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feat: template visualization for normalization variations #474

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feat: template visualization for normalization variations #474

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0284f89
support template visualization for pure normalization variations
alexander-held Apr 21, 2024
5f68e7b
pre-commit update
alexander-held Apr 21, 2024
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6 changes: 3 additions & 3 deletions .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
repos:
- repo: https://github.com/psf/black-pre-commit-mirror
rev: 24.3.0
rev: 24.4.0
hooks:
- id: black
- repo: https://github.com/pre-commit/mirrors-mypy
Expand All @@ -23,12 +23,12 @@ repos:
- id: flake8
additional_dependencies: [flake8-bugbear, flake8-import-order, flake8-print]
- repo: https://github.com/asottile/pyupgrade
rev: v3.15.1
rev: v3.15.2
hooks:
- id: pyupgrade
args: ["--py38-plus"]
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.5.0
rev: v4.6.0
hooks:
- id: check-added-large-files
args: ["--maxkb=100"]
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91 changes: 62 additions & 29 deletions src/cabinetry/visualize/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -347,9 +347,27 @@ def templates(
v for v in variation_paths if ("Up" in v.name or "Down" in v.name)
)

# check for histogram-based variations and pure normalization variations
if len(variation_paths) == 0:
# no associated templates (normalization systematics)
continue
# no histograms for variations found, check for norm systematic
sys_region = configuration.region_contains_modifier(
region, systematic
)
sys_sample = configuration.sample_contains_modifier(
sample, systematic
)
if not (sys_region and sys_sample):
continue # systematic does not act here

up_size = systematic["Up"].get("Normalization", None)
down_size = systematic["Down"].get("Normalization", None)
if up_size is None or down_size is None:
# not a normalization systematic, continue
continue

has_norm_variation = True
else:
has_norm_variation = False

# extract nominal histogram
nominal_histo = histo.Histogram.from_config(
Expand All @@ -362,35 +380,50 @@ def templates(
variable = region.get("Variable", "observable")
nominal = {"yields": nominal_histo.yields, "stdev": nominal_histo.stdev}

# extract original and modified (after post-processing) variation
# histograms, if they exist
up_orig = {}
down_orig = {}
up_mod = {}
down_mod = {}
for variation_path in variation_paths:
# original variation, before post-processing
variation_path_orig = pathlib.Path(
str(variation_path).replace("_modified", "")
)
var_histo_orig = histo.Histogram.from_path(variation_path_orig)
var_orig = {
"yields": var_histo_orig.yields,
"stdev": var_histo_orig.stdev,
if has_norm_variation:
# scale nominal histogram to show normalization variations
up_orig = {
"yields": (1 + up_size) * nominal_histo.yields,
"stdev": np.zeros_like(nominal["stdev"]),
}

# variation after post-processing
var_histo_mod = histo.Histogram.from_path(variation_path)
var_mod = {
"yields": var_histo_mod.yields,
"stdev": var_histo_mod.stdev,
down_orig = {
"yields": (1 + down_size) * nominal_histo.yields,
"stdev": np.zeros_like(nominal["stdev"]),
}
if "Up" in variation_path.parts[-1]:
up_orig.update(var_orig)
up_mod.update(var_mod)
else:
down_orig.update(var_orig)
down_mod.update(var_mod)
# no post-processing exists for pure norm variations
up_mod: dict = {}
down_mod: dict = {}

else:
# extract original and modified (after post-processing) variation
# histograms, if they exist
up_orig = {}
down_orig = {}
up_mod = {}
down_mod = {}
for variation_path in variation_paths:
# original variation, before post-processing
variation_path_orig = pathlib.Path(
str(variation_path).replace("_modified", "")
)
var_histo_orig = histo.Histogram.from_path(variation_path_orig)
var_orig = {
"yields": var_histo_orig.yields,
"stdev": var_histo_orig.stdev,
}

# variation after post-processing
var_histo_mod = histo.Histogram.from_path(variation_path)
var_mod = {
"yields": var_histo_mod.yields,
"stdev": var_histo_mod.stdev,
}
if "Up" in variation_path.parts[-1]:
up_orig.update(var_orig)
up_mod.update(var_mod)
else:
down_orig.update(var_orig)
down_mod.update(var_mod)

figure_label = (
f"region: {region['Name']}\nsample: {sample['Name']}"
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