From 5aed534d4d3c64025728f563392c727c9c43cfc0 Mon Sep 17 00:00:00 2001
From: rkansal47 <raghavk1997@yahoo.co.in>
Date: Wed, 10 Apr 2024 18:11:27 -0500
Subject: [PATCH 1/2] first pass

---
 src/HHbbVV/combine/run_blinded.sh             |   6 +-
 .../submit/submit_ftest_nonres.templ.sh       |   4 +-
 .../postprocessing/CombineTemplates.ipynb     |  14 +-
 src/HHbbVV/postprocessing/CreateDatacard.py   | 240 +++++++++++-------
 src/HHbbVV/postprocessing/datacardHelpers.py  |   4 +-
 src/HHbbVV/postprocessing/postprocessing.py   |   6 +-
 src/HHbbVV/postprocessing/regions.py          |  25 +-
 .../2016APV_templates.pkl                     | Bin 1088743 -> 1088707 bytes
 .../24Apr10NonresCombined/2016_templates.pkl  | Bin 1088743 -> 1088707 bytes
 .../24Apr10NonresCombined/2017_templates.pkl  | Bin 1088743 -> 1088707 bytes
 .../24Apr10NonresCombined/2018_templates.pkl  | Bin 1088743 -> 1088707 bytes
 .../24Apr10NonresCombined/systematics.json    |  20 +-
 12 files changed, 191 insertions(+), 128 deletions(-)

diff --git a/src/HHbbVV/combine/run_blinded.sh b/src/HHbbVV/combine/run_blinded.sh
index 7ccc7184..0e33a1c8 100755
--- a/src/HHbbVV/combine/run_blinded.sh
+++ b/src/HHbbVV/combine/run_blinded.sh
@@ -159,10 +159,12 @@ if [ $resonant = 0 ]; then
     freezeparamsblinded=""
     for bin in {5..9}
     do
-        setparamsblinded+="${CMS_PARAMS_LABEL}_tf_dataResidual_Bin${bin}=0,"
-        freezeparamsblinded+="${CMS_PARAMS_LABEL}_tf_dataResidual_Bin${bin},"
+        setparamsblinded+="rgx{${CMS_PARAMS_LABEL}_tf_dataResidual.*_Bin${bin}}=0,"
+        freezeparamsblinded+="rgx{${CMS_PARAMS_LABEL}_tf_dataResidual.*_Bin${bin}},"
     done
 
+    echo "CHECK FROZEN BLINDED QCD PARAMS"
+
     # remove last comma
     setparamsblinded=${setparamsblinded%,}
     freezeparamsblinded=${freezeparamsblinded%,}
diff --git a/src/HHbbVV/combine/submit/submit_ftest_nonres.templ.sh b/src/HHbbVV/combine/submit/submit_ftest_nonres.templ.sh
index 7b5e0cb9..da262391 100644
--- a/src/HHbbVV/combine/submit/submit_ftest_nonres.templ.sh
+++ b/src/HHbbVV/combine/submit/submit_ftest_nonres.templ.sh
@@ -61,8 +61,8 @@ setparamsblinded=""
 freezeparamsblinded=""
 for bin in {5..9}
 do
-    setparamsblinded+="${CMS_PARAMS_LABEL}_tf_dataResidual_Bin${bin}=0,"
-    freezeparamsblinded+="${CMS_PARAMS_LABEL}_tf_dataResidual_Bin${bin},"
+    setparamsblinded+="rgx{${CMS_PARAMS_LABEL}_tf_dataResidual.*_Bin${bin}}=0,"
+    freezeparamsblinded+="rgx{${CMS_PARAMS_LABEL}_tf_dataResidual.*_Bin${bin}},"
 done
 
 # remove last comma
diff --git a/src/HHbbVV/postprocessing/CombineTemplates.ipynb b/src/HHbbVV/postprocessing/CombineTemplates.ipynb
index 7bc9f509..1d52ed53 100644
--- a/src/HHbbVV/postprocessing/CombineTemplates.ipynb
+++ b/src/HHbbVV/postprocessing/CombineTemplates.ipynb
@@ -60,14 +60,14 @@
     "    for region, h in ggf_templates.items():\n",
     "        if region.startswith(\"pass\"):\n",
     "            # rename pass regions\n",
-    "            combined_templates[region.replace(\"pass\", \"pass_ggf\")] = h\n",
+    "            combined_templates[region.replace(\"pass\", \"passggf\")] = h\n",
     "        else:\n",
     "            combined_templates[region] = h\n",
     "\n",
     "    for region, h in vbf_templates.items():\n",
     "        if region.startswith(\"pass\"):\n",
     "            # rename pass regions\n",
-    "            combined_templates[region.replace(\"pass\", \"pass_vbf\")] = h\n",
+    "            combined_templates[region.replace(\"pass\", \"passvbf\")] = h\n",
     "        else:\n",
     "            # checking that fail regions are identical\n",
     "            assert combined_templates[region] == h\n",
@@ -90,7 +90,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "combined_systematics = {\"pass_ggf\": {}, \"pass_vbf\": {}}\n",
+    "combined_systematics = {\"passggf\": {}, \"passvbf\": {}}\n",
     "\n",
     "with (ggf_templates_path / \"systematics.json\").open(\"r\") as f:\n",
     "    ggf_systematics = json.load(f)\n",
@@ -104,25 +104,25 @@
     "        for region, val in sval.items():\n",
     "            if region.startswith(\"pass\"):\n",
     "                # rename pass regions\n",
-    "                combined_systematics[skey][region.replace(\"pass\", \"pass_ggf\")] = val\n",
+    "                combined_systematics[skey][region.replace(\"pass\", \"passggf\")] = val\n",
     "            else:\n",
     "                combined_systematics[skey][region] = val\n",
     "    else:\n",
     "        # LP SFs\n",
-    "        combined_systematics[\"pass_ggf\"][skey] = sval\n",
+    "        combined_systematics[\"passggf\"][skey] = sval\n",
     "\n",
     "for skey, sval in vbf_systematics.items():\n",
     "    if skey in years:\n",
     "        for region, val in sval.items():\n",
     "            if region.startswith(\"pass\"):\n",
     "                # rename pass regions\n",
-    "                combined_systematics[skey][region.replace(\"pass\", \"pass_vbf\")] = val\n",
+    "                combined_systematics[skey][region.replace(\"pass\", \"passvbf\")] = val\n",
     "            else:\n",
     "                # checking that fail regions are identical\n",
     "                assert combined_systematics[skey][region] == val\n",
     "    else:\n",
     "        # LP SFs\n",
-    "        combined_systematics[\"pass_vbf\"][skey] = sval\n",
+    "        combined_systematics[\"passvbf\"][skey] = sval\n",
     "\n",
     "with open(templates_path / \"systematics.json\", \"w\") as f:\n",
     "    json.dump(combined_systematics, f, indent=4)"
diff --git a/src/HHbbVV/postprocessing/CreateDatacard.py b/src/HHbbVV/postprocessing/CreateDatacard.py
index a6bea068..ec562a0f 100644
--- a/src/HHbbVV/postprocessing/CreateDatacard.py
+++ b/src/HHbbVV/postprocessing/CreateDatacard.py
@@ -16,6 +16,7 @@
 import logging
 import pickle
 import sys
+import warnings
 from collections import OrderedDict
 from pathlib import Path
 
@@ -69,6 +70,13 @@
     default=False,
 )
 
+parser.add_argument(
+    "--nonres-regions",
+    default="all",
+    type=str,
+    help="nonresonant regions for which to make cards",
+    choices=["ggf", "vbf", "all"],
+)
 parser.add_argument("--cards-dir", default="cards", type=str, help="output card directory")
 
 parser.add_argument("--mcstats-threshold", default=100, type=float, help="mcstats threshold n_eff")
@@ -94,8 +102,8 @@
     default=None,
     nargs="*",
     type=int,
-    help="order of polynomial for TF in [dim 1, dim 2] = [mH(bb), -] for nonresonant or [mY, mX] for resonant."
-    "Default is 0 for nonresonant and (1, 2) for resonant.",
+    help="order of polynomial for TF in [dim/cat 1, dim/cat 2] = [mH(bb) for ggF, mH(bb) for VBF] for nonresonant or [mY, mX] for resonant."
+    "Default is [0, 1] for nonresonant and [1, 2] for resonant.",
 )
 
 parser.add_argument("--model-name", default=None, type=str, help="output model name")
@@ -113,10 +121,29 @@
 
 
 CMS_PARAMS_LABEL = "CMS_bbWW_hadronic" if not args.resonant else "CMS_XHYbbWW_boosted"
+MCB_LABEL = "Blinded"  # for templates where MC is "blinded" to get background estimates
 qcd_data_key = "qcd_datadriven"
 
 if args.nTF is None:
-    args.nTF = [1, 2] if args.resonant else [0]
+    if args.resonant:
+        args.nTF = [1, 2]
+    else:
+        if args.nonres_regions == "all":
+            args.nTF = [0, 1]
+        elif args.nonres_regions == "ggf":
+            args.nTF = [0]
+        elif args.nonres_regions == "vbf":
+            args.nTF = [1]
+
+if not args.resonant:
+    if args.nonres_regions == "all":
+        signal_regions = ["passggf", "passvbf"]
+    elif args.nonres_regions == "ggf":
+        signal_regions = ["passggf"]
+    elif args.nonres_regions == "vbf":
+        signal_regions = ["passvbf"]
+else:
+    signal_regions = ["pass"]
 
 # (name in templates, name in cards)
 mc_samples = OrderedDict(
@@ -246,11 +273,13 @@
     ),
 }
 
-for sig_key in sig_keys:
-    # values will be added in from the systematics JSON
-    nuisance_params[f"{CMS_PARAMS_LABEL}_lp_sf_{mc_samples[sig_key]}"] = Syst(
-        prior="lnN", samples=[sig_key]
-    )
+# LP SFs - uncorrelated across regions to be more conservative (?)
+for sr in signal_regions:
+    for sig_key in sig_keys:
+        # values will be added in from the systematics JSON
+        nuisance_params[f"{CMS_PARAMS_LABEL}_lp_sf_{sr}_{mc_samples[sig_key]}"] = Syst(
+            prior="lnN", samples=[sig_key], regions=[sr]
+        )
 
 if args.year != "all":
     # remove other years' keys
@@ -295,6 +324,10 @@
     # "top_pt": Syst(name="CMS_top_pT_reweighting", prior="shape", samples=["TT"])  # TODO
 }
 
+if not args.resonant:
+    # AK4 jets only used for nonresonant
+    corr_year_shape_systs["pileupID"] = Syst(name="CMS_pileup_id", prior="shape", samples=all_mc)
+
 uncorr_year_shape_systs = {
     "pileup": Syst(name="CMS_pileup", prior="shape", samples=all_mc),
     # TODO: add 2016APV template into this
@@ -393,14 +426,25 @@ def get_templates(
     return templates_dict, templates_summed
 
 
-def process_systematics_combined(systematics: dict):
-    """Get total uncertainties from per-year systs in ``systematics``"""
-    for sig_key in sig_keys:
-        # already for all years
-        nuisance_params[f"{CMS_PARAMS_LABEL}_lp_sf_{mc_samples[sig_key]}"].value = (
-            1 + systematics[sig_key]["lp_sf_unc"]
-        )
+def _process_lpsfs(systematics: dict):
+    for sr in signal_regions:
+        for sig_key in sig_keys:
+            # already for all years
+            try:
+                lp_sf_unc = systematics[sr][sig_key]["lp_sf_unc"]
+            except KeyError:
+                warnings.warn(
+                    f"No {sr} region in systematics? Trying old convention for LP SFs.",
+                    stacklevel=2,
+                )
+                lp_sf_unc = systematics[sig_key]["lp_sf_unc"]
+
+            nuisance_params[f"{CMS_PARAMS_LABEL}_lp_sf_{sr}_{mc_samples[sig_key]}"].value = (
+                1 + lp_sf_unc
+            )
+
 
+def _process_triggereffs(systematics: dict):
     tdict = {}
     for region in systematics[years[0]]:
         if len(years) > 1:
@@ -423,35 +467,18 @@ def process_systematics_combined(systematics: dict):
     nuisance_params[f"{CMS_PARAMS_LABEL}_triggerEffSF_uncorrelated"].value = tdict
 
 
-def process_systematics_separate(bg_systematics: dict, sig_systs: dict[str, dict]):
-    """Get total uncertainties from per-year systs separated into bg and sig systs"""
-    for sig_key in sig_keys:
-        # already for all years
-        nuisance_params[f"{CMS_PARAMS_LABEL}_lp_sf_{mc_samples[sig_key]}"].value = (
-            1 + sig_systs[sig_key][sig_key]["lp_sf_unc"]
-        )
-
-    # use only bg trig uncs.
-    tdict = {}
-    for region in bg_systematics[years[0]]:
-        if len(years) > 1:
-            trig_totals, trig_total_errs = [], []
-            for year in years:
-                trig_totals.append(bg_systematics[year][region]["trig_total"])
-                trig_total_errs.append(bg_systematics[year][region]["trig_total_err"])
+def process_systematics_combined(systematics: dict):
+    """Get total uncertainties from per-year systs in ``systematics``"""
+    _process_lpsfs(systematics)
+    _process_triggereffs(systematics)
 
-            trig_total = np.sum(trig_totals)
-            trig_total_errs = np.linalg.norm(trig_total_errs)
+    print("Nuisance Parameters\n", nuisance_params)
 
-            tdict[region] = 1 + (trig_total_errs / trig_total)
-        else:
-            year = years[0]
-            tdict[region] = 1 + (
-                bg_systematics[year][region]["trig_total_err"]
-                / bg_systematics[year][region]["trig_total"]
-            )
 
-    nuisance_params[f"{CMS_PARAMS_LABEL}_triggerEffSF_uncorrelated"].value = tdict
+def process_systematics_separate(bg_systs: dict, sig_systs: dict[str, dict]):
+    """Get total uncertainties from per-year systs separated into bg and sig systs"""
+    _process_lpsfs(sig_systs)
+    _process_triggereffs(bg_systs)
 
     print("Nuisance Parameters\n", nuisance_params)
 
@@ -465,14 +492,14 @@ def process_systematics(templates_dir: str, sig_separate: bool):
         process_systematics_combined(systematics)  # LP SF and trig effs.
     else:
         with (templates_dir / "backgrounds/systematics.json").open("r") as f:
-            bg_systematics = json.load(f)
+            bg_systs = json.load(f)
 
         sig_systs = {}
         for sig_key in sig_keys:
             with (templates_dir / f"{hist_names[sig_key]}/systematics.json").open("r") as f:
                 sig_systs[sig_key] = json.load(f)
 
-        process_systematics_separate(bg_systematics, sig_systs)  # LP SF and trig effs.
+        process_systematics_separate(bg_systs, sig_systs)  # LP SF and trig effs.
 
 
 # TODO: separate function for VBF?
@@ -562,8 +589,8 @@ def fill_regions(
             region_templates = templates_summed[region][:, :, mX_bin]
 
         pass_region = region.startswith("pass")
-        region_noblinded = region.split("Blinded")[0]
-        blind_str = "Blinded" if region.endswith("Blinded") else ""
+        region_noblinded = region.split(MCB_LABEL)[0]
+        blind_str = MCB_LABEL if region.endswith(MCB_LABEL) else ""
 
         logging.info("starting region: %s" % region)
         binstr = "" if mX_bin is None else f"mXbin{mX_bin}"
@@ -574,7 +601,8 @@ def fill_regions(
             # don't add signals in fail regions
             # also skip resonant signals in pass blinded - they are ignored in the validation fits anyway
             if sample_name in sig_keys and (
-                not pass_region or (mX_bin is not None and region == "passBlinded")
+                not pass_region
+                or (mX_bin is not None and region not in [sr + MCB_LABEL for sr in signal_regions])
             ):
                 logging.info(f"\nSkipping {sample_name} in {region} region\n")
                 continue
@@ -624,7 +652,13 @@ def fill_regions(
 
             # rate systematics
             for skey, syst in nuisance_params.items():
-                if sample_name not in syst.samples or (not pass_region and syst.pass_only):
+                region_name = region if args.resonant else region_noblinded
+
+                if (
+                    sample_name not in syst.samples
+                    or (not pass_region and syst.pass_only)
+                    or (syst.regions is not None and region_name not in syst.regions)
+                ):
                     continue
 
                 logging.info(f"Getting {skey} rate")
@@ -633,7 +667,6 @@ def fill_regions(
 
                 val, val_down = syst.value, syst.value_down
                 if syst.diff_regions:
-                    region_name = region if args.resonant else region_noblinded
                     val = val[region_name]
                     val_down = val_down[region_name] if val_down is not None else val_down
                 if syst.diff_samples:
@@ -737,25 +770,11 @@ def nonres_alphabet_fit(
     shape_var = shape_vars[0]
     m_obs = rl.Observable(shape_var.name, shape_var.bins)
 
-    # QCD overall pass / fail efficiency
-    qcd_eff = (
-        templates_summed["pass"][qcd_key, :].sum().value
-        / templates_summed["fail"][qcd_key, :].sum().value
-    )
-
-    # transfer factor
-    tf_dataResidual = rl.BasisPoly(
-        f"{CMS_PARAMS_LABEL}_tf_dataResidual",
-        (shape_var.order,),
-        [shape_var.name],
-        basis="Bernstein",
-        limits=(-20, 20),
-        square_params=True,
-    )
-    tf_dataResidual_params = tf_dataResidual(shape_var.scaled)
-    tf_params_pass = qcd_eff * tf_dataResidual_params  # scale params initially by qcd eff
+    ##########################
+    # Setup fail region first
+    ##########################
 
-    # qcd params
+    # Independent nuisances to float QCD in each fail bin
     qcd_params = np.array(
         [
             rl.IndependentParameter(f"{CMS_PARAMS_LABEL}_tf_dataResidual_Bin{i}", 0)
@@ -763,23 +782,17 @@ def nonres_alphabet_fit(
         ]
     )
 
-    for blind_str in ["", "Blinded"]:
-        # for blind_str in ["Blinded"]:
-        passChName = f"pass{blind_str}".replace("_", "")
+    for blind_str in ["", MCB_LABEL]:
         failChName = f"fail{blind_str}".replace("_", "")
-        logging.info(
-            f"setting transfer factor for pass region {passChName}, fail region {failChName}"
-        )
+        logging.info(f"Setting up fail region {failChName}")
         failCh = model[failChName]
-        passCh = model[passChName]
 
-        # sideband fail
         # was integer, and numpy complained about subtracting float from it
         initial_qcd = failCh.getObservation().astype(float)
         for sample in failCh:
             if sample.sampletype == rl.Sample.SIGNAL:
                 continue
-            logging.debug("subtracting %s from qcd" % sample._name)
+            logging.debug("Subtracting %s from qcd" % sample._name)
             initial_qcd -= sample.getExpectation(nominal=True)
 
         if np.any(initial_qcd < 0.0):
@@ -806,14 +819,44 @@ def nonres_alphabet_fit(
         )
         failCh.addSample(fail_qcd)
 
-        pass_qcd = rl.TransferFactorSample(
-            f"{passChName}_{CMS_PARAMS_LABEL}_qcd_datadriven",
-            rl.Sample.BACKGROUND,
-            tf_params_pass,
-            fail_qcd,
-            min_val=min_qcd_val,
+    ##########################
+    # Now do signal regions
+    ##########################
+
+    for sr in signal_regions:
+        # QCD overall pass / fail efficiency
+        qcd_eff = (
+            templates_summed[sr][qcd_key, :].sum().value
+            / templates_summed["fail"][qcd_key, :].sum().value
         )
-        passCh.addSample(pass_qcd)
+
+        # transfer factor
+        tf_dataResidual = rl.BasisPoly(
+            f"{CMS_PARAMS_LABEL}_tf_dataResidual_{sr}",
+            (shape_var.order[sr],),
+            [shape_var.name],
+            basis="Bernstein",
+            limits=(-20, 20),
+            square_params=True,
+        )
+        # dependent parameters of the TF params representing QCD in each bin of pass region
+        tf_dataResidual_params = tf_dataResidual(shape_var.scaled)
+        tf_params_pass = qcd_eff * tf_dataResidual_params  # scale params initially by qcd eff
+
+        for blind_str in ["", MCB_LABEL]:
+            # for blind_str in [MCB_LABEL]:
+            passChName = f"{sr}{blind_str}".replace("_", "")
+            logging.info(f"setting transfer factor for pass region {passChName}")
+            passCh = model[passChName]
+
+            pass_qcd = rl.TransferFactorSample(
+                f"{passChName}_{CMS_PARAMS_LABEL}_qcd_datadriven",
+                rl.Sample.BACKGROUND,
+                tf_params_pass,
+                fail_qcd,
+                min_val=min_qcd_val,
+            )
+            passCh.addSample(pass_qcd)
 
 
 def res_alphabet_fit(
@@ -859,8 +902,8 @@ def res_alphabet_fit(
             ]
         )
 
-        for blind_str in ["", "Blinded"]:
-            # for blind_str in ["Blinded"]:
+        for blind_str in ["", MCB_LABEL]:
+            # for blind_str in [MCB_LABEL]:
             passChName = f"mXbin{mX_bin}pass{blind_str}".replace("_", "")
             failChName = f"mXbin{mX_bin}fail{blind_str}".replace("_", "")
             logging.info(
@@ -914,9 +957,9 @@ def res_alphabet_fit(
 
 
 def createDatacardAlphabet(args, templates_dict, templates_summed, shape_vars):
-    # (pass, fail) x (unblinded, blinded)
+    # (*signal_regions, fail) x (MC not-blinded, MC blinded)
     regions: list[str] = [
-        f"{pf}{blind_str}" for pf in ["pass", "fail"] for blind_str in ["", "Blinded"]
+        f"{pf}{blind_str}" for pf in [*signal_regions, "fail"] for blind_str in ["", MCB_LABEL]
     ]
 
     # build actual fit model now
@@ -1212,14 +1255,29 @@ def main(args):
     # TODO: check if / how to include signal trig eff uncs. (rn only using bg uncs.)
     process_systematics(args.templates_dir, args.sig_separate)
 
-    # random template from which to extract shape vars
+    # arbitrary template from which to extract shape vars
     sample_templates: Hist = templates_summed[next(iter(templates_summed.keys()))]
 
     # [mH(bb)] for nonresonant, [mY, mX] for resonant
-    shape_vars = [
-        ShapeVar(name=axis.name, bins=axis.edges, order=args.nTF[i])
-        for i, axis in enumerate(sample_templates.axes[1:])
-    ]
+    if not args.resonant:
+        shape_vars = [
+            ShapeVar(
+                name=axis.name,
+                bins=axis.edges,
+                order={sr: args.nTF[i] for i, sr in enumerate(signal_regions)},
+            )
+            for _, axis in enumerate(sample_templates.axes[1:])
+        ]
+    else:
+        if len(signal_regions) != 1:
+            raise NotImplementedError(
+                "Need to update shape vars for multiple resonant signal regions."
+            )
+
+        shape_vars = [
+            ShapeVar(name=axis.name, bins=axis.edges, order={signal_regions[0]: args.nTF[i]})
+            for i, axis in enumerate(sample_templates.axes[1:])
+        ]
 
     args.cards_dir.mkdir(parents=True, exist_ok=True)
     with (args.cards_dir / "templates.txt").open("w") as f:
diff --git a/src/HHbbVV/postprocessing/datacardHelpers.py b/src/HHbbVV/postprocessing/datacardHelpers.py
index 8d61b1db..55f106a0 100644
--- a/src/HHbbVV/postprocessing/datacardHelpers.py
+++ b/src/HHbbVV/postprocessing/datacardHelpers.py
@@ -38,6 +38,8 @@ class Syst:
     uncorr_years: list[str] = field(default_factory=lambda: all_years)
     pass_only: bool = False  # is it applied only in the pass regions
 
+    regions: list[str] = None  # regions affected by it (if None, this is ignored)
+
     def __post_init__(self):
         if isinstance(self.value, dict) and not (self.diff_regions or self.diff_samples):
             raise RuntimeError(
@@ -51,7 +53,7 @@ class ShapeVar:
 
     name: str = None
     bins: np.ndarray = None  # bin edges
-    order: int = None  # TF order
+    orders: dict = None  # TF order: dict of categories -> order
 
     def __post_init__(self):
         # use bin centers for polynomial fit
diff --git a/src/HHbbVV/postprocessing/postprocessing.py b/src/HHbbVV/postprocessing/postprocessing.py
index 7854460a..4d3491d3 100644
--- a/src/HHbbVV/postprocessing/postprocessing.py
+++ b/src/HHbbVV/postprocessing/postprocessing.py
@@ -387,7 +387,7 @@ def main(args):
             )
 
             with systs_file.open("w") as f:
-                json.dump(systematics, f)
+                json.dump(systematics, f, index=4)
 
 
 def _init(args):
@@ -1153,7 +1153,7 @@ def _lpsfs(args, filters, scan, scan_cuts, scan_wps, sig_keys, sig_samples):
                     wsysts[region.lpsf_region] = systematics[region.lpsf_region + "_" + cutstr]
 
             with systs_file.open("w") as f:
-                json.dump(wsysts, f)
+                json.dump(wsysts, f, index=4)
 
 
 def _get_signal_all_years(
@@ -1312,7 +1312,7 @@ def lpsfs(
 
             if systs_file is not None:
                 with systs_file.open("w") as f:
-                    json.dump(systematics, f)
+                    json.dump(systematics, f, index=4)
 
     if template_dir is not None:
         sf_table = OrderedDict()  # format SFs for each sig key in a table
diff --git a/src/HHbbVV/postprocessing/regions.py b/src/HHbbVV/postprocessing/regions.py
index 1c17910f..6fbb1ac7 100644
--- a/src/HHbbVV/postprocessing/regions.py
+++ b/src/HHbbVV/postprocessing/regions.py
@@ -1,5 +1,6 @@
 """
 Defines all the analysis regions.
+****Important****: Region names used in the analysis cannot have underscores because of a rhalphalib convention.
 Author(s): Raghav Kansal
 """
 
@@ -65,7 +66,7 @@ def get_nonres_selection_regions(
 
     regions = {
         # {label: {cutvar: [min, max], ...}, ...}
-        "pass_vbf": Region(
+        "passvbf": Region(
             cuts={
                 "bbFatJetPt": pt_cuts,
                 "VVFatJetPt": pt_cuts,
@@ -76,7 +77,7 @@ def get_nonres_selection_regions(
             signal=True,
             label="VBF",
         ),
-        "pass_ggf": Region(
+        "passggf": Region(
             cuts={
                 "bbFatJetPt": pt_cuts,
                 "VVFatJetPt": pt_cuts,
@@ -98,37 +99,37 @@ def get_nonres_selection_regions(
             label="Fail",
         ),
         # cuts for which LP SF is calculated
-        "lpsf_pass_vbf": Region(
+        "lpsf_passvbf": Region(
             cuts={
                 "BDTScoreVBF": [vbf_bdt_wp, CUT_MAX_VAL],
             },
             lpsf=True,
-            lpsf_region="pass_vbf",
+            lpsf_region="passvbf",
             label="LP SF VBF Cut",
         ),
-        "lpsf_pass_ggf": Region(
+        "lpsf_passggf": Region(
             cuts={
                 "BDTScoreVBF": [-CUT_MAX_VAL, vbf_bdt_wp],  # veto VBF BDT cut
                 "BDTScore": [ggf_bdt_wp, CUT_MAX_VAL],
             },
             lpsf=True,
-            lpsf_region="pass_ggf",
+            lpsf_region="passggf",
             label="LP SF ggF Cut",
         ),
     }
 
     if region == "ggf":
-        regions.pop("pass_vbf")
-        region.pop("lpsf_pass_vbf")
+        regions.pop("passvbf")
+        region.pop("lpsf_passvbf")
     elif region == "vbf":
-        regions.pop("pass_ggf")
-        region.pop("lpsf_pass_ggf")
+        regions.pop("passggf")
+        region.pop("lpsf_passggf")
     elif region == "ggf_no_vbf":
         # old version without any VBF category
-        lpregion = regions["lpsf_pass_ggf"]
+        lpregion = regions["lpsf_passggf"]
         lpregion.lpsf_region = "pass"
         regions = {
-            "pass": regions["pass_ggf"],
+            "pass": regions["passggf"],
             "fail": regions["fail"],
             "lpsf": lpregion,
         }
diff --git a/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/2016APV_templates.pkl
index 540ef4574f55d80e2248125c22455fda36419bf7..1aeb996552adcc1b807d6b8958460c2c778bb235 100644
GIT binary patch
delta 1334
zcmZXTU1$_n6vuPt!<pH=Gdr7Rv(1BHnvP4bluUil5LX-xq0vTJqAwClHbIkz=3`qS
zP}|atZH!38NL+6r_^}cUNWlc%oFpaCn1_}WlYJ2yqowUbTUu;w#iyiA@7z1vZ0o|n
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zJ!tfUM@FS@8~ytaA8*{ZZkHb+Lfgnn=AACD5^1Z@x53e=Kth9sJ@Wgz)Pgti#{+E}
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o=^5@Fqx<4)Lhw^P9_Ifi)|?X=)iQ6{e0?x67dj|-tJ_@l4}l!hp8x;=

delta 1510
zcmdT^+e;Kt7~eB9?as{ZamLtMsUAk1q#I~YF4@L**)r2wjKT-2Z4sri+OWdRYK7Dy
ztF2s5q1^+`2P?$BtRO22wvq~pvW&!{=!y}PAY`+%Gr8LK2Q&=x@}2K@`Ofc~+wF7S
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Rn>N7deEP6rWt;dW={M5xK&Su!

diff --git a/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/2016_templates.pkl
index 1b66ed611cd279e5a59d4f733c998d1fc6b1d6d4..4f0812358a5c763f80674a1863a7b4f911cd6bda 100644
GIT binary patch
delta 1209
zcmZXST}TvB6vw?Ycbb{q8F$uP7hhzvNunT^9&9V6O^Z}CMCu`|{6O>}zbX*2qO~w3
ziWJ>$B#CR55QIssM=infDJzqr2Q|ZbsG#q}Cj+~4XSS9cn3sFcIsgCp-`~gK%<<vO
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zgoy2_T8nNHVYsG5tZ;p0QK{{tL78}f{i>SoG(l$KCKPTtctkgIgMmkm!yM4e)Z{-<
CH?Bqi

delta 1245
zcmZ`(ZAcSw9Nz7&?e4a}cZ=ml^<lcpih|ashPtR=y_q!(L&(xbD23I+6f=trQj6C6
z*1z``D<nT?A^OV*T16j(go-FMqhZp!U<nln+JE=bX4rAum)||l@A*H^bN9Gw*~hMB
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/2017_templates.pkl
index e1b9902578b75dd477aa08c9fe1d8fd27f6ab430..dd8b890a1cc42c4aeb7faec1d2afd71d845bb6e9 100644
GIT binary patch
delta 1171
zcmZWnT}TvB6!y-K^RqMVthp{i%4U;9J}5nyl+vn5YHKW3PZH`9`jDC>2w9<9nIc7s
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MZ9l>orlqF;1(v6sF8}}l

delta 1458
zcmds1+e;L26yDhx-I;NI&KR3(<-_VumK0=9iMmn3nt7?kD3nZX3#G8yFl{pph17&i
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/2018_templates.pkl
index 5ee88f2f10004040a9dcb3e6c4a295a6a3cc0dcd..a8c0283ef838c0294e6d06d717534c04f525a7db 100644
GIT binary patch
delta 1197
zcmZvbT}TvB6vw?Ycbu8sncZ1)U8In0o0iZAr3aHzTC+&YLZY6mT$j*?)GR^B4A(-9
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sLHK8Y+sjIo;hAQ#0rop;q00oAshdQ2<j85=^bnp6J5O>z{ZoB^0OF9S8~^|S

delta 1236
zcmZ`(ZAcSg9N+D3?r!e!wph+gV!F$cKDa(5>cWJX`BG*SelT4ll+tQpim8Qz)PgCR
z&P(h?B@q4K3#NaCAnlC=1tTadBQq$<1fxL7(4O5(PFcrsU;fYU<^I3l-II~TFC&Rt
zD=CjNQXyPdns&Qs=c%HiT$iEBKvWr&g9dVCU-)(fA<UaDH97EIT;1ewv%<Oj{Jh*0
z+cEetPPUNnB94r;A^nEQMI*XJz^IG*Ni;^s==g$QDg%1cm`VadRm`Fa&bY}6Yk)`x
z-^R(e$+XM>g+_G82MieeG@MX?C5Ear)3OEl5Qkc&V_?grQf%uC8FtLTWBDR5Mrw{S
zu+&SHTeUJ2yHAJ3aq>3-VHY%Z2Hq@FWmc_hKo~oXs70XIulAG{dKbYHvE(j@9rgmo
z4yY=u>sI<E(JIHMrm6E`f(v}4ABc`|W<h-(`#|si*`^<0zcxIsnT}G!bQFEV9j5TJ
z?O<uAU@v7m1T&`QG93kNGt8W@SMTme)n17yHDsld@p?XIjZp*rj&^YYAp#ug*61yV
z7?~>%Ea#X`5{{%Xr)@|)BIcP1i?`W(er0e1|72EnQdlNoh>2u8L6T|c&n7#Fb#JgV
z!pxXxSrh6tx=sF*4$AiC&6*ANY8y00r8k#fNxt^dGkI+9q|$R7%7-+A$&6Up5=S2I
zTA=pycg=A%HZBlsoEECWk)Lgf>0^N0-DZNHy{t3wSF2GY%$5)d1el(&vjo)6vnCQ~
z7dxed;z6x;o78R@i`!kGg5_7*KRcujlsMq~IN9o#a7xWjL?8=4TYyZ^wW@s>5+F7W
z&Kl8qPW}kq$Du+_o*cIOOvbL^iPTQPLOPGz`BLc#g1~Nc*DNvA^h_e?nGE8)L;qhU
z$SGy3Ia<seyRKiO?+dP9dpBT_H-O&_f23lshCgcK^-_PZythB?n;|nMIZom;dz}jq
zlZQHu&R1-~tdYbz`y7ao_$!Oh27X)sk^ed9PUylSBxZhZsm{Q7iaFt@q=5Ep@+JW|
Iz)ul>0nHW2v;Y7A

diff --git a/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/systematics.json b/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/systematics.json
index 6c8691a5..7cf72086 100644
--- a/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/systematics.json
+++ b/src/HHbbVV/postprocessing/templates/24Apr10NonresCombined/systematics.json
@@ -1,5 +1,5 @@
 {
-    "pass_ggf": {
+    "passggf": {
         "HHbbVV": {
             "lp_sf": 1.0662593442284205,
             "lp_sf_unc": 0.2047565290328658,
@@ -101,7 +101,7 @@
             }
         }
     },
-    "pass_vbf": {
+    "passvbf": {
         "qqHH_CV_1_C2V_0_kl_1_HHbbVV": {
             "lp_sf": 0.9319151497178899,
             "lp_sf_unc": 0.2875145732655031,
@@ -204,7 +204,7 @@
         }
     },
     "2016": {
-        "pass_ggf": {
+        "passggf": {
             "trig_total": 35428.46861688447,
             "trig_total_err": 169.70344916739498
         },
@@ -212,13 +212,13 @@
             "trig_total": 2191129.51425094,
             "trig_total_err": 3393.9771949494093
         },
-        "pass_vbf": {
+        "passvbf": {
             "trig_total": 154861.2273932935,
             "trig_total_err": 3894.1821702776856
         }
     },
     "2016APV": {
-        "pass_ggf": {
+        "passggf": {
             "trig_total": 38500.12454948732,
             "trig_total_err": 145.32670570366125
         },
@@ -226,13 +226,13 @@
             "trig_total": 2578476.2790518906,
             "trig_total_err": 3693.285504726755
         },
-        "pass_vbf": {
+        "passvbf": {
             "trig_total": 175190.49526402235,
             "trig_total_err": 4318.649481220402
         }
     },
     "2017": {
-        "pass_ggf": {
+        "passggf": {
             "trig_total": 65678.89161826814,
             "trig_total_err": 207.86047980915436
         },
@@ -240,13 +240,13 @@
             "trig_total": 2460502.005127463,
             "trig_total_err": 2704.983599867493
         },
-        "pass_vbf": {
+        "passvbf": {
             "trig_total": 389745.69783708075,
             "trig_total_err": 7746.5100600706855
         }
     },
     "2018": {
-        "pass_ggf": {
+        "passggf": {
             "trig_total": 46677.8895927491,
             "trig_total_err": 137.1808889147677
         },
@@ -254,7 +254,7 @@
             "trig_total": 1876156.0255485063,
             "trig_total_err": 1795.8409437101016
         },
-        "pass_vbf": {
+        "passvbf": {
             "trig_total": 276742.9830432943,
             "trig_total_err": 5028.429129818956
         }

From 2bc68f2bf5a1ed42ffbc2b7b84e5ce364f17273f Mon Sep 17 00:00:00 2001
From: rkansal47 <rkansal@cern.ch>
Date: Wed, 10 Apr 2024 20:53:00 -0500
Subject: [PATCH 2/2] bug fixes

---
 pyproject.toml                               |   1 +
 src/HHbbVV/combine/run_blinded.sh            |  57 ++++++--
 src/HHbbVV/combine/run_ftest_nonres.sh       |   4 +-
 src/HHbbVV/postprocessing/CreateDatacard.py  |  45 ++++--
 src/HHbbVV/postprocessing/PlotFits.ipynb     | 139 ++++++-------------
 src/HHbbVV/postprocessing/datacardHelpers.py |   9 +-
 6 files changed, 130 insertions(+), 125 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 9e76f9c2..2475231c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -136,6 +136,7 @@ ignore = [
   "ISC001",   # Conflicts with formatter
   "PLR",    # Design related pylint codes
   "T201",   # print statements
+  "T203",   # pprint statements
   "EM101",  # Error message
   "EM102",
   "G002",   # Logging statement format
diff --git a/src/HHbbVV/combine/run_blinded.sh b/src/HHbbVV/combine/run_blinded.sh
index 0e33a1c8..c86f3d92 100755
--- a/src/HHbbVV/combine/run_blinded.sh
+++ b/src/HHbbVV/combine/run_blinded.sh
@@ -19,6 +19,9 @@
 # Specify resonant with --resonant / -r, otherwise does nonresonant
 # Specify seed with --seed (default 42) and number of toys with --numtoys (default 100)
 #
+# For nonresonant, will try to load all the regions automatically based on which text files exist
+# Can use --noggf, --novbf to exclude ggF and VBF regions respectively
+#
 # Usage ./run_blinded.sh [-wblsdgt] [--numtoys 100] [--seed 42]
 #
 # Author: Raghav Kansal
@@ -46,8 +49,10 @@ numtoys=100
 bias=-1
 mintol=0.1  # --cminDefaultMinimizerTolerance
 # maxcalls=1000000000  # --X-rtd MINIMIZER_MaxCalls
+nonresggf=1
+nonresvbf=1
 
-options=$(getopt -o "wblsdrgti" --long "workspace,bfit,limits,significance,dfit,dfitasimov,resonant,gofdata,goftoys,impactsi,impactsf:,impactsc:,bias:,seed:,numtoys:,mintol:" -- "$@")
+options=$(getopt -o "wblsdrgti" --long "workspace,bfit,limits,significance,dfit,dfitasimov,resonant,noggf,novbf,gofdata,goftoys,impactsi,impactsf:,impactsc:,bias:,seed:,numtoys:,mintol:" -- "$@")
 eval set -- "$options"
 
 while true; do
@@ -73,6 +78,12 @@ while true; do
         -r|--resonant)
             resonant=1
             ;;
+        --noggf)
+            nonresggf=0
+            ;;
+        --novbf)
+            nonresvbf=0
+            ;;
         -g|--gofdata)
             gofdata=1
             ;;
@@ -143,33 +154,53 @@ outsdir=${cards_dir}/outs
 mkdir -p $outsdir
 
 if [ $resonant = 0 ]; then
+    # nonresonant args
+
     if [ -f "mXbin0pass.txt" ]; then
         echo -e "\nWARNING: This is doing nonresonant fits - did you mean to pass -r|--resonant?\n"
     fi
 
     CMS_PARAMS_LABEL="CMS_bbWW_hadronic"
 
-    # nonresonant args
-    ccargs="fail=${cards_dir}/fail.txt failBlinded=${cards_dir}/failBlinded.txt pass=${cards_dir}/pass.txt passBlinded=${cards_dir}/passBlinded.txt"
-    maskunblindedargs="mask_pass=1,mask_fail=1,mask_passBlinded=0,mask_failBlinded=0"
-    maskblindedargs="mask_pass=0,mask_fail=0,mask_passBlinded=1,mask_failBlinded=1"
+    if [ -f "pass.txt" ]; then
+        echo "Single pass region"
+        ccargs="fail=${cards_dir}/fail.txt failBlinded=${cards_dir}/failBlinded.txt pass=${cards_dir}/pass.txt passBlinded=${cards_dir}/passBlinded.txt"
+        maskunblindedargs="mask_pass=1,mask_fail=1,mask_passBlinded=0,mask_failBlinded=0"
+        maskblindedargs="mask_pass=0,mask_fail=0,mask_passBlinded=1,mask_failBlinded=1"
+    else
+        ccargs="fail=${cards_dir}/fail.txt failBlinded=${cards_dir}/failBlinded.txt"
+        maskunblindedargs="mask_fail=1,mask_failBlinded=0"
+        maskblindedargs="mask_fail=0,mask_failBlinded=1"
+
+        if [ -f "passggf.txt" ] && [ $nonresggf = 1 ]; then
+            echo "passggf region"
+            ccargs+=" passggf=${cards_dir}/passggf.txt passggfBlinded=${cards_dir}/passggfBlinded.txt"
+            maskunblindedargs+=",mask_passggf=1,mask_passggfBlinded=0"
+            maskblindedargs+=",mask_passggf=0,mask_passggfBlinded=1"
+        fi
+
+        if [ -f "passvbf.txt" ] && [ $nonresvbf = 1 ]; then
+            echo "passvbf region"
+            ccargs+=" passvbf=${cards_dir}/passvbf.txt passvbfBlinded=${cards_dir}/passvbfBlinded.txt"
+            maskunblindedargs+=",mask_passvbf=1,mask_passvbfBlinded=0"
+            maskblindedargs+=",mask_passvbf=0,mask_passvbfBlinded=1"
+        fi
+    fi
 
-    # freeze qcd params in blinded bins
+    # freeze fail region qcd params in blinded bins
     setparamsblinded=""
     freezeparamsblinded=""
     for bin in {5..9}
     do
-        setparamsblinded+="rgx{${CMS_PARAMS_LABEL}_tf_dataResidual.*_Bin${bin}}=0,"
-        freezeparamsblinded+="rgx{${CMS_PARAMS_LABEL}_tf_dataResidual.*_Bin${bin}},"
+        # would need to use regex here for multiple fail regions
+        setparamsblinded+="${CMS_PARAMS_LABEL}_tf_dataResidual_Bin${bin}=0,"
+        freezeparamsblinded+="${CMS_PARAMS_LABEL}_tf_dataResidual_Bin${bin},"
     done
 
-    echo "CHECK FROZEN BLINDED QCD PARAMS"
-
     # remove last comma
     setparamsblinded=${setparamsblinded%,}
     freezeparamsblinded=${freezeparamsblinded%,}
 
-
     # floating parameters using var{} floats a bunch of parameters which shouldn't be floated,
     # so countering this inside --freezeParameters which takes priority.
     # Although, practically even if those are set to "float", I didn't see them ever being fitted,
@@ -241,7 +272,7 @@ echo "$unblindedparams"
 ulimit -s unlimited
 
 if [ $workspace = 1 ]; then
-    echo "Combining cards"
+    echo "Combining cards $ccargs"
     combineCards.py $ccargs > $ws.txt
 
     echo "Running text2workspace"
@@ -265,7 +296,7 @@ if [ $bfit = 1 ]; then
     -n Snapshot 2>&1 | tee $outsdir/MultiDimFit.txt
 else
     if [ ! -f "higgsCombineSnapshot.MultiDimFit.mH125.root" ]; then
-        echo "Background-only fit snapshot doesn't exist! Use the -b|--bfit option to run fit first"
+        echo "Background-only fit snapshot doesn't exist! Use the -b|--bfit option to run fit first. (Ignore this if you're only creating the workspace.)"
         exit 1
     fi
 fi
diff --git a/src/HHbbVV/combine/run_ftest_nonres.sh b/src/HHbbVV/combine/run_ftest_nonres.sh
index 5e067c85..19e12b4b 100755
--- a/src/HHbbVV/combine/run_ftest_nonres.sh
+++ b/src/HHbbVV/combine/run_ftest_nonres.sh
@@ -103,8 +103,8 @@ setparamsblinded=""
 freezeparamsblinded=""
 for bin in {5..9}
 do
-    setparamsblinded+="CMS_bbWW_boosted_ggf_qcdparam_msdbin${bin}=0,"
-    freezeparamsblinded+="CMS_bbWW_boosted_ggf_qcdparam_msdbin${bin},"
+    setparamsblinded+="${CMS_PARAMS_LABEL}_tf_dataResidual_Bin${bin}=0,"
+    freezeparamsblinded+="${CMS_PARAMS_LABEL}_tf_dataResidual_Bin${bin},"
 done
 
 # remove last comma
diff --git a/src/HHbbVV/postprocessing/CreateDatacard.py b/src/HHbbVV/postprocessing/CreateDatacard.py
index ec562a0f..2b828aec 100644
--- a/src/HHbbVV/postprocessing/CreateDatacard.py
+++ b/src/HHbbVV/postprocessing/CreateDatacard.py
@@ -15,6 +15,7 @@
 import json
 import logging
 import pickle
+import pprint
 import sys
 import warnings
 from collections import OrderedDict
@@ -472,7 +473,8 @@ def process_systematics_combined(systematics: dict):
     _process_lpsfs(systematics)
     _process_triggereffs(systematics)
 
-    print("Nuisance Parameters\n", nuisance_params)
+    print("Nuisance Parameters")
+    pprint.pprint(nuisance_params)
 
 
 def process_systematics_separate(bg_systs: dict, sig_systs: dict[str, dict]):
@@ -480,7 +482,8 @@ def process_systematics_separate(bg_systs: dict, sig_systs: dict[str, dict]):
     _process_lpsfs(sig_systs)
     _process_triggereffs(bg_systs)
 
-    print("Nuisance Parameters\n", nuisance_params)
+    print("Nuisance Parameters")
+    pprint.pprint(nuisance_params)
 
 
 def process_systematics(templates_dir: str, sig_separate: bool):
@@ -592,27 +595,29 @@ def fill_regions(
         region_noblinded = region.split(MCB_LABEL)[0]
         blind_str = MCB_LABEL if region.endswith(MCB_LABEL) else ""
 
+        print("\n\n")
         logging.info("starting region: %s" % region)
         binstr = "" if mX_bin is None else f"mXbin{mX_bin}"
         ch = rl.Channel(binstr + region.replace("_", ""))  # can't have '_'s in name
         model.addChannel(ch)
 
         for sample_name, card_name in mc_samples.items():
+            print("")
             # don't add signals in fail regions
             # also skip resonant signals in pass blinded - they are ignored in the validation fits anyway
             if sample_name in sig_keys and (
                 not pass_region
                 or (mX_bin is not None and region not in [sr + MCB_LABEL for sr in signal_regions])
             ):
-                logging.info(f"\nSkipping {sample_name} in {region} region\n")
+                logging.info(f"Skipping {sample_name} in {region} region")
                 continue
 
             # single top only in fail regions
             if sample_name == "ST" and pass_region:
-                logging.info(f"\nSkipping ST in {region} region\n")
+                logging.info(f"Skipping ST in {region} region")
                 continue
 
-            logging.info("get templates for: %s" % sample_name)
+            logging.info("Getting templates for: %s" % sample_name)
 
             sample_template = region_templates[sample_name, :]
 
@@ -659,6 +664,7 @@ def fill_regions(
                     or (not pass_region and syst.pass_only)
                     or (syst.regions is not None and region_name not in syst.regions)
                 ):
+                    logging.info(f"Skipping {skey} rate")
                     continue
 
                 logging.info(f"Getting {skey} rate")
@@ -677,7 +683,12 @@ def fill_regions(
 
             # correlated shape systematics
             for skey, syst in corr_year_shape_systs.items():
-                if sample_name not in syst.samples or (not pass_region and syst.pass_only):
+                if (
+                    sample_name not in syst.samples
+                    or (not pass_region and syst.pass_only)
+                    or (syst.regions is not None and region_name not in syst.regions)
+                ):
+                    logging.info(f"Skipping {skey} shapes")
                     continue
 
                 logging.info(f"Getting {skey} shapes")
@@ -717,7 +728,12 @@ def fill_regions(
 
             # uncorrelated shape systematics
             for skey, syst in uncorr_year_shape_systs.items():
-                if sample_name not in syst.samples or (not pass_region and syst.pass_only):
+                if (
+                    sample_name not in syst.samples
+                    or (not pass_region and syst.pass_only)
+                    or (syst.regions is not None and region_name not in syst.regions)
+                ):
+                    logging.info(f"Skipping {skey} shapes")
                     continue
 
                 logging.info(f"Getting {skey} shapes")
@@ -782,6 +798,8 @@ def nonres_alphabet_fit(
         ]
     )
 
+    fail_qcd_samples = {}
+
     for blind_str in ["", MCB_LABEL]:
         failChName = f"fail{blind_str}".replace("_", "")
         logging.info(f"Setting up fail region {failChName}")
@@ -819,6 +837,8 @@ def nonres_alphabet_fit(
         )
         failCh.addSample(fail_qcd)
 
+        fail_qcd_samples[blind_str] = fail_qcd
+
     ##########################
     # Now do signal regions
     ##########################
@@ -833,7 +853,7 @@ def nonres_alphabet_fit(
         # transfer factor
         tf_dataResidual = rl.BasisPoly(
             f"{CMS_PARAMS_LABEL}_tf_dataResidual_{sr}",
-            (shape_var.order[sr],),
+            (shape_var.orders[sr],),
             [shape_var.name],
             basis="Bernstein",
             limits=(-20, 20),
@@ -853,7 +873,7 @@ def nonres_alphabet_fit(
                 f"{passChName}_{CMS_PARAMS_LABEL}_qcd_datadriven",
                 rl.Sample.BACKGROUND,
                 tf_params_pass,
-                fail_qcd,
+                fail_qcd_samples[blind_str],
                 min_val=min_qcd_val,
             )
             passCh.addSample(pass_qcd)
@@ -868,6 +888,7 @@ def res_alphabet_fit(
 ):
     shape_var_mY, shape_var_mX = shape_vars
     m_obs = rl.Observable(shape_var_mY.name, shape_var_mY.bins)
+    sr = signal_regions[0]
 
     # QCD overall pass / fail efficiency
     qcd_eff = (
@@ -878,7 +899,7 @@ def res_alphabet_fit(
     # transfer factor
     tf_dataResidual = rl.BasisPoly(
         f"{CMS_PARAMS_LABEL}_tf_dataResidual",
-        (shape_var_mX.order, shape_var_mY.order),
+        (shape_var_mX.orders[sr], shape_var_mY.orders[sr]),
         [shape_var_mX.name, shape_var_mY.name],
         basis="Bernstein",
         limits=(-20, 20),
@@ -1264,7 +1285,7 @@ def main(args):
             ShapeVar(
                 name=axis.name,
                 bins=axis.edges,
-                order={sr: args.nTF[i] for i, sr in enumerate(signal_regions)},
+                orders={sr: args.nTF[i] for i, sr in enumerate(signal_regions)},
             )
             for _, axis in enumerate(sample_templates.axes[1:])
         ]
@@ -1275,7 +1296,7 @@ def main(args):
             )
 
         shape_vars = [
-            ShapeVar(name=axis.name, bins=axis.edges, order={signal_regions[0]: args.nTF[i]})
+            ShapeVar(name=axis.name, bins=axis.edges, orders={signal_regions[0]: args.nTF[i]})
             for i, axis in enumerate(sample_templates.axes[1:])
         ]
 
diff --git a/src/HHbbVV/postprocessing/PlotFits.ipynb b/src/HHbbVV/postprocessing/PlotFits.ipynb
index e3b94ff5..0eb9718c 100644
--- a/src/HHbbVV/postprocessing/PlotFits.ipynb
+++ b/src/HHbbVV/postprocessing/PlotFits.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,18 +26,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -45,30 +36,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "MAIN_DIR = Path(\"../../../\")\n",
     "\n",
-    "# plot_dir = MAIN_DIR / \"plots/PostFit/24Apr9VBFHP999\"\n",
-    "plot_dir = (\n",
-    "    MAIN_DIR\n",
-    "    / \"plots/PostFit/24Apr9ggFScan/nTF1/ggf_txbb_MP_ggf_bdt_0.9965_vbf_txbb_HP_vbf_bdt_0.999_lepton_veto_Hbb\"\n",
-    ")\n",
-    "plot_dir.mkdir(exist_ok=True, parents=True)\n",
+    "vbf_only = False\n",
+    "ggf_only = False\n",
+    "k2v0sig = True\n",
     "\n",
-    "vbf = False"
+    "plot_dir = MAIN_DIR / \"plots/PostFit/24Apr10NonresCombined\"\n",
+    "# plot_dir = (\n",
+    "#     MAIN_DIR\n",
+    "#     / \"plots/PostFit/24Apr9ggFScan/nTF1/ggf_txbb_MP_ggf_bdt_0.9965_vbf_txbb_HP_vbf_bdt_0.999_lepton_veto_Hbb\"\n",
+    "# )\n",
+    "plot_dir.mkdir(exist_ok=True, parents=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# cards_dir = \"24Apr8VBFHP999\"\n",
-    "cards_dir = \"24Apr9ggFScan_nTF0/ggf_txbb_HP_ggf_bdt_0.996_vbf_txbb_HP_vbf_bdt_0.999_lepton_veto_Hbb\"\n",
+    "cards_dir = \"24Apr10NonresCombined/\"\n",
+    "# cards_dir = \"24Apr9ggFScan_nTF0/ggf_txbb_HP_ggf_bdt_0.996_vbf_txbb_HP_vbf_bdt_0.999_lepton_veto_Hbb\"\n",
     "asimov = False\n",
     "\n",
     "asimov_label = \"Asimov\" if asimov else \"\"\n",
@@ -80,14 +73,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# templates_dir = Path(f\"templates/{cards_dir}\")\n",
-    "templates_dir = Path(\n",
-    "    f\"templates/24Apr9ggFScan/ggf_txbb_HP_ggf_bdt_0.996_vbf_txbb_HP_vbf_bdt_0.999_lepton_veto_Hbb\"\n",
-    ")\n",
+    "templates_dir = Path(f\"templates/24Apr10NonresCombined\")\n",
+    "# templates_dir = Path(\n",
+    "#     f\"templates/24Apr9ggFScan/ggf_txbb_HP_ggf_bdt_0.996_vbf_txbb_HP_vbf_bdt_0.999_lepton_veto_Hbb\"\n",
+    "# )\n",
     "templates_dict = {}\n",
     "for year in years:\n",
     "    with (templates_dir / f\"{year}_templates.pkl\").open(\"rb\") as f:\n",
@@ -97,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,14 +114,14 @@
     "\n",
     "hist_label_map = {val: key for key, val in hist_label_map_inverse.items()}\n",
     "\n",
-    "sig_keys = [\"HHbbVV\", \"VBFHHbbVV\"]\n",
-    "sig_keys = [\"qqHH_CV_1_C2V_0_kl_1_HHbbVV\"] if vbf else [\"HHbbVV\"]\n",
+    "sig_keys = [\"HHbbVV\", \"VBFHHbbVV\", \"qqHH_CV_1_C2V_0_kl_1_HHbbVV\", \"qqHH_CV_1_C2V_2_kl_1_HHbbVV\"]\n",
+    "# sig_keys = [\"qqHH_CV_1_C2V_0_kl_1_HHbbVV\"] if k2v0sig else [\"HHbbVV\"]\n",
     "samples = bg_keys + sig_keys + [data_key]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,27 +134,23 @@
     "shape_vars = nonres_shape_vars\n",
     "\n",
     "selection_regions = {\n",
-    "    \"pass\": \"VBF\" if vbf else \"ggF\",\n",
+    "    \"passvbf\": \"VBF\",\n",
+    "    \"passggf\": \"ggF\",\n",
     "    \"fail\": \"Fail\",\n",
-    "}"
+    "}\n",
+    "\n",
+    "if vbf_only:\n",
+    "    selection_regions.pop(\"passggf\")\n",
+    "\n",
+    "if ggf_only:\n",
+    "    selection_regions.pop(\"passvbf\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "prefit\n",
-      "No ST in pass\n",
-      "postfit\n",
-      "No ST in pass\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "hists = {}\n",
     "bgerrs = {}\n",
@@ -204,55 +194,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "pass_ylim = 8 if vbf else 22\n",
-    "fail_ylim = 600000\n",
+    "ylims = {\"passggf\": 20, \"passvbf\": 7, \"fail\": 6e5}\n",
     "title_label = \" Asimov Dataset\" if asimov else \"\"\n",
-    "sig_scale_dict = {\"HHbbVV\": 100, \"VBFHHbbVV\": 2000, \"qqHH_CV_1_C2V_0_kl_1_HHbbVV\": 1}\n",
+    "sig_scale_dict = {\n",
+    "    \"HHbbVV\": 100,\n",
+    "    \"VBFHHbbVV\": 2000,\n",
+    "    \"qqHH_CV_1_C2V_0_kl_1_HHbbVV\": 1,\n",
+    "    \"qqHH_CV_1_C2V_2_kl_1_HHbbVV\": 1,\n",
+    "}\n",
     "sig_scale_dict = {key: val for key, val in sig_scale_dict.items() if key in sig_keys}\n",
     "\n",
     "for shape, shape_label in shapes.items():\n",
@@ -267,7 +220,7 @@
     "                \"sig_scale_dict\": sig_scale_dict if pass_region else None,\n",
     "                \"show\": True,\n",
     "                \"year\": \"all\",\n",
-    "                \"ylim\": pass_ylim if pass_region else fail_ylim,\n",
+    "                \"ylim\": ylims[region],\n",
     "                \"title\": f\"{shape_label} {region_label} Region{title_label}\",\n",
     "                \"name\": f\"{plot_dir}/{shape}_{region}_{shape_var.var}.pdf\",\n",
     "            }\n",
diff --git a/src/HHbbVV/postprocessing/datacardHelpers.py b/src/HHbbVV/postprocessing/datacardHelpers.py
index 55f106a0..f817df85 100644
--- a/src/HHbbVV/postprocessing/datacardHelpers.py
+++ b/src/HHbbVV/postprocessing/datacardHelpers.py
@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import logging
 from dataclasses import dataclass, field
 from string import Template
 
@@ -237,11 +236,11 @@ def get_effect_updown(values_nominal, values_up, values_down, mask, logger, epsi
     effect_up[mask_up & zero_up] = values_nominal[mask_up & zero_up] * epsilon
     effect_down[mask_down & zero_down] = values_nominal[mask_down & zero_down] * epsilon
 
-    _shape_checks(values_up, values_down, values_nominal, effect_up, effect_down, logger)
+    # _shape_checks(values_up, values_down, values_nominal, effect_up, effect_down, logger)
 
-    logging.debug(f"nominal   : {values_nominal}")
-    logging.debug(f"effect_up  : {effect_up}")
-    logging.debug(f"effect_down: {effect_down}")
+    logger.debug(f"nominal   : {values_nominal}")
+    logger.debug(f"effect_up  : {effect_up}")
+    logger.debug(f"effect_down: {effect_down}")
 
     return effect_up, effect_down