From 081cbfe7c5aa36b7920e170a2de265d17c76d822 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 21 May 2024 22:15:57 +0000
Subject: [PATCH] style: pre-commit fixes

---
 src/HH4b/postprocessing/CombineTemplates.ipynb |  2 +-
 src/HH4b/postprocessing/CreateDatacard.py      | 17 ++++++++++++++---
 src/HH4b/postprocessing/datacardHelpers.py     |  6 ++++--
 3 files changed, 19 insertions(+), 6 deletions(-)

diff --git a/src/HH4b/postprocessing/CombineTemplates.ipynb b/src/HH4b/postprocessing/CombineTemplates.ipynb
index 0683e72d..828be3cc 100644
--- a/src/HH4b/postprocessing/CombineTemplates.ipynb
+++ b/src/HH4b/postprocessing/CombineTemplates.ipynb
@@ -192,7 +192,7 @@
     "for rname, region in selection_regions.items():\n",
     "    plot_dir = main_plot_dir / year / \"wshifts\"\n",
     "    plot_dir.mkdir(exist_ok=True, parents=True)\n",
-    "    #for wshift in list((jecs).keys()):\n",
+    "    # for wshift in list((jecs).keys()):\n",
     "    for wshift in [\"trigger\"]:\n",
     "        if wshift in jecs:\n",
     "            # adding jshift-ed histograms into the same histogram\n",
diff --git a/src/HH4b/postprocessing/CreateDatacard.py b/src/HH4b/postprocessing/CreateDatacard.py
index 3e76daea..27e30118 100644
--- a/src/HH4b/postprocessing/CreateDatacard.py
+++ b/src/HH4b/postprocessing/CreateDatacard.py
@@ -71,7 +71,7 @@
 )
 
 add_bool_arg(parser, "only-sm", "Only add SM HH samples", default=False)
-parser.add_argument("--sig-samples", default="hh4b", nargs='*', type=str, help="specify signals")
+parser.add_argument("--sig-samples", default="hh4b", nargs="*", type=str, help="specify signals")
 
 parser.add_argument(
     "--nTF",
@@ -234,7 +234,12 @@
     # ),
     # TODO: separate into individual
     "JES": Syst(name="CMS_scale_j", prior="shape", samples=sig_keys),  # TODO: update to all_mc
-    "ttbarSF": Syst(name=f"{CMS_PARAMS_LABEL}_ttbar_sf", prior="shape", samples=["ttbar"], convert_shape_to_lnN=True),
+    "ttbarSF": Syst(
+        name=f"{CMS_PARAMS_LABEL}_ttbar_sf",
+        prior="shape",
+        samples=["ttbar"],
+        convert_shape_to_lnN=True,
+    ),
     # "trigger": Syst(name=f"{CMS_PARAMS_LABEL}_trigger", prior="shape", samples=all_mc),  # TODO: fix
     # "txbb": Syst(
     #     name=f"{CMS_PARAMS_LABEL}_PNetHbbScaleFactors_correlated",
@@ -484,7 +489,13 @@ def fill_regions(
                 logger = logging.getLogger(f"validate_shapes_{region}_{sample_name}_{skey}")
 
                 effect_up, effect_down = get_effect_updown(
-                    values_nominal, values_up, values_down, mask, logger, args.epsilon, syst.convert_shape_to_lnN
+                    values_nominal,
+                    values_up,
+                    values_down,
+                    mask,
+                    logger,
+                    args.epsilon,
+                    syst.convert_shape_to_lnN,
                 )
                 sample.setParamEffect(shape_systs_dict[skey], effect_up, effect_down)
 
diff --git a/src/HH4b/postprocessing/datacardHelpers.py b/src/HH4b/postprocessing/datacardHelpers.py
index 13f8df47..d1bf9c76 100644
--- a/src/HH4b/postprocessing/datacardHelpers.py
+++ b/src/HH4b/postprocessing/datacardHelpers.py
@@ -34,7 +34,7 @@ class Syst:
     # in case of uncorrelated unc., which years to split into
     uncorr_years: list[str] = field(default_factory=lambda: all_years)
     pass_only: bool = False  # is it applied only in the pass regions
-    convert_shape_to_lnN: bool = False # take shape uncertainty and conert to lnN
+    convert_shape_to_lnN: bool = False  # take shape uncertainty and conert to lnN
 
     def __post_init__(self):
         if isinstance(self.value, dict) and not (self.diff_regions or self.diff_samples):
@@ -181,7 +181,9 @@ def _shape_checks(values_up, values_down, values_nominal, effect_up, effect_down
         logger.warning("Up and Down vary norm in the same direction")
 
 
-def get_effect_updown(values_nominal, values_up, values_down, mask, logger, epsilon, convert_shape_to_lnN=False):
+def get_effect_updown(
+    values_nominal, values_up, values_down, mask, logger, epsilon, convert_shape_to_lnN=False
+):
     if convert_shape_to_lnN:
         effect_up = np.sum(values_up) / np.sum(values_nominal)
         effect_down = np.sum(values_down) / np.sum(values_nominal)