hist = input_hist.Clone()
# Reset so we can just Add() all hists without worrying which hist is being processed
output_hist.Reset()
# NOTE: Sumw2 is kept even after resetting.
output_hist.Add(input_hist)
# Save the final result
utils.recursive_setattr(output_analysis_object, hist_attribute_name, output_hist)
def test_recursive_setattr(setup_recursive_setattr: Any) -> None:
""" Test setting an attribute with recursive setattr. """
# Setup
obj, path = setup_recursive_setattr
# Set the attribute and check the result
new_value = "new value"
utils.recursive_setattr(obj, path, new_value)
assert utils.recursive_getattr(obj, path) == new_value
def _get_hists_from_analysis_objects(analyses: Mapping[str, analysis_objects.JetHBase],
hist_attribute_name: str) -> Dict[str, Hist]:
""" Retrieve histograms from an analysis object stored under specified attribute names.
Args:
analyses: Analysis objects to be processed according to values in this pt hard bin.
hist_attribute_name: Names of the attributes to retrieve the histograms.
Returns:
Extracted histograms.
"""
hists: Dict[str, Hist] = {}
for key, analysis in analyses.items():
hists[key] = utils.recursive_getattr(analysis, hist_attribute_name)
return hists
def test_recursive_getattr(logging_mixin, mocker, path, expected):
""" Tests for recursive getattr. """
# Setup mock objects from which we will recursively grab attributes
mock_obj1 = mocker.MagicMock(spec=["standard_attr", "attr1"])
mock_obj2 = mocker.MagicMock(spec=["attr3"])
mock_obj3 = mocker.MagicMock(spec=["my_attr"])
mock_obj1.standard_attr = "standard_attr_value"
mock_obj1.attr1 = mock_obj2
mock_obj2.attr3 = mock_obj3
mock_obj3.my_attr = "recursive_attr_value"
# For convenience
obj = mock_obj1
# Check the returned value
assert expected == utils.recursive_getattr(obj, path)
def _plot_response_spectra_with_ROOT(
plot_labels: plot_base.PlotLabels, output_name: str,
merged_analysis: analysis_objects.JetHBase, pt_hard_analyses: Analyses,
hist_attribute_name: str, colors: Sequence[Tuple[float, float,
float]]) -> None:
""" Plot 1D response spectra with ROOT.
Args:
plot_labels: Labels for the plot.
output_name: Name under which the plot should be stored.
merged_analysis: Full merged together analysis object.
pt_hard_analyses: Pt hard dependent analysis objects to be plotted.
hist_attribute_name: Name of the attribute under which the histogram is stored.
colors: List of colors to be used for plotting the pt hard spectra.
"""
# Setup
canvas = ROOT.TCanvas("canvas", "canvas")
canvas.SetLogy(True)
# Legend
legend = ROOT.TLegend(0.37, 0.55, 0.9, 0.9)
legend.SetHeader(r"p_{\mathrm{T}}\:\mathrm{bins}", "C")
# Increase text size
legend.SetTextSize(0.025)
# Use two columns because we have a lot of entries.
legend.SetNColumns(2)
# Remove the legend border
legend.SetBorderSize(0)
# Make the legend transparent
legend.SetFillStyle(0)
# First, we plot the merged analysis. This is the sum of the various pt hard bin contributions.
merged_hist = utils.recursive_getattr(merged_analysis, hist_attribute_name)
# Apply axis labels (which must be set on the hist)
plot_labels.apply_labels(merged_hist)
# Style the merged hist to ensure that it is possible to see the points
merged_hist.SetMarkerStyle(ROOT.kFullCircle)
merged_hist.SetMarkerSize(1)
merged_hist.SetMarkerColor(ROOT.kBlack)
merged_hist.SetLineColor(ROOT.kBlack)
# Ensure that the max is never beyond 300 for better presentation.
max_limit = merged_hist.GetXaxis().GetXmax()
if max_limit > 300:
max_limit = 300
merged_hist.GetXaxis().SetRangeUser(0, max_limit)
# Label and draw
legend.AddEntry(merged_hist, "Merged")
merged_hist.Draw("same")
# Now, we plot the pt hard dependent hists
for i, ((key_index, analysis),
color) in enumerate(zip(pt_hard_analyses.items(), colors)):
# Setup
color = ROOT.TColor.GetColor(*color)
# Determine the proper label.
label = labels.pt_range_string(
pt_bin=key_index.pt_hard_bin,
lower_label="T",
upper_label="hard",
only_show_lower_value_for_last_bin=True,
)
# Retrieve and style the hist
hist = utils.recursive_getattr(analysis, hist_attribute_name)
hist.SetMarkerStyle(ROOT.kFullCircle + i)
hist.SetMarkerSize(1)
hist.SetMarkerColor(color)
hist.SetLineColor(color)
# Label and draw
legend.AddEntry(hist, labels.use_label_with_root(label))
hist.Draw("same")
# Final presentation settings
legend.Draw()
# Save and cleanup
output_name += "_ROOT"
plot_base.save_plot(merged_analysis.output_info, canvas, output_name)
def _plot_response_spectra_with_matplotlib(
plot_labels: plot_base.PlotLabels, output_name: str,
merged_analysis: analysis_objects.JetHBase, pt_hard_analyses: Analyses,
hist_attribute_name: str, colors: Sequence[Tuple[float, float,
float]]) -> None:
""" Plot 1D response spectra with matplotlib.
Args:
plot_labels: Labels for the plot.
output_name: Name under which the plot should be stored.
merged_analysis: Full merged together analysis object.
pt_hard_analyses: Pt hard dependent analysis objects to be plotted.
hist_attribute_name: Name of the attribute under which the histogram is stored.
colors: List of colors to be used for plotting the pt hard spectra.
"""
# Setup
fig, ax = plt.subplots(figsize=(8, 6))
plot_labels.apply_labels(ax)
# First, we plot the merged analysis. This is the sum of the various pt hard bin contributions.
merged_hist = utils.recursive_getattr(merged_analysis, hist_attribute_name)
merged_hist = histogram.Histogram1D.from_existing_hist(merged_hist)
ax.errorbar(
merged_hist.x,
merged_hist.y,
yerr=merged_hist.errors,
label="Merged",
color="black",
marker=".",
linestyle="",
)
# Now, we plot the pt hard dependent hists
for (key_index, analysis), color in zip(pt_hard_analyses.items(), colors):
# Determine the proper label.
label = labels.pt_range_string(
pt_bin=key_index.pt_hard_bin,
lower_label="T",
upper_label="hard",
only_show_lower_value_for_last_bin=True,
)
# Plot the histogram.
hist = utils.recursive_getattr(analysis, hist_attribute_name)
h = histogram.Histogram1D.from_existing_hist(hist)
ax.errorbar(
h.x,
h.y,
yerr=h.errors,
label=label,
color=color,
marker=".",
linestyle="",
)
# Final presentation settings
# Ensure that the max is never beyond 300 for better presentation.
max_limit = np.max(merged_hist.x)
if max_limit > 300:
max_limit = 300
ax.set_xlim(0, max_limit)
ax.set_yscale("log")
ax.legend(loc="best", frameon=False)
fig.tight_layout()
# Save and cleanup
output_name += "_mpl"
plot_base.save_plot(merged_analysis.output_info, fig, output_name)
plt.close(fig)
def test_recursive_getattr_defualt_value(logging_mixin: Any,
mocker: Any) -> None:
""" Test for retrieving a default value with getattr. """
obj = mocker.MagicMock(spec=["sole_attr"])
assert "default_value" == utils.recursive_getattr(obj, "nonexistent_attr",
"default_value")